diff --git a/docs/articles_en/about-openvino/release-notes-openvino.rst b/docs/articles_en/about-openvino/release-notes-openvino.rst index 1d0c7f8d99e..1cb9b34dc9f 100644 --- a/docs/articles_en/about-openvino/release-notes-openvino.rst +++ b/docs/articles_en/about-openvino/release-notes-openvino.rst @@ -232,18 +232,18 @@ Jupyter Notebooks The following notebooks have been updated or newly added: -* `Mobile language assistant with MobileVLM `__ -* `Depth estimation with DepthAnything `__ -* `Kosmos-2 `__ -* `Zero-shot Image Classification with SigLIP `__ -* `Personalized image generation with PhotoMaker `__ -* `Voice tone cloning with OpenVoice `__ -* `Line-level text detection with Surya `__ -* `InstantID: Zero-shot Identity-Preserving Generation using OpenVINO `__ -* `Tutorial for Big Image Transfer (BIT) model quantization using NNCF `__ -* `Tutorial for OpenVINO Tokenizers integration into inference pipelines `__ -* `LLM chatbot `__ and - `LLM RAG pipeline `__ +* `Mobile language assistant with MobileVLM `__ +* `Depth estimation with DepthAnything `__ +* `Kosmos-2 `__ +* `Zero-shot Image Classification with SigLIP `__ +* `Personalized image generation with PhotoMaker `__ +* `Voice tone cloning with OpenVoice `__ +* `Line-level text detection with Surya `__ +* `InstantID: Zero-shot Identity-Preserving Generation using OpenVINO `__ +* `Tutorial for Big Image Transfer (BIT) model quantization using NNCF `__ +* `Tutorial for OpenVINO Tokenizers integration into inference pipelines `__ +* `LLM chatbot `__ and + `LLM RAG pipeline `__ have received integration with new models: minicpm-2b-dpo, gemma-7b-it, qwen1.5-7b-chat, baichuan2-7b-chat diff --git a/docs/articles_en/documentation/legacy-features/install-dev-tools.rst b/docs/articles_en/documentation/legacy-features/install-dev-tools.rst index 1c631599b73..59e97b69ab7 100644 --- a/docs/articles_en/documentation/legacy-features/install-dev-tools.rst +++ b/docs/articles_en/documentation/legacy-features/install-dev-tools.rst @@ -176,13 +176,13 @@ Get started with Python .. image:: ../../_static/images/get_started_with_python.gif :width: 400 -Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser. +Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser. Visit the :doc:`Tutorials <../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as: -* `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__ -* `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__ -* `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__ +* `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__ +* `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__ +* `Convert a PyTorch model and use it for image background removal <../../notebooks/vision-background-removal-with-output.html>`__ Get started with C++ ++++++++++++++++++++ diff --git a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats.rst b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats.rst index 8fada51305c..b4296d69a8a 100644 --- a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats.rst +++ b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats.rst @@ -76,7 +76,7 @@ Here are code examples of how to use these methods with different model formats: For more details on conversion, refer to the :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-pytorch>` - and an example `tutorial `__ + and an example `tutorial `__ on this topic. .. tab-item:: TensorFlow @@ -115,14 +115,14 @@ Here are code examples of how to use these methods with different model formats: import openvino from openvino.tools.mo import convert_model - + core = openvino.Core() ov_model = convert_model("saved_model.pb") compiled_model = core.compile_model(ov_model, "AUTO") For more details on conversion, refer to the :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-tensorflow>` - and an example `tutorial `__ + and an example `tutorial `__ on this topic. * The ``read_model()`` and ``compile_model()`` methods: @@ -227,7 +227,7 @@ Here are code examples of how to use these methods with different model formats: For more details on conversion, refer to the :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-tensorflow>` - and an example `tutorial `__ + and an example `tutorial `__ on this topic. @@ -260,7 +260,7 @@ Here are code examples of how to use these methods with different model formats: :force: import openvino - + core = openvino.Core() compiled_model = core.compile_model(".tflite", "AUTO") @@ -355,7 +355,7 @@ Here are code examples of how to use these methods with different model formats: For more details on conversion, refer to the :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-onnx>` - and an example `tutorial `__ + and an example `tutorial `__ on this topic. @@ -486,7 +486,7 @@ Here are code examples of how to use these methods with different model formats: For more details on conversion, refer to the :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-paddle>` - and an example `tutorial `__ + and an example `tutorial `__ on this topic. * The ``read_model()`` method: diff --git a/docs/articles_en/get-started.rst b/docs/articles_en/get-started.rst index edae30592b6..05d15de18e1 100644 --- a/docs/articles_en/get-started.rst +++ b/docs/articles_en/get-started.rst @@ -24,7 +24,7 @@ GET STARTED

Welcome to OpenVINO! This guide introduces installation and learning materials for Intel® Distribution of OpenVINO™ toolkit. The guide walks through the following steps:
- Quick Start Example + Quick Start Example Install OpenVINO Learn OpenVINO

@@ -43,7 +43,7 @@ For a quick reference, check out .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif :width: 400 -Try out OpenVINO's capabilities with this `quick start example `__ that estimates depth in a scene using an OpenVINO monodepth model to quickly see how to load a model, prepare an image, inference the image, and display the result. +Try out OpenVINO's capabilities with this `quick start example `__ that estimates depth in a scene using an OpenVINO monodepth model to quickly see how to load a model, prepare an image, inference the image, and display the result. .. _install-openvino-gsg: @@ -75,10 +75,10 @@ Interactive Tutorials - Jupyter Notebooks Start with :doc:`interactive Python ` that show the basics of model inferencing, the OpenVINO API, how to convert models to OpenVINO format, and more. -* `Hello Image Classification `__ - Load an image classification model in OpenVINO and use it to apply a label to an image -* `OpenVINO Runtime API Tutorial `__ - Learn the basic Python API for working with models in OpenVINO -* `Convert TensorFlow Models to OpenVINO `__ -* `Convert PyTorch Models to OpenVINO `__ +* `Hello Image Classification `__ - Load an image classification model in OpenVINO and use it to apply a label to an image +* `OpenVINO Runtime API Tutorial `__ - Learn the basic Python API for working with models in OpenVINO +* `Convert TensorFlow Models to OpenVINO `__ +* `Convert PyTorch Models to OpenVINO `__ .. _code-samples: @@ -106,8 +106,8 @@ Model Compression and Quantization Use OpenVINO’s model compression tools to reduce your model’s latency and memory footprint while maintaining good accuracy. -* Tutorial - `Quantization-Aware Training in TensorFlow with OpenVINO NNCF `__ -* Tutorial - `Quantization-Aware Training in PyTorch with NNCF `__ +* Tutorial - `Quantization-Aware Training in TensorFlow with OpenVINO NNCF `__ +* Tutorial - `Quantization-Aware Training in PyTorch with NNCF `__ * :doc:`Model Optimization Guide ` Automated Device Configuration diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst b/docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst index 7cee296cb7c..6364d598587 100644 --- a/docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst +++ b/docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst @@ -283,7 +283,7 @@ Learn more about how to integrate a model in OpenVINO applications by trying out .. tab-item:: Get started with Python :sync: get-started-py - Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__ + Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser. .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif @@ -291,9 +291,9 @@ Learn more about how to integrate a model in OpenVINO applications by trying out Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as: - * `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__ - * `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__ - * `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__ + * `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__ + * `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__ + * `Convert a PyTorch model and use it for image background removal <../../notebooks/vision-background-removal-with-output.html>`__ .. tab-item:: Get started with C++ diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst b/docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst index 91907cd3ec5..2421ec54c32 100644 --- a/docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst +++ b/docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst @@ -156,16 +156,16 @@ Now that you've installed OpenVINO Runtime, you're ready to run your own machine .. tab-item:: Get started with Python :sync: get-started-py - Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser. + Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser. .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif :width: 400 Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as: - * `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__ - * `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__ - * `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__ + * `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__ + * `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__ + * `Convert a PyTorch model and use it for image background removal <../../notebooks/vision-background-removal-with-output.html>`__ .. tab-item:: Get started with C++ :sync: get-started-cpp diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-archive-windows.rst b/docs/articles_en/get-started/install-openvino/install-openvino-archive-windows.rst index 94b4eb2d7fc..ffa5e612ab1 100644 --- a/docs/articles_en/get-started/install-openvino/install-openvino-archive-windows.rst +++ b/docs/articles_en/get-started/install-openvino/install-openvino-archive-windows.rst @@ -194,16 +194,16 @@ Now that you've installed OpenVINO Runtime, you're ready to run your own machine .. tab-item:: Get started with Python :sync: get-started-py - Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser. + Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser. .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif :width: 400 Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as: - * `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__ - * `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__ - * `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__ + * `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__ + * `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__ + * `Convert a PyTorch model and use it for image background removal <../../notebooks/vision-background-removal-with-output.html>`__ .. tab-item:: Get started with C++ :sync: get-started-cpp diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-pip.rst b/docs/articles_en/get-started/install-openvino/install-openvino-pip.rst index 6d00c1bc0b0..ed3c1b12428 100644 --- a/docs/articles_en/get-started/install-openvino/install-openvino-pip.rst +++ b/docs/articles_en/get-started/install-openvino/install-openvino-pip.rst @@ -135,16 +135,16 @@ Now that you've installed OpenVINO Runtime, you're ready to run your own machine .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif :width: 400 -Try the `Python Quick Start Example `__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser. +Try the `Python Quick Start Example `__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser. Get started with Python +++++++++++++++++++++++ Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as: -* `OpenVINO Python API Tutorial `__ -* `Basic image classification program with Hello Image Classification `__ -* `Convert a PyTorch model and use it for image background removal `__ +* `OpenVINO Python API Tutorial `__ +* `Basic image classification program with Hello Image Classification `__ +* `Convert a PyTorch model and use it for image background removal `__ diff --git a/docs/articles_en/learn-openvino/interactive-tutorials-python.rst b/docs/articles_en/learn-openvino/interactive-tutorials-python.rst index b0656649119..98719478526 100644 --- a/docs/articles_en/learn-openvino/interactive-tutorials-python.rst +++ b/docs/articles_en/learn-openvino/interactive-tutorials-python.rst @@ -43,7 +43,7 @@ on how to run and manage the notebooks on your system. Additional Resources ###################### -* `OpenVINO™ Notebooks - Github Repository `_ +* `OpenVINO™ Notebooks - Github Repository `_ * `Binder documentation `_ * `Google Colab `__ diff --git a/docs/articles_en/learn-openvino/interactive-tutorials-python/notebooks-installation.rst b/docs/articles_en/learn-openvino/interactive-tutorials-python/notebooks-installation.rst index c091a5a8d1e..bda27362fbf 100644 --- a/docs/articles_en/learn-openvino/interactive-tutorials-python/notebooks-installation.rst +++ b/docs/articles_en/learn-openvino/interactive-tutorials-python/notebooks-installation.rst @@ -175,7 +175,7 @@ Installing prerequisites .. code-block:: sh - :caption: Source: https://github.com/openvinotoolkit/openvino_notebooks/blob/main/Dockerfile + :caption: Source: https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/Dockerfile FROM quay.io/thoth-station/s2i-thoth-ubi8-py38:v0.29.0 @@ -485,7 +485,7 @@ If you want to launch only one notebook, such as the *Monodepth* notebook, run t .. code:: bash - jupyter lab notebooks/201-vision-monodepth/201-vision-monodepth.ipynb + jupyter lab notebooks/vision-monodepth/vision-monodepth.ipynb Launch All Notebooks ++++++++++++++++++++ @@ -605,7 +605,7 @@ or create an Additional Resources #################### -* `OpenVINO™ Notebooks - Github Repository `_ +* `OpenVINO™ Notebooks - Github Repository `_ .. |launch-jupyter| image:: https://user-images.githubusercontent.com/15709723/120527271-006fd200-c38f-11eb-9935-2d36d50bab9f.gif diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-hf.rst b/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-hf.rst index 571108f07dc..aa5ad1066b5 100644 --- a/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-hf.rst +++ b/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-hf.rst @@ -171,15 +171,15 @@ parameters. Below are some examples of using Optimum-Intel for model conversion and inference: -* `Instruction following using Databricks Dolly 2.0 and OpenVINO `__ -* `Create an LLM-powered Chatbot using OpenVINO `__ +* `Instruction following using Databricks Dolly 2.0 and OpenVINO `__ +* `Create an LLM-powered Chatbot using OpenVINO `__ .. note:: Optimum-Intel can be used for other generative AI models. See - `Stable Diffusion v2.1 using Optimum-Intel OpenVINO `__ + `Stable Diffusion v2.1 using Optimum-Intel OpenVINO `__ and - `Image generation with Stable Diffusion XL and OpenVINO `__ + `Image generation with Stable Diffusion XL and OpenVINO `__ for more examples. Inference Example diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst b/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst index 3a35b00be6e..e7e59ba5755 100644 --- a/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst +++ b/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst @@ -25,7 +25,7 @@ tokenizer conversion for seamless integration into your project. With OpenVINO T * Convert Hugging Face tokenizers into OpenVINO tokenizer and detokenizer for efficient deployment across different environments. See the `conversion example `__ for more details. -* Combine OpenVINO models into a single model. Recommended for specific models, like classifiers or RAG Embedders, where both tokenizer and a model are used once in each pipeline inference. For more information, see the `OpenVINO Tokenizers Notebook `__. +* Combine OpenVINO models into a single model. Recommended for specific models, like classifiers or RAG Embedders, where both tokenizer and a model are used once in each pipeline inference. For more information, see the `OpenVINO Tokenizers Notebook `__. * Add greedy decoding pipeline to text generation models. @@ -273,7 +273,7 @@ Convert Tokenizers: tokenizer, detokenizer = compile_model(ov_tokenizer), compile_model(ov_detokenizer) The result is two OpenVINO models: ``ov_tokenizer`` and ``ov_detokenizer``. -You can find more information and code snippets in the `OpenVINO Tokenizers Notebook `__. +You can find more information and code snippets in the `OpenVINO Tokenizers Notebook `__. 2. Tokenize and Prepare Inputs +++++++++++++++++++++++++++++++ @@ -337,7 +337,7 @@ Additional Resources #################### * `OpenVINO Tokenizers repo `__ -* `OpenVINO Tokenizers Notebook `__ +* `OpenVINO Tokenizers Notebook `__ * `Text generation C++ samples that support most popular models like LLaMA 2 `__ * `OpenVINO GenAI Repo `__ diff --git a/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training.rst b/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training.rst index 618ec460c3f..c1796c87113 100644 --- a/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training.rst +++ b/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training.rst @@ -30,8 +30,8 @@ NNCF Quick Start Examples See the following Jupyter Notebooks for step-by-step examples showing how to add model compression to a PyTorch or Tensorflow training pipeline with NNCF: -- `Quantization Aware Training with NNCF and PyTorch `__. -- `Quantization Aware Training with NNCF and TensorFlow `__. +- `Quantization Aware Training with NNCF and PyTorch `__. +- `Quantization Aware Training with NNCF and TensorFlow `__. Installation #################### @@ -111,6 +111,6 @@ Additional Resources - :doc:`Quantizing Models Post-training ` - `NNCF GitHub repository `__ - `NNCF FAQ `__ -- `Quantization Aware Training with NNCF and PyTorch `__. -- `Quantization Aware Training with NNCF and TensorFlow `__. +- `Quantization Aware Training with NNCF and PyTorch `__. +- `Quantization Aware Training with NNCF and TensorFlow `__. diff --git a/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/quantization-aware-training.rst b/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/quantization-aware-training.rst index d39303396eb..cce63315939 100644 --- a/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/quantization-aware-training.rst +++ b/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/quantization-aware-training.rst @@ -228,7 +228,7 @@ required in this case. For more details, see the corresponding :doc:`documentati Examples #################### -* `Quantizing PyTorch model with NNCF `__ +* `Quantizing PyTorch model with NNCF `__ -* `Quantizing TensorFlow model with NNCF `__ +* `Quantizing TensorFlow model with NNCF `__ diff --git a/docs/articles_en/openvino-workflow/model-preparation/convert-model-pytorch.rst b/docs/articles_en/openvino-workflow/model-preparation/convert-model-pytorch.rst index 2a6eb48a79b..a395e5ed942 100644 --- a/docs/articles_en/openvino-workflow/model-preparation/convert-model-pytorch.rst +++ b/docs/articles_en/openvino-workflow/model-preparation/convert-model-pytorch.rst @@ -124,8 +124,8 @@ If your model has a ``dict`` input, such as, ``{"x": a, "y": b, "z": c}``, it wi Check out more examples of model conversion with non-tensor data types in the following tutorials: -* `Video Subtitle Generation using Whisper and OpenVINO™ `__ -* `Visual Question Answering and Image Captioning using BLIP and OpenVINO `__ +* `Video Subtitle Generation using Whisper and OpenVINO™ `__ +* `Visual Question Answering and Image Captioning using BLIP and OpenVINO `__ Input and output names of the model ################################### diff --git a/docs/articles_en/openvino-workflow/model-preparation/convert-model-to-ir.rst b/docs/articles_en/openvino-workflow/model-preparation/convert-model-to-ir.rst index 9220b5d5c32..ac008631e49 100644 --- a/docs/articles_en/openvino-workflow/model-preparation/convert-model-to-ir.rst +++ b/docs/articles_en/openvino-workflow/model-preparation/convert-model-to-ir.rst @@ -61,7 +61,7 @@ Here are code examples of how to use these methods with different model formats: For more details on conversion, refer to the :doc:`guide ` - and an example `tutorial `__ + and an example `tutorial `__ on this topic. .. tab-item:: TensorFlow @@ -105,7 +105,7 @@ Here are code examples of how to use these methods with different model formats: For more details on conversion, refer to the :doc:`guide ` - and an example `tutorial `__ + and an example `tutorial `__ on this topic. * The ``read_model()`` and ``compile_model()`` methods: @@ -211,7 +211,7 @@ Here are code examples of how to use these methods with different model formats: For more details on conversion, refer to the :doc:`guide ` - and an example `tutorial `__ + and an example `tutorial `__ on this topic. @@ -336,7 +336,7 @@ Here are code examples of how to use these methods with different model formats: For more details on conversion, refer to the :doc:`guide ` - and an example `tutorial `__ + and an example `tutorial `__ on this topic. @@ -464,7 +464,7 @@ Here are code examples of how to use these methods with different model formats: For more details on conversion, refer to the :doc:`guide ` - and an example `tutorial `__ + and an example `tutorial `__ on this topic. * The ``read_model()`` method: diff --git a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection.rst b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection.rst index c17afa0ad05..80471869497 100644 --- a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection.rst +++ b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection.rst @@ -314,8 +314,8 @@ asynchronous inference pipeline. For information on asynchronous inference, see The following notebooks provide examples of how to set up an asynchronous pipeline: * :doc:`Image Classification Async Sample <../../../learn-openvino/openvino-samples/image-classification-async>` -* `Notebook - Asynchronous Inference with OpenVINO™ <./../../../notebooks/115-async-api-with-output.html>`__ -* `Notebook - Automatic Device Selection with OpenVINO <./../../../notebooks/106-auto-device-with-output.html>`__ +* `Notebook - Asynchronous Inference with OpenVINO™ <./../../../notebooks/async-api-with-output.html>`__ +* `Notebook - Automatic Device Selection with OpenVINO <./../../../notebooks/auto-device-with-output.html>`__ LATENCY -------------------- diff --git a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.rst b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.rst index ae5bd055fbb..f0b5505e867 100644 --- a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.rst +++ b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.rst @@ -153,7 +153,7 @@ guaranteed. Additional Resources ############################# -* `Vision colorization Notebook `__ +* `Vision colorization Notebook `__ * `Classification Benchmark C++ Demo `__ * `3D Human Pose Estimation Python Demo `__ * `Object Detection C++ Demo `__ diff --git a/docs/articles_en/openvino-workflow/running-inference/stateful-models.rst b/docs/articles_en/openvino-workflow/running-inference/stateful-models.rst index 735f30b07dd..19517adf0af 100644 --- a/docs/articles_en/openvino-workflow/running-inference/stateful-models.rst +++ b/docs/articles_en/openvino-workflow/running-inference/stateful-models.rst @@ -141,5 +141,5 @@ sequences. You can find more examples demonstrating how to work with states in other articles: -* `LLM Chatbot notebook <../../notebooks/273-stable-zephyr-3b-chatbot-with-output.html>`__ +* `LLM Chatbot notebook <../../notebooks/stable-zephyr-3b-chatbot-with-output.html>`__ * :doc:`Serving Stateful Models with OpenVINO Model Server <../../ovms_docs_stateful_models>` diff --git a/docs/nbdoc/consts.py b/docs/nbdoc/consts.py index 3993b7835c1..4c069874647 100644 --- a/docs/nbdoc/consts.py +++ b/docs/nbdoc/consts.py @@ -6,11 +6,11 @@ repo_directory = "notebooks" repo_owner = "openvinotoolkit" repo_name = "openvino_notebooks" repo_branch = "tree/main" -artifacts_link = "http://repository.toolbox.iotg.sclab.intel.com/projects/ov-notebook/0.1.0-latest/20240312220809/dist/rst_files/" +artifacts_link = "http://repository.toolbox.iotg.sclab.intel.com/projects/ov-notebook/0.1.0-latest/20240417220808/dist/rst_files/" blacklisted_extensions = ['.xml', '.bin'] -notebooks_repo = "https://github.com/openvinotoolkit/openvino_notebooks/blob/main/" +notebooks_repo = "https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/" notebooks_binder = "https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=" -notebooks_colab = "https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/" +notebooks_colab = "https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/" file_with_binder_notebooks = Path('../../docs/notebooks/notebooks_with_binder_buttons.txt').resolve(strict=True) file_with_colab_notebooks = Path('../../docs/notebooks/notebooks_with_colab_buttons.txt').resolve(strict=True) openvino_notebooks_ipynb_list = Path('../../docs/notebooks/all_notebooks_paths.txt').resolve(strict=True) diff --git a/docs/nbdoc/nbdoc.py b/docs/nbdoc/nbdoc.py index ed390586164..dd6bb0eaa29 100644 --- a/docs/nbdoc/nbdoc.py +++ b/docs/nbdoc/nbdoc.py @@ -37,8 +37,6 @@ import os import re import sys -matching_notebooks_paths = [] - def fetch_binder_list(binder_list_file) -> list: """Function that fetches list of notebooks with binder buttons @@ -127,25 +125,6 @@ class NbProcessor: def __init__(self, nb_path: str = notebooks_path): self.nb_path = nb_path - with open(openvino_notebooks_ipynb_list, 'r+', encoding='cp437') as ipynb_file: - openvino_notebooks_paths_list = ipynb_file.readlines() - - for notebook_name in [ - nb for nb in os.listdir(self.nb_path) if - verify_notebook_name(nb) - ]: - - if not os.path.exists(openvino_notebooks_ipynb_list): - raise FileNotFoundError("all_notebooks_paths.txt is not found") - else: - ipynb_list = [x for x in openvino_notebooks_paths_list if re.match("notebooks/[0-9]{3}.*\.ipynb$", x)] - notebook_with_ext = notebook_name[:-16] + ".ipynb" - matching_notebooks = [re.sub('[\n]', '', match) for match in ipynb_list if notebook_with_ext in match] - - if matching_notebooks is not None: - for n in matching_notebooks: - matching_notebooks_paths.append(n) - def add_binder(self, buttons_list: list, cbuttons_list: list, template_with_colab_and_binder: str = binder_colab_template, template_without_binder: str = no_binder_template): """A function working as an example of how to add Binder or Google Colab buttons to existing RST files. @@ -161,22 +140,30 @@ class NbProcessor: """ - for notebook_file, nb_path in zip([ - nb for nb in os.listdir(self.nb_path) if verify_notebook_name(nb) - ], matching_notebooks_paths): + if not os.path.exists(openvino_notebooks_ipynb_list): + raise FileNotFoundError("all_notebooks_paths.txt is not found") + else: + with open(openvino_notebooks_ipynb_list, 'r+', encoding='cp437') as ipynb_file: + openvino_notebooks_paths_list = ipynb_file.read() + for notebook_file in [nb for nb in os.listdir(self.nb_path) if verify_notebook_name(nb)]: + + notebook_ipynb_ext = notebook_file[:-16] + ".ipynb" + nb_path_match = [line for line in openvino_notebooks_paths_list.split('\n') if notebook_ipynb_ext in line] + nb_repo_path = ''.join(nb_path_match) notebook_item = '-'.join(notebook_file.split('-')[:-2]) + local_install = ".. |installation_link| raw:: html\n\n local installation \n\n" binder_badge = ".. raw:: html\n\n Binder\n\n" colab_badge = ".. raw:: html\n\n Google Colab\n\n" github_badge = ".. raw:: html\n\n Github

\n\n" binder_data = { diff --git a/docs/nbdoc/utils.py b/docs/nbdoc/utils.py index bbef186640b..6533c23a3de 100644 --- a/docs/nbdoc/utils.py +++ b/docs/nbdoc/utils.py @@ -47,11 +47,11 @@ def verify_notebook_name(notebook_name: str) -> bool: """Verification based on notebook name :param notebook_name: Notebook name by default keeps convention: - [3 digit]-name-with-dashes-with-output.rst, - example: 001-hello-world-with-output.rst + name-with-dashes-with-output.rst, + example: hello-world-with-output.rst :type notebook_name: str :returns: Return if notebook meets requirements :rtype: bool """ - return notebook_name[:3].isdigit() and notebook_name[-4:] == ".rst" \ No newline at end of file + return notebook_name[-16:] == "-with-output.rst" \ No newline at end of file diff --git a/docs/notebooks/001-hello-world-with-output_files/001-hello-world-with-output_11_1.png b/docs/notebooks/001-hello-world-with-output_files/001-hello-world-with-output_11_1.png deleted file mode 100644 index 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a/docs/notebooks/103-paddle-to-openvino-classification-with-output_files/103-paddle-to-openvino-classification-with-output_30_1.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1381e5922057c6bc70eb4ba9a04f3164382ad01191d320c1acbc819e7261f8c1 -size 224886 diff --git a/docs/notebooks/103-paddle-to-openvino-classification-with-output_files/103-paddle-to-openvino-classification-with-output_8_2.png b/docs/notebooks/103-paddle-to-openvino-classification-with-output_files/103-paddle-to-openvino-classification-with-output_8_2.png deleted file mode 100644 index 35c91e327be..00000000000 --- a/docs/notebooks/103-paddle-to-openvino-classification-with-output_files/103-paddle-to-openvino-classification-with-output_8_2.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1381e5922057c6bc70eb4ba9a04f3164382ad01191d320c1acbc819e7261f8c1 -size 224886 diff --git a/docs/notebooks/104-model-tools-with-output.rst b/docs/notebooks/104-model-tools-with-output.rst deleted file mode 100644 index ed9b11c69f1..00000000000 --- a/docs/notebooks/104-model-tools-with-output.rst +++ /dev/null @@ -1,1034 +0,0 @@ -Working with Open Model Zoo Models -================================== - -This tutorial shows how to download a model from `Open Model -Zoo `__, convert it -to OpenVINO™ IR format, show information about the model, and benchmark -the model. - -Table of contents: -^^^^^^^^^^^^^^^^^^ - -- `OpenVINO and Open Model Zoo - Tools <#openvino-and-open-model-zoo-tools>`__ -- `Preparation <#preparation>`__ - - - `Model Name <#model-name>`__ - - `Imports <#imports>`__ - - `Settings and Configuration <#settings-and-configuration>`__ - -- `Download a Model from Open Model - Zoo <#download-a-model-from-open-model-zoo>`__ -- `Convert a Model to OpenVINO IR - format <#convert-a-model-to-openvino-ir-format>`__ -- `Get Model Information <#get-model-information>`__ -- `Run Benchmark Tool <#run-benchmark-tool>`__ - - - `Benchmark with Different - Settings <#benchmark-with-different-settings>`__ - -OpenVINO and Open Model Zoo Tools ---------------------------------- - - - -OpenVINO and Open Model Zoo tools are listed in the table below. - -+------------+--------------+-----------------------------------------+ -| Tool | Command | Description | -+============+==============+=========================================+ -| Model | ``omz_downlo | Download models from Open Model Zoo. | -| Downloader | ader`` | | -+------------+--------------+-----------------------------------------+ -| Model | ``omz_conver | Convert Open Model Zoo models to | -| Converter | ter`` | OpenVINO’s IR format. | -+------------+--------------+-----------------------------------------+ -| Info | ``omz_info_d | Print information about Open Model Zoo | -| Dumper | umper`` | models. | -+------------+--------------+-----------------------------------------+ -| Benchmark | ``benchmark_ | Benchmark model performance by | -| Tool | app`` | computing inference time. | -+------------+--------------+-----------------------------------------+ - -.. code:: ipython3 - - # Install openvino package - %pip install -q "openvino-dev>=2024.0.0" - - -.. parsed-literal:: - - Note: you may need to restart the kernel to use updated packages. - - -Preparation ------------ - - - -Model Name -~~~~~~~~~~ - - - -Set ``model_name`` to the name of the Open Model Zoo model to use in -this notebook. Refer to the list of -`public `__ -and -`Intel `__ -pre-trained models for a full list of models that can be used. Set -``model_name`` to the model you want to use. - -.. code:: ipython3 - - # model_name = "resnet-50-pytorch" - model_name = "mobilenet-v2-pytorch" - -Imports -~~~~~~~ - - - -.. code:: ipython3 - - import json - from pathlib import Path - - import openvino as ov - from IPython.display import Markdown, display - - # Fetch `notebook_utils` module - import urllib.request - urllib.request.urlretrieve( - url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py', - filename='notebook_utils.py' - ) - from notebook_utils import DeviceNotFoundAlert, NotebookAlert - -Settings and Configuration -~~~~~~~~~~~~~~~~~~~~~~~~~~ - - - -Set the file and directory paths. By default, this notebook downloads -models from Open Model Zoo to the ``open_model_zoo_models`` directory in -your ``$HOME`` directory. On Windows, the $HOME directory is usually -``c:\users\username``, on Linux ``/home/username``. To change the -folder, change ``base_model_dir`` in the cell below. - -The following settings can be changed: - -- ``base_model_dir``: Models will be downloaded into the ``intel`` and - ``public`` folders in this directory. -- ``omz_cache_dir``: Cache folder for Open Model Zoo. Specifying a - cache directory is not required for Model Downloader and Model - Converter, but it speeds up subsequent downloads. -- ``precision``: If specified, only models with this precision will be - downloaded and converted. - -.. code:: ipython3 - - base_model_dir = Path("model") - omz_cache_dir = Path("cache") - precision = "FP16" - - # Check if an GPU is available on this system to use with Benchmark App. - core = ov.Core() - gpu_available = "GPU" in core.available_devices - - print( - f"base_model_dir: {base_model_dir}, omz_cache_dir: {omz_cache_dir}, gpu_availble: {gpu_available}" - ) - - -.. parsed-literal:: - - base_model_dir: model, omz_cache_dir: cache, gpu_availble: False - - -Download a Model from Open Model Zoo ------------------------------------- - - - -Specify, display and run the Model Downloader command to download the -model. - -.. code:: ipython3 - - ## Uncomment the next line to show help in omz_downloader which explains the command-line options. - - # !omz_downloader --help - -.. code:: ipython3 - - download_command = ( - f"omz_downloader --name {model_name} --output_dir {base_model_dir} --cache_dir {omz_cache_dir}" - ) - display(Markdown(f"Download command: `{download_command}`")) - display(Markdown(f"Downloading {model_name}...")) - ! $download_command - - - -Download command: -``omz_downloader --name mobilenet-v2-pytorch --output_dir model --cache_dir cache`` - - - -Downloading mobilenet-v2-pytorch… - - -.. parsed-literal:: - - ################|| Downloading mobilenet-v2-pytorch ||################ - - - -.. parsed-literal:: - - ========== Downloading model/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth - - -.. parsed-literal:: - - ... 0%, 32 KB, 967 KB/s, 0 seconds passed -... 0%, 64 KB, 961 KB/s, 0 seconds passed -... 0%, 96 KB, 1403 KB/s, 0 seconds passed - -.. parsed-literal:: - - ... 0%, 128 KB, 1260 KB/s, 0 seconds passed -... 1%, 160 KB, 1556 KB/s, 0 seconds passed -... 1%, 192 KB, 1836 KB/s, 0 seconds passed -... 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Model conversion may take a while. The -output of the Model Converter command will be displayed. When the -conversion is successful, the last lines of the output will include: -``[ SUCCESS ] Generated IR version 11 model.`` For downloaded models -that are already in OpenVINO IR format, conversion will be skipped. - -.. code:: ipython3 - - ## Uncomment the next line to show Help in omz_converter which explains the command-line options. - - # !omz_converter --help - -.. code:: ipython3 - - convert_command = f"omz_converter --name {model_name} --precisions {precision} --download_dir {base_model_dir} --output_dir {base_model_dir}" - display(Markdown(f"Convert command: `{convert_command}`")) - display(Markdown(f"Converting {model_name}...")) - - ! $convert_command - - - -Convert command: -``omz_converter --name mobilenet-v2-pytorch --precisions FP16 --download_dir model --output_dir model`` - - - -Converting mobilenet-v2-pytorch… - - -.. parsed-literal:: - - ========== Converting mobilenet-v2-pytorch to ONNX - Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-name=mobilenet_v2 --weights=model/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth --import-module=torchvision.models --input-shape=1,3,224,224 --output-file=model/public/mobilenet-v2-pytorch/mobilenet-v2.onnx --input-names=data --output-names=prob - - - -.. parsed-literal:: - - ONNX check passed successfully. - - -.. parsed-literal:: - - - ========== Converting mobilenet-v2-pytorch to IR (FP16) - Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/mobilenet-v2-pytorch/FP16 --model_name=mobilenet-v2-pytorch --input=data '--mean_values=data[123.675,116.28,103.53]' '--scale_values=data[58.624,57.12,57.375]' --reverse_input_channels --output=prob --input_model=model/public/mobilenet-v2-pytorch/mobilenet-v2.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 224, 224]' --compress_to_fp16=True - - - -.. parsed-literal:: - - [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False. - Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html - [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. - Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html - [ SUCCESS ] Generated IR version 11 model. - [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/104-model-tools/model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml - [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/104-model-tools/model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.bin - - - -Get Model Information ---------------------- - - - -The Info Dumper prints the following information for Open Model Zoo -models: - -- Model name -- Description -- Framework that was used to train the model -- License URL -- Precisions supported by the model -- Subdirectory: the location of the downloaded model -- Task type - -This information can be shown by running -``omz_info_dumper --name model_name`` in a terminal. The information can -also be parsed and used in scripts. - -In the next cell, run Info Dumper and use ``json`` to load the -information in a dictionary. - -.. code:: ipython3 - - model_info_output = %sx omz_info_dumper --name $model_name - model_info = json.loads(model_info_output.get_nlstr()) - - if len(model_info) > 1: - NotebookAlert( - f"There are multiple IR files for the {model_name} model. The first model in the " - "omz_info_dumper output will be used for benchmarking. Change " - "`selected_model_info` in the cell below to select a different model from the list.", - "warning", - ) - - model_info - - - - -.. parsed-literal:: - - [{'name': 'mobilenet-v2-pytorch', - 'composite_model_name': None, - 'description': 'MobileNet V2 is image classification model pre-trained on ImageNet dataset. This is a PyTorch* implementation of MobileNetV2 architecture as described in the paper "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation" .\nThe model input is a blob that consists of a single image of "1, 3, 224, 224" in "RGB" order.\nThe model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.', - 'framework': 'pytorch', - 'license_url': 'https://raw.githubusercontent.com/pytorch/vision/master/LICENSE', - 'accuracy_config': '/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/mobilenet-v2-pytorch/accuracy-check.yml', - 'model_config': '/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/mobilenet-v2-pytorch/model.yml', - 'precisions': ['FP16', 'FP32'], - 'subdirectory': 'public/mobilenet-v2-pytorch', - 'task_type': 'classification', - 'input_info': [{'name': 'data', - 'shape': [1, 3, 224, 224], - 'layout': 'NCHW'}], - 'model_stages': []}] - - - -Having information of the model in a JSON file enables extraction of the -path to the model directory, and building the path to the OpenVINO IR -file. - -.. code:: ipython3 - - selected_model_info = model_info[0] - model_path = ( - base_model_dir - / Path(selected_model_info["subdirectory"]) - / Path(f"{precision}/{selected_model_info['name']}.xml") - ) - print(model_path, "exists:", model_path.exists()) - - -.. parsed-literal:: - - model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml exists: True - - -Run Benchmark Tool ------------------- - - - -By default, Benchmark Tool runs inference for 60 seconds in asynchronous -mode on CPU. It returns inference speed as latency (milliseconds per -image) and throughput values (frames per second). - -.. code:: ipython3 - - ## Uncomment the next line to show Help in benchmark_app which explains the command-line options. - # !benchmark_app --help - -.. code:: ipython3 - - benchmark_command = f"benchmark_app -m {model_path} -t 15" - display(Markdown(f"Benchmark command: `{benchmark_command}`")) - display(Markdown(f"Benchmarking {model_name} on CPU with async inference for 15 seconds...")) - - ! $benchmark_command - - - -Benchmark command: -``benchmark_app -m model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml -t 15`` - - - -Benchmarking mobilenet-v2-pytorch on CPU with async inference for 15 -seconds… - - -.. parsed-literal:: - - [Step 1/11] Parsing and validating input arguments - [ INFO ] Parsing input parameters - [Step 2/11] Loading OpenVINO Runtime - [ INFO ] OpenVINO: - [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0 - [ INFO ] - [ INFO ] Device info: - - -.. parsed-literal:: - - [ INFO ] CPU - [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0 - [ INFO ] - [ INFO ] - [Step 3/11] Setting device configuration - [ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT. - [Step 4/11] Reading model files - [ INFO ] Loading model files - - -.. parsed-literal:: - - [ INFO ] Read model took 30.94 ms - [ INFO ] Original model I/O parameters: - [ INFO ] Model inputs: - [ INFO ] data (node: data) : f32 / [N,C,H,W] / [1,3,224,224] - [ INFO ] Model outputs: - [ INFO ] prob (node: prob) : f32 / [...] / [1,1000] - [Step 5/11] Resizing model to match image sizes and given batch - [ INFO ] Model batch size: 1 - [Step 6/11] Configuring input of the model - [ INFO ] Model inputs: - [ INFO ] data (node: data) : u8 / [N,C,H,W] / [1,3,224,224] - [ INFO ] Model outputs: - [ INFO ] prob (node: prob) : f32 / [...] / [1,1000] - [Step 7/11] Loading the model to the device - - -.. parsed-literal:: - - [ INFO ] Compile model took 136.02 ms - [Step 8/11] Querying optimal runtime parameters - [ INFO ] Model: - [ INFO ] NETWORK_NAME: main_graph - [ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6 - [ INFO ] NUM_STREAMS: 6 - [ INFO ] AFFINITY: Affinity.CORE - [ INFO ] INFERENCE_NUM_THREADS: 24 - [ INFO ] PERF_COUNT: NO - [ INFO ] INFERENCE_PRECISION_HINT: - [ INFO ] PERFORMANCE_HINT: THROUGHPUT - [ INFO ] EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE - [ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0 - [ INFO ] ENABLE_CPU_PINNING: True - [ INFO ] SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE - [ INFO ] ENABLE_HYPER_THREADING: True - [ INFO ] EXECUTION_DEVICES: ['CPU'] - [ INFO ] CPU_DENORMALS_OPTIMIZATION: False - [ INFO ] LOG_LEVEL: Level.NO - [ INFO ] CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0 - [ INFO ] DYNAMIC_QUANTIZATION_GROUP_SIZE: 0 - [ INFO ] KV_CACHE_PRECISION: - [Step 9/11] Creating infer requests and preparing input tensors - [ WARNING ] No input files were given for input 'data'!. This input will be filled with random values! - [ INFO ] Fill input 'data' with random values - [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration) - [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop). - [ INFO ] First inference took 6.30 ms - - -.. parsed-literal:: - - [Step 11/11] Dumping statistics report - [ INFO ] Execution Devices:['CPU'] - [ INFO ] Count: 20280 iterations - [ INFO ] Duration: 15006.67 ms - [ INFO ] Latency: - [ INFO ] Median: 4.31 ms - [ INFO ] Average: 4.31 ms - [ INFO ] Min: 2.75 ms - [ INFO ] Max: 13.73 ms - [ INFO ] Throughput: 1351.40 FPS - - -Benchmark with Different Settings -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - - -The ``benchmark_app`` tool displays logging information that is not -always necessary. A more compact result is achieved when the output is -parsed with ``json``. - -The following cells show some examples of ``benchmark_app`` with -different parameters. Below are some useful parameters: - -- ``-d`` A device to use for inference. For example: CPU, GPU, MULTI. - Default: CPU. -- ``-t`` Time expressed in number of seconds to run inference. Default: - 60. -- ``-api`` Use asynchronous (async) or synchronous (sync) inference. - Default: async. -- ``-b`` Batch size. Default: 1. - -Run ``! benchmark_app --help`` to get an overview of all possible -command-line parameters. - -In the next cell, define the ``benchmark_model()`` function that calls -``benchmark_app``. This makes it easy to try different combinations. In -the cell below that, you display available devices on the system. - - **Note**: In this notebook, ``benchmark_app`` runs for 15 seconds to - give a quick indication of performance. For more accurate - performance, it is recommended to run inference for at least one - minute by setting the ``t`` parameter to 60 or higher, and run - ``benchmark_app`` in a terminal/command prompt after closing other - applications. Copy the **benchmark command** and paste it in a - command prompt where you have activated the ``openvino_env`` - environment. - -.. code:: ipython3 - - def benchmark_model(model_xml, device="CPU", seconds=60, api="async", batch=1): - core = ov.Core() - model_path = Path(model_xml) - if ("GPU" in device) and ("GPU" not in core.available_devices): - DeviceNotFoundAlert("GPU") - else: - benchmark_command = f"benchmark_app -m {model_path} -d {device} -t {seconds} -api {api} -b {batch}" - display(Markdown(f"**Benchmark {model_path.name} with {device} for {seconds} seconds with {api} inference**")) - display(Markdown(f"Benchmark command: `{benchmark_command}`")) - - benchmark_output = %sx $benchmark_command - print("command ended") - benchmark_result = [line for line in benchmark_output - if not (line.startswith(r"[") or line.startswith(" ") or line == "")] - print("\n".join(benchmark_result)) - -.. code:: ipython3 - - core = ov.Core() - - # Show devices available for OpenVINO Runtime - for device in core.available_devices: - device_name = core.get_property(device, "FULL_DEVICE_NAME") - print(f"{device}: {device_name}") - - -.. parsed-literal:: - - CPU: Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz - - -You can select inference device using device widget - -.. code:: ipython3 - - import ipywidgets as widgets - - device = widgets.Dropdown( - options=core.available_devices + ["AUTO"], - value='CPU', - description='Device:', - disabled=False, - ) - - device - - - - -.. parsed-literal:: - - Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU') - - - -.. code:: ipython3 - - benchmark_model(model_path, device=device.value, seconds=15, api="async") - - - -**Benchmark mobilenet-v2-pytorch.xml with CPU for 15 seconds with async -inference** - - - -Benchmark command: -``benchmark_app -m model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml -d CPU -t 15 -api async -b 1`` - - -.. parsed-literal:: - - command ended - - diff --git 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8-bit quantization -with accuracy control in post-training mode (without the fine-tuning -pipeline). This notebook uses a fine-tuned -`Wav2Vec2-Base-960h `__ -`PyTorch `__ model trained on the `LibriSpeech ASR -corpus `__. The tutorial is designed to be -extendable to custom models and datasets. It consists of the following -steps: - -- Download and prepare the Wav2Vec2 model and LibriSpeech dataset. -- Define data loading and accuracy validation functionality. -- Model quantization with accuracy control. -- Compare Accuracy of original PyTorch model, OpenVINO FP16 and INT8 - models. -- Compare performance of the original and quantized models. - -The advanced quantization flow allows to apply 8-bit quantization to the -model with control of accuracy metric. This is achieved by keeping the -most impactful operations within the model in the original precision. -The flow is based on the `Basic 8-bit -quantization `__ -and has the following differences: - -- Besides the calibration dataset, a validation dataset is required to - compute the accuracy metric. Both datasets can refer to the same data - in the simplest case. -- Validation function, used to compute accuracy metric is required. It - can be a function that is already available in the source framework - or a custom function. -- Since accuracy validation is run several times during the - quantization process, quantization with accuracy control can take - more time than the Basic 8-bit quantization flow. -- The resulted model can provide smaller performance improvement than - the Basic 8-bit quantization flow because some of the operations are - kept in the original precision. - -.. - - **NOTE**: Currently, 8-bit quantization with accuracy control in NNCF - is available only for models in OpenVINO representation. - -The steps for the quantization with accuracy control are described -below. - -Table of contents: -^^^^^^^^^^^^^^^^^^ - -- `Imports <#imports>`__ -- `Prepare the Model <#prepare-the-model>`__ -- `Prepare LibriSpeech Dataset <#prepare-librispeech-dataset>`__ -- `Prepare calibration dataset <#prepare-calibration-dataset>`__ -- `Prepare validation function <#prepare-validation-function>`__ -- `Run quantization with accuracy - control <#run-quantization-with-accuracy-control>`__ -- `Model Usage Example <#model-usage-example>`__ -- `Compare Accuracy of the Original and Quantized - Models <#compare-accuracy-of-the-original-and-quantized-models>`__ - -.. code:: ipython3 - - %pip install -q "openvino>=2023.1.0" - %pip install -q "nncf>=2.6.0" - %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu soundfile librosa transformers torch datasets torchmetrics - -Imports -------- - - - -.. code:: ipython3 - - import numpy as np - import torch - - from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor - - -.. parsed-literal:: - - 2023-10-10 09:32:06.465943: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. - 2023-10-10 09:32:06.505459: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. - To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. - 2023-10-10 09:32:07.113533: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT - - -Prepare the Model ------------------ - - - -For instantiating PyTorch model class, we should use -``Wav2Vec2ForCTC.from_pretrained`` method with providing model ID for -downloading from HuggingFace hub. Model weights and configuration files -will be downloaded automatically in first time usage. Keep in mind that -downloading the files can take several minutes and depends on your -internet connection. - -Additionally, we can create processor class which is responsible for -model specific pre- and post-processing steps. - -.. code:: ipython3 - - BATCH_SIZE = 1 - MAX_SEQ_LENGTH = 30480 - - - torch_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h", ctc_loss_reduction="mean") - processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") - - -.. parsed-literal:: - - Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-960h and are newly initialized: ['wav2vec2.masked_spec_embed'] - You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. - - -Convert it to the OpenVINO Intermediate Representation (OpenVINO IR) - -.. code:: ipython3 - - import openvino as ov - - - default_input = torch.zeros([1, MAX_SEQ_LENGTH], dtype=torch.float) - ov_model = ov.convert_model(torch_model, example_input=default_input) - - -.. parsed-literal:: - - WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11. - - -.. parsed-literal:: - - [ WARNING ] Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s. - /home/ea/work/ov_venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:595: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! - if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): - /home/ea/work/ov_venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:634: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! - if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): - - -Prepare LibriSpeech Dataset ---------------------------- - - - -For demonstration purposes, we will use short dummy version of -LibriSpeech dataset - ``patrickvonplaten/librispeech_asr_dummy`` to -speed up model evaluation. Model accuracy can be different from reported -in the paper. For reproducing original accuracy, use ``librispeech_asr`` -dataset. - -.. code:: ipython3 - - from datasets import load_dataset - - - dataset = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") - test_sample = dataset[0]["audio"] - - - # define preprocessing function for converting audio to input values for model - def map_to_input(batch): - preprocessed_signal = processor(batch["audio"]["array"], return_tensors="pt", padding="longest", sampling_rate=batch['audio']['sampling_rate']) - input_values = preprocessed_signal.input_values - batch['input_values'] = input_values - return batch - - - # apply preprocessing function to dataset and remove audio column, to save memory as we do not need it anymore - dataset = dataset.map(map_to_input, batched=False, remove_columns=["audio"]) - - -.. parsed-literal:: - - Found cached dataset librispeech_asr_dummy (/home/ea/.cache/huggingface/datasets/patrickvonplaten___librispeech_asr_dummy/clean/2.1.0/f2c70a4d03ab4410954901bde48c54b85ca1b7f9bf7d616e7e2a72b5ee6ddbfc) - Loading cached processed dataset at /home/ea/.cache/huggingface/datasets/patrickvonplaten___librispeech_asr_dummy/clean/2.1.0/f2c70a4d03ab4410954901bde48c54b85ca1b7f9bf7d616e7e2a72b5ee6ddbfc/cache-dcb48242e67b91b1.arrow - - -Prepare calibration dataset ---------------------------- - - - -.. code:: ipython3 - - import nncf - - - def transform_fn(data_item): - """ - Extract the model's input from the data item. - The data item here is the data item that is returned from the data source per iteration. - This function should be passed when the data item cannot be used as model's input. - """ - return np.array(data_item["input_values"]) - - - calibration_dataset = nncf.Dataset(dataset, transform_fn) - - -.. parsed-literal:: - - INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino - - -Prepare validation function ---------------------------- - - - -Define the validation function. - -.. code:: ipython3 - - from torchmetrics import WordErrorRate - from tqdm.notebook import tqdm - - - def validation_fn(model, dataset): - """ - Calculate and returns a metric for the model. - """ - wer = WordErrorRate() - for sample in dataset: - # run infer function on sample - output = model.output(0) - logits = model(np.array(sample['input_values']))[output] - predicted_ids = np.argmax(logits, axis=-1) - transcription = processor.batch_decode(torch.from_numpy(predicted_ids)) - - # update metric on sample result - wer.update(transcription, [sample['text']]) - - result = wer.compute() - - return 1 - result - -Run quantization with accuracy control --------------------------------------- - - - -You should provide the calibration dataset and the validation dataset. -It can be the same dataset. - parameter ``max_drop`` defines the -accuracy drop threshold. The quantization process stops when the -degradation of accuracy metric on the validation dataset is less than -the ``max_drop``. The default value is 0.01. NNCF will stop the -quantization and report an error if the ``max_drop`` value can’t be -reached. - ``drop_type`` defines how the accuracy drop will be -calculated: ABSOLUTE (used by default) or RELATIVE. - -``ranking_subset_size`` - size of a subset that is used to rank layers -by their contribution to the accuracy drop. Default value is 300, and -the more samples it has the better ranking, potentially. Here we use the -value 25 to speed up the execution. - - **NOTE**: Execution can take tens of minutes and requires up to 10 GB - of free memory - -.. code:: ipython3 - - from nncf.quantization.advanced_parameters import AdvancedAccuracyRestorerParameters - from nncf.parameters import ModelType - - quantized_model = nncf.quantize_with_accuracy_control( - ov_model, - calibration_dataset=calibration_dataset, - validation_dataset=calibration_dataset, - validation_fn=validation_fn, - max_drop=0.01, - drop_type=nncf.DropType.ABSOLUTE, - model_type=ModelType.TRANSFORMER, - advanced_accuracy_restorer_parameters=AdvancedAccuracyRestorerParameters( - ranking_subset_size=25 - ), - ) - - -.. parsed-literal:: - - Statistics collection: 24%|███████████████████████████████████▎ | 73/300 [00:12<00:37, 5.98it/s] - Applying Smooth Quant: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 41.01it/s] - - -.. parsed-literal:: - - INFO:nncf:36 ignored nodes was found by name in the NNCFGraph - - -.. parsed-literal:: - - Statistics collection: 24%|███████████████████████████████████▎ | 73/300 [00:22<01:08, 3.31it/s] - Applying Fast Bias correction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 74/74 [00:23<00:00, 3.09it/s] - -.. parsed-literal:: - - INFO:nncf:Validation of initial model was started - - - - - - - -.. parsed-literal:: - - INFO:nncf:Elapsed Time: 00:00:00 - INFO:nncf:Elapsed Time: 00:00:11 - INFO:nncf:Metric of initial model: 0.9469565153121948 - INFO:nncf:Collecting values for each data item using the initial model - INFO:nncf:Elapsed Time: 00:00:09 - INFO:nncf:Validation of quantized model was started - INFO:nncf:Elapsed Time: 00:00:22 - INFO:nncf:Elapsed Time: 00:00:11 - INFO:nncf:Metric of quantized model: 0.5 - INFO:nncf:Collecting values for each data item using the quantized model - INFO:nncf:Elapsed Time: 00:00:06 - INFO:nncf:Accuracy drop: 0.4469565153121948 (DropType.ABSOLUTE) - INFO:nncf:Accuracy drop: 0.4469565153121948 (DropType.ABSOLUTE) - INFO:nncf:Total number of quantized operations in the model: 94 - INFO:nncf:Number of parallel processes to rank quantized operations: 14 - INFO:nncf:ORIGINAL metric is used to rank quantizers - INFO:nncf:Calculating ranking score for groups of quantizers - INFO:nncf:Elapsed Time: 00:04:36 - INFO:nncf:Changing the scope of quantizer nodes was started - INFO:nncf:Reverted 1 operations to the floating-point precision: - __module.wav2vec2.feature_extractor.conv_layers.2.conv/aten::_convolution/Convolution_11 - INFO:nncf:Accuracy drop with the new quantization scope is 0.06173914670944214 (DropType.ABSOLUTE) - INFO:nncf:Reverted 1 operations to the floating-point precision: - __module.wav2vec2.feature_extractor.conv_layers.1.conv/aten::_convolution/Convolution_10 - INFO:nncf:Accuracy drop with the new quantization scope is 0.010434746742248535 (DropType.ABSOLUTE) - INFO:nncf:Reverted 1 operations to the floating-point precision: - __module.wav2vec2.feature_extractor.conv_layers.3.conv/aten::_convolution/Convolution_12 - INFO:nncf:Algorithm completed: achieved required accuracy drop 0.006956517696380615 (DropType.ABSOLUTE) - INFO:nncf:3 out of 94 were reverted back to the floating-point precision: - __module.wav2vec2.feature_extractor.conv_layers.2.conv/aten::_convolution/Convolution_11 - __module.wav2vec2.feature_extractor.conv_layers.1.conv/aten::_convolution/Convolution_10 - __module.wav2vec2.feature_extractor.conv_layers.3.conv/aten::_convolution/Convolution_12 - - -Model Usage Example -------------------- - - - -.. code:: ipython3 - - import IPython.display as ipd - - - ipd.Audio(test_sample["array"], rate=16000) - - - - -.. raw:: html - - - - - - - -Select device for inference - -.. code:: ipython3 - - import ipywidgets as widgets - - core = ov.Core() - - device = widgets.Dropdown( - options=core.available_devices + ["AUTO"], - value='CPU', - description='Device:', - disabled=False, - ) - - device - -.. code:: ipython3 - - compiled_quantized_model = core.compile_model(model=quantized_model, device_name=device.value) - - input_data = np.expand_dims(test_sample["array"], axis=0) - -Next, make a prediction. - -.. code:: ipython3 - - predictions = compiled_quantized_model([input_data])[0] - predicted_ids = np.argmax(predictions, axis=-1) - transcription = processor.batch_decode(torch.from_numpy(predicted_ids)) - transcription - - - - -.. parsed-literal:: - - ['I E O WE WORD I O O FAGGI FARE E BO'] - - - -Compare Accuracy of the Original and Quantized Models ------------------------------------------------------ - - - -- Define dataloader for test dataset. -- Define functions to get inference for PyTorch and OpenVINO models. -- Define functions to compute Word Error Rate. - -.. code:: ipython3 - - # inference function for pytorch - def torch_infer(model, sample): - logits = model(torch.Tensor(sample['input_values'])).logits - # take argmax and decode - predicted_ids = torch.argmax(logits, dim=-1) - transcription = processor.batch_decode(predicted_ids) - return transcription - - - # inference function for openvino - def ov_infer(model, sample): - output = model.output(0) - logits = model(np.array(sample['input_values']))[output] - predicted_ids = np.argmax(logits, axis=-1) - transcription = processor.batch_decode(torch.from_numpy(predicted_ids)) - return transcription - - - def compute_wer(dataset, model, infer_fn): - wer = WordErrorRate() - for sample in tqdm(dataset): - # run infer function on sample - transcription = infer_fn(model, sample) - # update metric on sample result - wer.update(transcription, [sample['text']]) - # finalize metric calculation - result = wer.compute() - return result - -Now, compute WER for the original PyTorch model and quantized model. - -.. code:: ipython3 - - pt_result = compute_wer(dataset, torch_model, torch_infer) - quantized_result = compute_wer(dataset, compiled_quantized_model, ov_infer) - - print(f'[PyTorch] Word Error Rate: {pt_result:.4f}') - print(f'[Quantized OpenVino] Word Error Rate: {quantized_result:.4f}') - - - -.. parsed-literal:: - - 0%| | 0/73 [00:00`__. It uses -`BiT-M-R50x1/1 `__ -model, which is trained on ImageNet-21k. Big Transfer is a recipe for -pre-training image classification models on large supervised datasets -and efficiently fine-tuning them on any given target task. The recipe -achieves excellent performance on a wide variety of tasks, even when -using very few labeled examples from the target dataset. This tutorial -uses OpenVINO backend for performing model quantization in NNCF. - -Table of contents: -^^^^^^^^^^^^^^^^^^ - -- `Prepare Dataset <#prepare-dataset>`__ -- `Plotting data samples <#plotting-data-samples>`__ -- `Model Fine-tuning <#model-fine-tuning>`__ -- `Perform model optimization (IR) - step <#perform-model-optimization-ir-step>`__ -- `Compute accuracy of the TF - model <#compute-accuracy-of-the-tf-model>`__ -- `Compute accuracy of the OpenVINO - model <#compute-accuracy-of-the-openvino-model>`__ -- `Quantize OpenVINO model using - NNCF <#quantize-openvino-model-using-nncf>`__ -- `Compute accuracy of the quantized - model <#compute-accuracy-of-the-quantized-model>`__ -- `Compare FP32 and INT8 accuracy <#compare-fp32-and-int8-accuracy>`__ -- `Compare inference results on one - picture <#compare-inference-results-on-one-picture>`__ - -.. code:: ipython3 - - %pip install -q "openvino>=2024.0.0" "nncf>=2.7.0" "tensorflow-hub>=0.15.0" "tensorflow_datasets" - %pip install -q "scikit-learn>=1.3.2" - - -.. parsed-literal:: - - Note: you may need to restart the kernel to use updated packages. - - -.. parsed-literal:: - - Note: you may need to restart the kernel to use updated packages. - - -.. code:: ipython3 - - import os - import sys - import numpy as np - from pathlib import Path - - from openvino.runtime import Core - import openvino as ov - import nncf - import logging - - sys.path.append("../utils") - from nncf.common.logging.logger import set_log_level - set_log_level(logging.ERROR) - - from sklearn.metrics import accuracy_score - - import tensorflow as tf - import tensorflow_datasets as tfds - import tensorflow_hub as hub - - tfds.core.utils.gcs_utils._is_gcs_disabled = True - os.environ['NO_GCE_CHECK'] = 'true' - - -.. parsed-literal:: - - INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino - - -.. parsed-literal:: - - 2024-03-12 22:55:31.874883: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. - 2024-03-12 22:55:31.909243: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. - To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. - - -.. parsed-literal:: - - 2024-03-12 22:55:32.550119: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT - - -.. code:: ipython3 - - core = Core() - tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) - os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' - - - # For top 5 labels. - MAX_PREDS = 1 - TRAINING_BATCH_SIZE = 128 - BATCH_SIZE = 1 - IMG_SIZE = (256, 256) # Default Imagenet image size - NUM_CLASSES = 10 # For Imagenette dataset - FINE_TUNING_STEPS = 1 - LR = 1e-5 - - MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255) # From Imagenet dataset - STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255) # From Imagenet dataset - - -Prepare Dataset -~~~~~~~~~~~~~~~ - - - -.. code:: ipython3 - - datasets, datasets_info = tfds.load('imagenette/160px', shuffle_files=True, as_supervised=True, with_info=True, - read_config=tfds.ReadConfig(shuffle_seed=0)) - train_ds, validation_ds = datasets['train'], datasets['validation'] - - - -.. parsed-literal:: - - 2024-03-12 22:55:35.401645: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW - 2024-03-12 22:55:35.401677: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07 - 2024-03-12 22:55:35.401681: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07 - 2024-03-12 22:55:35.401861: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2 - 2024-03-12 22:55:35.401877: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3 - 2024-03-12 22:55:35.401880: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration - - -.. code:: ipython3 - - def preprocessing(image, label): - image = tf.image.resize(image, IMG_SIZE) - image = tf.cast(image, tf.float32) / 255.0 - label = tf.one_hot(label, NUM_CLASSES) - return image, label - - train_dataset = (train_ds.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE) - .batch(TRAINING_BATCH_SIZE).prefetch(tf.data.experimental.AUTOTUNE)) - validation_dataset = (validation_ds.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE) - .batch(TRAINING_BATCH_SIZE).prefetch(tf.data.experimental.AUTOTUNE)) - -.. code:: ipython3 - - # Class labels dictionary with imagenette sample names and classes - lbl_dict = dict( - n01440764='tench', - n02102040='English springer', - n02979186='cassette player', - n03000684='chain saw', - n03028079='church', - n03394916='French horn', - n03417042='garbage truck', - n03425413='gas pump', - n03445777='golf ball', - n03888257='parachute' - ) - - # Imagenette samples name index - class_idx_dict = ['n01440764', 'n02102040', 'n02979186', 'n03000684', - 'n03028079', 'n03394916', 'n03417042', 'n03425413', - 'n03445777', 'n03888257'] - - def label_func(key): - return lbl_dict[key] - -Plotting data samples -~~~~~~~~~~~~~~~~~~~~~ - - - -.. code:: ipython3 - - import matplotlib.pyplot as plt - - # Get the class labels from the dataset info - class_labels = datasets_info.features['label'].names - - # Display labels along with the examples - num_examples_to_display = 4 - fig, axes = plt.subplots(nrows=1, ncols=num_examples_to_display, figsize=(10, 5)) - - for i, (image, label_index) in enumerate(train_ds.take(num_examples_to_display)): - label_name = class_labels[label_index.numpy()] - - axes[i].imshow(image.numpy()) - axes[i].set_title(f"{label_func(label_name)}") - axes[i].axis('off') - plt.tight_layout() - plt.show() - - -.. parsed-literal:: - - 2024-03-12 22:55:35.857921: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1] - [[{{node Placeholder/_1}}]] - 2024-03-12 22:55:35.858264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int64 and shape [1] - [[{{node Placeholder/_4}}]] - - -.. parsed-literal:: - - 2024-03-12 22:55:36.118290: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead. - - - -.. image:: 127-tensorflow-bit-image-classification-nncf-quantization-with-output_files/127-tensorflow-bit-image-classification-nncf-quantization-with-output_9_2.png - - -.. code:: ipython3 - - # Get the class labels from the dataset info - class_labels = datasets_info.features['label'].names - - # Display labels along with the examples - num_examples_to_display = 4 - fig, axes = plt.subplots(nrows=1, ncols=num_examples_to_display, figsize=(10, 5)) - - for i, (image, label_index) in enumerate(validation_ds.take(num_examples_to_display)): - label_name = class_labels[label_index.numpy()] - - axes[i].imshow(image.numpy()) - axes[i].set_title(f"{label_func(label_name)}") - axes[i].axis('off') - plt.tight_layout() - plt.show() - - -.. parsed-literal:: - - 2024-03-12 22:55:36.596528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_2' with dtype string and shape [1] - [[{{node Placeholder/_2}}]] - 2024-03-12 22:55:36.596875: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1] - [[{{node Placeholder/_1}}]] - - -.. parsed-literal:: - - 2024-03-12 22:55:36.801171: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead. - - - -.. image:: 127-tensorflow-bit-image-classification-nncf-quantization-with-output_files/127-tensorflow-bit-image-classification-nncf-quantization-with-output_10_2.png - - -Model Fine-tuning -~~~~~~~~~~~~~~~~~ - - - -.. code:: ipython3 - - # Load the Big Transfer model - bit_model_url = "https://www.kaggle.com/models/google/bit/frameworks/TensorFlow2/variations/m-r50x1/versions/1" - bit_m = hub.KerasLayer(bit_model_url, trainable=True) - - # Customize the model for the new task - model = tf.keras.Sequential([ - bit_m, - tf.keras.layers.Dense(NUM_CLASSES, activation='softmax') - ]) - - # Compile the model - model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=LR), - loss='categorical_crossentropy', - metrics=['accuracy']) - - # Fine-tune the model - model.fit(train_dataset.take(3000), - epochs=FINE_TUNING_STEPS, - validation_data=validation_dataset.take(1000)) - model.save("./bit_tf_model/", save_format='tf') - - -.. parsed-literal:: - - 2024-03-12 22:55:45.506934: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1] - [[{{node Placeholder/_1}}]] - 2024-03-12 22:55:45.507317: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [1] - [[{{node Placeholder/_0}}]] - - -.. parsed-literal:: - - 2024-03-12 22:55:46.103799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/global_average_pooling2d/PartitionedCall_grad/global_average_pooling2d/PartitionedCall' with dtype float and shape [?,?,?,?] - [[{{node gradients/global_average_pooling2d/PartitionedCall_grad/global_average_pooling2d/PartitionedCall}}]] - 2024-03-12 22:55:46.103863: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/global_average_pooling2d/PartitionedCall_grad/global_average_pooling2d/PartitionedCall_1' with dtype int32 and shape [2] - [[{{node gradients/global_average_pooling2d/PartitionedCall_grad/global_average_pooling2d/PartitionedCall_1}}]] - 2024-03-12 22:55:46.163066: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall}}]] - 2024-03-12 22:55:46.163144: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_1' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_1}}]] - 2024-03-12 22:55:46.163184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_2' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_2}}]] - 2024-03-12 22:55:46.163219: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_3' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_3}}]] - 2024-03-12 22:55:46.163253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_4' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_4}}]] - 2024-03-12 22:55:46.163287: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_5' with dtype float and shape [32,64] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_5}}]] - 2024-03-12 22:55:46.163322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_6' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_6}}]] - 2024-03-12 22:55:46.163357: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_7' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_7}}]] - 2024-03-12 22:55:46.163392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_8' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_8}}]] - 2024-03-12 22:55:46.163428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_9' with dtype float and shape [32,64] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_9}}]] - 2024-03-12 22:55:46.163462: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_10' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_10}}]] - 2024-03-12 22:55:46.163497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_11' with dtype float - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_11}}]] - 2024-03-12 22:55:46.163532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_12' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_12}}]] - 2024-03-12 22:55:46.163576: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_13' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_13}}]] - 2024-03-12 22:55:46.163608: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_14' with dtype float and shape [?,?,?,2048] - [[{{node gradients/group_norm/StatefulPartitionedCall_grad/group_norm/StatefulPartitionedCall_14}}]] - - -.. parsed-literal:: - - 2024-03-12 22:55:46.920175: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall' with dtype float and shape [?,?,?,2048] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall}}]] - 2024-03-12 22:55:46.920248: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_1' with dtype float and shape [?,?,?,2048] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_1}}]] - 2024-03-12 22:55:46.920285: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_2' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_2}}]] - 2024-03-12 22:55:46.920319: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_6' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_6}}]] - 2024-03-12 22:55:46.920351: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_7' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_7}}]] - 2024-03-12 22:55:46.920382: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_8' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_8}}]] - 2024-03-12 22:55:46.920430: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_9' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_9}}]] - 2024-03-12 22:55:46.920463: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_10' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_10}}]] - 2024-03-12 22:55:46.920496: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_11' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_11}}]] - 2024-03-12 22:55:46.920528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_12' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_12}}]] - 2024-03-12 22:55:46.920560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_13' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_13}}]] - 2024-03-12 22:55:46.920594: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_16' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_16}}]] - 2024-03-12 22:55:46.920626: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_17' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_17}}]] - 2024-03-12 22:55:46.920658: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_18' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_18}}]] - 2024-03-12 22:55:46.920691: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_19' with dtype float - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_19}}]] - 2024-03-12 22:55:46.920724: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_20' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_20}}]] - 2024-03-12 22:55:46.920756: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_21' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_21}}]] - 2024-03-12 22:55:46.920787: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_22' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_22}}]] - 2024-03-12 22:55:46.920819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_23' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_23}}]] - 2024-03-12 22:55:46.920852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_25' with dtype int32 and shape [4,2] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_25}}]] - 2024-03-12 22:55:46.920884: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_26' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_26}}]] - 2024-03-12 22:55:46.920917: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_29' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_29}}]] - 2024-03-12 22:55:46.920950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_30' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_30}}]] - 2024-03-12 22:55:46.920982: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_31' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_31}}]] - 2024-03-12 22:55:46.921014: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_32' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_32}}]] - 2024-03-12 22:55:46.921046: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_33' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_33}}]] - 2024-03-12 22:55:46.921078: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_34' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_34}}]] - 2024-03-12 22:55:46.921113: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_35' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_35}}]] - 2024-03-12 22:55:46.921148: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_38' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_38}}]] - 2024-03-12 22:55:46.921183: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_39' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_39}}]] - 2024-03-12 22:55:46.921217: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_40' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_40}}]] - 2024-03-12 22:55:46.921251: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_41' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_41}}]] - 2024-03-12 22:55:46.921286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_42' with dtype float - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_42}}]] - 2024-03-12 22:55:46.921322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_43' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_43}}]] - 2024-03-12 22:55:46.921357: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_44' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_44}}]] - 2024-03-12 22:55:46.921391: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_45' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_45}}]] - 2024-03-12 22:55:46.921426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_46' with dtype float and shape [?,?,?,2048] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_46}}]] - 2024-03-12 22:55:46.921467: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_50' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_50}}]] - 2024-03-12 22:55:46.921500: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_51' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_51}}]] - 2024-03-12 22:55:46.921536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_52' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_52}}]] - 2024-03-12 22:55:46.921585: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_53' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_53}}]] - 2024-03-12 22:55:46.921618: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_54' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_54}}]] - 2024-03-12 22:55:46.921652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_55' with dtype float and shape [32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_55}}]] - 2024-03-12 22:55:46.921686: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_56' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_56}}]] - 2024-03-12 22:55:46.921719: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_57' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_57}}]] - 2024-03-12 22:55:46.921753: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_60' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_60}}]] - 2024-03-12 22:55:46.921792: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_61' with dtype float and shape [32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_61}}]] - 2024-03-12 22:55:46.921827: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_62' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_62}}]] - 2024-03-12 22:55:46.921863: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_63' with dtype float - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_63}}]] - 2024-03-12 22:55:46.921898: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_64' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_64}}]] - 2024-03-12 22:55:46.921934: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_65' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_65}}]] - 2024-03-12 22:55:46.921969: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_66' with dtype float and shape [?,?,?,2048] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_66}}]] - 2024-03-12 22:55:46.922004: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_67' with dtype float and shape [?,?,?,2048] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_67}}]] - 2024-03-12 22:55:46.922050: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_68' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_68}}]] - 2024-03-12 22:55:46.922084: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_72' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_72}}]] - 2024-03-12 22:55:46.922118: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_73' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_73}}]] - 2024-03-12 22:55:46.922153: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_74' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_74}}]] - 2024-03-12 22:55:46.922187: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_75' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_75}}]] - 2024-03-12 22:55:46.922222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_76' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_76}}]] - 2024-03-12 22:55:46.922256: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_77' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_77}}]] - 2024-03-12 22:55:46.922291: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_78' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_78}}]] - 2024-03-12 22:55:46.922326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_79' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_79}}]] - 2024-03-12 22:55:46.922361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_82' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_82}}]] - 2024-03-12 22:55:46.922396: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_83' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_83}}]] - 2024-03-12 22:55:46.922431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_84' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_84}}]] - 2024-03-12 22:55:46.922465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_85' with dtype float - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_85}}]] - 2024-03-12 22:55:46.922500: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_86' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_86}}]] - 2024-03-12 22:55:46.922535: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_87' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_87}}]] - 2024-03-12 22:55:46.922569: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_88' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_88}}]] - 2024-03-12 22:55:46.922604: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_89' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_89}}]] - 2024-03-12 22:55:46.922639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_91' with dtype int32 and shape [4,2] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_91}}]] - 2024-03-12 22:55:46.922673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_92' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_92}}]] - 2024-03-12 22:55:46.922708: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_95' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_95}}]] - 2024-03-12 22:55:46.922742: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_96' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_96}}]] - 2024-03-12 22:55:46.922776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_97' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_97}}]] - 2024-03-12 22:55:46.922811: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_98' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_98}}]] - 2024-03-12 22:55:46.922845: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_99' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_99}}]] - 2024-03-12 22:55:46.922879: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_100' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_100}}]] - 2024-03-12 22:55:46.922914: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_101' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_101}}]] - 2024-03-12 22:55:46.922949: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_104' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_104}}]] - 2024-03-12 22:55:46.922984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_105' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_105}}]] - 2024-03-12 22:55:46.923018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_106' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_106}}]] - 2024-03-12 22:55:46.923053: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_107' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_107}}]] - 2024-03-12 22:55:46.923087: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_108' with dtype float - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_108}}]] - 2024-03-12 22:55:46.923121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_109' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_109}}]] - 2024-03-12 22:55:46.923156: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_110' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_110}}]] - 2024-03-12 22:55:46.923190: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_111' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_111}}]] - 2024-03-12 22:55:46.923224: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_112' with dtype float and shape [?,?,?,2048] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_112}}]] - 2024-03-12 22:55:46.923258: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_116' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_116}}]] - 2024-03-12 22:55:46.923293: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_117' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_117}}]] - 2024-03-12 22:55:46.923328: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_118' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_118}}]] - 2024-03-12 22:55:46.923362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_119' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_119}}]] - 2024-03-12 22:55:46.923396: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_120' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_120}}]] - 2024-03-12 22:55:46.923431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_121' with dtype float and shape [32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_121}}]] - 2024-03-12 22:55:46.923465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_122' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_122}}]] - 2024-03-12 22:55:46.923500: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_123' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_123}}]] - 2024-03-12 22:55:46.923533: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_126' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_126}}]] - 2024-03-12 22:55:46.923567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_127' with dtype float and shape [32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_127}}]] - 2024-03-12 22:55:46.923601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_128' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_128}}]] - 2024-03-12 22:55:46.923635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_129' with dtype float - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_129}}]] - 2024-03-12 22:55:46.923670: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_130' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_130}}]] - 2024-03-12 22:55:46.923704: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_131' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_131}}]] - 2024-03-12 22:55:46.923739: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_132' with dtype float and shape [?,?,?,2048] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_132}}]] - 2024-03-12 22:55:46.923773: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_133' with dtype float and shape [?,?,?,2048] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_133}}]] - 2024-03-12 22:55:46.923807: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_134' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_134}}]] - 2024-03-12 22:55:46.923841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_136' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_136}}]] - 2024-03-12 22:55:46.923874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_142' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_142}}]] - 2024-03-12 22:55:46.923909: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_143' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_143}}]] - 2024-03-12 22:55:46.923943: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_144' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_144}}]] - 2024-03-12 22:55:46.923978: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_145' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_145}}]] - 2024-03-12 22:55:46.924012: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_146' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_146}}]] - 2024-03-12 22:55:46.924047: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_147' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_147}}]] - 2024-03-12 22:55:46.924080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_148' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_148}}]] - 2024-03-12 22:55:46.924114: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_149' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_149}}]] - 2024-03-12 22:55:46.924149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_154' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_154}}]] - 2024-03-12 22:55:46.924183: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_155' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_155}}]] - 2024-03-12 22:55:46.924217: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_156' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_156}}]] - 2024-03-12 22:55:46.924250: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_157' with dtype float - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_157}}]] - 2024-03-12 22:55:46.924285: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_158' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_158}}]] - 2024-03-12 22:55:46.924319: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_159' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_159}}]] - 2024-03-12 22:55:46.924354: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_160' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_160}}]] - 2024-03-12 22:55:46.924388: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_161' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_161}}]] - 2024-03-12 22:55:46.924422: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_163' with dtype int32 and shape [4,2] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_163}}]] - 2024-03-12 22:55:46.924456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_164' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_164}}]] - 2024-03-12 22:55:46.924491: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_167' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_167}}]] - 2024-03-12 22:55:46.924525: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_168' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_168}}]] - 2024-03-12 22:55:46.924559: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_169' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_169}}]] - 2024-03-12 22:55:46.924592: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_170' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_170}}]] - 2024-03-12 22:55:46.924625: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_171' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_171}}]] - 2024-03-12 22:55:46.924660: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_172' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_172}}]] - 2024-03-12 22:55:46.924694: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_173' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_173}}]] - 2024-03-12 22:55:46.924727: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_176' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_176}}]] - 2024-03-12 22:55:46.924761: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_177' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_177}}]] - 2024-03-12 22:55:46.924795: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_178' with dtype float and shape [32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_178}}]] - 2024-03-12 22:55:46.924829: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_179' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_179}}]] - 2024-03-12 22:55:46.924879: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_180' with dtype float - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_180}}]] - 2024-03-12 22:55:46.924915: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_181' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_181}}]] - 2024-03-12 22:55:46.924950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_182' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_182}}]] - 2024-03-12 22:55:46.924984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_183' with dtype float and shape [?,?,?,512] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_183}}]] - 2024-03-12 22:55:46.925020: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_187' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_187}}]] - 2024-03-12 22:55:46.925055: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_188' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_188}}]] - 2024-03-12 22:55:46.925090: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_189' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_189}}]] - 2024-03-12 22:55:46.925124: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_190' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_190}}]] - 2024-03-12 22:55:46.925159: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_191' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_191}}]] - 2024-03-12 22:55:46.925194: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_192' with dtype float and shape [32,32] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_192}}]] - 2024-03-12 22:55:46.925228: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_193' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_193}}]] - 2024-03-12 22:55:46.925263: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_194' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_194}}]] - 2024-03-12 22:55:46.925298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_197' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_197}}]] - 2024-03-12 22:55:46.925333: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_198' with dtype float and shape [32,32] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_198}}]] - 2024-03-12 22:55:46.925368: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_199' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_199}}]] - 2024-03-12 22:55:46.925402: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_200' with dtype float - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_200}}]] - 2024-03-12 22:55:46.925441: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_201' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_201}}]] - 2024-03-12 22:55:46.925478: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_202' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_202}}]] - 2024-03-12 22:55:46.925513: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_203' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block4/StatefulPartitionedCall_grad/block4/StatefulPartitionedCall_203}}]] - - -.. parsed-literal:: - - 2024-03-12 22:55:48.389526: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall}}]] - 2024-03-12 22:55:48.389618: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_1' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_1}}]] - 2024-03-12 22:55:48.389656: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_2' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_2}}]] - 2024-03-12 22:55:48.389691: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_6' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_6}}]] - 2024-03-12 22:55:48.389726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_7' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_7}}]] - 2024-03-12 22:55:48.389760: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_8' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_8}}]] - 2024-03-12 22:55:48.389794: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_9' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_9}}]] - 2024-03-12 22:55:48.389827: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_10' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_10}}]] - 2024-03-12 22:55:48.389860: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_11' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_11}}]] - 2024-03-12 22:55:48.389892: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_12' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_12}}]] - 2024-03-12 22:55:48.389927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_13' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_13}}]] - 2024-03-12 22:55:48.389959: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_16' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_16}}]] - 2024-03-12 22:55:48.389992: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_17' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_17}}]] - 2024-03-12 22:55:48.390026: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_18' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_18}}]] - 2024-03-12 22:55:48.390059: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_19' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_19}}]] - 2024-03-12 22:55:48.390092: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_20' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_20}}]] - 2024-03-12 22:55:48.390125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_21' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_21}}]] - 2024-03-12 22:55:48.390157: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_22' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_22}}]] - 2024-03-12 22:55:48.390191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_23' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_23}}]] - 2024-03-12 22:55:48.390223: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_25' with dtype int32 and shape [4,2] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_25}}]] - 2024-03-12 22:55:48.390255: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_26' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_26}}]] - 2024-03-12 22:55:48.390288: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_29' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_29}}]] - 2024-03-12 22:55:48.390320: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_30' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_30}}]] - 2024-03-12 22:55:48.390353: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_31' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_31}}]] - 2024-03-12 22:55:48.390385: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_32' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_32}}]] - 2024-03-12 22:55:48.390421: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_33' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_33}}]] - 2024-03-12 22:55:48.390456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_34' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_34}}]] - 2024-03-12 22:55:48.390491: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_35' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_35}}]] - 2024-03-12 22:55:48.390525: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_38' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_38}}]] - 2024-03-12 22:55:48.390559: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_39' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_39}}]] - 2024-03-12 22:55:48.390594: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_40' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_40}}]] - 2024-03-12 22:55:48.390629: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_41' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_41}}]] - 2024-03-12 22:55:48.390663: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_42' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_42}}]] - 2024-03-12 22:55:48.390698: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_43' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_43}}]] - 2024-03-12 22:55:48.390732: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_44' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_44}}]] - 2024-03-12 22:55:48.390767: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_45' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_45}}]] - 2024-03-12 22:55:48.390801: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_46' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_46}}]] - 2024-03-12 22:55:48.390835: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_50' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_50}}]] - 2024-03-12 22:55:48.390870: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_51' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_51}}]] - 2024-03-12 22:55:48.390905: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_52' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_52}}]] - 2024-03-12 22:55:48.390939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_53' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_53}}]] - 2024-03-12 22:55:48.390973: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_54' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_54}}]] - 2024-03-12 22:55:48.391008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_55' with dtype float and shape [32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_55}}]] - 2024-03-12 22:55:48.391043: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_56' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_56}}]] - 2024-03-12 22:55:48.391077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_57' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_57}}]] - 2024-03-12 22:55:48.391110: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_60' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_60}}]] - 2024-03-12 22:55:48.391144: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_61' with dtype float and shape [32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_61}}]] - 2024-03-12 22:55:48.391179: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_62' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_62}}]] - 2024-03-12 22:55:48.391213: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_63' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_63}}]] - 2024-03-12 22:55:48.391247: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_64' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_64}}]] - 2024-03-12 22:55:48.391281: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_65' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_65}}]] - 2024-03-12 22:55:48.391316: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_66' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_66}}]] - 2024-03-12 22:55:48.391350: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_67' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_67}}]] - 2024-03-12 22:55:48.391384: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_68' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_68}}]] - 2024-03-12 22:55:48.391419: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_72' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_72}}]] - 2024-03-12 22:55:48.391455: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_73' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_73}}]] - 2024-03-12 22:55:48.391490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_74' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_74}}]] - 2024-03-12 22:55:48.391524: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_75' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_75}}]] - 2024-03-12 22:55:48.391559: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_76' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_76}}]] - 2024-03-12 22:55:48.391593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_77' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_77}}]] - 2024-03-12 22:55:48.391628: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_78' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_78}}]] - 2024-03-12 22:55:48.391661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_79' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_79}}]] - 2024-03-12 22:55:48.391697: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_82' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_82}}]] - 2024-03-12 22:55:48.391733: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_83' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_83}}]] - 2024-03-12 22:55:48.391768: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_84' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_84}}]] - 2024-03-12 22:55:48.391804: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_85' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_85}}]] - 2024-03-12 22:55:48.391838: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_86' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_86}}]] - 2024-03-12 22:55:48.391874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_87' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_87}}]] - 2024-03-12 22:55:48.391908: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_88' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_88}}]] - 2024-03-12 22:55:48.391943: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_89' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_89}}]] - 2024-03-12 22:55:48.391978: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_91' with dtype int32 and shape [4,2] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_91}}]] - 2024-03-12 22:55:48.392014: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_92' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_92}}]] - 2024-03-12 22:55:48.392049: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_95' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_95}}]] - 2024-03-12 22:55:48.392083: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_96' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_96}}]] - 2024-03-12 22:55:48.392118: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_97' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_97}}]] - 2024-03-12 22:55:48.392152: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_98' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_98}}]] - 2024-03-12 22:55:48.392187: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_99' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_99}}]] - 2024-03-12 22:55:48.392222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_100' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_100}}]] - 2024-03-12 22:55:48.392257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_101' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_101}}]] - 2024-03-12 22:55:48.392292: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_104' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_104}}]] - 2024-03-12 22:55:48.392337: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_105' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_105}}]] - 2024-03-12 22:55:48.392371: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_106' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_106}}]] - 2024-03-12 22:55:48.392404: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_107' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_107}}]] - 2024-03-12 22:55:48.392438: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_108' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_108}}]] - 2024-03-12 22:55:48.392471: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_109' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_109}}]] - 2024-03-12 22:55:48.392505: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_110' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_110}}]] - 2024-03-12 22:55:48.392538: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_111' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_111}}]] - 2024-03-12 22:55:48.392571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_112' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_112}}]] - 2024-03-12 22:55:48.392605: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_116' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_116}}]] - 2024-03-12 22:55:48.392639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_117' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_117}}]] - 2024-03-12 22:55:48.392672: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_118' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_118}}]] - 2024-03-12 22:55:48.392706: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_119' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_119}}]] - 2024-03-12 22:55:48.392740: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_120' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_120}}]] - 2024-03-12 22:55:48.392773: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_121' with dtype float and shape [32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_121}}]] - 2024-03-12 22:55:48.392806: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_122' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_122}}]] - 2024-03-12 22:55:48.392840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_123' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_123}}]] - 2024-03-12 22:55:48.392874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_126' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_126}}]] - 2024-03-12 22:55:48.392908: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_127' with dtype float and shape [32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_127}}]] - 2024-03-12 22:55:48.392941: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_128' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_128}}]] - 2024-03-12 22:55:48.392974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_129' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_129}}]] - 2024-03-12 22:55:48.393008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_130' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_130}}]] - 2024-03-12 22:55:48.393042: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_131' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_131}}]] - 2024-03-12 22:55:48.393076: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_132' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_132}}]] - 2024-03-12 22:55:48.393110: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_133' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_133}}]] - 2024-03-12 22:55:48.393143: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_134' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_134}}]] - 2024-03-12 22:55:48.393176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_138' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_138}}]] - 2024-03-12 22:55:48.393210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_139' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_139}}]] - 2024-03-12 22:55:48.393244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_140' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_140}}]] - 2024-03-12 22:55:48.393277: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_141' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_141}}]] - 2024-03-12 22:55:48.393310: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_142' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_142}}]] - 2024-03-12 22:55:48.393344: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_143' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_143}}]] - 2024-03-12 22:55:48.393378: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_144' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_144}}]] - 2024-03-12 22:55:48.393411: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_145' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_145}}]] - 2024-03-12 22:55:48.393449: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_148' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_148}}]] - 2024-03-12 22:55:48.393501: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_149' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_149}}]] - 2024-03-12 22:55:48.393537: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_150' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_150}}]] - 2024-03-12 22:55:48.393572: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_151' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_151}}]] - 2024-03-12 22:55:48.393621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_152' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_152}}]] - 2024-03-12 22:55:48.393655: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_153' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_153}}]] - 2024-03-12 22:55:48.393689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_154' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_154}}]] - 2024-03-12 22:55:48.393722: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_155' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_155}}]] - 2024-03-12 22:55:48.393756: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_157' with dtype int32 and shape [4,2] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_157}}]] - 2024-03-12 22:55:48.393789: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_158' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_158}}]] - 2024-03-12 22:55:48.393828: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_161' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_161}}]] - 2024-03-12 22:55:48.393864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_162' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_162}}]] - 2024-03-12 22:55:48.393900: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_163' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_163}}]] - 2024-03-12 22:55:48.393935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_164' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_164}}]] - 2024-03-12 22:55:48.393970: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_165' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_165}}]] - 2024-03-12 22:55:48.394006: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_166' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_166}}]] - 2024-03-12 22:55:48.394041: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_167' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_167}}]] - 2024-03-12 22:55:48.394078: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_170' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_170}}]] - 2024-03-12 22:55:48.394114: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_171' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_171}}]] - 2024-03-12 22:55:48.394149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_172' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_172}}]] - 2024-03-12 22:55:48.394185: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_173' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_173}}]] - 2024-03-12 22:55:48.394221: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_174' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_174}}]] - 2024-03-12 22:55:48.394257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_175' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_175}}]] - 2024-03-12 22:55:48.394292: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_176' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_176}}]] - 2024-03-12 22:55:48.394328: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_177' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_177}}]] - 2024-03-12 22:55:48.394363: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_178' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_178}}]] - 2024-03-12 22:55:48.394399: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_182' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_182}}]] - 2024-03-12 22:55:48.394435: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_183' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_183}}]] - 2024-03-12 22:55:48.394471: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_184' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_184}}]] - 2024-03-12 22:55:48.394506: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_185' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_185}}]] - 2024-03-12 22:55:48.394542: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_186' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_186}}]] - 2024-03-12 22:55:48.394578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_187' with dtype float and shape [32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_187}}]] - 2024-03-12 22:55:48.394613: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_188' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_188}}]] - 2024-03-12 22:55:48.394659: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_189' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_189}}]] - 2024-03-12 22:55:48.394694: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_192' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_192}}]] - 2024-03-12 22:55:48.394729: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_193' with dtype float and shape [32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_193}}]] - 2024-03-12 22:55:48.394764: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_194' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_194}}]] - 2024-03-12 22:55:48.394799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_195' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_195}}]] - 2024-03-12 22:55:48.394834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_196' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_196}}]] - 2024-03-12 22:55:48.394869: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_197' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_197}}]] - 2024-03-12 22:55:48.394903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_198' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_198}}]] - 2024-03-12 22:55:48.394938: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_199' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_199}}]] - 2024-03-12 22:55:48.394974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_200' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_200}}]] - 2024-03-12 22:55:48.395009: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_204' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_204}}]] - 2024-03-12 22:55:48.395043: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_205' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_205}}]] - 2024-03-12 22:55:48.395078: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_206' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_206}}]] - 2024-03-12 22:55:48.395113: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_207' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_207}}]] - 2024-03-12 22:55:48.395148: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_208' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_208}}]] - 2024-03-12 22:55:48.395183: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_209' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_209}}]] - 2024-03-12 22:55:48.395217: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_210' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_210}}]] - 2024-03-12 22:55:48.395251: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_211' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_211}}]] - 2024-03-12 22:55:48.395286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_214' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_214}}]] - 2024-03-12 22:55:48.395321: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_215' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_215}}]] - 2024-03-12 22:55:48.395356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_216' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_216}}]] - 2024-03-12 22:55:48.395391: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_217' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_217}}]] - 2024-03-12 22:55:48.395425: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_218' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_218}}]] - 2024-03-12 22:55:48.395460: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_219' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_219}}]] - 2024-03-12 22:55:48.395495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_220' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_220}}]] - 2024-03-12 22:55:48.395530: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_221' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_221}}]] - 2024-03-12 22:55:48.395565: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_223' with dtype int32 and shape [4,2] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_223}}]] - 2024-03-12 22:55:48.395601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_224' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_224}}]] - 2024-03-12 22:55:48.395635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_227' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_227}}]] - 2024-03-12 22:55:48.395670: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_228' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_228}}]] - 2024-03-12 22:55:48.395705: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_229' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_229}}]] - 2024-03-12 22:55:48.395740: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_230' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_230}}]] - 2024-03-12 22:55:48.395775: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_231' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_231}}]] - 2024-03-12 22:55:48.395810: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_232' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_232}}]] - 2024-03-12 22:55:48.395845: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_233' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_233}}]] - 2024-03-12 22:55:48.395879: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_236' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_236}}]] - 2024-03-12 22:55:48.395914: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_237' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_237}}]] - 2024-03-12 22:55:48.395950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_238' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_238}}]] - 2024-03-12 22:55:48.395984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_239' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_239}}]] - 2024-03-12 22:55:48.396019: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_240' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_240}}]] - 2024-03-12 22:55:48.396054: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_241' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_241}}]] - 2024-03-12 22:55:48.396088: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_242' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_242}}]] - 2024-03-12 22:55:48.396122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_243' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_243}}]] - 2024-03-12 22:55:48.396157: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_244' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_244}}]] - 2024-03-12 22:55:48.396193: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_248' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_248}}]] - 2024-03-12 22:55:48.396229: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_249' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_249}}]] - 2024-03-12 22:55:48.396263: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_250' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_250}}]] - 2024-03-12 22:55:48.396298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_251' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_251}}]] - 2024-03-12 22:55:48.396332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_252' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_252}}]] - 2024-03-12 22:55:48.396368: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_253' with dtype float and shape [32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_253}}]] - 2024-03-12 22:55:48.396403: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_254' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_254}}]] - 2024-03-12 22:55:48.396437: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_255' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_255}}]] - 2024-03-12 22:55:48.396472: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_258' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_258}}]] - 2024-03-12 22:55:48.396508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_259' with dtype float and shape [32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_259}}]] - 2024-03-12 22:55:48.396543: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_260' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_260}}]] - 2024-03-12 22:55:48.396578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_261' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_261}}]] - 2024-03-12 22:55:48.396613: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_262' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_262}}]] - 2024-03-12 22:55:48.396648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_263' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_263}}]] - 2024-03-12 22:55:48.396682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_264' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_264}}]] - 2024-03-12 22:55:48.396716: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_265' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_265}}]] - 2024-03-12 22:55:48.396750: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_266' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_266}}]] - 2024-03-12 22:55:48.396785: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_270' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_270}}]] - 2024-03-12 22:55:48.396818: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_271' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_271}}]] - 2024-03-12 22:55:48.396852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_272' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_272}}]] - 2024-03-12 22:55:48.396887: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_273' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_273}}]] - 2024-03-12 22:55:48.396922: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_274' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_274}}]] - 2024-03-12 22:55:48.396958: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_275' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_275}}]] - 2024-03-12 22:55:48.396994: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_276' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_276}}]] - 2024-03-12 22:55:48.397030: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_277' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_277}}]] - 2024-03-12 22:55:48.397064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_280' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_280}}]] - 2024-03-12 22:55:48.397099: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_281' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_281}}]] - 2024-03-12 22:55:48.397134: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_282' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_282}}]] - 2024-03-12 22:55:48.397169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_283' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_283}}]] - 2024-03-12 22:55:48.397204: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_284' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_284}}]] - 2024-03-12 22:55:48.397239: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_285' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_285}}]] - 2024-03-12 22:55:48.397274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_286' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_286}}]] - 2024-03-12 22:55:48.397308: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_287' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_287}}]] - 2024-03-12 22:55:48.397343: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_289' with dtype int32 and shape [4,2] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_289}}]] - 2024-03-12 22:55:48.397378: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_290' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_290}}]] - 2024-03-12 22:55:48.397412: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_293' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_293}}]] - 2024-03-12 22:55:48.397465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_294' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_294}}]] - 2024-03-12 22:55:48.397501: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_295' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_295}}]] - 2024-03-12 22:55:48.397536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_296' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_296}}]] - 2024-03-12 22:55:48.397571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_297' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_297}}]] - 2024-03-12 22:55:48.397607: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_298' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_298}}]] - 2024-03-12 22:55:48.397642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_299' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_299}}]] - 2024-03-12 22:55:48.397676: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_302' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_302}}]] - 2024-03-12 22:55:48.397710: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_303' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_303}}]] - 2024-03-12 22:55:48.397745: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_304' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_304}}]] - 2024-03-12 22:55:48.397780: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_305' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_305}}]] - 2024-03-12 22:55:48.397815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_306' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_306}}]] - 2024-03-12 22:55:48.397851: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_307' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_307}}]] - 2024-03-12 22:55:48.397885: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_308' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_308}}]] - 2024-03-12 22:55:48.397919: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_309' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_309}}]] - 2024-03-12 22:55:48.397953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_310' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_310}}]] - 2024-03-12 22:55:48.397988: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_314' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_314}}]] - 2024-03-12 22:55:48.398023: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_315' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_315}}]] - 2024-03-12 22:55:48.398057: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_316' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_316}}]] - 2024-03-12 22:55:48.398091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_317' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_317}}]] - 2024-03-12 22:55:48.398126: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_318' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_318}}]] - 2024-03-12 22:55:48.398160: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_319' with dtype float and shape [32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_319}}]] - 2024-03-12 22:55:48.398195: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_320' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_320}}]] - 2024-03-12 22:55:48.398229: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_321' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_321}}]] - 2024-03-12 22:55:48.398263: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_324' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_324}}]] - 2024-03-12 22:55:48.398298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_325' with dtype float and shape [32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_325}}]] - 2024-03-12 22:55:48.398332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_326' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_326}}]] - 2024-03-12 22:55:48.398366: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_327' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_327}}]] - 2024-03-12 22:55:48.398400: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_328' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_328}}]] - 2024-03-12 22:55:48.398435: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_329' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_329}}]] - 2024-03-12 22:55:48.398470: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_330' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_330}}]] - 2024-03-12 22:55:48.398503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_331' with dtype float and shape [?,?,?,1024] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_331}}]] - 2024-03-12 22:55:48.398537: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_332' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_332}}]] - 2024-03-12 22:55:48.398572: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_334' with dtype float and shape [?,?,?,512] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_334}}]] - 2024-03-12 22:55:48.398607: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_340' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_340}}]] - 2024-03-12 22:55:48.398642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_341' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_341}}]] - 2024-03-12 22:55:48.398676: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_342' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_342}}]] - 2024-03-12 22:55:48.398712: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_343' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_343}}]] - 2024-03-12 22:55:48.398747: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_344' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_344}}]] - 2024-03-12 22:55:48.398781: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_345' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_345}}]] - 2024-03-12 22:55:48.398815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_346' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_346}}]] - 2024-03-12 22:55:48.398849: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_347' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_347}}]] - 2024-03-12 22:55:48.398884: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_352' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_352}}]] - 2024-03-12 22:55:48.398919: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_353' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_353}}]] - 2024-03-12 22:55:48.398953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_354' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_354}}]] - 2024-03-12 22:55:48.398987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_355' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_355}}]] - 2024-03-12 22:55:48.399022: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_356' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_356}}]] - 2024-03-12 22:55:48.399056: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_357' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_357}}]] - 2024-03-12 22:55:48.399091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_358' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_358}}]] - 2024-03-12 22:55:48.399125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_359' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_359}}]] - 2024-03-12 22:55:48.399160: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_361' with dtype int32 and shape [4,2] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_361}}]] - 2024-03-12 22:55:48.399194: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_362' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_362}}]] - 2024-03-12 22:55:48.399228: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_365' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_365}}]] - 2024-03-12 22:55:48.399263: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_366' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_366}}]] - 2024-03-12 22:55:48.399298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_367' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_367}}]] - 2024-03-12 22:55:48.399333: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_368' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_368}}]] - 2024-03-12 22:55:48.399367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_369' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_369}}]] - 2024-03-12 22:55:48.399401: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_370' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_370}}]] - 2024-03-12 22:55:48.399435: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_371' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_371}}]] - 2024-03-12 22:55:48.399469: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_374' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_374}}]] - 2024-03-12 22:55:48.399503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_375' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_375}}]] - 2024-03-12 22:55:48.399537: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_376' with dtype float and shape [32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_376}}]] - 2024-03-12 22:55:48.399570: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_377' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_377}}]] - 2024-03-12 22:55:48.399605: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_378' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_378}}]] - 2024-03-12 22:55:48.399638: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_379' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_379}}]] - 2024-03-12 22:55:48.399673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_380' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_380}}]] - 2024-03-12 22:55:48.399708: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_381' with dtype float and shape [?,?,?,256] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_381}}]] - 2024-03-12 22:55:48.399742: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_385' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_385}}]] - 2024-03-12 22:55:48.399776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_386' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_386}}]] - 2024-03-12 22:55:48.399811: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_387' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_387}}]] - 2024-03-12 22:55:48.399845: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_388' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_388}}]] - 2024-03-12 22:55:48.399879: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_389' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_389}}]] - 2024-03-12 22:55:48.399914: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_390' with dtype float and shape [32,16] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_390}}]] - 2024-03-12 22:55:48.399949: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_391' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_391}}]] - 2024-03-12 22:55:48.399983: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_392' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_392}}]] - 2024-03-12 22:55:48.400017: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_395' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_395}}]] - 2024-03-12 22:55:48.400051: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_396' with dtype float and shape [32,16] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_396}}]] - 2024-03-12 22:55:48.400086: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_397' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_397}}]] - 2024-03-12 22:55:48.400121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_398' with dtype float - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_398}}]] - 2024-03-12 22:55:48.400156: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_399' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_399}}]] - 2024-03-12 22:55:48.400190: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_400' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_400}}]] - 2024-03-12 22:55:48.400224: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_401' with dtype float and shape [?,?,?,512] - [[{{node gradients/block3/StatefulPartitionedCall_grad/block3/StatefulPartitionedCall_401}}]] - - -.. parsed-literal:: - - 2024-03-12 22:55:49.391775: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall}}]] - 2024-03-12 22:55:49.391848: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_1' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_1}}]] - 2024-03-12 22:55:49.391885: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_2' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_2}}]] - 2024-03-12 22:55:49.391919: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_6' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_6}}]] - 2024-03-12 22:55:49.391951: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_7' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_7}}]] - 2024-03-12 22:55:49.391982: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_8' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_8}}]] - 2024-03-12 22:55:49.392029: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_9' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_9}}]] - 2024-03-12 22:55:49.392062: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_10' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_10}}]] - 2024-03-12 22:55:49.392094: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_11' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_11}}]] - 2024-03-12 22:55:49.392126: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_12' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_12}}]] - 2024-03-12 22:55:49.392158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_13' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_13}}]] - 2024-03-12 22:55:49.392193: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_16' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_16}}]] - 2024-03-12 22:55:49.392225: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_17' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_17}}]] - 2024-03-12 22:55:49.392257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_18' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_18}}]] - 2024-03-12 22:55:49.392289: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_19' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_19}}]] - 2024-03-12 22:55:49.392322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_20' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_20}}]] - 2024-03-12 22:55:49.392354: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_21' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_21}}]] - 2024-03-12 22:55:49.392385: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_22' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_22}}]] - 2024-03-12 22:55:49.392417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_23' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_23}}]] - 2024-03-12 22:55:49.392451: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_25' with dtype int32 and shape [4,2] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_25}}]] - 2024-03-12 22:55:49.392483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_26' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_26}}]] - 2024-03-12 22:55:49.392516: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_29' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_29}}]] - 2024-03-12 22:55:49.392548: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_30' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_30}}]] - 2024-03-12 22:55:49.392579: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_31' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_31}}]] - 2024-03-12 22:55:49.392612: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_32' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_32}}]] - 2024-03-12 22:55:49.392648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_33' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_33}}]] - 2024-03-12 22:55:49.392683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_34' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_34}}]] - 2024-03-12 22:55:49.392717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_35' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_35}}]] - 2024-03-12 22:55:49.392752: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_38' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_38}}]] - 2024-03-12 22:55:49.392786: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_39' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_39}}]] - 2024-03-12 22:55:49.392820: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_40' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_40}}]] - 2024-03-12 22:55:49.392854: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_41' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_41}}]] - 2024-03-12 22:55:49.392888: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_42' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_42}}]] - 2024-03-12 22:55:49.392922: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_43' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_43}}]] - 2024-03-12 22:55:49.392956: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_44' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_44}}]] - 2024-03-12 22:55:49.392991: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_45' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_45}}]] - 2024-03-12 22:55:49.393025: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_46' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_46}}]] - 2024-03-12 22:55:49.393060: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_50' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_50}}]] - 2024-03-12 22:55:49.393093: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_51' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_51}}]] - 2024-03-12 22:55:49.393127: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_52' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_52}}]] - 2024-03-12 22:55:49.393162: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_53' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_53}}]] - 2024-03-12 22:55:49.393196: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_54' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_54}}]] - 2024-03-12 22:55:49.393229: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_55' with dtype float and shape [32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_55}}]] - 2024-03-12 22:55:49.393264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_56' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_56}}]] - 2024-03-12 22:55:49.393297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_57' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_57}}]] - 2024-03-12 22:55:49.393332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_60' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_60}}]] - 2024-03-12 22:55:49.393367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_61' with dtype float and shape [32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_61}}]] - 2024-03-12 22:55:49.393400: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_62' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_62}}]] - 2024-03-12 22:55:49.393439: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_63' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_63}}]] - 2024-03-12 22:55:49.393475: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_64' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_64}}]] - 2024-03-12 22:55:49.393510: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_65' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_65}}]] - 2024-03-12 22:55:49.393543: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_66' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_66}}]] - 2024-03-12 22:55:49.393578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_67' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_67}}]] - 2024-03-12 22:55:49.393612: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_68' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_68}}]] - 2024-03-12 22:55:49.393645: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_72' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_72}}]] - 2024-03-12 22:55:49.393679: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_73' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_73}}]] - 2024-03-12 22:55:49.393713: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_74' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_74}}]] - 2024-03-12 22:55:49.393746: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_75' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_75}}]] - 2024-03-12 22:55:49.393781: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_76' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_76}}]] - 2024-03-12 22:55:49.393814: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_77' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_77}}]] - 2024-03-12 22:55:49.393849: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_78' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_78}}]] - 2024-03-12 22:55:49.393883: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_79' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_79}}]] - 2024-03-12 22:55:49.393916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_82' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_82}}]] - 2024-03-12 22:55:49.393950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_83' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_83}}]] - 2024-03-12 22:55:49.393984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_84' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_84}}]] - 2024-03-12 22:55:49.394018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_85' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_85}}]] - 2024-03-12 22:55:49.394052: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_86' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_86}}]] - 2024-03-12 22:55:49.394086: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_87' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_87}}]] - 2024-03-12 22:55:49.394121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_88' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_88}}]] - 2024-03-12 22:55:49.394155: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_89' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_89}}]] - 2024-03-12 22:55:49.394190: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_91' with dtype int32 and shape [4,2] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_91}}]] - 2024-03-12 22:55:49.394223: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_92' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_92}}]] - 2024-03-12 22:55:49.394257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_95' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_95}}]] - 2024-03-12 22:55:49.394291: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_96' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_96}}]] - 2024-03-12 22:55:49.394325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_97' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_97}}]] - 2024-03-12 22:55:49.394359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_98' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_98}}]] - 2024-03-12 22:55:49.394394: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_99' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_99}}]] - 2024-03-12 22:55:49.394428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_100' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_100}}]] - 2024-03-12 22:55:49.394463: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_101' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_101}}]] - 2024-03-12 22:55:49.394497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_104' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_104}}]] - 2024-03-12 22:55:49.394531: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_105' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_105}}]] - 2024-03-12 22:55:49.394565: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_106' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_106}}]] - 2024-03-12 22:55:49.394609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_107' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_107}}]] - 2024-03-12 22:55:49.394642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_108' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_108}}]] - 2024-03-12 22:55:49.394675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_109' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_109}}]] - 2024-03-12 22:55:49.394708: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_110' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_110}}]] - 2024-03-12 22:55:49.394741: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_111' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_111}}]] - 2024-03-12 22:55:49.394774: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_112' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_112}}]] - 2024-03-12 22:55:49.394808: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_116' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_116}}]] - 2024-03-12 22:55:49.394841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_117' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_117}}]] - 2024-03-12 22:55:49.394874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_118' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_118}}]] - 2024-03-12 22:55:49.394906: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_119' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_119}}]] - 2024-03-12 22:55:49.394939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_120' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_120}}]] - 2024-03-12 22:55:49.394972: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_121' with dtype float and shape [32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_121}}]] - 2024-03-12 22:55:49.395004: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_122' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_122}}]] - 2024-03-12 22:55:49.395036: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_123' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_123}}]] - 2024-03-12 22:55:49.395069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_126' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_126}}]] - 2024-03-12 22:55:49.395101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_127' with dtype float and shape [32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_127}}]] - 2024-03-12 22:55:49.395133: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_128' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_128}}]] - 2024-03-12 22:55:49.395165: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_129' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_129}}]] - 2024-03-12 22:55:49.395198: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_130' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_130}}]] - 2024-03-12 22:55:49.395231: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_131' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_131}}]] - 2024-03-12 22:55:49.395263: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_132' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_132}}]] - 2024-03-12 22:55:49.395295: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_133' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_133}}]] - 2024-03-12 22:55:49.395328: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_134' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_134}}]] - 2024-03-12 22:55:49.395360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_138' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_138}}]] - 2024-03-12 22:55:49.395393: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_139' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_139}}]] - 2024-03-12 22:55:49.395426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_140' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_140}}]] - 2024-03-12 22:55:49.395458: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_141' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_141}}]] - 2024-03-12 22:55:49.395490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_142' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_142}}]] - 2024-03-12 22:55:49.395522: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_143' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_143}}]] - 2024-03-12 22:55:49.395555: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_144' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_144}}]] - 2024-03-12 22:55:49.395587: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_145' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_145}}]] - 2024-03-12 22:55:49.395619: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_148' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_148}}]] - 2024-03-12 22:55:49.395652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_149' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_149}}]] - 2024-03-12 22:55:49.395685: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_150' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_150}}]] - 2024-03-12 22:55:49.395717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_151' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_151}}]] - 2024-03-12 22:55:49.395749: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_152' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_152}}]] - 2024-03-12 22:55:49.395782: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_153' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_153}}]] - 2024-03-12 22:55:49.395814: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_154' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_154}}]] - 2024-03-12 22:55:49.395847: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_155' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_155}}]] - 2024-03-12 22:55:49.395879: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_157' with dtype int32 and shape [4,2] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_157}}]] - 2024-03-12 22:55:49.395912: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_158' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_158}}]] - 2024-03-12 22:55:49.395945: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_161' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_161}}]] - 2024-03-12 22:55:49.395978: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_162' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_162}}]] - 2024-03-12 22:55:49.396011: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_163' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_163}}]] - 2024-03-12 22:55:49.396043: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_164' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_164}}]] - 2024-03-12 22:55:49.396075: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_165' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_165}}]] - 2024-03-12 22:55:49.396108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_166' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_166}}]] - 2024-03-12 22:55:49.396140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_167' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_167}}]] - 2024-03-12 22:55:49.396189: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_170' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_170}}]] - 2024-03-12 22:55:49.396223: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_171' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_171}}]] - 2024-03-12 22:55:49.396257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_172' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_172}}]] - 2024-03-12 22:55:49.396291: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_173' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_173}}]] - 2024-03-12 22:55:49.396325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_174' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_174}}]] - 2024-03-12 22:55:49.396359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_175' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_175}}]] - 2024-03-12 22:55:49.396393: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_176' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_176}}]] - 2024-03-12 22:55:49.396427: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_177' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_177}}]] - 2024-03-12 22:55:49.396461: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_178' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_178}}]] - 2024-03-12 22:55:49.396495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_182' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_182}}]] - 2024-03-12 22:55:49.396529: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_183' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_183}}]] - 2024-03-12 22:55:49.396563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_184' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_184}}]] - 2024-03-12 22:55:49.396597: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_185' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_185}}]] - 2024-03-12 22:55:49.396631: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_186' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_186}}]] - 2024-03-12 22:55:49.396665: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_187' with dtype float and shape [32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_187}}]] - 2024-03-12 22:55:49.396699: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_188' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_188}}]] - 2024-03-12 22:55:49.396732: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_189' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_189}}]] - 2024-03-12 22:55:49.396766: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_192' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_192}}]] - 2024-03-12 22:55:49.396800: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_193' with dtype float and shape [32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_193}}]] - 2024-03-12 22:55:49.396833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_194' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_194}}]] - 2024-03-12 22:55:49.396867: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_195' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_195}}]] - 2024-03-12 22:55:49.396901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_196' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_196}}]] - 2024-03-12 22:55:49.396935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_197' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_197}}]] - 2024-03-12 22:55:49.396969: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_198' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_198}}]] - 2024-03-12 22:55:49.397003: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_199' with dtype float and shape [?,?,?,512] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_199}}]] - 2024-03-12 22:55:49.397037: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_200' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_200}}]] - 2024-03-12 22:55:49.397071: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_202' with dtype float and shape [?,?,?,256] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_202}}]] - 2024-03-12 22:55:49.397105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_208' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_208}}]] - 2024-03-12 22:55:49.397140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_209' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_209}}]] - 2024-03-12 22:55:49.397174: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_210' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_210}}]] - 2024-03-12 22:55:49.397208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_211' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_211}}]] - 2024-03-12 22:55:49.397242: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_212' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_212}}]] - 2024-03-12 22:55:49.397276: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_213' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_213}}]] - 2024-03-12 22:55:49.397310: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_214' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_214}}]] - 2024-03-12 22:55:49.397345: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_215' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_215}}]] - 2024-03-12 22:55:49.397389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_220' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_220}}]] - 2024-03-12 22:55:49.397421: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_221' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_221}}]] - 2024-03-12 22:55:49.397459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_222' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_222}}]] - 2024-03-12 22:55:49.397510: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_223' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_223}}]] - 2024-03-12 22:55:49.397545: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_224' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_224}}]] - 2024-03-12 22:55:49.397580: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_225' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_225}}]] - 2024-03-12 22:55:49.397614: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_226' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_226}}]] - 2024-03-12 22:55:49.397648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_227' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_227}}]] - 2024-03-12 22:55:49.397683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_229' with dtype int32 and shape [4,2] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_229}}]] - 2024-03-12 22:55:49.397717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_230' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_230}}]] - 2024-03-12 22:55:49.397751: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_233' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_233}}]] - 2024-03-12 22:55:49.397785: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_234' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_234}}]] - 2024-03-12 22:55:49.397819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_235' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_235}}]] - 2024-03-12 22:55:49.397853: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_236' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_236}}]] - 2024-03-12 22:55:49.397888: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_237' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_237}}]] - 2024-03-12 22:55:49.397922: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_238' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_238}}]] - 2024-03-12 22:55:49.397957: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_239' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_239}}]] - 2024-03-12 22:55:49.397990: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_242' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_242}}]] - 2024-03-12 22:55:49.398024: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_243' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_243}}]] - 2024-03-12 22:55:49.398059: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_244' with dtype float and shape [32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_244}}]] - 2024-03-12 22:55:49.398093: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_245' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_245}}]] - 2024-03-12 22:55:49.398128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_246' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_246}}]] - 2024-03-12 22:55:49.398162: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_247' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_247}}]] - 2024-03-12 22:55:49.398196: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_248' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_248}}]] - 2024-03-12 22:55:49.398231: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_249' with dtype float and shape [?,?,?,128] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_249}}]] - 2024-03-12 22:55:49.398265: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_253' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_253}}]] - 2024-03-12 22:55:49.398299: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_254' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_254}}]] - 2024-03-12 22:55:49.398333: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_255' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_255}}]] - 2024-03-12 22:55:49.398368: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_256' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_256}}]] - 2024-03-12 22:55:49.398402: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_257' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_257}}]] - 2024-03-12 22:55:49.398437: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_258' with dtype float and shape [32,8] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_258}}]] - 2024-03-12 22:55:49.398472: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_259' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_259}}]] - 2024-03-12 22:55:49.398506: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_260' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_260}}]] - 2024-03-12 22:55:49.398540: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_263' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_263}}]] - 2024-03-12 22:55:49.398574: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_264' with dtype float and shape [32,8] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_264}}]] - 2024-03-12 22:55:49.398608: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_265' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_265}}]] - 2024-03-12 22:55:49.398643: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_266' with dtype float - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_266}}]] - 2024-03-12 22:55:49.398677: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_267' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_267}}]] - 2024-03-12 22:55:49.398722: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_268' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_268}}]] - 2024-03-12 22:55:49.398755: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_269' with dtype float and shape [?,?,?,256] - [[{{node gradients/block2/StatefulPartitionedCall_grad/block2/StatefulPartitionedCall_269}}]] - - -.. parsed-literal:: - - 2024-03-12 22:55:50.151184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall' with dtype float and shape [?,?,?,256] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall}}]] - 2024-03-12 22:55:50.151258: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_1' with dtype float and shape [?,?,?,256] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_1}}]] - 2024-03-12 22:55:50.151295: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_2' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_2}}]] - 2024-03-12 22:55:50.151328: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_6' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_6}}]] - 2024-03-12 22:55:50.151359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_7' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_7}}]] - 2024-03-12 22:55:50.151391: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_8' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_8}}]] - 2024-03-12 22:55:50.151438: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_9' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_9}}]] - 2024-03-12 22:55:50.151471: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_10' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_10}}]] - 2024-03-12 22:55:50.151503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_11' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_11}}]] - 2024-03-12 22:55:50.151536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_12' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_12}}]] - 2024-03-12 22:55:50.151568: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_13' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_13}}]] - 2024-03-12 22:55:50.151601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_16' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_16}}]] - 2024-03-12 22:55:50.151633: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_17' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_17}}]] - 2024-03-12 22:55:50.151664: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_18' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_18}}]] - 2024-03-12 22:55:50.151696: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_19' with dtype float - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_19}}]] - 2024-03-12 22:55:50.151729: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_20' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_20}}]] - 2024-03-12 22:55:50.151761: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_21' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_21}}]] - 2024-03-12 22:55:50.151792: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_22' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_22}}]] - 2024-03-12 22:55:50.151824: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_23' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_23}}]] - 2024-03-12 22:55:50.151857: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_25' with dtype int32 and shape [4,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_25}}]] - 2024-03-12 22:55:50.151889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_26' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_26}}]] - 2024-03-12 22:55:50.151920: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_29' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_29}}]] - 2024-03-12 22:55:50.151951: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_30' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_30}}]] - 2024-03-12 22:55:50.151983: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_31' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_31}}]] - 2024-03-12 22:55:50.152014: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_32' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_32}}]] - 2024-03-12 22:55:50.152046: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_33' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_33}}]] - 2024-03-12 22:55:50.152081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_34' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_34}}]] - 2024-03-12 22:55:50.152115: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_35' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_35}}]] - 2024-03-12 22:55:50.152150: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_38' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_38}}]] - 2024-03-12 22:55:50.152183: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_39' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_39}}]] - 2024-03-12 22:55:50.152217: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_40' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_40}}]] - 2024-03-12 22:55:50.152251: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_41' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_41}}]] - 2024-03-12 22:55:50.152284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_42' with dtype float - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_42}}]] - 2024-03-12 22:55:50.152318: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_43' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_43}}]] - 2024-03-12 22:55:50.152351: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_44' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_44}}]] - 2024-03-12 22:55:50.152385: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_45' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_45}}]] - 2024-03-12 22:55:50.152419: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_46' with dtype float and shape [?,?,?,256] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_46}}]] - 2024-03-12 22:55:50.152453: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_50' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_50}}]] - 2024-03-12 22:55:50.152486: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_51' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_51}}]] - 2024-03-12 22:55:50.152521: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_52' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_52}}]] - 2024-03-12 22:55:50.152555: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_53' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_53}}]] - 2024-03-12 22:55:50.152588: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_54' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_54}}]] - 2024-03-12 22:55:50.152622: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_55' with dtype float and shape [32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_55}}]] - 2024-03-12 22:55:50.152656: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_56' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_56}}]] - 2024-03-12 22:55:50.152689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_57' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_57}}]] - 2024-03-12 22:55:50.152723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_60' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_60}}]] - 2024-03-12 22:55:50.152757: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_61' with dtype float and shape [32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_61}}]] - 2024-03-12 22:55:50.152791: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_62' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_62}}]] - 2024-03-12 22:55:50.152824: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_63' with dtype float - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_63}}]] - 2024-03-12 22:55:50.152858: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_64' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_64}}]] - 2024-03-12 22:55:50.152892: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_65' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_65}}]] - 2024-03-12 22:55:50.152926: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_66' with dtype float and shape [?,?,?,256] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_66}}]] - 2024-03-12 22:55:50.152959: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_67' with dtype float and shape [?,?,?,256] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_67}}]] - 2024-03-12 22:55:50.152992: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_68' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_68}}]] - 2024-03-12 22:55:50.153036: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_72' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_72}}]] - 2024-03-12 22:55:50.153069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_73' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_73}}]] - 2024-03-12 22:55:50.153101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_74' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_74}}]] - 2024-03-12 22:55:50.153133: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_75' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_75}}]] - 2024-03-12 22:55:50.153166: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_76' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_76}}]] - 2024-03-12 22:55:50.153198: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_77' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_77}}]] - 2024-03-12 22:55:50.153230: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_78' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_78}}]] - 2024-03-12 22:55:50.153262: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_79' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_79}}]] - 2024-03-12 22:55:50.153295: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_82' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_82}}]] - 2024-03-12 22:55:50.153327: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_83' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_83}}]] - 2024-03-12 22:55:50.153359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_84' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_84}}]] - 2024-03-12 22:55:50.153392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_85' with dtype float - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_85}}]] - 2024-03-12 22:55:50.153425: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_86' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_86}}]] - 2024-03-12 22:55:50.153479: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_87' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_87}}]] - 2024-03-12 22:55:50.153514: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_88' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_88}}]] - 2024-03-12 22:55:50.153549: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_89' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_89}}]] - 2024-03-12 22:55:50.153583: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_91' with dtype int32 and shape [4,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_91}}]] - 2024-03-12 22:55:50.153617: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_92' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_92}}]] - 2024-03-12 22:55:50.153651: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_95' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_95}}]] - 2024-03-12 22:55:50.153685: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_96' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_96}}]] - 2024-03-12 22:55:50.153719: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_97' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_97}}]] - 2024-03-12 22:55:50.153753: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_98' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_98}}]] - 2024-03-12 22:55:50.153787: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_99' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_99}}]] - 2024-03-12 22:55:50.153821: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_100' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_100}}]] - 2024-03-12 22:55:50.153855: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_101' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_101}}]] - 2024-03-12 22:55:50.153889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_104' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_104}}]] - 2024-03-12 22:55:50.153923: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_105' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_105}}]] - 2024-03-12 22:55:50.153957: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_106' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_106}}]] - 2024-03-12 22:55:50.153991: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_107' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_107}}]] - 2024-03-12 22:55:50.154024: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_108' with dtype float - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_108}}]] - 2024-03-12 22:55:50.154057: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_109' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_109}}]] - 2024-03-12 22:55:50.154091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_110' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_110}}]] - 2024-03-12 22:55:50.154124: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_111' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_111}}]] - 2024-03-12 22:55:50.154158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_112' with dtype float and shape [?,?,?,256] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_112}}]] - 2024-03-12 22:55:50.154191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_116' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_116}}]] - 2024-03-12 22:55:50.154225: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_117' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_117}}]] - 2024-03-12 22:55:50.154259: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_118' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_118}}]] - 2024-03-12 22:55:50.154292: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_119' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_119}}]] - 2024-03-12 22:55:50.154326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_120' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_120}}]] - 2024-03-12 22:55:50.154361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_121' with dtype float and shape [32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_121}}]] - 2024-03-12 22:55:50.154395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_122' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_122}}]] - 2024-03-12 22:55:50.154429: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_123' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_123}}]] - 2024-03-12 22:55:50.154463: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_126' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_126}}]] - 2024-03-12 22:55:50.154496: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_127' with dtype float and shape [32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_127}}]] - 2024-03-12 22:55:50.154530: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_128' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_128}}]] - 2024-03-12 22:55:50.154564: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_129' with dtype float - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_129}}]] - 2024-03-12 22:55:50.154597: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_130' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_130}}]] - 2024-03-12 22:55:50.154631: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_131' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_131}}]] - 2024-03-12 22:55:50.154665: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_132' with dtype float and shape [?,?,?,256] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_132}}]] - 2024-03-12 22:55:50.154699: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_133' with dtype float and shape [?,?,?,256] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_133}}]] - 2024-03-12 22:55:50.154743: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_134' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_134}}]] - 2024-03-12 22:55:50.154775: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_136' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_136}}]] - 2024-03-12 22:55:50.154809: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_142' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_142}}]] - 2024-03-12 22:55:50.154841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_143' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_143}}]] - 2024-03-12 22:55:50.154874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_144' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_144}}]] - 2024-03-12 22:55:50.154906: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_145' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_145}}]] - 2024-03-12 22:55:50.154938: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_146' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_146}}]] - 2024-03-12 22:55:50.154971: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_147' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_147}}]] - 2024-03-12 22:55:50.155004: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_148' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_148}}]] - 2024-03-12 22:55:50.155037: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_149' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_149}}]] - 2024-03-12 22:55:50.155069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_154' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_154}}]] - 2024-03-12 22:55:50.155102: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_155' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_155}}]] - 2024-03-12 22:55:50.155135: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_156' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_156}}]] - 2024-03-12 22:55:50.155167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_157' with dtype float - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_157}}]] - 2024-03-12 22:55:50.155199: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_158' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_158}}]] - 2024-03-12 22:55:50.155232: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_159' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_159}}]] - 2024-03-12 22:55:50.155264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_160' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_160}}]] - 2024-03-12 22:55:50.155297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_161' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_161}}]] - 2024-03-12 22:55:50.155330: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_163' with dtype int32 and shape [4,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_163}}]] - 2024-03-12 22:55:50.155363: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_164' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_164}}]] - 2024-03-12 22:55:50.155395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_167' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_167}}]] - 2024-03-12 22:55:50.155427: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_168' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_168}}]] - 2024-03-12 22:55:50.155460: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_169' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_169}}]] - 2024-03-12 22:55:50.155492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_170' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_170}}]] - 2024-03-12 22:55:50.155525: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_171' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_171}}]] - 2024-03-12 22:55:50.155557: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_172' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_172}}]] - 2024-03-12 22:55:50.155589: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_173' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_173}}]] - 2024-03-12 22:55:50.155621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_176' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_176}}]] - 2024-03-12 22:55:50.155654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_177' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_177}}]] - 2024-03-12 22:55:50.155686: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_178' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_178}}]] - 2024-03-12 22:55:50.155718: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_179' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_179}}]] - 2024-03-12 22:55:50.155750: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_180' with dtype float - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_180}}]] - 2024-03-12 22:55:50.155782: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_181' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_181}}]] - 2024-03-12 22:55:50.155815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_182' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_182}}]] - 2024-03-12 22:55:50.155847: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_183' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_183}}]] - 2024-03-12 22:55:50.155879: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_187' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_187}}]] - 2024-03-12 22:55:50.155911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_188' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_188}}]] - 2024-03-12 22:55:50.155943: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_189' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_189}}]] - 2024-03-12 22:55:50.155975: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_190' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_190}}]] - 2024-03-12 22:55:50.156007: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_191' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_191}}]] - 2024-03-12 22:55:50.156040: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_192' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_192}}]] - 2024-03-12 22:55:50.156072: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_193' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_193}}]] - 2024-03-12 22:55:50.156104: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_194' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_194}}]] - 2024-03-12 22:55:50.156136: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_197' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_197}}]] - 2024-03-12 22:55:50.156169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_198' with dtype float and shape [32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_198}}]] - 2024-03-12 22:55:50.156201: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_199' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_199}}]] - 2024-03-12 22:55:50.156234: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_200' with dtype float - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_200}}]] - 2024-03-12 22:55:50.156266: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_201' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_201}}]] - 2024-03-12 22:55:50.156298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_202' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_202}}]] - 2024-03-12 22:55:50.156331: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_203' with dtype float and shape [?,?,?,64] - [[{{node gradients/block1/StatefulPartitionedCall_grad/block1/StatefulPartitionedCall_203}}]] - 2024-03-12 22:55:50.200639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/root_block/StatefulPartitionedCall_grad/root_block/StatefulPartitionedCall_2' with dtype int32 and shape [4,2] - [[{{node gradients/root_block/StatefulPartitionedCall_grad/root_block/StatefulPartitionedCall_2}}]] - 2024-03-12 22:55:50.200697: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/root_block/StatefulPartitionedCall_grad/root_block/StatefulPartitionedCall_3' with dtype float and shape [?,?,?,64] - [[{{node gradients/root_block/StatefulPartitionedCall_grad/root_block/StatefulPartitionedCall_3}}]] - 2024-03-12 22:55:50.200734: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/root_block/StatefulPartitionedCall_grad/root_block/StatefulPartitionedCall_4' with dtype float and shape [?,?,?,3] - [[{{node gradients/root_block/StatefulPartitionedCall_grad/root_block/StatefulPartitionedCall_4}}]] - 2024-03-12 22:55:50.200768: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/root_block/StatefulPartitionedCall_grad/root_block/StatefulPartitionedCall_6' with dtype int32 and shape [4,2] - [[{{node gradients/root_block/StatefulPartitionedCall_grad/root_block/StatefulPartitionedCall_6}}]] - 2024-03-12 22:55:50.200800: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/root_block/StatefulPartitionedCall_grad/root_block/StatefulPartitionedCall_7' with dtype float and shape [?,?,?,3] - [[{{node gradients/root_block/StatefulPartitionedCall_grad/root_block/StatefulPartitionedCall_7}}]] - - -.. parsed-literal:: - - 2024-03-12 22:55:50.677798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall' with dtype float and shape [?,?,?,?] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall}}]] - 2024-03-12 22:55:50.677871: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1' with dtype int32 and shape [2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1}}]] - 2024-03-12 22:55:50.677911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_4' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_4}}]] - 2024-03-12 22:55:50.677948: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_5' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_5}}]] - 2024-03-12 22:55:50.677999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_6' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_6}}]] - 2024-03-12 22:55:50.678035: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_7' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_7}}]] - 2024-03-12 22:55:50.678070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_8' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_8}}]] - 2024-03-12 22:55:50.678105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_9' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_9}}]] - 2024-03-12 22:55:50.678142: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_10' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_10}}]] - 2024-03-12 22:55:50.678178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_11' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_11}}]] - 2024-03-12 22:55:50.678212: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_12' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_12}}]] - 2024-03-12 22:55:50.678248: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_13' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_13}}]] - 2024-03-12 22:55:50.678284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_14' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_14}}]] - 2024-03-12 22:55:50.678320: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_15' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_15}}]] - 2024-03-12 22:55:50.678357: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_16' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_16}}]] - 2024-03-12 22:55:50.678394: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_17' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_17}}]] - 2024-03-12 22:55:50.678429: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_18' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_18}}]] - 2024-03-12 22:55:50.678465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_19' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_19}}]] - 2024-03-12 22:55:50.678500: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_20' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_20}}]] - 2024-03-12 22:55:50.678534: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_21' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_21}}]] - 2024-03-12 22:55:50.678571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_25' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_25}}]] - 2024-03-12 22:55:50.678607: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_26' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_26}}]] - 2024-03-12 22:55:50.678643: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_27' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_27}}]] - 2024-03-12 22:55:50.678679: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_28' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_28}}]] - 2024-03-12 22:55:50.678717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_29' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_29}}]] - 2024-03-12 22:55:50.678756: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_30' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_30}}]] - 2024-03-12 22:55:50.678793: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_31' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_31}}]] - 2024-03-12 22:55:50.678831: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_32' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_32}}]] - 2024-03-12 22:55:50.678869: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_35' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_35}}]] - 2024-03-12 22:55:50.678906: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_36' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_36}}]] - 2024-03-12 22:55:50.678943: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_37' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_37}}]] - 2024-03-12 22:55:50.678979: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_38' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_38}}]] - 2024-03-12 22:55:50.679018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_39' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_39}}]] - 2024-03-12 22:55:50.679056: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_40' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_40}}]] - 2024-03-12 22:55:50.679094: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_41' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_41}}]] - 2024-03-12 22:55:50.679131: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_42' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_42}}]] - 2024-03-12 22:55:50.679168: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_44' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_44}}]] - 2024-03-12 22:55:50.679205: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_45' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_45}}]] - 2024-03-12 22:55:50.679242: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_48' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_48}}]] - 2024-03-12 22:55:50.679280: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_49' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_49}}]] - 2024-03-12 22:55:50.679317: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_50' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_50}}]] - 2024-03-12 22:55:50.679356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_51' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_51}}]] - 2024-03-12 22:55:50.679392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_52' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_52}}]] - 2024-03-12 22:55:50.679429: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_53' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_53}}]] - 2024-03-12 22:55:50.679466: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_54' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_54}}]] - 2024-03-12 22:55:50.679503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_57' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_57}}]] - 2024-03-12 22:55:50.679541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_58' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_58}}]] - 2024-03-12 22:55:50.679577: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_59' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_59}}]] - 2024-03-12 22:55:50.679614: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_60' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_60}}]] - 2024-03-12 22:55:50.679650: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_61' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_61}}]] - 2024-03-12 22:55:50.679688: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_62' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_62}}]] - 2024-03-12 22:55:50.679726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_63' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_63}}]] - 2024-03-12 22:55:50.679762: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_64' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_64}}]] - 2024-03-12 22:55:50.679799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_65' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_65}}]] - 2024-03-12 22:55:50.679836: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_69' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_69}}]] - 2024-03-12 22:55:50.679873: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_70' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_70}}]] - 2024-03-12 22:55:50.679910: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_71' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_71}}]] - 2024-03-12 22:55:50.679947: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_72' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_72}}]] - 2024-03-12 22:55:50.679983: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_73' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_73}}]] - 2024-03-12 22:55:50.680021: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_74' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_74}}]] - 2024-03-12 22:55:50.680058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_75' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_75}}]] - 2024-03-12 22:55:50.680095: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_76' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_76}}]] - 2024-03-12 22:55:50.680132: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_79' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_79}}]] - 2024-03-12 22:55:50.680168: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_80' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_80}}]] - 2024-03-12 22:55:50.680205: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_81' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_81}}]] - 2024-03-12 22:55:50.680242: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_82' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_82}}]] - 2024-03-12 22:55:50.680279: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_83' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_83}}]] - 2024-03-12 22:55:50.680316: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_84' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_84}}]] - 2024-03-12 22:55:50.680353: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_85' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_85}}]] - 2024-03-12 22:55:50.680390: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_86' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_86}}]] - 2024-03-12 22:55:50.680426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_87' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_87}}]] - 2024-03-12 22:55:50.680463: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_91' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_91}}]] - 2024-03-12 22:55:50.680499: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_92' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_92}}]] - 2024-03-12 22:55:50.680536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_93' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_93}}]] - 2024-03-12 22:55:50.680572: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_94' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_94}}]] - 2024-03-12 22:55:50.680608: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_95' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_95}}]] - 2024-03-12 22:55:50.680644: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_96' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_96}}]] - 2024-03-12 22:55:50.680680: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_97' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_97}}]] - 2024-03-12 22:55:50.680717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_98' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_98}}]] - 2024-03-12 22:55:50.680754: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_101' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_101}}]] - 2024-03-12 22:55:50.680791: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_102' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_102}}]] - 2024-03-12 22:55:50.680829: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_103' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_103}}]] - 2024-03-12 22:55:50.680865: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_104' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_104}}]] - 2024-03-12 22:55:50.680902: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_105' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_105}}]] - 2024-03-12 22:55:50.680938: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_106' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_106}}]] - 2024-03-12 22:55:50.680976: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_107' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_107}}]] - 2024-03-12 22:55:50.681013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_108' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_108}}]] - 2024-03-12 22:55:50.681049: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_110' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_110}}]] - 2024-03-12 22:55:50.681085: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_111' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_111}}]] - 2024-03-12 22:55:50.681122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_114' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_114}}]] - 2024-03-12 22:55:50.681159: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_115' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_115}}]] - 2024-03-12 22:55:50.681195: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_116' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_116}}]] - 2024-03-12 22:55:50.681232: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_117' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_117}}]] - 2024-03-12 22:55:50.681269: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_118' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_118}}]] - 2024-03-12 22:55:50.681305: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_119' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_119}}]] - 2024-03-12 22:55:50.681342: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_120' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_120}}]] - 2024-03-12 22:55:50.681379: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_123' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_123}}]] - 2024-03-12 22:55:50.681417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_124' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_124}}]] - 2024-03-12 22:55:50.681459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_125' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_125}}]] - 2024-03-12 22:55:50.681513: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_126' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_126}}]] - 2024-03-12 22:55:50.681551: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_127' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_127}}]] - 2024-03-12 22:55:50.681588: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_128' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_128}}]] - 2024-03-12 22:55:50.681626: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_129' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_129}}]] - 2024-03-12 22:55:50.681664: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_130' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_130}}]] - 2024-03-12 22:55:50.681702: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_131' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_131}}]] - 2024-03-12 22:55:50.681740: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_135' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_135}}]] - 2024-03-12 22:55:50.681777: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_136' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_136}}]] - 2024-03-12 22:55:50.681815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_137' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_137}}]] - 2024-03-12 22:55:50.681852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_138' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_138}}]] - 2024-03-12 22:55:50.681891: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_139' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_139}}]] - 2024-03-12 22:55:50.681928: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_140' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_140}}]] - 2024-03-12 22:55:50.681965: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_141' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_141}}]] - 2024-03-12 22:55:50.682003: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_142' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_142}}]] - 2024-03-12 22:55:50.682041: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_145' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_145}}]] - 2024-03-12 22:55:50.682079: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_146' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_146}}]] - 2024-03-12 22:55:50.682117: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_147' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_147}}]] - 2024-03-12 22:55:50.682154: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_148' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_148}}]] - 2024-03-12 22:55:50.682193: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_149' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_149}}]] - 2024-03-12 22:55:50.682232: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_150' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_150}}]] - 2024-03-12 22:55:50.682270: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_151' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_151}}]] - 2024-03-12 22:55:50.682307: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_152' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_152}}]] - 2024-03-12 22:55:50.682344: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_153' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_153}}]] - 2024-03-12 22:55:50.682383: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_155' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_155}}]] - 2024-03-12 22:55:50.682420: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_161' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_161}}]] - 2024-03-12 22:55:50.682457: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_162' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_162}}]] - 2024-03-12 22:55:50.682495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_163' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_163}}]] - 2024-03-12 22:55:50.682532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_164' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_164}}]] - 2024-03-12 22:55:50.682570: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_165' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_165}}]] - 2024-03-12 22:55:50.682609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_166' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_166}}]] - 2024-03-12 22:55:50.682646: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_167' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_167}}]] - 2024-03-12 22:55:50.682683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_168' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_168}}]] - 2024-03-12 22:55:50.682721: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_173' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_173}}]] - 2024-03-12 22:55:50.682769: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_174' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_174}}]] - 2024-03-12 22:55:50.682805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_175' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_175}}]] - 2024-03-12 22:55:50.682840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_176' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_176}}]] - 2024-03-12 22:55:50.682877: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_177' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_177}}]] - 2024-03-12 22:55:50.682914: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_178' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_178}}]] - 2024-03-12 22:55:50.682951: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_179' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_179}}]] - 2024-03-12 22:55:50.682988: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_180' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_180}}]] - 2024-03-12 22:55:50.683025: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_182' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_182}}]] - 2024-03-12 22:55:50.683062: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_183' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_183}}]] - 2024-03-12 22:55:50.683099: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_186' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_186}}]] - 2024-03-12 22:55:50.683136: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_187' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_187}}]] - 2024-03-12 22:55:50.683173: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_188' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_188}}]] - 2024-03-12 22:55:50.683210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_189' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_189}}]] - 2024-03-12 22:55:50.683247: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_190' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_190}}]] - 2024-03-12 22:55:50.683284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_191' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_191}}]] - 2024-03-12 22:55:50.683321: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_192' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_192}}]] - 2024-03-12 22:55:50.683357: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_195' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_195}}]] - 2024-03-12 22:55:50.683394: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_196' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_196}}]] - 2024-03-12 22:55:50.683431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_197' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_197}}]] - 2024-03-12 22:55:50.683467: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_198' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_198}}]] - 2024-03-12 22:55:50.683505: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_199' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_199}}]] - 2024-03-12 22:55:50.683542: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_200' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_200}}]] - 2024-03-12 22:55:50.683578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_201' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_201}}]] - 2024-03-12 22:55:50.683615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_202' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_202}}]] - 2024-03-12 22:55:50.683652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_206' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_206}}]] - 2024-03-12 22:55:50.683689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_207' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_207}}]] - 2024-03-12 22:55:50.683727: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_208' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_208}}]] - 2024-03-12 22:55:50.683764: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_209' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_209}}]] - 2024-03-12 22:55:50.683801: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_210' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_210}}]] - 2024-03-12 22:55:50.683839: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_211' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_211}}]] - 2024-03-12 22:55:50.683875: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_212' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_212}}]] - 2024-03-12 22:55:50.683913: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_213' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_213}}]] - 2024-03-12 22:55:50.683950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_216' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_216}}]] - 2024-03-12 22:55:50.683987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_217' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_217}}]] - 2024-03-12 22:55:50.684025: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_218' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_218}}]] - 2024-03-12 22:55:50.684063: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_219' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_219}}]] - 2024-03-12 22:55:50.684101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_220' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_220}}]] - 2024-03-12 22:55:50.684138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_221' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_221}}]] - 2024-03-12 22:55:50.684174: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_222' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_222}}]] - 2024-03-12 22:55:50.684212: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_223' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_223}}]] - 2024-03-12 22:55:50.684248: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_224' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_224}}]] - 2024-03-12 22:55:50.684286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_225' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_225}}]] - 2024-03-12 22:55:50.684323: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_229' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_229}}]] - 2024-03-12 22:55:50.684360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_230' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_230}}]] - 2024-03-12 22:55:50.684397: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_231' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_231}}]] - 2024-03-12 22:55:50.684434: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_232' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_232}}]] - 2024-03-12 22:55:50.684472: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_233' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_233}}]] - 2024-03-12 22:55:50.684508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_234' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_234}}]] - 2024-03-12 22:55:50.684548: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_235' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_235}}]] - 2024-03-12 22:55:50.684586: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_236' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_236}}]] - 2024-03-12 22:55:50.684622: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_239' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_239}}]] - 2024-03-12 22:55:50.684659: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_240' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_240}}]] - 2024-03-12 22:55:50.684696: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_241' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_241}}]] - 2024-03-12 22:55:50.684733: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_242' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_242}}]] - 2024-03-12 22:55:50.684771: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_243' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_243}}]] - 2024-03-12 22:55:50.684807: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_244' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_244}}]] - 2024-03-12 22:55:50.684843: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_245' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_245}}]] - 2024-03-12 22:55:50.684880: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_246' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_246}}]] - 2024-03-12 22:55:50.684917: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_248' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_248}}]] - 2024-03-12 22:55:50.684954: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_249' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_249}}]] - 2024-03-12 22:55:50.684991: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_252' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_252}}]] - 2024-03-12 22:55:50.685027: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_253' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_253}}]] - 2024-03-12 22:55:50.685064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_254' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_254}}]] - 2024-03-12 22:55:50.685100: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_255' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_255}}]] - 2024-03-12 22:55:50.685137: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_256' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_256}}]] - 2024-03-12 22:55:50.685174: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_257' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_257}}]] - 2024-03-12 22:55:50.685211: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_258' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_258}}]] - 2024-03-12 22:55:50.685248: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_261' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_261}}]] - 2024-03-12 22:55:50.685284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_262' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_262}}]] - 2024-03-12 22:55:50.685321: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_263' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_263}}]] - 2024-03-12 22:55:50.685358: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_264' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_264}}]] - 2024-03-12 22:55:50.685395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_265' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_265}}]] - 2024-03-12 22:55:50.685438: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_266' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_266}}]] - 2024-03-12 22:55:50.685495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_267' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_267}}]] - 2024-03-12 22:55:50.685532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_268' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_268}}]] - 2024-03-12 22:55:50.685568: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_269' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_269}}]] - 2024-03-12 22:55:50.685604: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_273' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_273}}]] - 2024-03-12 22:55:50.685639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_274' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_274}}]] - 2024-03-12 22:55:50.685675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_275' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_275}}]] - 2024-03-12 22:55:50.685710: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_276' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_276}}]] - 2024-03-12 22:55:50.685745: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_277' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_277}}]] - 2024-03-12 22:55:50.685781: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_278' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_278}}]] - 2024-03-12 22:55:50.685817: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_279' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_279}}]] - 2024-03-12 22:55:50.685854: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_280' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_280}}]] - 2024-03-12 22:55:50.685889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_283' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_283}}]] - 2024-03-12 22:55:50.685925: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_284' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_284}}]] - 2024-03-12 22:55:50.685959: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_285' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_285}}]] - 2024-03-12 22:55:50.685995: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_286' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_286}}]] - 2024-03-12 22:55:50.686031: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_287' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_287}}]] - 2024-03-12 22:55:50.686066: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_288' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_288}}]] - 2024-03-12 22:55:50.686101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_289' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_289}}]] - 2024-03-12 22:55:50.686137: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_290' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_290}}]] - 2024-03-12 22:55:50.686173: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_291' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_291}}]] - 2024-03-12 22:55:50.686208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_295' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_295}}]] - 2024-03-12 22:55:50.686243: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_296' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_296}}]] - 2024-03-12 22:55:50.686278: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_297' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_297}}]] - 2024-03-12 22:55:50.686314: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_298' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_298}}]] - 2024-03-12 22:55:50.686349: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_299' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_299}}]] - 2024-03-12 22:55:50.686385: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_300' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_300}}]] - 2024-03-12 22:55:50.686420: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_301' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_301}}]] - 2024-03-12 22:55:50.686458: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_302' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_302}}]] - 2024-03-12 22:55:50.686496: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_305' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_305}}]] - 2024-03-12 22:55:50.686534: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_306' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_306}}]] - 2024-03-12 22:55:50.686573: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_307' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_307}}]] - 2024-03-12 22:55:50.686611: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_308' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_308}}]] - 2024-03-12 22:55:50.686659: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_309' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_309}}]] - 2024-03-12 22:55:50.686696: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_310' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_310}}]] - 2024-03-12 22:55:50.686733: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_311' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_311}}]] - 2024-03-12 22:55:50.686770: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_312' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_312}}]] - 2024-03-12 22:55:50.686807: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_314' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_314}}]] - 2024-03-12 22:55:50.686844: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_315' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_315}}]] - 2024-03-12 22:55:50.686880: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_318' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_318}}]] - 2024-03-12 22:55:50.686917: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_319' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_319}}]] - 2024-03-12 22:55:50.686954: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_320' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_320}}]] - 2024-03-12 22:55:50.686990: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_321' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_321}}]] - 2024-03-12 22:55:50.687028: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_322' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_322}}]] - 2024-03-12 22:55:50.687064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_323' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_323}}]] - 2024-03-12 22:55:50.687101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_324' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_324}}]] - 2024-03-12 22:55:50.687138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_327' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_327}}]] - 2024-03-12 22:55:50.687175: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_328' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_328}}]] - 2024-03-12 22:55:50.687211: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_329' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_329}}]] - 2024-03-12 22:55:50.687248: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_330' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_330}}]] - 2024-03-12 22:55:50.687286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_331' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_331}}]] - 2024-03-12 22:55:50.687324: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_332' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_332}}]] - 2024-03-12 22:55:50.687360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_333' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_333}}]] - 2024-03-12 22:55:50.687397: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_334' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_334}}]] - 2024-03-12 22:55:50.687434: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_335' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_335}}]] - 2024-03-12 22:55:50.687472: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_339' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_339}}]] - 2024-03-12 22:55:50.687509: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_340' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_340}}]] - 2024-03-12 22:55:50.687545: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_341' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_341}}]] - 2024-03-12 22:55:50.687582: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_342' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_342}}]] - 2024-03-12 22:55:50.687619: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_343' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_343}}]] - 2024-03-12 22:55:50.687658: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_344' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_344}}]] - 2024-03-12 22:55:50.687695: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_345' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_345}}]] - 2024-03-12 22:55:50.687732: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_346' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_346}}]] - 2024-03-12 22:55:50.687768: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_349' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_349}}]] - 2024-03-12 22:55:50.687805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_350' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_350}}]] - 2024-03-12 22:55:50.687841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_351' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_351}}]] - 2024-03-12 22:55:50.687878: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_352' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_352}}]] - 2024-03-12 22:55:50.687915: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_353' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_353}}]] - 2024-03-12 22:55:50.687952: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_354' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_354}}]] - 2024-03-12 22:55:50.687989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_355' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_355}}]] - 2024-03-12 22:55:50.688026: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_356' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_356}}]] - 2024-03-12 22:55:50.688062: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_357' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_357}}]] - 2024-03-12 22:55:50.688098: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_361' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_361}}]] - 2024-03-12 22:55:50.688136: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_362' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_362}}]] - 2024-03-12 22:55:50.688173: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_363' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_363}}]] - 2024-03-12 22:55:50.688209: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_364' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_364}}]] - 2024-03-12 22:55:50.688245: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_365' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_365}}]] - 2024-03-12 22:55:50.688282: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_366' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_366}}]] - 2024-03-12 22:55:50.688319: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_367' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_367}}]] - 2024-03-12 22:55:50.688356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_368' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_368}}]] - 2024-03-12 22:55:50.688393: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_371' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_371}}]] - 2024-03-12 22:55:50.688429: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_372' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_372}}]] - 2024-03-12 22:55:50.688466: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_373' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_373}}]] - 2024-03-12 22:55:50.688503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_374' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_374}}]] - 2024-03-12 22:55:50.688540: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_375' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_375}}]] - 2024-03-12 22:55:50.688577: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_376' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_376}}]] - 2024-03-12 22:55:50.688614: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_377' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_377}}]] - 2024-03-12 22:55:50.688651: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_378' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_378}}]] - 2024-03-12 22:55:50.688688: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_380' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_380}}]] - 2024-03-12 22:55:50.688724: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_381' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_381}}]] - 2024-03-12 22:55:50.688761: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_384' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_384}}]] - 2024-03-12 22:55:50.688797: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_385' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_385}}]] - 2024-03-12 22:55:50.688834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_386' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_386}}]] - 2024-03-12 22:55:50.688871: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_387' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_387}}]] - 2024-03-12 22:55:50.688908: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_388' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_388}}]] - 2024-03-12 22:55:50.688944: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_389' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_389}}]] - 2024-03-12 22:55:50.688981: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_390' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_390}}]] - 2024-03-12 22:55:50.689019: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_393' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_393}}]] - 2024-03-12 22:55:50.689056: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_394' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_394}}]] - 2024-03-12 22:55:50.689094: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_395' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_395}}]] - 2024-03-12 22:55:50.689130: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_396' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_396}}]] - 2024-03-12 22:55:50.689167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_397' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_397}}]] - 2024-03-12 22:55:50.689205: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_398' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_398}}]] - 2024-03-12 22:55:50.689242: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_399' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_399}}]] - 2024-03-12 22:55:50.689279: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_400' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_400}}]] - 2024-03-12 22:55:50.689317: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_401' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_401}}]] - 2024-03-12 22:55:50.689354: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_405' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_405}}]] - 2024-03-12 22:55:50.689392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_406' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_406}}]] - 2024-03-12 22:55:50.689429: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_407' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_407}}]] - 2024-03-12 22:55:50.689488: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_408' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_408}}]] - 2024-03-12 22:55:50.689528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_409' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_409}}]] - 2024-03-12 22:55:50.689566: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_410' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_410}}]] - 2024-03-12 22:55:50.689604: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_411' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_411}}]] - 2024-03-12 22:55:50.689642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_412' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_412}}]] - 2024-03-12 22:55:50.689682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_415' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_415}}]] - 2024-03-12 22:55:50.689719: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_416' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_416}}]] - 2024-03-12 22:55:50.689757: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_417' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_417}}]] - 2024-03-12 22:55:50.689793: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_418' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_418}}]] - 2024-03-12 22:55:50.689831: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_419' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_419}}]] - 2024-03-12 22:55:50.689869: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_420' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_420}}]] - 2024-03-12 22:55:50.689906: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_421' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_421}}]] - 2024-03-12 22:55:50.689944: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_422' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_422}}]] - 2024-03-12 22:55:50.689983: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_423' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_423}}]] - 2024-03-12 22:55:50.690021: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_427' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_427}}]] - 2024-03-12 22:55:50.690059: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_428' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_428}}]] - 2024-03-12 22:55:50.690097: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_429' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_429}}]] - 2024-03-12 22:55:50.690135: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_430' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_430}}]] - 2024-03-12 22:55:50.690173: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_431' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_431}}]] - 2024-03-12 22:55:50.690211: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_432' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_432}}]] - 2024-03-12 22:55:50.690248: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_433' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_433}}]] - 2024-03-12 22:55:50.690284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_434' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_434}}]] - 2024-03-12 22:55:50.690322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_437' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_437}}]] - 2024-03-12 22:55:50.690360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_438' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_438}}]] - 2024-03-12 22:55:50.690399: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_439' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_439}}]] - 2024-03-12 22:55:50.690437: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_440' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_440}}]] - 2024-03-12 22:55:50.690475: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_441' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_441}}]] - 2024-03-12 22:55:50.690514: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_442' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_442}}]] - 2024-03-12 22:55:50.690551: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_443' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_443}}]] - 2024-03-12 22:55:50.690590: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_444' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_444}}]] - 2024-03-12 22:55:50.690628: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_446' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_446}}]] - 2024-03-12 22:55:50.690677: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_447' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_447}}]] - 2024-03-12 22:55:50.690723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_450' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_450}}]] - 2024-03-12 22:55:50.690760: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_451' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_451}}]] - 2024-03-12 22:55:50.690796: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_452' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_452}}]] - 2024-03-12 22:55:50.690831: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_453' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_453}}]] - 2024-03-12 22:55:50.690868: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_454' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_454}}]] - 2024-03-12 22:55:50.690905: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_455' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_455}}]] - 2024-03-12 22:55:50.690939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_456' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_456}}]] - 2024-03-12 22:55:50.690974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_459' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_459}}]] - 2024-03-12 22:55:50.691010: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_460' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_460}}]] - 2024-03-12 22:55:50.691045: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_461' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_461}}]] - 2024-03-12 22:55:50.691081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_462' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_462}}]] - 2024-03-12 22:55:50.691116: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_463' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_463}}]] - 2024-03-12 22:55:50.691154: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_464' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_464}}]] - 2024-03-12 22:55:50.691190: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_465' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_465}}]] - 2024-03-12 22:55:50.691226: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_466' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_466}}]] - 2024-03-12 22:55:50.691262: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_467' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_467}}]] - 2024-03-12 22:55:50.691298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_471' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_471}}]] - 2024-03-12 22:55:50.691332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_472' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_472}}]] - 2024-03-12 22:55:50.691367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_473' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_473}}]] - 2024-03-12 22:55:50.691402: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_474' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_474}}]] - 2024-03-12 22:55:50.691439: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_475' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_475}}]] - 2024-03-12 22:55:50.691475: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_476' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_476}}]] - 2024-03-12 22:55:50.691511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_477' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_477}}]] - 2024-03-12 22:55:50.691548: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_478' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_478}}]] - 2024-03-12 22:55:50.691584: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_481' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_481}}]] - 2024-03-12 22:55:50.691619: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_482' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_482}}]] - 2024-03-12 22:55:50.691654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_483' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_483}}]] - 2024-03-12 22:55:50.691690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_484' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_484}}]] - 2024-03-12 22:55:50.691726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_485' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_485}}]] - 2024-03-12 22:55:50.691762: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_486' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_486}}]] - 2024-03-12 22:55:50.691798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_487' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_487}}]] - 2024-03-12 22:55:50.691832: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_488' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_488}}]] - 2024-03-12 22:55:50.691867: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_489' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_489}}]] - 2024-03-12 22:55:50.691903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_493' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_493}}]] - 2024-03-12 22:55:50.691939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_494' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_494}}]] - 2024-03-12 22:55:50.691974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_495' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_495}}]] - 2024-03-12 22:55:50.692009: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_496' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_496}}]] - 2024-03-12 22:55:50.692044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_497' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_497}}]] - 2024-03-12 22:55:50.692079: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_498' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_498}}]] - 2024-03-12 22:55:50.692114: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_499' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_499}}]] - 2024-03-12 22:55:50.692149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_500' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_500}}]] - 2024-03-12 22:55:50.692185: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_503' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_503}}]] - 2024-03-12 22:55:50.692221: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_504' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_504}}]] - 2024-03-12 22:55:50.692255: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_505' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_505}}]] - 2024-03-12 22:55:50.692291: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_506' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_506}}]] - 2024-03-12 22:55:50.692326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_507' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_507}}]] - 2024-03-12 22:55:50.692362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_508' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_508}}]] - 2024-03-12 22:55:50.692397: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_509' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_509}}]] - 2024-03-12 22:55:50.692433: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_510' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_510}}]] - 2024-03-12 22:55:50.692468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_512' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_512}}]] - 2024-03-12 22:55:50.692504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_513' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_513}}]] - 2024-03-12 22:55:50.692540: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_516' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_516}}]] - 2024-03-12 22:55:50.692575: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_517' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_517}}]] - 2024-03-12 22:55:50.692609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_518' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_518}}]] - 2024-03-12 22:55:50.692644: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_519' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_519}}]] - 2024-03-12 22:55:50.692680: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_520' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_520}}]] - 2024-03-12 22:55:50.692714: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_521' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_521}}]] - 2024-03-12 22:55:50.692748: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_522' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_522}}]] - 2024-03-12 22:55:50.692783: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_525' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_525}}]] - 2024-03-12 22:55:50.692817: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_526' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_526}}]] - 2024-03-12 22:55:50.692852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_527' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_527}}]] - 2024-03-12 22:55:50.692887: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_528' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_528}}]] - 2024-03-12 22:55:50.692921: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_529' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_529}}]] - 2024-03-12 22:55:50.692956: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_530' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_530}}]] - 2024-03-12 22:55:50.692991: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_531' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_531}}]] - 2024-03-12 22:55:50.693026: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_532' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_532}}]] - 2024-03-12 22:55:50.693060: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_533' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_533}}]] - 2024-03-12 22:55:50.693096: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_537' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_537}}]] - 2024-03-12 22:55:50.693130: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_538' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_538}}]] - 2024-03-12 22:55:50.693166: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_539' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_539}}]] - 2024-03-12 22:55:50.693200: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_540' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_540}}]] - 2024-03-12 22:55:50.693235: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_541' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_541}}]] - 2024-03-12 22:55:50.693269: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_542' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_542}}]] - 2024-03-12 22:55:50.693304: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_543' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_543}}]] - 2024-03-12 22:55:50.693339: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_544' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_544}}]] - 2024-03-12 22:55:50.693374: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_547' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_547}}]] - 2024-03-12 22:55:50.693408: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_548' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_548}}]] - 2024-03-12 22:55:50.693448: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_549' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_549}}]] - 2024-03-12 22:55:50.693503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_550' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_550}}]] - 2024-03-12 22:55:50.693541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_551' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_551}}]] - 2024-03-12 22:55:50.693578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_552' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_552}}]] - 2024-03-12 22:55:50.693616: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_553' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_553}}]] - 2024-03-12 22:55:50.693654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_554' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_554}}]] - 2024-03-12 22:55:50.693691: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_555' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_555}}]] - 2024-03-12 22:55:50.693728: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_557' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_557}}]] - 2024-03-12 22:55:50.693766: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_563' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_563}}]] - 2024-03-12 22:55:50.693803: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_564' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_564}}]] - 2024-03-12 22:55:50.693840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_565' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_565}}]] - 2024-03-12 22:55:50.693877: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_566' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_566}}]] - 2024-03-12 22:55:50.693913: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_567' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_567}}]] - 2024-03-12 22:55:50.693951: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_568' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_568}}]] - 2024-03-12 22:55:50.693988: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_569' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_569}}]] - 2024-03-12 22:55:50.694026: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_570' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_570}}]] - 2024-03-12 22:55:50.694063: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_575' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_575}}]] - 2024-03-12 22:55:50.694100: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_576' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_576}}]] - 2024-03-12 22:55:50.694137: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_577' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_577}}]] - 2024-03-12 22:55:50.694176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_578' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_578}}]] - 2024-03-12 22:55:50.694215: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_579' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_579}}]] - 2024-03-12 22:55:50.694253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_580' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_580}}]] - 2024-03-12 22:55:50.694290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_581' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_581}}]] - 2024-03-12 22:55:50.694327: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_582' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_582}}]] - 2024-03-12 22:55:50.694363: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_584' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_584}}]] - 2024-03-12 22:55:50.694400: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_585' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_585}}]] - 2024-03-12 22:55:50.694437: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_588' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_588}}]] - 2024-03-12 22:55:50.694474: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_589' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_589}}]] - 2024-03-12 22:55:50.694510: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_590' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_590}}]] - 2024-03-12 22:55:50.694547: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_591' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_591}}]] - 2024-03-12 22:55:50.694584: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_592' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_592}}]] - 2024-03-12 22:55:50.694621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_593' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_593}}]] - 2024-03-12 22:55:50.694669: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_594' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_594}}]] - 2024-03-12 22:55:50.694705: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_597' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_597}}]] - 2024-03-12 22:55:50.694741: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_598' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_598}}]] - 2024-03-12 22:55:50.694778: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_599' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_599}}]] - 2024-03-12 22:55:50.694813: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_600' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_600}}]] - 2024-03-12 22:55:50.694849: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_601' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_601}}]] - 2024-03-12 22:55:50.694887: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_602' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_602}}]] - 2024-03-12 22:55:50.694923: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_603' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_603}}]] - 2024-03-12 22:55:50.694960: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_604' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_604}}]] - 2024-03-12 22:55:50.694997: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_608' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_608}}]] - 2024-03-12 22:55:50.695033: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_609' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_609}}]] - 2024-03-12 22:55:50.695071: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_610' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_610}}]] - 2024-03-12 22:55:50.695108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_611' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_611}}]] - 2024-03-12 22:55:50.695145: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_612' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_612}}]] - 2024-03-12 22:55:50.695183: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_613' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_613}}]] - 2024-03-12 22:55:50.695219: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_614' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_614}}]] - 2024-03-12 22:55:50.695257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_615' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_615}}]] - 2024-03-12 22:55:50.695294: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_618' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_618}}]] - 2024-03-12 22:55:50.695330: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_619' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_619}}]] - 2024-03-12 22:55:50.695367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_620' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_620}}]] - 2024-03-12 22:55:50.695403: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_621' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_621}}]] - 2024-03-12 22:55:50.695439: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_622' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_622}}]] - 2024-03-12 22:55:50.695475: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_623' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_623}}]] - 2024-03-12 22:55:50.695522: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_624' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_624}}]] - 2024-03-12 22:55:50.695557: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_625' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_625}}]] - 2024-03-12 22:55:50.695592: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_626' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_626}}]] - 2024-03-12 22:55:50.695628: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_627' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_627}}]] - 2024-03-12 22:55:50.695664: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_631' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_631}}]] - 2024-03-12 22:55:50.695699: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_632' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_632}}]] - 2024-03-12 22:55:50.695734: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_633' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_633}}]] - 2024-03-12 22:55:50.695769: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_634' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_634}}]] - 2024-03-12 22:55:50.695806: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_635' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_635}}]] - 2024-03-12 22:55:50.695841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_636' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_636}}]] - 2024-03-12 22:55:50.695876: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_637' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_637}}]] - 2024-03-12 22:55:50.695910: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_638' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_638}}]] - 2024-03-12 22:55:50.695946: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_641' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_641}}]] - 2024-03-12 22:55:50.695982: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_642' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_642}}]] - 2024-03-12 22:55:50.696018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_643' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_643}}]] - 2024-03-12 22:55:50.696053: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_644' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_644}}]] - 2024-03-12 22:55:50.696090: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_645' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_645}}]] - 2024-03-12 22:55:50.696125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_646' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_646}}]] - 2024-03-12 22:55:50.696160: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_647' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_647}}]] - 2024-03-12 22:55:50.696195: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_648' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_648}}]] - 2024-03-12 22:55:50.696231: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_650' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_650}}]] - 2024-03-12 22:55:50.696267: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_651' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_651}}]] - 2024-03-12 22:55:50.696301: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_654' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_654}}]] - 2024-03-12 22:55:50.696336: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_655' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_655}}]] - 2024-03-12 22:55:50.696371: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_656' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_656}}]] - 2024-03-12 22:55:50.696407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_657' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_657}}]] - 2024-03-12 22:55:50.696443: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_658' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_658}}]] - 2024-03-12 22:55:50.696479: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_659' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_659}}]] - 2024-03-12 22:55:50.696515: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_660' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_660}}]] - 2024-03-12 22:55:50.696550: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_663' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_663}}]] - 2024-03-12 22:55:50.696601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_664' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_664}}]] - 2024-03-12 22:55:50.696637: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_665' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_665}}]] - 2024-03-12 22:55:50.696675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_666' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_666}}]] - 2024-03-12 22:55:50.696713: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_667' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_667}}]] - 2024-03-12 22:55:50.696752: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_668' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_668}}]] - 2024-03-12 22:55:50.696790: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_669' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_669}}]] - 2024-03-12 22:55:50.696829: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_670' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_670}}]] - 2024-03-12 22:55:50.696866: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_671' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_671}}]] - 2024-03-12 22:55:50.696903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_675' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_675}}]] - 2024-03-12 22:55:50.696940: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_676' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_676}}]] - 2024-03-12 22:55:50.696977: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_677' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_677}}]] - 2024-03-12 22:55:50.697015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_678' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_678}}]] - 2024-03-12 22:55:50.697051: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_679' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_679}}]] - 2024-03-12 22:55:50.697087: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_680' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_680}}]] - 2024-03-12 22:55:50.697124: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_681' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_681}}]] - 2024-03-12 22:55:50.697162: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_682' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_682}}]] - 2024-03-12 22:55:50.697199: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_685' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_685}}]] - 2024-03-12 22:55:50.697235: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_686' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_686}}]] - 2024-03-12 22:55:50.697271: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_687' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_687}}]] - 2024-03-12 22:55:50.697309: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_688' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_688}}]] - 2024-03-12 22:55:50.697346: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_689' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_689}}]] - 2024-03-12 22:55:50.697383: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_690' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_690}}]] - 2024-03-12 22:55:50.697421: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_691' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_691}}]] - 2024-03-12 22:55:50.697480: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_692' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_692}}]] - 2024-03-12 22:55:50.697518: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_693' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_693}}]] - 2024-03-12 22:55:50.697556: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_697' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_697}}]] - 2024-03-12 22:55:50.697594: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_698' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_698}}]] - 2024-03-12 22:55:50.697633: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_699' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_699}}]] - 2024-03-12 22:55:50.697670: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_700' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_700}}]] - 2024-03-12 22:55:50.697707: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_701' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_701}}]] - 2024-03-12 22:55:50.697743: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_702' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_702}}]] - 2024-03-12 22:55:50.697780: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_703' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_703}}]] - 2024-03-12 22:55:50.697818: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_704' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_704}}]] - 2024-03-12 22:55:50.697854: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_707' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_707}}]] - 2024-03-12 22:55:50.697892: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_708' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_708}}]] - 2024-03-12 22:55:50.697928: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_709' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_709}}]] - 2024-03-12 22:55:50.697966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_710' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_710}}]] - 2024-03-12 22:55:50.698003: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_711' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_711}}]] - 2024-03-12 22:55:50.698040: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_712' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_712}}]] - 2024-03-12 22:55:50.698079: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_713' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_713}}]] - 2024-03-12 22:55:50.698114: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_714' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_714}}]] - 2024-03-12 22:55:50.698151: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_716' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_716}}]] - 2024-03-12 22:55:50.698188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_717' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_717}}]] - 2024-03-12 22:55:50.698225: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_720' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_720}}]] - 2024-03-12 22:55:50.698261: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_721' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_721}}]] - 2024-03-12 22:55:50.698298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_722' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_722}}]] - 2024-03-12 22:55:50.698335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_723' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_723}}]] - 2024-03-12 22:55:50.698371: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_724' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_724}}]] - 2024-03-12 22:55:50.698408: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_725' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_725}}]] - 2024-03-12 22:55:50.698444: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_726' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_726}}]] - 2024-03-12 22:55:50.698481: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_729' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_729}}]] - 2024-03-12 22:55:50.698518: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_730' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_730}}]] - 2024-03-12 22:55:50.698554: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_731' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_731}}]] - 2024-03-12 22:55:50.698592: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_732' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_732}}]] - 2024-03-12 22:55:50.698630: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_733' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_733}}]] - 2024-03-12 22:55:50.698687: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_734' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_734}}]] - 2024-03-12 22:55:50.698723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_735' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_735}}]] - 2024-03-12 22:55:50.698758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_736' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_736}}]] - 2024-03-12 22:55:50.698793: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_737' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_737}}]] - 2024-03-12 22:55:50.698827: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_741' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_741}}]] - 2024-03-12 22:55:50.698862: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_742' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_742}}]] - 2024-03-12 22:55:50.698897: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_743' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_743}}]] - 2024-03-12 22:55:50.698931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_744' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_744}}]] - 2024-03-12 22:55:50.698966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_745' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_745}}]] - 2024-03-12 22:55:50.699001: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_746' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_746}}]] - 2024-03-12 22:55:50.699035: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_747' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_747}}]] - 2024-03-12 22:55:50.699070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_748' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_748}}]] - 2024-03-12 22:55:50.699105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_751' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_751}}]] - 2024-03-12 22:55:50.699139: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_752' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_752}}]] - 2024-03-12 22:55:50.699174: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_753' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_753}}]] - 2024-03-12 22:55:50.699209: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_754' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_754}}]] - 2024-03-12 22:55:50.699244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_755' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_755}}]] - 2024-03-12 22:55:50.699278: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_756' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_756}}]] - 2024-03-12 22:55:50.699313: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_757' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_757}}]] - 2024-03-12 22:55:50.699348: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_758' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_758}}]] - 2024-03-12 22:55:50.699382: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_759' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_759}}]] - 2024-03-12 22:55:50.699417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_763' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_763}}]] - 2024-03-12 22:55:50.699452: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_764' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_764}}]] - 2024-03-12 22:55:50.699486: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_765' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_765}}]] - 2024-03-12 22:55:50.699521: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_766' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_766}}]] - 2024-03-12 22:55:50.699555: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_767' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_767}}]] - 2024-03-12 22:55:50.699589: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_768' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_768}}]] - 2024-03-12 22:55:50.699624: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_769' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_769}}]] - 2024-03-12 22:55:50.699658: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_770' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_770}}]] - 2024-03-12 22:55:50.699693: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_773' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_773}}]] - 2024-03-12 22:55:50.699728: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_774' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_774}}]] - 2024-03-12 22:55:50.699763: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_775' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_775}}]] - 2024-03-12 22:55:50.699797: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_776' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_776}}]] - 2024-03-12 22:55:50.699832: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_777' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_777}}]] - 2024-03-12 22:55:50.699867: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_778' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_778}}]] - 2024-03-12 22:55:50.699902: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_779' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_779}}]] - 2024-03-12 22:55:50.699937: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_780' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_780}}]] - 2024-03-12 22:55:50.699972: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_782' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_782}}]] - 2024-03-12 22:55:50.700008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_783' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_783}}]] - 2024-03-12 22:55:50.700042: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_786' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_786}}]] - 2024-03-12 22:55:50.700077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_787' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_787}}]] - 2024-03-12 22:55:50.700111: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_788' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_788}}]] - 2024-03-12 22:55:50.700146: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_789' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_789}}]] - 2024-03-12 22:55:50.700181: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_790' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_790}}]] - 2024-03-12 22:55:50.700216: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_791' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_791}}]] - 2024-03-12 22:55:50.700251: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_792' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_792}}]] - 2024-03-12 22:55:50.700286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_795' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_795}}]] - 2024-03-12 22:55:50.700322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_796' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_796}}]] - 2024-03-12 22:55:50.700357: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_797' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_797}}]] - 2024-03-12 22:55:50.700392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_798' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_798}}]] - 2024-03-12 22:55:50.700426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_799' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_799}}]] - 2024-03-12 22:55:50.700461: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_800' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_800}}]] - 2024-03-12 22:55:50.700496: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_801' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_801}}]] - 2024-03-12 22:55:50.700531: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_802' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_802}}]] - 2024-03-12 22:55:50.700567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_803' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_803}}]] - 2024-03-12 22:55:50.700602: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_807' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_807}}]] - 2024-03-12 22:55:50.700637: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_808' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_808}}]] - 2024-03-12 22:55:50.700672: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_809' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_809}}]] - 2024-03-12 22:55:50.700707: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_810' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_810}}]] - 2024-03-12 22:55:50.700743: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_811' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_811}}]] - 2024-03-12 22:55:50.700777: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_812' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_812}}]] - 2024-03-12 22:55:50.700812: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_813' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_813}}]] - 2024-03-12 22:55:50.700847: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_814' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_814}}]] - 2024-03-12 22:55:50.700881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_817' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_817}}]] - 2024-03-12 22:55:50.700918: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_818' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_818}}]] - 2024-03-12 22:55:50.700954: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_819' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_819}}]] - 2024-03-12 22:55:50.700990: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_820' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_820}}]] - 2024-03-12 22:55:50.701027: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_821' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_821}}]] - 2024-03-12 22:55:50.701062: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_822' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_822}}]] - 2024-03-12 22:55:50.701100: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_823' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_823}}]] - 2024-03-12 22:55:50.701135: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_824' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_824}}]] - 2024-03-12 22:55:50.701171: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_825' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_825}}]] - 2024-03-12 22:55:50.701207: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_827' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_827}}]] - 2024-03-12 22:55:50.701243: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_833' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_833}}]] - 2024-03-12 22:55:50.701279: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_834' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_834}}]] - 2024-03-12 22:55:50.701315: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_835' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_835}}]] - 2024-03-12 22:55:50.701352: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_836' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_836}}]] - 2024-03-12 22:55:50.701388: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_837' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_837}}]] - 2024-03-12 22:55:50.701424: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_838' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_838}}]] - 2024-03-12 22:55:50.701484: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_839' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_839}}]] - 2024-03-12 22:55:50.701522: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_840' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_840}}]] - 2024-03-12 22:55:50.701560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_845' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_845}}]] - 2024-03-12 22:55:50.701600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_846' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_846}}]] - 2024-03-12 22:55:50.701638: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_847' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_847}}]] - 2024-03-12 22:55:50.701676: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_848' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_848}}]] - 2024-03-12 22:55:50.701715: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_849' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_849}}]] - 2024-03-12 22:55:50.701753: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_850' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_850}}]] - 2024-03-12 22:55:50.701791: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_851' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_851}}]] - 2024-03-12 22:55:50.701829: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_852' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_852}}]] - 2024-03-12 22:55:50.701869: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_854' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_854}}]] - 2024-03-12 22:55:50.701907: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_855' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_855}}]] - 2024-03-12 22:55:50.701945: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_858' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_858}}]] - 2024-03-12 22:55:50.701984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_859' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_859}}]] - 2024-03-12 22:55:50.702023: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_860' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_860}}]] - 2024-03-12 22:55:50.702062: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_861' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_861}}]] - 2024-03-12 22:55:50.702100: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_862' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_862}}]] - 2024-03-12 22:55:50.702138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_863' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_863}}]] - 2024-03-12 22:55:50.702176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_864' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_864}}]] - 2024-03-12 22:55:50.702214: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_867' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_867}}]] - 2024-03-12 22:55:50.702253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_868' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_868}}]] - 2024-03-12 22:55:50.702292: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_869' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_869}}]] - 2024-03-12 22:55:50.702330: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_870' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_870}}]] - 2024-03-12 22:55:50.702368: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_871' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_871}}]] - 2024-03-12 22:55:50.702407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_872' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_872}}]] - 2024-03-12 22:55:50.702446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_873' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_873}}]] - 2024-03-12 22:55:50.702484: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_874' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_874}}]] - 2024-03-12 22:55:50.702523: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_878' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_878}}]] - 2024-03-12 22:55:50.702561: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_879' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_879}}]] - 2024-03-12 22:55:50.702599: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_880' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_880}}]] - 2024-03-12 22:55:50.702639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_881' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_881}}]] - 2024-03-12 22:55:50.702687: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_882' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_882}}]] - 2024-03-12 22:55:50.702734: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_883' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_883}}]] - 2024-03-12 22:55:50.702769: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_884' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_884}}]] - 2024-03-12 22:55:50.702805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_885' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_885}}]] - 2024-03-12 22:55:50.702841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_888' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_888}}]] - 2024-03-12 22:55:50.702876: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_889' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_889}}]] - 2024-03-12 22:55:50.702911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_890' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_890}}]] - 2024-03-12 22:55:50.702948: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_891' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_891}}]] - 2024-03-12 22:55:50.702984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_892' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_892}}]] - 2024-03-12 22:55:50.703022: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_893' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_893}}]] - 2024-03-12 22:55:50.703058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_894' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_894}}]] - 2024-03-12 22:55:50.703095: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_895' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_895}}]] - 2024-03-12 22:55:50.703131: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_896' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_896}}]] - 2024-03-12 22:55:50.703167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_897' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_897}}]] - 2024-03-12 22:55:50.703204: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_901' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_901}}]] - 2024-03-12 22:55:50.703240: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_902' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_902}}]] - 2024-03-12 22:55:50.703276: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_903' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_903}}]] - 2024-03-12 22:55:50.703312: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_904' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_904}}]] - 2024-03-12 22:55:50.703348: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_905' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_905}}]] - 2024-03-12 22:55:50.703384: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_906' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_906}}]] - 2024-03-12 22:55:50.703419: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_907' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_907}}]] - 2024-03-12 22:55:50.703455: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_908' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_908}}]] - 2024-03-12 22:55:50.703491: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_911' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_911}}]] - 2024-03-12 22:55:50.703527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_912' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_912}}]] - 2024-03-12 22:55:50.703563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_913' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_913}}]] - 2024-03-12 22:55:50.703600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_914' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_914}}]] - 2024-03-12 22:55:50.703636: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_915' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_915}}]] - 2024-03-12 22:55:50.703672: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_916' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_916}}]] - 2024-03-12 22:55:50.703708: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_917' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_917}}]] - 2024-03-12 22:55:50.703744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_918' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_918}}]] - 2024-03-12 22:55:50.703780: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_920' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_920}}]] - 2024-03-12 22:55:50.703816: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_921' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_921}}]] - 2024-03-12 22:55:50.703852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_924' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_924}}]] - 2024-03-12 22:55:50.703889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_925' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_925}}]] - 2024-03-12 22:55:50.703925: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_926' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_926}}]] - 2024-03-12 22:55:50.703961: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_927' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_927}}]] - 2024-03-12 22:55:50.703998: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_928' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_928}}]] - 2024-03-12 22:55:50.704035: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_929' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_929}}]] - 2024-03-12 22:55:50.704072: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_930' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_930}}]] - 2024-03-12 22:55:50.704109: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_933' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_933}}]] - 2024-03-12 22:55:50.704145: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_934' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_934}}]] - 2024-03-12 22:55:50.704181: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_935' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_935}}]] - 2024-03-12 22:55:50.704217: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_936' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_936}}]] - 2024-03-12 22:55:50.704253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_937' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_937}}]] - 2024-03-12 22:55:50.704290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_938' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_938}}]] - 2024-03-12 22:55:50.704326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_939' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_939}}]] - 2024-03-12 22:55:50.704363: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_940' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_940}}]] - 2024-03-12 22:55:50.704400: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_941' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_941}}]] - 2024-03-12 22:55:50.704436: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_945' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_945}}]] - 2024-03-12 22:55:50.704473: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_946' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_946}}]] - 2024-03-12 22:55:50.704509: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_947' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_947}}]] - 2024-03-12 22:55:50.704546: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_948' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_948}}]] - 2024-03-12 22:55:50.704583: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_949' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_949}}]] - 2024-03-12 22:55:50.704619: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_950' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_950}}]] - 2024-03-12 22:55:50.704654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_951' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_951}}]] - 2024-03-12 22:55:50.704690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_952' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_952}}]] - 2024-03-12 22:55:50.704727: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_955' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_955}}]] - 2024-03-12 22:55:50.704763: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_956' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_956}}]] - 2024-03-12 22:55:50.704800: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_957' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_957}}]] - 2024-03-12 22:55:50.704837: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_958' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_958}}]] - 2024-03-12 22:55:50.704872: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_959' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_959}}]] - 2024-03-12 22:55:50.704908: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_960' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_960}}]] - 2024-03-12 22:55:50.704943: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_961' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_961}}]] - 2024-03-12 22:55:50.704979: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_962' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_962}}]] - 2024-03-12 22:55:50.705015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_963' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_963}}]] - 2024-03-12 22:55:50.705051: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_967' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_967}}]] - 2024-03-12 22:55:50.705086: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_968' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_968}}]] - 2024-03-12 22:55:50.705122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_969' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_969}}]] - 2024-03-12 22:55:50.705158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_970' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_970}}]] - 2024-03-12 22:55:50.705195: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_971' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_971}}]] - 2024-03-12 22:55:50.705231: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_972' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_972}}]] - 2024-03-12 22:55:50.705267: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_973' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_973}}]] - 2024-03-12 22:55:50.705303: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_974' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_974}}]] - 2024-03-12 22:55:50.705340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_977' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_977}}]] - 2024-03-12 22:55:50.705376: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_978' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_978}}]] - 2024-03-12 22:55:50.705412: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_979' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_979}}]] - 2024-03-12 22:55:50.705455: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_980' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_980}}]] - 2024-03-12 22:55:50.705510: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_981' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_981}}]] - 2024-03-12 22:55:50.705549: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_982' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_982}}]] - 2024-03-12 22:55:50.705588: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_983' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_983}}]] - 2024-03-12 22:55:50.705627: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_984' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_984}}]] - 2024-03-12 22:55:50.705666: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_986' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_986}}]] - 2024-03-12 22:55:50.705705: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_987' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_987}}]] - 2024-03-12 22:55:50.705744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_990' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_990}}]] - 2024-03-12 22:55:50.705782: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_991' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_991}}]] - 2024-03-12 22:55:50.705820: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_992' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_992}}]] - 2024-03-12 22:55:50.705858: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_993' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_993}}]] - 2024-03-12 22:55:50.705897: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_994' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_994}}]] - 2024-03-12 22:55:50.705935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_995' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_995}}]] - 2024-03-12 22:55:50.705973: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_996' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_996}}]] - 2024-03-12 22:55:50.706010: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_999' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_999}}]] - 2024-03-12 22:55:50.706048: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1000' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1000}}]] - 2024-03-12 22:55:50.706086: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1001' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1001}}]] - 2024-03-12 22:55:50.706125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1002' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1002}}]] - 2024-03-12 22:55:50.706163: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1003' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1003}}]] - 2024-03-12 22:55:50.706202: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1004' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1004}}]] - 2024-03-12 22:55:50.706240: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1005' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1005}}]] - 2024-03-12 22:55:50.706277: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1006' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1006}}]] - 2024-03-12 22:55:50.706316: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1007' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1007}}]] - 2024-03-12 22:55:50.706354: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1011' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1011}}]] - 2024-03-12 22:55:50.706392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1012' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1012}}]] - 2024-03-12 22:55:50.706430: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1013' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1013}}]] - 2024-03-12 22:55:50.706468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1014' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1014}}]] - 2024-03-12 22:55:50.706507: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1015' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1015}}]] - 2024-03-12 22:55:50.706546: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1016' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1016}}]] - 2024-03-12 22:55:50.706585: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1017' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1017}}]] - 2024-03-12 22:55:50.706623: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1018' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1018}}]] - 2024-03-12 22:55:50.706661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1021' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1021}}]] - 2024-03-12 22:55:50.706708: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1022' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1022}}]] - 2024-03-12 22:55:50.706744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1023' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1023}}]] - 2024-03-12 22:55:50.706781: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1024' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1024}}]] - 2024-03-12 22:55:50.706817: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1025' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1025}}]] - 2024-03-12 22:55:50.706854: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1026' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1026}}]] - 2024-03-12 22:55:50.706890: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1027' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1027}}]] - 2024-03-12 22:55:50.706943: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1028' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1028}}]] - 2024-03-12 22:55:50.706980: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1029' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1029}}]] - 2024-03-12 22:55:50.707018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1031' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1031}}]] - 2024-03-12 22:55:50.707056: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1037' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1037}}]] - 2024-03-12 22:55:50.707094: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1038' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1038}}]] - 2024-03-12 22:55:50.707132: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1039' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1039}}]] - 2024-03-12 22:55:50.707171: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1040' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1040}}]] - 2024-03-12 22:55:50.707208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1041' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1041}}]] - 2024-03-12 22:55:50.707246: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1042' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1042}}]] - 2024-03-12 22:55:50.707283: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1043' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1043}}]] - 2024-03-12 22:55:50.707321: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1044' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1044}}]] - 2024-03-12 22:55:50.707358: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1049' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1049}}]] - 2024-03-12 22:55:50.707395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1050' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1050}}]] - 2024-03-12 22:55:50.707433: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1051' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1051}}]] - 2024-03-12 22:55:50.707471: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1052' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1052}}]] - 2024-03-12 22:55:50.707508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1053' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1053}}]] - 2024-03-12 22:55:50.707547: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1054' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1054}}]] - 2024-03-12 22:55:50.707585: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1055' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1055}}]] - 2024-03-12 22:55:50.707623: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1056' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1056}}]] - 2024-03-12 22:55:50.707661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1058' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1058}}]] - 2024-03-12 22:55:50.707698: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1059' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1059}}]] - 2024-03-12 22:55:50.707737: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1062' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1062}}]] - 2024-03-12 22:55:50.707775: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1063' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1063}}]] - 2024-03-12 22:55:50.707813: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1064' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1064}}]] - 2024-03-12 22:55:50.707850: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1065' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1065}}]] - 2024-03-12 22:55:50.707888: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1066' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1066}}]] - 2024-03-12 22:55:50.707926: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1067' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1067}}]] - 2024-03-12 22:55:50.707963: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1068' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1068}}]] - 2024-03-12 22:55:50.708003: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1071' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1071}}]] - 2024-03-12 22:55:50.708042: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1072' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1072}}]] - 2024-03-12 22:55:50.708079: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1073' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1073}}]] - 2024-03-12 22:55:50.708127: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1074' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1074}}]] - 2024-03-12 22:55:50.708164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1075' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1075}}]] - 2024-03-12 22:55:50.708201: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1076' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1076}}]] - 2024-03-12 22:55:50.708238: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1077' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1077}}]] - 2024-03-12 22:55:50.708274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1078' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1078}}]] - 2024-03-12 22:55:50.708311: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1082' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1082}}]] - 2024-03-12 22:55:50.708348: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1083' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1083}}]] - 2024-03-12 22:55:50.708385: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1084' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1084}}]] - 2024-03-12 22:55:50.708422: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1085' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1085}}]] - 2024-03-12 22:55:50.708459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1086' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1086}}]] - 2024-03-12 22:55:50.708497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1087' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1087}}]] - 2024-03-12 22:55:50.708532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1088' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1088}}]] - 2024-03-12 22:55:50.708568: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1089' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1089}}]] - 2024-03-12 22:55:50.708605: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1092' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1092}}]] - 2024-03-12 22:55:50.708642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1093' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1093}}]] - 2024-03-12 22:55:50.708679: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1094' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1094}}]] - 2024-03-12 22:55:50.708716: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1095' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1095}}]] - 2024-03-12 22:55:50.708753: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1096' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1096}}]] - 2024-03-12 22:55:50.708790: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1097' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1097}}]] - 2024-03-12 22:55:50.708827: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1098' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1098}}]] - 2024-03-12 22:55:50.708864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1101' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1101}}]] - 2024-03-12 22:55:50.708901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1102' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1102}}]] - 2024-03-12 22:55:50.708956: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1103' with dtype float and shape [?,?,?,3] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1103}}]] - 2024-03-12 22:55:50.708994: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1105' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1105}}]] - 2024-03-12 22:55:50.709031: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1106' with dtype float and shape [?,?,?,3] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1106}}]] - 2024-03-12 22:55:50.709070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1111' with dtype float and shape [?,?,?,3] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1111}}]] - 2024-03-12 22:55:50.709108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1112' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1112}}]] - 2024-03-12 22:55:50.709146: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1113' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1113}}]] - 2024-03-12 22:55:50.709185: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1114' with dtype float and shape [?,?,?,3] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1114}}]] - - -.. parsed-literal:: - - 2024-03-12 22:55:51.217700: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall' with dtype float and shape [?,?,?,?] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall}}]] - 2024-03-12 22:55:51.217779: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1' with dtype int32 and shape [2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1}}]] - 2024-03-12 22:55:51.217819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_4' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_4}}]] - 2024-03-12 22:55:51.217864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_5' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_5}}]] - 2024-03-12 22:55:51.217902: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_6' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_6}}]] - 2024-03-12 22:55:51.217941: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_7' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_7}}]] - 2024-03-12 22:55:51.217994: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_8' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_8}}]] - 2024-03-12 22:55:51.218034: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_9' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_9}}]] - 2024-03-12 22:55:51.218073: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_10' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_10}}]] - 2024-03-12 22:55:51.218108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_11' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_11}}]] - 2024-03-12 22:55:51.218145: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_12' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_12}}]] - 2024-03-12 22:55:51.218183: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_13' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_13}}]] - 2024-03-12 22:55:51.218223: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_14' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_14}}]] - 2024-03-12 22:55:51.218261: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_15' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_15}}]] - 2024-03-12 22:55:51.218300: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_16' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_16}}]] - 2024-03-12 22:55:51.218337: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_17' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_17}}]] - 2024-03-12 22:55:51.218375: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_18' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_18}}]] - 2024-03-12 22:55:51.218412: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_19' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_19}}]] - 2024-03-12 22:55:51.218450: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_20' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_20}}]] - 2024-03-12 22:55:51.218487: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_21' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_21}}]] - 2024-03-12 22:55:51.218526: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_25' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_25}}]] - 2024-03-12 22:55:51.218572: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_26' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_26}}]] - 2024-03-12 22:55:51.218614: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_27' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_27}}]] - 2024-03-12 22:55:51.218654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_28' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_28}}]] - 2024-03-12 22:55:51.218694: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_29' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_29}}]] - 2024-03-12 22:55:51.218735: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_30' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_30}}]] - 2024-03-12 22:55:51.218773: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_31' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_31}}]] - 2024-03-12 22:55:51.218812: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_32' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_32}}]] - 2024-03-12 22:55:51.218852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_35' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_35}}]] - 2024-03-12 22:55:51.218891: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_36' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_36}}]] - 2024-03-12 22:55:51.218930: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_37' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_37}}]] - 2024-03-12 22:55:51.218969: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_38' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_38}}]] - 2024-03-12 22:55:51.219009: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_39' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_39}}]] - 2024-03-12 22:55:51.219048: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_40' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_40}}]] - 2024-03-12 22:55:51.219088: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_41' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_41}}]] - 2024-03-12 22:55:51.219125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_42' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_42}}]] - 2024-03-12 22:55:51.219161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_44' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_44}}]] - 2024-03-12 22:55:51.219200: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_45' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_45}}]] - 2024-03-12 22:55:51.219239: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_48' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_48}}]] - 2024-03-12 22:55:51.219279: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_49' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_49}}]] - 2024-03-12 22:55:51.219318: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_50' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_50}}]] - 2024-03-12 22:55:51.219358: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_51' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_51}}]] - 2024-03-12 22:55:51.219397: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_52' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_52}}]] - 2024-03-12 22:55:51.219437: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_53' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_53}}]] - 2024-03-12 22:55:51.219476: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_54' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_54}}]] - 2024-03-12 22:55:51.219515: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_57' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_57}}]] - 2024-03-12 22:55:51.219553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_58' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_58}}]] - 2024-03-12 22:55:51.219600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_59' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_59}}]] - 2024-03-12 22:55:51.219637: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_60' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_60}}]] - 2024-03-12 22:55:51.219675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_61' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_61}}]] - 2024-03-12 22:55:51.219713: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_62' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_62}}]] - 2024-03-12 22:55:51.219751: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_63' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_63}}]] - 2024-03-12 22:55:51.219788: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_64' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_64}}]] - 2024-03-12 22:55:51.219826: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_65' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_65}}]] - 2024-03-12 22:55:51.219864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_69' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_69}}]] - 2024-03-12 22:55:51.219902: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_70' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_70}}]] - 2024-03-12 22:55:51.219940: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_71' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_71}}]] - 2024-03-12 22:55:51.219978: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_72' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_72}}]] - 2024-03-12 22:55:51.220015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_73' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_73}}]] - 2024-03-12 22:55:51.220053: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_74' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_74}}]] - 2024-03-12 22:55:51.220091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_75' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_75}}]] - 2024-03-12 22:55:51.220144: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_76' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_76}}]] - 2024-03-12 22:55:51.220183: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_79' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_79}}]] - 2024-03-12 22:55:51.220222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_80' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_80}}]] - 2024-03-12 22:55:51.220261: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_81' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_81}}]] - 2024-03-12 22:55:51.220299: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_82' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_82}}]] - 2024-03-12 22:55:51.220339: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_83' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_83}}]] - 2024-03-12 22:55:51.220378: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_84' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_84}}]] - 2024-03-12 22:55:51.220417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_85' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_85}}]] - 2024-03-12 22:55:51.220456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_86' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_86}}]] - 2024-03-12 22:55:51.220495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_87' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_87}}]] - 2024-03-12 22:55:51.220535: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_91' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_91}}]] - 2024-03-12 22:55:51.220573: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_92' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_92}}]] - 2024-03-12 22:55:51.220612: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_93' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_93}}]] - 2024-03-12 22:55:51.220651: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_94' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_94}}]] - 2024-03-12 22:55:51.220690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_95' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_95}}]] - 2024-03-12 22:55:51.220730: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_96' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_96}}]] - 2024-03-12 22:55:51.220768: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_97' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_97}}]] - 2024-03-12 22:55:51.220806: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_98' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_98}}]] - 2024-03-12 22:55:51.220845: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_101' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_101}}]] - 2024-03-12 22:55:51.220884: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_102' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_102}}]] - 2024-03-12 22:55:51.220921: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_103' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_103}}]] - 2024-03-12 22:55:51.220960: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_104' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_104}}]] - 2024-03-12 22:55:51.220999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_105' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_105}}]] - 2024-03-12 22:55:51.221037: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_106' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_106}}]] - 2024-03-12 22:55:51.221075: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_107' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_107}}]] - 2024-03-12 22:55:51.221114: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_108' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_108}}]] - 2024-03-12 22:55:51.221153: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_110' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_110}}]] - 2024-03-12 22:55:51.221188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_111' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_111}}]] - 2024-03-12 22:55:51.221227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_114' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_114}}]] - 2024-03-12 22:55:51.221265: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_115' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_115}}]] - 2024-03-12 22:55:51.221304: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_116' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_116}}]] - 2024-03-12 22:55:51.221351: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_117' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_117}}]] - 2024-03-12 22:55:51.221386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_118' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_118}}]] - 2024-03-12 22:55:51.221422: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_119' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_119}}]] - 2024-03-12 22:55:51.221481: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_120' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_120}}]] - 2024-03-12 22:55:51.221522: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_123' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_123}}]] - 2024-03-12 22:55:51.221561: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_124' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_124}}]] - 2024-03-12 22:55:51.221600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_125' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_125}}]] - 2024-03-12 22:55:51.221636: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_126' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_126}}]] - 2024-03-12 22:55:51.221672: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_127' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_127}}]] - 2024-03-12 22:55:51.221712: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_128' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_128}}]] - 2024-03-12 22:55:51.221751: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_129' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_129}}]] - 2024-03-12 22:55:51.221789: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_130' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_130}}]] - 2024-03-12 22:55:51.221828: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_131' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_131}}]] - 2024-03-12 22:55:51.221866: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_135' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_135}}]] - 2024-03-12 22:55:51.221904: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_136' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_136}}]] - 2024-03-12 22:55:51.221942: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_137' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_137}}]] - 2024-03-12 22:55:51.221980: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_138' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_138}}]] - 2024-03-12 22:55:51.222019: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_139' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_139}}]] - 2024-03-12 22:55:51.222058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_140' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_140}}]] - 2024-03-12 22:55:51.222094: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_141' with dtype float and shape [?,1,1,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_141}}]] - 2024-03-12 22:55:51.222130: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_142' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_142}}]] - 2024-03-12 22:55:51.222169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_145' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_145}}]] - 2024-03-12 22:55:51.222208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_146' with dtype float and shape [32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_146}}]] - 2024-03-12 22:55:51.222247: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_147' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_147}}]] - 2024-03-12 22:55:51.222286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_148' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_148}}]] - 2024-03-12 22:55:51.222322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_149' with dtype float and shape [?,?,?,32,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_149}}]] - 2024-03-12 22:55:51.222358: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_150' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_150}}]] - 2024-03-12 22:55:51.222395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_151' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_151}}]] - 2024-03-12 22:55:51.222435: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_152' with dtype float and shape [?,?,?,2048] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_152}}]] - 2024-03-12 22:55:51.222473: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_153' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_153}}]] - 2024-03-12 22:55:51.222512: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_155' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_155}}]] - 2024-03-12 22:55:51.222548: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_161' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_161}}]] - 2024-03-12 22:55:51.222585: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_162' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_162}}]] - 2024-03-12 22:55:51.222623: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_163' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_163}}]] - 2024-03-12 22:55:51.222662: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_164' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_164}}]] - 2024-03-12 22:55:51.222700: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_165' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_165}}]] - 2024-03-12 22:55:51.222746: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_166' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_166}}]] - 2024-03-12 22:55:51.222781: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_167' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_167}}]] - 2024-03-12 22:55:51.222817: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_168' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_168}}]] - 2024-03-12 22:55:51.222852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_173' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_173}}]] - 2024-03-12 22:55:51.222886: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_174' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_174}}]] - 2024-03-12 22:55:51.222922: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_175' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_175}}]] - 2024-03-12 22:55:51.222959: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_176' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_176}}]] - 2024-03-12 22:55:51.222997: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_177' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_177}}]] - 2024-03-12 22:55:51.223034: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_178' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_178}}]] - 2024-03-12 22:55:51.223071: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_179' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_179}}]] - 2024-03-12 22:55:51.223106: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_180' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_180}}]] - 2024-03-12 22:55:51.223140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_182' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_182}}]] - 2024-03-12 22:55:51.223177: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_183' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_183}}]] - 2024-03-12 22:55:51.223215: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_186' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_186}}]] - 2024-03-12 22:55:51.223253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_187' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_187}}]] - 2024-03-12 22:55:51.223293: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_188' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_188}}]] - 2024-03-12 22:55:51.223327: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_189' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_189}}]] - 2024-03-12 22:55:51.223362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_190' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_190}}]] - 2024-03-12 22:55:51.223398: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_191' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_191}}]] - 2024-03-12 22:55:51.223435: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_192' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_192}}]] - 2024-03-12 22:55:51.223473: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_195' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_195}}]] - 2024-03-12 22:55:51.223511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_196' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_196}}]] - 2024-03-12 22:55:51.223547: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_197' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_197}}]] - 2024-03-12 22:55:51.223581: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_198' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_198}}]] - 2024-03-12 22:55:51.223618: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_199' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_199}}]] - 2024-03-12 22:55:51.223656: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_200' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_200}}]] - 2024-03-12 22:55:51.223694: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_201' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_201}}]] - 2024-03-12 22:55:51.223731: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_202' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_202}}]] - 2024-03-12 22:55:51.223770: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_206' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_206}}]] - 2024-03-12 22:55:51.223808: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_207' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_207}}]] - 2024-03-12 22:55:51.223842: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_208' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_208}}]] - 2024-03-12 22:55:51.223876: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_209' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_209}}]] - 2024-03-12 22:55:51.223911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_210' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_210}}]] - 2024-03-12 22:55:51.223946: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_211' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_211}}]] - 2024-03-12 22:55:51.223984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_212' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_212}}]] - 2024-03-12 22:55:51.224022: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_213' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_213}}]] - 2024-03-12 22:55:51.224060: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_216' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_216}}]] - 2024-03-12 22:55:51.224095: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_217' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_217}}]] - 2024-03-12 22:55:51.224130: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_218' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_218}}]] - 2024-03-12 22:55:51.224165: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_219' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_219}}]] - 2024-03-12 22:55:51.224203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_220' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_220}}]] - 2024-03-12 22:55:51.224241: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_221' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_221}}]] - 2024-03-12 22:55:51.224279: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_222' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_222}}]] - 2024-03-12 22:55:51.224317: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_223' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_223}}]] - 2024-03-12 22:55:51.224352: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_224' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_224}}]] - 2024-03-12 22:55:51.224386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_225' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_225}}]] - 2024-03-12 22:55:51.224423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_229' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_229}}]] - 2024-03-12 22:55:51.224461: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_230' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_230}}]] - 2024-03-12 22:55:51.224498: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_231' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_231}}]] - 2024-03-12 22:55:51.224536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_232' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_232}}]] - 2024-03-12 22:55:51.224574: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_233' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_233}}]] - 2024-03-12 22:55:51.224609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_234' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_234}}]] - 2024-03-12 22:55:51.224646: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_235' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_235}}]] - 2024-03-12 22:55:51.224683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_236' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_236}}]] - 2024-03-12 22:55:51.224720: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_239' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_239}}]] - 2024-03-12 22:55:51.224758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_240' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_240}}]] - 2024-03-12 22:55:51.224795: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_241' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_241}}]] - 2024-03-12 22:55:51.224833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_242' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_242}}]] - 2024-03-12 22:55:51.224871: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_243' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_243}}]] - 2024-03-12 22:55:51.224909: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_244' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_244}}]] - 2024-03-12 22:55:51.224947: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_245' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_245}}]] - 2024-03-12 22:55:51.224984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_246' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_246}}]] - 2024-03-12 22:55:51.225022: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_248' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_248}}]] - 2024-03-12 22:55:51.225060: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_249' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_249}}]] - 2024-03-12 22:55:51.225096: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_252' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_252}}]] - 2024-03-12 22:55:51.225135: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_253' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_253}}]] - 2024-03-12 22:55:51.225173: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_254' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_254}}]] - 2024-03-12 22:55:51.225210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_255' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_255}}]] - 2024-03-12 22:55:51.225264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_256' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_256}}]] - 2024-03-12 22:55:51.225303: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_257' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_257}}]] - 2024-03-12 22:55:51.225342: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_258' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_258}}]] - 2024-03-12 22:55:51.225381: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_261' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_261}}]] - 2024-03-12 22:55:51.225420: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_262' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_262}}]] - 2024-03-12 22:55:51.225480: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_263' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_263}}]] - 2024-03-12 22:55:51.225522: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_264' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_264}}]] - 2024-03-12 22:55:51.225562: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_265' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_265}}]] - 2024-03-12 22:55:51.225603: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_266' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_266}}]] - 2024-03-12 22:55:51.225643: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_267' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_267}}]] - 2024-03-12 22:55:51.225684: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_268' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_268}}]] - 2024-03-12 22:55:51.225724: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_269' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_269}}]] - 2024-03-12 22:55:51.225765: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_273' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_273}}]] - 2024-03-12 22:55:51.225805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_274' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_274}}]] - 2024-03-12 22:55:51.225845: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_275' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_275}}]] - 2024-03-12 22:55:51.225886: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_276' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_276}}]] - 2024-03-12 22:55:51.225925: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_277' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_277}}]] - 2024-03-12 22:55:51.225965: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_278' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_278}}]] - 2024-03-12 22:55:51.226005: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_279' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_279}}]] - 2024-03-12 22:55:51.226045: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_280' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_280}}]] - 2024-03-12 22:55:51.226086: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_283' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_283}}]] - 2024-03-12 22:55:51.226126: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_284' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_284}}]] - 2024-03-12 22:55:51.226166: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_285' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_285}}]] - 2024-03-12 22:55:51.226206: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_286' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_286}}]] - 2024-03-12 22:55:51.226246: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_287' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_287}}]] - 2024-03-12 22:55:51.226286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_288' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_288}}]] - 2024-03-12 22:55:51.226326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_289' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_289}}]] - 2024-03-12 22:55:51.226367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_290' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_290}}]] - 2024-03-12 22:55:51.226407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_291' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_291}}]] - 2024-03-12 22:55:51.226447: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_295' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_295}}]] - 2024-03-12 22:55:51.226487: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_296' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_296}}]] - 2024-03-12 22:55:51.226527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_297' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_297}}]] - 2024-03-12 22:55:51.226567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_298' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_298}}]] - 2024-03-12 22:55:51.226607: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_299' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_299}}]] - 2024-03-12 22:55:51.226648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_300' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_300}}]] - 2024-03-12 22:55:51.226698: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_301' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_301}}]] - 2024-03-12 22:55:51.226737: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_302' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_302}}]] - 2024-03-12 22:55:51.226776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_305' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_305}}]] - 2024-03-12 22:55:51.226816: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_306' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_306}}]] - 2024-03-12 22:55:51.226854: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_307' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_307}}]] - 2024-03-12 22:55:51.226893: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_308' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_308}}]] - 2024-03-12 22:55:51.226932: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_309' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_309}}]] - 2024-03-12 22:55:51.226972: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_310' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_310}}]] - 2024-03-12 22:55:51.227011: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_311' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_311}}]] - 2024-03-12 22:55:51.227051: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_312' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_312}}]] - 2024-03-12 22:55:51.227091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_314' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_314}}]] - 2024-03-12 22:55:51.227130: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_315' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_315}}]] - 2024-03-12 22:55:51.227169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_318' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_318}}]] - 2024-03-12 22:55:51.227208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_319' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_319}}]] - 2024-03-12 22:55:51.227247: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_320' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_320}}]] - 2024-03-12 22:55:51.227286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_321' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_321}}]] - 2024-03-12 22:55:51.227326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_322' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_322}}]] - 2024-03-12 22:55:51.227365: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_323' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_323}}]] - 2024-03-12 22:55:51.227404: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_324' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_324}}]] - 2024-03-12 22:55:51.227444: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_327' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_327}}]] - 2024-03-12 22:55:51.227483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_328' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_328}}]] - 2024-03-12 22:55:51.227523: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_329' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_329}}]] - 2024-03-12 22:55:51.227573: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_330' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_330}}]] - 2024-03-12 22:55:51.227608: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_331' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_331}}]] - 2024-03-12 22:55:51.227645: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_332' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_332}}]] - 2024-03-12 22:55:51.227683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_333' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_333}}]] - 2024-03-12 22:55:51.227721: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_334' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_334}}]] - 2024-03-12 22:55:51.227775: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_335' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_335}}]] - 2024-03-12 22:55:51.227815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_339' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_339}}]] - 2024-03-12 22:55:51.227854: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_340' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_340}}]] - 2024-03-12 22:55:51.227893: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_341' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_341}}]] - 2024-03-12 22:55:51.227933: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_342' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_342}}]] - 2024-03-12 22:55:51.227972: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_343' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_343}}]] - 2024-03-12 22:55:51.228011: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_344' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_344}}]] - 2024-03-12 22:55:51.228051: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_345' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_345}}]] - 2024-03-12 22:55:51.228091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_346' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_346}}]] - 2024-03-12 22:55:51.228129: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_349' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_349}}]] - 2024-03-12 22:55:51.228170: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_350' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_350}}]] - 2024-03-12 22:55:51.228208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_351' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_351}}]] - 2024-03-12 22:55:51.228247: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_352' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_352}}]] - 2024-03-12 22:55:51.228286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_353' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_353}}]] - 2024-03-12 22:55:51.228325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_354' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_354}}]] - 2024-03-12 22:55:51.228364: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_355' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_355}}]] - 2024-03-12 22:55:51.228404: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_356' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_356}}]] - 2024-03-12 22:55:51.228442: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_357' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_357}}]] - 2024-03-12 22:55:51.228481: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_361' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_361}}]] - 2024-03-12 22:55:51.228521: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_362' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_362}}]] - 2024-03-12 22:55:51.228560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_363' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_363}}]] - 2024-03-12 22:55:51.228600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_364' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_364}}]] - 2024-03-12 22:55:51.228639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_365' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_365}}]] - 2024-03-12 22:55:51.228679: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_366' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_366}}]] - 2024-03-12 22:55:51.228718: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_367' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_367}}]] - 2024-03-12 22:55:51.228757: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_368' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_368}}]] - 2024-03-12 22:55:51.228794: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_371' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_371}}]] - 2024-03-12 22:55:51.228833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_372' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_372}}]] - 2024-03-12 22:55:51.228873: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_373' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_373}}]] - 2024-03-12 22:55:51.228912: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_374' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_374}}]] - 2024-03-12 22:55:51.228952: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_375' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_375}}]] - 2024-03-12 22:55:51.228991: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_376' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_376}}]] - 2024-03-12 22:55:51.229030: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_377' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_377}}]] - 2024-03-12 22:55:51.229070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_378' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_378}}]] - 2024-03-12 22:55:51.229109: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_380' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_380}}]] - 2024-03-12 22:55:51.229148: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_381' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_381}}]] - 2024-03-12 22:55:51.229187: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_384' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_384}}]] - 2024-03-12 22:55:51.229224: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_385' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_385}}]] - 2024-03-12 22:55:51.229260: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_386' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_386}}]] - 2024-03-12 22:55:51.229299: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_387' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_387}}]] - 2024-03-12 22:55:51.229338: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_388' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_388}}]] - 2024-03-12 22:55:51.229377: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_389' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_389}}]] - 2024-03-12 22:55:51.229415: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_390' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_390}}]] - 2024-03-12 22:55:51.229456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_393' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_393}}]] - 2024-03-12 22:55:51.229511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_394' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_394}}]] - 2024-03-12 22:55:51.229551: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_395' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_395}}]] - 2024-03-12 22:55:51.229591: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_396' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_396}}]] - 2024-03-12 22:55:51.229631: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_397' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_397}}]] - 2024-03-12 22:55:51.229682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_398' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_398}}]] - 2024-03-12 22:55:51.229721: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_399' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_399}}]] - 2024-03-12 22:55:51.229761: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_400' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_400}}]] - 2024-03-12 22:55:51.229797: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_401' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_401}}]] - 2024-03-12 22:55:51.229836: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_405' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_405}}]] - 2024-03-12 22:55:51.229875: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_406' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_406}}]] - 2024-03-12 22:55:51.229916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_407' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_407}}]] - 2024-03-12 22:55:51.229956: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_408' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_408}}]] - 2024-03-12 22:55:51.229992: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_409' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_409}}]] - 2024-03-12 22:55:51.230028: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_410' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_410}}]] - 2024-03-12 22:55:51.230064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_411' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_411}}]] - 2024-03-12 22:55:51.230100: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_412' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_412}}]] - 2024-03-12 22:55:51.230138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_415' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_415}}]] - 2024-03-12 22:55:51.230174: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_416' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_416}}]] - 2024-03-12 22:55:51.230210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_417' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_417}}]] - 2024-03-12 22:55:51.230249: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_418' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_418}}]] - 2024-03-12 22:55:51.230287: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_419' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_419}}]] - 2024-03-12 22:55:51.230324: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_420' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_420}}]] - 2024-03-12 22:55:51.230361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_421' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_421}}]] - 2024-03-12 22:55:51.230397: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_422' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_422}}]] - 2024-03-12 22:55:51.230434: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_423' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_423}}]] - 2024-03-12 22:55:51.230470: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_427' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_427}}]] - 2024-03-12 22:55:51.230506: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_428' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_428}}]] - 2024-03-12 22:55:51.230542: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_429' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_429}}]] - 2024-03-12 22:55:51.230579: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_430' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_430}}]] - 2024-03-12 22:55:51.230615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_431' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_431}}]] - 2024-03-12 22:55:51.230651: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_432' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_432}}]] - 2024-03-12 22:55:51.230689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_433' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_433}}]] - 2024-03-12 22:55:51.230728: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_434' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_434}}]] - 2024-03-12 22:55:51.230765: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_437' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_437}}]] - 2024-03-12 22:55:51.230802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_438' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_438}}]] - 2024-03-12 22:55:51.230839: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_439' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_439}}]] - 2024-03-12 22:55:51.230875: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_440' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_440}}]] - 2024-03-12 22:55:51.230914: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_441' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_441}}]] - 2024-03-12 22:55:51.230950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_442' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_442}}]] - 2024-03-12 22:55:51.230987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_443' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_443}}]] - 2024-03-12 22:55:51.231023: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_444' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_444}}]] - 2024-03-12 22:55:51.231060: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_446' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_446}}]] - 2024-03-12 22:55:51.231096: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_447' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_447}}]] - 2024-03-12 22:55:51.231133: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_450' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_450}}]] - 2024-03-12 22:55:51.231170: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_451' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_451}}]] - 2024-03-12 22:55:51.231207: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_452' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_452}}]] - 2024-03-12 22:55:51.231243: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_453' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_453}}]] - 2024-03-12 22:55:51.231280: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_454' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_454}}]] - 2024-03-12 22:55:51.231317: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_455' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_455}}]] - 2024-03-12 22:55:51.231355: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_456' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_456}}]] - 2024-03-12 22:55:51.231392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_459' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_459}}]] - 2024-03-12 22:55:51.231428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_460' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_460}}]] - 2024-03-12 22:55:51.231467: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_461' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_461}}]] - 2024-03-12 22:55:51.231504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_462' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_462}}]] - 2024-03-12 22:55:51.231541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_463' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_463}}]] - 2024-03-12 22:55:51.231578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_464' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_464}}]] - 2024-03-12 22:55:51.231617: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_465' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_465}}]] - 2024-03-12 22:55:51.231653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_466' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_466}}]] - 2024-03-12 22:55:51.231688: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_467' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_467}}]] - 2024-03-12 22:55:51.231724: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_471' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_471}}]] - 2024-03-12 22:55:51.231761: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_472' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_472}}]] - 2024-03-12 22:55:51.231799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_473' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_473}}]] - 2024-03-12 22:55:51.231835: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_474' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_474}}]] - 2024-03-12 22:55:51.231872: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_475' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_475}}]] - 2024-03-12 22:55:51.231908: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_476' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_476}}]] - 2024-03-12 22:55:51.231945: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_477' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_477}}]] - 2024-03-12 22:55:51.231981: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_478' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_478}}]] - 2024-03-12 22:55:51.232020: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_481' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_481}}]] - 2024-03-12 22:55:51.232060: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_482' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_482}}]] - 2024-03-12 22:55:51.232095: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_483' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_483}}]] - 2024-03-12 22:55:51.232133: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_484' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_484}}]] - 2024-03-12 22:55:51.232169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_485' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_485}}]] - 2024-03-12 22:55:51.232206: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_486' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_486}}]] - 2024-03-12 22:55:51.232245: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_487' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_487}}]] - 2024-03-12 22:55:51.232282: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_488' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_488}}]] - 2024-03-12 22:55:51.232320: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_489' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_489}}]] - 2024-03-12 22:55:51.232357: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_493' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_493}}]] - 2024-03-12 22:55:51.232392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_494' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_494}}]] - 2024-03-12 22:55:51.232429: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_495' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_495}}]] - 2024-03-12 22:55:51.232469: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_496' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_496}}]] - 2024-03-12 22:55:51.232507: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_497' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_497}}]] - 2024-03-12 22:55:51.232543: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_498' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_498}}]] - 2024-03-12 22:55:51.232579: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_499' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_499}}]] - 2024-03-12 22:55:51.232615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_500' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_500}}]] - 2024-03-12 22:55:51.232652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_503' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_503}}]] - 2024-03-12 22:55:51.232690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_504' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_504}}]] - 2024-03-12 22:55:51.232730: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_505' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_505}}]] - 2024-03-12 22:55:51.232767: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_506' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_506}}]] - 2024-03-12 22:55:51.232805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_507' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_507}}]] - 2024-03-12 22:55:51.232841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_508' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_508}}]] - 2024-03-12 22:55:51.232878: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_509' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_509}}]] - 2024-03-12 22:55:51.232914: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_510' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_510}}]] - 2024-03-12 22:55:51.232953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_512' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_512}}]] - 2024-03-12 22:55:51.232992: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_513' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_513}}]] - 2024-03-12 22:55:51.233029: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_516' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_516}}]] - 2024-03-12 22:55:51.233068: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_517' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_517}}]] - 2024-03-12 22:55:51.233107: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_518' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_518}}]] - 2024-03-12 22:55:51.233146: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_519' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_519}}]] - 2024-03-12 22:55:51.233185: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_520' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_520}}]] - 2024-03-12 22:55:51.233224: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_521' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_521}}]] - 2024-03-12 22:55:51.233264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_522' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_522}}]] - 2024-03-12 22:55:51.233302: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_525' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_525}}]] - 2024-03-12 22:55:51.233340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_526' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_526}}]] - 2024-03-12 22:55:51.233380: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_527' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_527}}]] - 2024-03-12 22:55:51.233418: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_528' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_528}}]] - 2024-03-12 22:55:51.233463: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_529' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_529}}]] - 2024-03-12 22:55:51.233519: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_530' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_530}}]] - 2024-03-12 22:55:51.233559: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_531' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_531}}]] - 2024-03-12 22:55:51.233598: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_532' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_532}}]] - 2024-03-12 22:55:51.233637: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_533' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_533}}]] - 2024-03-12 22:55:51.233676: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_537' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_537}}]] - 2024-03-12 22:55:51.233717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_538' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_538}}]] - 2024-03-12 22:55:51.233756: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_539' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_539}}]] - 2024-03-12 22:55:51.233796: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_540' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_540}}]] - 2024-03-12 22:55:51.233834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_541' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_541}}]] - 2024-03-12 22:55:51.233874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_542' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_542}}]] - 2024-03-12 22:55:51.233914: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_543' with dtype float and shape [?,1,1,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_543}}]] - 2024-03-12 22:55:51.233953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_544' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_544}}]] - 2024-03-12 22:55:51.233992: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_547' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_547}}]] - 2024-03-12 22:55:51.234031: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_548' with dtype float and shape [32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_548}}]] - 2024-03-12 22:55:51.234070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_549' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_549}}]] - 2024-03-12 22:55:51.234109: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_550' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_550}}]] - 2024-03-12 22:55:51.234149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_551' with dtype float and shape [?,?,?,32,32] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_551}}]] - 2024-03-12 22:55:51.234189: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_552' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_552}}]] - 2024-03-12 22:55:51.234228: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_553' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_553}}]] - 2024-03-12 22:55:51.234264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_554' with dtype float and shape [?,?,?,1024] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_554}}]] - 2024-03-12 22:55:51.234303: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_555' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_555}}]] - 2024-03-12 22:55:51.234343: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_557' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_557}}]] - 2024-03-12 22:55:51.234380: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_563' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_563}}]] - 2024-03-12 22:55:51.234418: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_564' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_564}}]] - 2024-03-12 22:55:51.234456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_565' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_565}}]] - 2024-03-12 22:55:51.234495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_566' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_566}}]] - 2024-03-12 22:55:51.234534: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_567' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_567}}]] - 2024-03-12 22:55:51.234573: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_568' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_568}}]] - 2024-03-12 22:55:51.234613: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_569' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_569}}]] - 2024-03-12 22:55:51.234649: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_570' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_570}}]] - 2024-03-12 22:55:51.234699: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_575' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_575}}]] - 2024-03-12 22:55:51.234737: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_576' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_576}}]] - 2024-03-12 22:55:51.234776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_577' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_577}}]] - 2024-03-12 22:55:51.234814: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_578' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_578}}]] - 2024-03-12 22:55:51.234850: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_579' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_579}}]] - 2024-03-12 22:55:51.234888: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_580' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_580}}]] - 2024-03-12 22:55:51.234927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_581' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_581}}]] - 2024-03-12 22:55:51.234966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_582' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_582}}]] - 2024-03-12 22:55:51.235004: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_584' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_584}}]] - 2024-03-12 22:55:51.235039: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_585' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_585}}]] - 2024-03-12 22:55:51.235077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_588' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_588}}]] - 2024-03-12 22:55:51.235115: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_589' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_589}}]] - 2024-03-12 22:55:51.235154: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_590' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_590}}]] - 2024-03-12 22:55:51.235192: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_591' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_591}}]] - 2024-03-12 22:55:51.235230: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_592' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_592}}]] - 2024-03-12 22:55:51.235271: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_593' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_593}}]] - 2024-03-12 22:55:51.235306: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_594' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_594}}]] - 2024-03-12 22:55:51.235345: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_597' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_597}}]] - 2024-03-12 22:55:51.235384: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_598' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_598}}]] - 2024-03-12 22:55:51.235423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_599' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_599}}]] - 2024-03-12 22:55:51.235461: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_600' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_600}}]] - 2024-03-12 22:55:51.235497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_601' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_601}}]] - 2024-03-12 22:55:51.235533: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_602' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_602}}]] - 2024-03-12 22:55:51.235569: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_603' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_603}}]] - 2024-03-12 22:55:51.235605: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_604' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_604}}]] - 2024-03-12 22:55:51.235644: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_608' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_608}}]] - 2024-03-12 22:55:51.235680: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_609' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_609}}]] - 2024-03-12 22:55:51.235716: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_610' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_610}}]] - 2024-03-12 22:55:51.235751: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_611' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_611}}]] - 2024-03-12 22:55:51.235788: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_612' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_612}}]] - 2024-03-12 22:55:51.235825: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_613' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_613}}]] - 2024-03-12 22:55:51.235864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_614' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_614}}]] - 2024-03-12 22:55:51.235903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_615' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_615}}]] - 2024-03-12 22:55:51.235939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_618' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_618}}]] - 2024-03-12 22:55:51.235976: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_619' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_619}}]] - 2024-03-12 22:55:51.236013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_620' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_620}}]] - 2024-03-12 22:55:51.236049: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_621' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_621}}]] - 2024-03-12 22:55:51.236085: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_622' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_622}}]] - 2024-03-12 22:55:51.236125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_623' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_623}}]] - 2024-03-12 22:55:51.236163: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_624' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_624}}]] - 2024-03-12 22:55:51.236200: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_625' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_625}}]] - 2024-03-12 22:55:51.236236: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_626' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_626}}]] - 2024-03-12 22:55:51.236272: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_627' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_627}}]] - 2024-03-12 22:55:51.236311: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_631' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_631}}]] - 2024-03-12 22:55:51.236350: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_632' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_632}}]] - 2024-03-12 22:55:51.236387: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_633' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_633}}]] - 2024-03-12 22:55:51.236423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_634' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_634}}]] - 2024-03-12 22:55:51.236460: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_635' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_635}}]] - 2024-03-12 22:55:51.236496: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_636' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_636}}]] - 2024-03-12 22:55:51.236533: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_637' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_637}}]] - 2024-03-12 22:55:51.236571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_638' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_638}}]] - 2024-03-12 22:55:51.236608: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_641' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_641}}]] - 2024-03-12 22:55:51.236643: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_642' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_642}}]] - 2024-03-12 22:55:51.236680: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_643' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_643}}]] - 2024-03-12 22:55:51.236717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_644' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_644}}]] - 2024-03-12 22:55:51.236755: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_645' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_645}}]] - 2024-03-12 22:55:51.236794: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_646' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_646}}]] - 2024-03-12 22:55:51.236832: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_647' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_647}}]] - 2024-03-12 22:55:51.236868: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_648' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_648}}]] - 2024-03-12 22:55:51.236905: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_650' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_650}}]] - 2024-03-12 22:55:51.236943: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_651' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_651}}]] - 2024-03-12 22:55:51.236983: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_654' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_654}}]] - 2024-03-12 22:55:51.237022: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_655' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_655}}]] - 2024-03-12 22:55:51.237059: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_656' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_656}}]] - 2024-03-12 22:55:51.237097: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_657' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_657}}]] - 2024-03-12 22:55:51.237134: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_658' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_658}}]] - 2024-03-12 22:55:51.237172: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_659' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_659}}]] - 2024-03-12 22:55:51.237210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_660' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_660}}]] - 2024-03-12 22:55:51.237250: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_663' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_663}}]] - 2024-03-12 22:55:51.237286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_664' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_664}}]] - 2024-03-12 22:55:51.237322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_665' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_665}}]] - 2024-03-12 22:55:51.237359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_666' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_666}}]] - 2024-03-12 22:55:51.237396: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_667' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_667}}]] - 2024-03-12 22:55:51.237438: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_668' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_668}}]] - 2024-03-12 22:55:51.237478: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_669' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_669}}]] - 2024-03-12 22:55:51.237517: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_670' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_670}}]] - 2024-03-12 22:55:51.237556: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_671' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_671}}]] - 2024-03-12 22:55:51.237593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_675' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_675}}]] - 2024-03-12 22:55:51.237631: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_676' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_676}}]] - 2024-03-12 22:55:51.237668: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_677' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_677}}]] - 2024-03-12 22:55:51.237707: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_678' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_678}}]] - 2024-03-12 22:55:51.237744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_679' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_679}}]] - 2024-03-12 22:55:51.237781: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_680' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_680}}]] - 2024-03-12 22:55:51.237817: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_681' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_681}}]] - 2024-03-12 22:55:51.237853: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_682' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_682}}]] - 2024-03-12 22:55:51.237890: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_685' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_685}}]] - 2024-03-12 22:55:51.237927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_686' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_686}}]] - 2024-03-12 22:55:51.237966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_687' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_687}}]] - 2024-03-12 22:55:51.238002: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_688' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_688}}]] - 2024-03-12 22:55:51.238039: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_689' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_689}}]] - 2024-03-12 22:55:51.238075: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_690' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_690}}]] - 2024-03-12 22:55:51.238112: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_691' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_691}}]] - 2024-03-12 22:55:51.238148: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_692' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_692}}]] - 2024-03-12 22:55:51.238185: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_693' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_693}}]] - 2024-03-12 22:55:51.238221: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_697' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_697}}]] - 2024-03-12 22:55:51.238258: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_698' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_698}}]] - 2024-03-12 22:55:51.238295: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_699' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_699}}]] - 2024-03-12 22:55:51.238334: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_700' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_700}}]] - 2024-03-12 22:55:51.238373: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_701' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_701}}]] - 2024-03-12 22:55:51.238411: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_702' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_702}}]] - 2024-03-12 22:55:51.238450: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_703' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_703}}]] - 2024-03-12 22:55:51.238487: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_704' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_704}}]] - 2024-03-12 22:55:51.238525: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_707' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_707}}]] - 2024-03-12 22:55:51.238563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_708' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_708}}]] - 2024-03-12 22:55:51.238602: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_709' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_709}}]] - 2024-03-12 22:55:51.238641: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_710' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_710}}]] - 2024-03-12 22:55:51.238678: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_711' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_711}}]] - 2024-03-12 22:55:51.238714: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_712' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_712}}]] - 2024-03-12 22:55:51.238752: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_713' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_713}}]] - 2024-03-12 22:55:51.238791: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_714' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_714}}]] - 2024-03-12 22:55:51.238830: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_716' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_716}}]] - 2024-03-12 22:55:51.238868: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_717' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_717}}]] - 2024-03-12 22:55:51.238907: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_720' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_720}}]] - 2024-03-12 22:55:51.238946: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_721' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_721}}]] - 2024-03-12 22:55:51.238985: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_722' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_722}}]] - 2024-03-12 22:55:51.239024: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_723' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_723}}]] - 2024-03-12 22:55:51.239063: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_724' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_724}}]] - 2024-03-12 22:55:51.239102: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_725' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_725}}]] - 2024-03-12 22:55:51.239141: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_726' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_726}}]] - 2024-03-12 22:55:51.239180: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_729' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_729}}]] - 2024-03-12 22:55:51.239218: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_730' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_730}}]] - 2024-03-12 22:55:51.239257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_731' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_731}}]] - 2024-03-12 22:55:51.239296: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_732' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_732}}]] - 2024-03-12 22:55:51.239335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_733' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_733}}]] - 2024-03-12 22:55:51.239374: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_734' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_734}}]] - 2024-03-12 22:55:51.239412: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_735' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_735}}]] - 2024-03-12 22:55:51.239450: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_736' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_736}}]] - 2024-03-12 22:55:51.239489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_737' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_737}}]] - 2024-03-12 22:55:51.239527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_741' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_741}}]] - 2024-03-12 22:55:51.239566: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_742' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_742}}]] - 2024-03-12 22:55:51.239604: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_743' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_743}}]] - 2024-03-12 22:55:51.239643: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_744' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_744}}]] - 2024-03-12 22:55:51.239682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_745' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_745}}]] - 2024-03-12 22:55:51.239720: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_746' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_746}}]] - 2024-03-12 22:55:51.239759: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_747' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_747}}]] - 2024-03-12 22:55:51.239798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_748' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_748}}]] - 2024-03-12 22:55:51.239836: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_751' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_751}}]] - 2024-03-12 22:55:51.239874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_752' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_752}}]] - 2024-03-12 22:55:51.239913: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_753' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_753}}]] - 2024-03-12 22:55:51.239952: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_754' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_754}}]] - 2024-03-12 22:55:51.239991: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_755' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_755}}]] - 2024-03-12 22:55:51.240030: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_756' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_756}}]] - 2024-03-12 22:55:51.240068: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_757' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_757}}]] - 2024-03-12 22:55:51.240107: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_758' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_758}}]] - 2024-03-12 22:55:51.240146: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_759' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_759}}]] - 2024-03-12 22:55:51.240184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_763' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_763}}]] - 2024-03-12 22:55:51.240222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_764' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_764}}]] - 2024-03-12 22:55:51.240261: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_765' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_765}}]] - 2024-03-12 22:55:51.240299: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_766' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_766}}]] - 2024-03-12 22:55:51.240338: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_767' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_767}}]] - 2024-03-12 22:55:51.240377: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_768' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_768}}]] - 2024-03-12 22:55:51.240415: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_769' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_769}}]] - 2024-03-12 22:55:51.240453: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_770' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_770}}]] - 2024-03-12 22:55:51.240492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_773' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_773}}]] - 2024-03-12 22:55:51.240531: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_774' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_774}}]] - 2024-03-12 22:55:51.240569: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_775' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_775}}]] - 2024-03-12 22:55:51.240608: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_776' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_776}}]] - 2024-03-12 22:55:51.240648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_777' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_777}}]] - 2024-03-12 22:55:51.240687: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_778' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_778}}]] - 2024-03-12 22:55:51.240725: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_779' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_779}}]] - 2024-03-12 22:55:51.240764: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_780' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_780}}]] - 2024-03-12 22:55:51.240802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_782' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_782}}]] - 2024-03-12 22:55:51.240842: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_783' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_783}}]] - 2024-03-12 22:55:51.240881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_786' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_786}}]] - 2024-03-12 22:55:51.240920: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_787' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_787}}]] - 2024-03-12 22:55:51.240960: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_788' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_788}}]] - 2024-03-12 22:55:51.240999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_789' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_789}}]] - 2024-03-12 22:55:51.241038: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_790' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_790}}]] - 2024-03-12 22:55:51.241077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_791' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_791}}]] - 2024-03-12 22:55:51.241115: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_792' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_792}}]] - 2024-03-12 22:55:51.241155: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_795' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_795}}]] - 2024-03-12 22:55:51.241194: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_796' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_796}}]] - 2024-03-12 22:55:51.241232: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_797' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_797}}]] - 2024-03-12 22:55:51.241272: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_798' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_798}}]] - 2024-03-12 22:55:51.241311: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_799' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_799}}]] - 2024-03-12 22:55:51.241350: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_800' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_800}}]] - 2024-03-12 22:55:51.241389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_801' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_801}}]] - 2024-03-12 22:55:51.241427: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_802' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_802}}]] - 2024-03-12 22:55:51.241489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_803' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_803}}]] - 2024-03-12 22:55:51.241530: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_807' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_807}}]] - 2024-03-12 22:55:51.241569: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_808' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_808}}]] - 2024-03-12 22:55:51.241609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_809' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_809}}]] - 2024-03-12 22:55:51.241648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_810' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_810}}]] - 2024-03-12 22:55:51.241688: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_811' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_811}}]] - 2024-03-12 22:55:51.241728: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_812' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_812}}]] - 2024-03-12 22:55:51.241767: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_813' with dtype float and shape [?,1,1,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_813}}]] - 2024-03-12 22:55:51.241806: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_814' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_814}}]] - 2024-03-12 22:55:51.241845: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_817' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_817}}]] - 2024-03-12 22:55:51.241882: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_818' with dtype float and shape [32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_818}}]] - 2024-03-12 22:55:51.241919: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_819' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_819}}]] - 2024-03-12 22:55:51.241956: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_820' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_820}}]] - 2024-03-12 22:55:51.241995: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_821' with dtype float and shape [?,?,?,32,16] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_821}}]] - 2024-03-12 22:55:51.242033: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_822' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_822}}]] - 2024-03-12 22:55:51.242069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_823' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_823}}]] - 2024-03-12 22:55:51.242106: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_824' with dtype float and shape [?,?,?,512] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_824}}]] - 2024-03-12 22:55:51.242143: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_825' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_825}}]] - 2024-03-12 22:55:51.242181: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_827' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_827}}]] - 2024-03-12 22:55:51.242218: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_833' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_833}}]] - 2024-03-12 22:55:51.242255: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_834' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_834}}]] - 2024-03-12 22:55:51.242293: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_835' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_835}}]] - 2024-03-12 22:55:51.242331: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_836' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_836}}]] - 2024-03-12 22:55:51.242369: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_837' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_837}}]] - 2024-03-12 22:55:51.242407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_838' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_838}}]] - 2024-03-12 22:55:51.242444: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_839' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_839}}]] - 2024-03-12 22:55:51.242480: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_840' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_840}}]] - 2024-03-12 22:55:51.242517: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_845' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_845}}]] - 2024-03-12 22:55:51.242554: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_846' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_846}}]] - 2024-03-12 22:55:51.242591: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_847' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_847}}]] - 2024-03-12 22:55:51.242639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_848' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_848}}]] - 2024-03-12 22:55:51.242675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_849' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_849}}]] - 2024-03-12 22:55:51.242721: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_850' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_850}}]] - 2024-03-12 22:55:51.242756: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_851' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_851}}]] - 2024-03-12 22:55:51.242792: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_852' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_852}}]] - 2024-03-12 22:55:51.242827: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_854' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_854}}]] - 2024-03-12 22:55:51.242862: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_855' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_855}}]] - 2024-03-12 22:55:51.242896: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_858' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_858}}]] - 2024-03-12 22:55:51.242931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_859' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_859}}]] - 2024-03-12 22:55:51.242966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_860' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_860}}]] - 2024-03-12 22:55:51.243001: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_861' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_861}}]] - 2024-03-12 22:55:51.243037: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_862' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_862}}]] - 2024-03-12 22:55:51.243072: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_863' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_863}}]] - 2024-03-12 22:55:51.243107: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_864' with dtype float and shape [?,1,1,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_864}}]] - 2024-03-12 22:55:51.243142: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_867' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_867}}]] - 2024-03-12 22:55:51.243177: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_868' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_868}}]] - 2024-03-12 22:55:51.243212: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_869' with dtype float and shape [32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_869}}]] - 2024-03-12 22:55:51.243247: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_870' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_870}}]] - 2024-03-12 22:55:51.243282: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_871' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_871}}]] - 2024-03-12 22:55:51.243317: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_872' with dtype float and shape [?,?,?,32,4] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_872}}]] - 2024-03-12 22:55:51.243352: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_873' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_873}}]] - 2024-03-12 22:55:51.243387: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_874' with dtype float and shape [?,?,?,128] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_874}}]] - 2024-03-12 22:55:51.243422: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_878' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_878}}]] - 2024-03-12 22:55:51.243456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_879' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_879}}]] - 2024-03-12 22:55:51.243491: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_880' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_880}}]] - 2024-03-12 22:55:51.243526: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_881' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_881}}]] - 2024-03-12 22:55:51.243561: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_882' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_882}}]] - 2024-03-12 22:55:51.243597: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_883' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_883}}]] - 2024-03-12 22:55:51.243632: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_884' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_884}}]] - 2024-03-12 22:55:51.243667: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_885' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_885}}]] - 2024-03-12 22:55:51.243703: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_888' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_888}}]] - 2024-03-12 22:55:51.243739: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_889' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_889}}]] - 2024-03-12 22:55:51.243774: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_890' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_890}}]] - 2024-03-12 22:55:51.243809: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_891' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_891}}]] - 2024-03-12 22:55:51.243845: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_892' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_892}}]] - 2024-03-12 22:55:51.243881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_893' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_893}}]] - 2024-03-12 22:55:51.243916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_894' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_894}}]] - 2024-03-12 22:55:51.243951: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_895' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_895}}]] - 2024-03-12 22:55:51.243986: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_896' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_896}}]] - 2024-03-12 22:55:51.244021: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_897' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_897}}]] - 2024-03-12 22:55:51.244057: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_901' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_901}}]] - 2024-03-12 22:55:51.244093: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_902' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_902}}]] - 2024-03-12 22:55:51.244128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_903' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_903}}]] - 2024-03-12 22:55:51.244164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_904' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_904}}]] - 2024-03-12 22:55:51.244199: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_905' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_905}}]] - 2024-03-12 22:55:51.244235: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_906' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_906}}]] - 2024-03-12 22:55:51.244270: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_907' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_907}}]] - 2024-03-12 22:55:51.244306: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_908' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_908}}]] - 2024-03-12 22:55:51.244341: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_911' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_911}}]] - 2024-03-12 22:55:51.244376: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_912' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_912}}]] - 2024-03-12 22:55:51.244411: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_913' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_913}}]] - 2024-03-12 22:55:51.244446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_914' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_914}}]] - 2024-03-12 22:55:51.244481: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_915' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_915}}]] - 2024-03-12 22:55:51.244516: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_916' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_916}}]] - 2024-03-12 22:55:51.244551: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_917' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_917}}]] - 2024-03-12 22:55:51.244586: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_918' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_918}}]] - 2024-03-12 22:55:51.244620: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_920' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_920}}]] - 2024-03-12 22:55:51.244655: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_921' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_921}}]] - 2024-03-12 22:55:51.244690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_924' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_924}}]] - 2024-03-12 22:55:51.244725: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_925' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_925}}]] - 2024-03-12 22:55:51.244760: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_926' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_926}}]] - 2024-03-12 22:55:51.244795: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_927' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_927}}]] - 2024-03-12 22:55:51.244830: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_928' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_928}}]] - 2024-03-12 22:55:51.244866: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_929' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_929}}]] - 2024-03-12 22:55:51.244903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_930' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_930}}]] - 2024-03-12 22:55:51.244938: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_933' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_933}}]] - 2024-03-12 22:55:51.244973: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_934' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_934}}]] - 2024-03-12 22:55:51.245008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_935' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_935}}]] - 2024-03-12 22:55:51.245043: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_936' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_936}}]] - 2024-03-12 22:55:51.245078: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_937' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_937}}]] - 2024-03-12 22:55:51.245113: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_938' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_938}}]] - 2024-03-12 22:55:51.245149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_939' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_939}}]] - 2024-03-12 22:55:51.245184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_940' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_940}}]] - 2024-03-12 22:55:51.245219: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_941' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_941}}]] - 2024-03-12 22:55:51.245255: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_945' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_945}}]] - 2024-03-12 22:55:51.245290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_946' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_946}}]] - 2024-03-12 22:55:51.245325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_947' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_947}}]] - 2024-03-12 22:55:51.245360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_948' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_948}}]] - 2024-03-12 22:55:51.245395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_949' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_949}}]] - 2024-03-12 22:55:51.245431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_950' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_950}}]] - 2024-03-12 22:55:51.245489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_951' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_951}}]] - 2024-03-12 22:55:51.245526: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_952' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_952}}]] - 2024-03-12 22:55:51.245564: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_955' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_955}}]] - 2024-03-12 22:55:51.245602: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_956' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_956}}]] - 2024-03-12 22:55:51.245638: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_957' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_957}}]] - 2024-03-12 22:55:51.245675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_958' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_958}}]] - 2024-03-12 22:55:51.245712: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_959' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_959}}]] - 2024-03-12 22:55:51.245749: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_960' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_960}}]] - 2024-03-12 22:55:51.245785: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_961' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_961}}]] - 2024-03-12 22:55:51.245823: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_962' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_962}}]] - 2024-03-12 22:55:51.245860: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_963' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_963}}]] - 2024-03-12 22:55:51.245897: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_967' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_967}}]] - 2024-03-12 22:55:51.245934: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_968' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_968}}]] - 2024-03-12 22:55:51.245970: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_969' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_969}}]] - 2024-03-12 22:55:51.246007: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_970' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_970}}]] - 2024-03-12 22:55:51.246043: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_971' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_971}}]] - 2024-03-12 22:55:51.246080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_972' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_972}}]] - 2024-03-12 22:55:51.246117: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_973' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_973}}]] - 2024-03-12 22:55:51.246153: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_974' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_974}}]] - 2024-03-12 22:55:51.246190: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_977' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_977}}]] - 2024-03-12 22:55:51.246227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_978' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_978}}]] - 2024-03-12 22:55:51.246264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_979' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_979}}]] - 2024-03-12 22:55:51.246300: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_980' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_980}}]] - 2024-03-12 22:55:51.246336: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_981' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_981}}]] - 2024-03-12 22:55:51.246372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_982' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_982}}]] - 2024-03-12 22:55:51.246408: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_983' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_983}}]] - 2024-03-12 22:55:51.246445: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_984' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_984}}]] - 2024-03-12 22:55:51.246481: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_986' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_986}}]] - 2024-03-12 22:55:51.246518: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_987' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_987}}]] - 2024-03-12 22:55:51.246554: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_990' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_990}}]] - 2024-03-12 22:55:51.246590: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_991' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_991}}]] - 2024-03-12 22:55:51.246627: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_992' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_992}}]] - 2024-03-12 22:55:51.246663: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_993' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_993}}]] - 2024-03-12 22:55:51.246710: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_994' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_994}}]] - 2024-03-12 22:55:51.246744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_995' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_995}}]] - 2024-03-12 22:55:51.246780: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_996' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_996}}]] - 2024-03-12 22:55:51.246815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_999' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_999}}]] - 2024-03-12 22:55:51.246851: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1000' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1000}}]] - 2024-03-12 22:55:51.246886: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1001' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1001}}]] - 2024-03-12 22:55:51.246921: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1002' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1002}}]] - 2024-03-12 22:55:51.246957: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1003' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1003}}]] - 2024-03-12 22:55:51.246993: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1004' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1004}}]] - 2024-03-12 22:55:51.247028: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1005' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1005}}]] - 2024-03-12 22:55:51.247062: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1006' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1006}}]] - 2024-03-12 22:55:51.247097: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1007' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1007}}]] - 2024-03-12 22:55:51.247132: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1011' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1011}}]] - 2024-03-12 22:55:51.247166: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1012' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1012}}]] - 2024-03-12 22:55:51.247200: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1013' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1013}}]] - 2024-03-12 22:55:51.247236: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1014' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1014}}]] - 2024-03-12 22:55:51.247270: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1015' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1015}}]] - 2024-03-12 22:55:51.247306: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1016' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1016}}]] - 2024-03-12 22:55:51.247341: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1017' with dtype float and shape [?,1,1,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1017}}]] - 2024-03-12 22:55:51.247376: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1018' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1018}}]] - 2024-03-12 22:55:51.247410: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1021' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1021}}]] - 2024-03-12 22:55:51.247445: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1022' with dtype float and shape [32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1022}}]] - 2024-03-12 22:55:51.247480: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1023' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1023}}]] - 2024-03-12 22:55:51.247514: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1024' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1024}}]] - 2024-03-12 22:55:51.247549: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1025' with dtype float and shape [?,?,?,32,8] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1025}}]] - 2024-03-12 22:55:51.247584: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1026' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1026}}]] - 2024-03-12 22:55:51.247620: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1027' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1027}}]] - 2024-03-12 22:55:51.247654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1028' with dtype float and shape [?,?,?,256] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1028}}]] - 2024-03-12 22:55:51.247690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1029' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1029}}]] - 2024-03-12 22:55:51.247725: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1031' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1031}}]] - 2024-03-12 22:55:51.247761: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1037' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1037}}]] - 2024-03-12 22:55:51.247796: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1038' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1038}}]] - 2024-03-12 22:55:51.247832: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1039' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1039}}]] - 2024-03-12 22:55:51.247868: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1040' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1040}}]] - 2024-03-12 22:55:51.247903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1041' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1041}}]] - 2024-03-12 22:55:51.247955: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1042' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1042}}]] - 2024-03-12 22:55:51.247992: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1043' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1043}}]] - 2024-03-12 22:55:51.248029: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1044' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1044}}]] - 2024-03-12 22:55:51.248065: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1049' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1049}}]] - 2024-03-12 22:55:51.248102: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1050' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1050}}]] - 2024-03-12 22:55:51.248139: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1051' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1051}}]] - 2024-03-12 22:55:51.248176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1052' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1052}}]] - 2024-03-12 22:55:51.248213: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1053' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1053}}]] - 2024-03-12 22:55:51.248250: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1054' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1054}}]] - 2024-03-12 22:55:51.248288: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1055' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1055}}]] - 2024-03-12 22:55:51.248325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1056' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1056}}]] - 2024-03-12 22:55:51.248361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1058' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1058}}]] - 2024-03-12 22:55:51.248398: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1059' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1059}}]] - 2024-03-12 22:55:51.248434: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1062' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1062}}]] - 2024-03-12 22:55:51.248471: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1063' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1063}}]] - 2024-03-12 22:55:51.248508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1064' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1064}}]] - 2024-03-12 22:55:51.248544: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1065' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1065}}]] - 2024-03-12 22:55:51.248580: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1066' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1066}}]] - 2024-03-12 22:55:51.248617: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1067' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1067}}]] - 2024-03-12 22:55:51.248655: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1068' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1068}}]] - 2024-03-12 22:55:51.248692: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1071' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1071}}]] - 2024-03-12 22:55:51.248728: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1072' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1072}}]] - 2024-03-12 22:55:51.248764: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1073' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1073}}]] - 2024-03-12 22:55:51.248800: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1074' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1074}}]] - 2024-03-12 22:55:51.248837: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1075' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1075}}]] - 2024-03-12 22:55:51.248874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1076' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1076}}]] - 2024-03-12 22:55:51.248911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1077' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1077}}]] - 2024-03-12 22:55:51.248949: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1078' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1078}}]] - 2024-03-12 22:55:51.248986: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1082' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1082}}]] - 2024-03-12 22:55:51.249024: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1083' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1083}}]] - 2024-03-12 22:55:51.249061: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1084' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1084}}]] - 2024-03-12 22:55:51.249098: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1085' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1085}}]] - 2024-03-12 22:55:51.249146: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1086' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1086}}]] - 2024-03-12 22:55:51.249182: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1087' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1087}}]] - 2024-03-12 22:55:51.249217: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1088' with dtype float and shape [?,1,1,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1088}}]] - 2024-03-12 22:55:51.249252: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1089' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1089}}]] - 2024-03-12 22:55:51.249289: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1092' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1092}}]] - 2024-03-12 22:55:51.249325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1093' with dtype float and shape [32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1093}}]] - 2024-03-12 22:55:51.249361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1094' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1094}}]] - 2024-03-12 22:55:51.249397: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1095' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1095}}]] - 2024-03-12 22:55:51.249438: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1096' with dtype float and shape [?,?,?,32,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1096}}]] - 2024-03-12 22:55:51.249493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1097' with dtype float and shape [?,1,1,32,1] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1097}}]] - 2024-03-12 22:55:51.249531: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1098' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1098}}]] - 2024-03-12 22:55:51.249569: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1101' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1101}}]] - 2024-03-12 22:55:51.249607: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1102' with dtype float and shape [?,?,?,64] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1102}}]] - 2024-03-12 22:55:51.249645: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1103' with dtype float and shape [?,?,?,3] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1103}}]] - 2024-03-12 22:55:51.249683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1105' with dtype int32 and shape [4,2] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1105}}]] - 2024-03-12 22:55:51.249721: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1106' with dtype float and shape [?,?,?,3] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1106}}]] - 2024-03-12 22:55:51.249758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1111' with dtype float and shape [?,?,?,3] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1111}}]] - 2024-03-12 22:55:51.249795: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1112' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1112}}]] - 2024-03-12 22:55:51.249834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1113' with dtype float - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1113}}]] - 2024-03-12 22:55:51.249871: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1114' with dtype float and shape [?,?,?,3] - [[{{node gradients/StatefulPartitionedCall_grad/StatefulPartitionedCall_1114}}]] - - -.. parsed-literal:: - - - 1/101 [..............................] - ETA: 46:22 - loss: 5.5159 - accuracy: 0.1484 - -.. parsed-literal:: - -  - 2/101 [..............................] - ETA: 15:12 - loss: 4.7963 - accuracy: 0.1719 - -.. parsed-literal:: - -  - 3/101 [..............................] - ETA: 15:03 - loss: 4.3933 - accuracy: 0.1719 - -.. parsed-literal:: - -  - 4/101 [>.............................] - ETA: 14:55 - loss: 4.1327 - accuracy: 0.1914 - -.. parsed-literal:: - -  - 5/101 [>.............................] - ETA: 14:46 - loss: 3.8522 - accuracy: 0.2266 - -.. parsed-literal:: - -  - 6/101 [>.............................] - ETA: 14:38 - loss: 3.6144 - accuracy: 0.2487 - -.. parsed-literal:: - -  - 7/101 [=>............................] - ETA: 14:28 - loss: 3.3752 - accuracy: 0.2846 - -.. parsed-literal:: - -  - 8/101 [=>............................] - ETA: 14:19 - loss: 3.1518 - accuracy: 0.3135 - 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-.. parsed-literal:: - -  - 96/101 [===========================>..] - ETA: 46s - loss: 0.4639 - accuracy: 0.8820 - -.. parsed-literal:: - -  - 97/101 [===========================>..] - ETA: 37s - loss: 0.4597 - accuracy: 0.8831 - -.. parsed-literal:: - -  - 98/101 [============================>.] - ETA: 27s - loss: 0.4557 - accuracy: 0.8840 - -.. parsed-literal:: - -  - 99/101 [============================>.] - ETA: 18s - loss: 0.4524 - accuracy: 0.8848 - -.. parsed-literal:: - -  -100/101 [============================>.] - ETA: 9s - loss: 0.4492 - accuracy: 0.8857 - -.. parsed-literal:: - -  -101/101 [==============================] - ETA: 0s - loss: 0.4470 - accuracy: 0.8862 - -.. parsed-literal:: - - 2024-03-12 23:11:37.488331: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [1] - [[{{node Placeholder/_0}}]] - 2024-03-12 23:11:37.488859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1] - [[{{node Placeholder/_1}}]] - - -.. parsed-literal:: - -  -101/101 [==============================] - 968s 9s/step - loss: 0.4470 - accuracy: 0.8862 - val_loss: 0.0844 - val_accuracy: 0.9800 - - -.. parsed-literal:: - - WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading. - - -Perform model optimization (IR) step -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - - -.. code:: ipython3 - - ir_path = Path("./bit_ov_model/bit_m_r50x1_1.xml") - if not ir_path.exists(): - print("Initiating model optimization..!!!") - ov_model = ov.convert_model("./bit_tf_model") - ov.save_model(ov_model, ir_path) - else: - print(f"IR model {ir_path} already exists.") - - -.. parsed-literal:: - - Initiating model optimization..!!! - - -Compute accuracy of the TF model -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - - -.. code:: ipython3 - - tf_model = tf.keras.models.load_model("./bit_tf_model/") - - tf_predictions = [] - gt_label = [] - - for _, label in validation_dataset: - for cls_label in label: - l_list = cls_label.numpy().tolist() - gt_label.append(l_list.index(1)) - - for img_batch, label_batch in validation_dataset: - tf_result_batch = tf_model.predict(img_batch, verbose=0) - for i in range(len(img_batch)): - tf_result = tf_result_batch[i] - tf_result = tf.reshape(tf_result, [-1]) - top5_label_idx = np.argsort(tf_result)[-MAX_PREDS::][::-1] - tf_predictions.append(top5_label_idx) - - # Convert the lists to NumPy arrays for accuracy calculation - tf_predictions = np.array(tf_predictions) - gt_label = np.array(gt_label) - - tf_acc_score = accuracy_score(tf_predictions, gt_label) - - - -.. parsed-literal:: - - 2024-03-12 23:12:15.853507: W tensorflow/core/common_runtime/graph_constructor.cc:812] Node 're_lu_48/PartitionedCall' has 1 outputs but the _output_shapes attribute specifies shapes for 2 outputs. Output shapes may be inaccurate. - 2024-03-12 23:12:15.853549: W tensorflow/core/common_runtime/graph_constructor.cc:812] Node 'global_average_pooling2d/PartitionedCall' has 1 outputs but the _output_shapes attribute specifies shapes for 4 outputs. Output shapes may be inaccurate. - - -.. parsed-literal:: - - 2024-03-12 23:12:18.002587: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_2' with dtype string and shape [1] - [[{{node Placeholder/_2}}]] - 2024-03-12 23:12:18.003104: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [1] - [[{{node Placeholder/_0}}]] - - -Compute accuracy of the OpenVINO model -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - - -Select device for inference: - -.. code:: ipython3 - - import ipywidgets as widgets - - core = ov.Core() - - device = widgets.Dropdown( - options=core.available_devices + ["AUTO"], - value='AUTO', - description='Device:', - disabled=False, - ) - - device - - - - -.. parsed-literal:: - - Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO') - - - -.. code:: ipython3 - - ov_fp32_model = core.read_model("./bit_ov_model/bit_m_r50x1_1.xml") - ov_fp32_model.reshape([1, IMG_SIZE[0], IMG_SIZE[1], 3]) - - # Target device set to CPU (Other options Ex: AUTO/GPU/dGPU/) - compiled_model = ov.compile_model(ov_fp32_model, device.value) - output = compiled_model.outputs[0] - - ov_predictions = [] - for img_batch, _ in validation_dataset: - for image in img_batch: - image = tf.expand_dims(image, axis=0) - pred = compiled_model(image)[output] - ov_result = tf.reshape(pred, [-1]) - top_label_idx = np.argsort(ov_result)[-MAX_PREDS::][::-1] - ov_predictions.append(top_label_idx) - - fp32_acc_score = accuracy_score(ov_predictions, gt_label) - - -Quantize OpenVINO model using NNCF -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - - -Model Quantization using NNCF - -1. Preprocessing and preparing validation samples for NNCF calibration -2. Perform NNCF Quantization on OpenVINO FP32 model -3. Serialize Quantized OpenVINO INT8 model - -.. code:: ipython3 - - def nncf_preprocessing(image, label): - image = tf.image.resize(image, IMG_SIZE) - image = image - MEAN_RGB - image = image / STDDEV_RGB - return image - - val_ds = (validation_ds.map(nncf_preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE) - .batch(1) - .prefetch(tf.data.experimental.AUTOTUNE)) - - calibration_dataset = nncf.Dataset(val_ds) - - ov_fp32_model = core.read_model("./bit_ov_model/bit_m_r50x1_1.xml") - - ov_int8_model = nncf.quantize(ov_fp32_model, calibration_dataset, fast_bias_correction=False) - - ov.save_model(ov_int8_model, "./bit_ov_int8_model/bit_m_r50x1_1_ov_int8.xml") - - - -.. parsed-literal:: - - Output() - - -.. parsed-literal:: - - 2024-03-12 23:12:51.514824: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_3' with dtype int64 and shape [1] - [[{{node Placeholder/_3}}]] - 2024-03-12 23:12:51.515930: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1] - [[{{node Placeholder/_1}}]] - - - -.. raw:: html - - 

-
-
-
-
-.. raw:: html
-
-    
-    

-
-
-
-
-.. parsed-literal::
-
-    Output()
-
-
-
-.. raw:: html
-
-    

-
-
-
-
-.. raw:: html
-
-    
-    

-
-
-
-Compute accuracy of the quantized model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    nncf_quantized_model = core.read_model("./bit_ov_int8_model/bit_m_r50x1_1_ov_int8.xml")
-    nncf_quantized_model.reshape([1, IMG_SIZE[0], IMG_SIZE[1], 3])
-    
-    # Target device set to CPU by default
-    compiled_model = ov.compile_model(nncf_quantized_model, device.value)
-    output = compiled_model.outputs[0]
-    
-    ov_predictions = []
-    inp_tensor = nncf_quantized_model.inputs[0]
-    out_tensor = nncf_quantized_model.outputs[0]
-            
-    for img_batch, _ in validation_dataset:
-        for image in img_batch:
-            image = tf.expand_dims(image, axis=0)
-            pred = compiled_model(image)[output]
-            ov_result = tf.reshape(pred, [-1])
-            top_label_idx = np.argsort(ov_result)[-MAX_PREDS::][::-1]
-            ov_predictions.append(top_label_idx)
-            
-    int8_acc_score = accuracy_score(ov_predictions, gt_label)
-
-
-Compare FP32 and INT8 accuracy
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    print(f"Accuracy of the tensorflow model (fp32): {tf_acc_score * 100: .2f}%")
-    print(f"Accuracy of the OpenVINO optimized model (fp32): {fp32_acc_score * 100: .2f}%")
-    print(f"Accuracy of the OpenVINO quantized model (int8): {int8_acc_score * 100: .2f}%")
-    accuracy_drop = fp32_acc_score - int8_acc_score
-    print(f"Accuracy drop between OV FP32 and INT8 model: {accuracy_drop * 100:.1f}% ")
-
-
-.. parsed-literal::
-
-    Accuracy of the tensorflow model (fp32):  98.00%
-    Accuracy of the OpenVINO optimized model (fp32):  98.00%
-    Accuracy of the OpenVINO quantized model (int8):  97.60%
-    Accuracy drop between OV FP32 and INT8 model: 0.4% 
-
-
-Compare inference results on one picture
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    
-    # Accessing validation sample
-    sample_idx = 50
-    vds = datasets['validation']
-    
-    if len(vds) > sample_idx:
-        sample = vds.take(sample_idx + 1).skip(sample_idx).as_numpy_iterator().next()
-    else:
-        print("Dataset does not have enough samples...!!!")
-    
-    # Image data
-    sample_data = sample[0]
-    
-    # Label info
-    sample_label = sample[1]
-    
-    # Image data pre-processing
-    image = tf.image.resize(sample_data, IMG_SIZE)
-    image = tf.expand_dims(image, axis=0)
-    image = tf.cast(image, tf.float32) / 255.0
-    
-    # OpenVINO inference 
-    def ov_inference(model: ov.Model, image) -> str:
-        compiled_model = ov.compile_model(model, device.value)
-        output = compiled_model.outputs[0]
-        pred = compiled_model(image)[output]
-        ov_result = tf.reshape(pred, [-1])
-        pred_label = np.argsort(ov_result)[-MAX_PREDS::][::-1]
-        return pred_label
-    
-    # OpenVINO FP32 model
-    ov_fp32_model = core.read_model("./bit_ov_model/bit_m_r50x1_1.xml")
-    ov_fp32_model.reshape([1, IMG_SIZE[0], IMG_SIZE[1], 3])
-    
-    # OpenVINO INT8 model
-    ov_int8_model = core.read_model("./bit_ov_int8_model/bit_m_r50x1_1_ov_int8.xml")
-    ov_int8_model.reshape([1, IMG_SIZE[0], IMG_SIZE[1], 3])
-    
-    # OpenVINO FP32 model inference
-    ov_fp32_pred_label = ov_inference(ov_fp32_model, image)
-    
-    print(f"Predicted label for the sample picture by float (fp32) model: {label_func(class_idx_dict[int(ov_fp32_pred_label)])}\n")
-    
-    # OpenVINO FP32 model inference
-    ov_int8_pred_label = ov_inference(ov_int8_model, image)
-    print(f"Predicted label for the sample picture by qunatized (int8) model: {label_func(class_idx_dict[int(ov_int8_pred_label)])}\n")
-    
-    # Plotting the image sample with ground truth
-    plt.figure()
-    plt.imshow(sample_data)
-    plt.title(f"Ground truth: {label_func(class_idx_dict[sample_label])}")
-    plt.axis('off')
-    plt.show()
-
-
-
-.. parsed-literal::
-
-    2024-03-12 23:13:18.735254: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1]
-    	 [[{{node Placeholder/_1}}]]
-    2024-03-12 23:13:18.735969: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [1]
-    	 [[{{node Placeholder/_0}}]]
-
-
-.. parsed-literal::
-
-    Predicted label for the sample picture by float (fp32) model: gas pump
-    
-
-
-.. parsed-literal::
-
-    Predicted label for the sample picture by qunatized (int8) model: gas pump
-    
-
-
-
-.. image:: 127-tensorflow-bit-image-classification-nncf-quantization-with-output_files/127-tensorflow-bit-image-classification-nncf-quantization-with-output_27_3.png
-
diff --git a/docs/notebooks/201-vision-monodepth-with-output_files/201-vision-monodepth-with-output_18_0.png b/docs/notebooks/201-vision-monodepth-with-output_files/201-vision-monodepth-with-output_18_0.png
deleted file mode 100644
index 40d91a2b7fb..00000000000
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-Optical Character Recognition (OCR) with OpenVINO™
-==================================================
-
-This tutorial demonstrates how to perform optical character recognition
-(OCR) with OpenVINO models. It is a continuation of the
-`004-hello-detection <004-hello-detection-with-output.html>`__
-tutorial, which shows only text detection.
-
-The
-`horizontal-text-detection-0001 `__
-and
-`text-recognition-resnet `__
-models are used together for text detection and then text recognition.
-
-In this tutorial, Open Model Zoo tools including Model Downloader, Model
-Converter and Info Dumper are used to download and convert the models
-from `Open Model
-Zoo `__. For more
-information, refer to the
-`104-model-tools <104-model-tools-with-output.html>`__ tutorial.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Imports <#imports>`__
--  `Settings <#settings>`__
--  `Download Models <#download-models>`__
--  `Convert Models <#convert-models>`__
--  `Select inference device <#select-inference-device>`__
--  `Object Detection <#object-detection>`__
-
-   -  `Load a Detection Model <#load-a-detection-model>`__
-   -  `Load an Image <#load-an-image>`__
-   -  `Do Inference <#do-inference>`__
-   -  `Get Detection Results <#get-detection-results>`__
-
--  `Text Recognition <#text-recognition>`__
-
-   -  `Load Text Recognition Model <#load-text-recognition-model>`__
-   -  `Do Inference <#do-inference>`__
-
--  `Show Results <#show-results>`__
-
-   -  `Show Detected Text Boxes and OCR Results for the
-      Image <#show-detected-text-boxes-and-ocr-results-for-the-image>`__
-   -  `Show the OCR Result per Bounding
-      Box <#show-the-ocr-result-per-bounding-box>`__
-   -  `Print Annotations in Plain Text
-      Format <#print-annotations-in-plain-text-format>`__
-
-.. code:: ipython3
-
-    import platform
-
-    # Install openvino-dev package
-    %pip install -q "openvino-dev>=2024.0.0"  onnx
-
-    if platform.system() != "Windows":
-        %pip install -q "matplotlib>=3.4"
-    else:
-        %pip install -q "matplotlib>=3.4,<3.7"
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Imports
--------
-
-
-
-.. code:: ipython3
-
-    from pathlib import Path
-
-    import cv2
-    import matplotlib.pyplot as plt
-    import numpy as np
-    import openvino as ov
-    from IPython.display import Markdown, display
-    from PIL import Image
-
-    # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
-    )
-    from notebook_utils import load_image
-
-Settings
---------
-
-
-
-.. code:: ipython3
-
-    core = ov.Core()
-
-    model_dir = Path("model")
-    precision = "FP16"
-    detection_model = "horizontal-text-detection-0001"
-    recognition_model = "text-recognition-resnet-fc"
-
-    model_dir.mkdir(exist_ok=True)
-
-Download Models
----------------
-
-
-
-The next cells will run Model Downloader to download the detection and
-recognition models. If the models have been downloaded before, they will
-not be downloaded again.
-
-.. code:: ipython3
-
-    download_command = f"omz_downloader --name {detection_model},{recognition_model} --output_dir {model_dir} --cache_dir {model_dir} --precision {precision}  --num_attempts 5"
-    display(Markdown(f"Download command: `{download_command}`"))
-    display(Markdown(f"Downloading {detection_model}, {recognition_model}..."))
-    !$download_command
-    display(Markdown(f"Finished downloading {detection_model}, {recognition_model}."))
-
-    detection_model_path = (model_dir / "intel/horizontal-text-detection-0001" / precision / detection_model).with_suffix(".xml")
-    recognition_model_path = (model_dir / "public/text-recognition-resnet-fc" / precision / recognition_model).with_suffix(".xml")
-
-
-
-Download command:
-``omz_downloader --name horizontal-text-detection-0001,text-recognition-resnet-fc --output_dir model --cache_dir model --precision FP16  --num_attempts 5``
-
-
-
-Downloading horizontal-text-detection-0001, text-recognition-resnet-fc…
-
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-
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-
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-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
-
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
-
-    ... 71%, 500640 KB, 43064 KB/s, 11 seconds passed
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-... 72%, 502656 KB, 43050 KB/s, 11 seconds passed
-
-.. parsed-literal::
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-    ... 72%, 502688 KB, 43051 KB/s, 11 seconds passed
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
-
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
-
-    ... 79%, 552960 KB, 42794 KB/s, 12 seconds passed
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-... 83%, 578144 KB, 43286 KB/s, 13 seconds passed
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-... 83%, 578336 KB, 43295 KB/s, 13 seconds passed
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-... 83%, 578496 KB, 43302 KB/s, 13 seconds passed
-... 83%, 578528 KB, 43304 KB/s, 13 seconds passed
-
-.. parsed-literal::
-
-    ... 83%, 578560 KB, 43227 KB/s, 13 seconds passed
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-... 83%, 582432 KB, 43398 KB/s, 13 seconds passed
-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
-
-    ... 87%, 607520 KB, 43925 KB/s, 13 seconds passed
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-
-.. parsed-literal::
-
-    ... 87%, 609280 KB, 43811 KB/s, 13 seconds passed
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
-
-    ... 88%, 616960 KB, 43956 KB/s, 14 seconds passed
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-
-.. parsed-literal::
-
-    ... 89%, 619520 KB, 43931 KB/s, 14 seconds passed
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
-
-    ... 90%, 629760 KB, 43957 KB/s, 14 seconds passed
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-... 90%, 631200 KB, 44011 KB/s, 14 seconds passed
-
-.. parsed-literal::
-
-    ... 90%, 631232 KB, 44005 KB/s, 14 seconds passed
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-
-.. parsed-literal::
-
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
-
-
-    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/addict-2.4.0-py3-none-any.whl
-
-
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-
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-
-
-    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/heads/__init__.py
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-    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/feature_extractors/encoders/backbones/__init__.py
-    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/feature_extractors/encoders/enhance_modules/__init__.py
-    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/utils/__init__.py
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-    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/utils/config.py
-    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/utils/config.py
-    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/feature_extractors/encoders/backbones/resnet.py
-    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/feature_extractors/encoders/backbones/resnet.py
-    ========== Unpacking model/public/text-recognition-resnet-fc/vedastr/addict-2.4.0-py3-none-any.whl
-
-
-
-
-Finished downloading horizontal-text-detection-0001,
-text-recognition-resnet-fc.
-
-
-.. code:: ipython3
-
-    ### The text-recognition-resnet-fc model consists of many files. All filenames are printed in
-    ### the output of Model Downloader. Uncomment the next two lines to show this output.
-
-    # for line in download_result:
-    #    print(line)
-
-Convert Models
---------------
-
-
-
-The downloaded detection model is an Intel model, which is already in
-OpenVINO Intermediate Representation (OpenVINO IR) format. The text
-recognition model is a public model which needs to be converted to
-OpenVINO IR. Since this model was downloaded from Open Model Zoo, use
-Model Converter to convert the model to OpenVINO IR format.
-
-The output of Model Converter will be displayed. When the conversion is
-successful, the last lines of output will include
-``[ SUCCESS ] Generated IR version 11 model.``
-
-.. code:: ipython3
-
-    convert_command = f"omz_converter --name {recognition_model} --precisions {precision} --download_dir {model_dir} --output_dir {model_dir}"
-    display(Markdown(f"Convert command: `{convert_command}`"))
-    display(Markdown(f"Converting {recognition_model}..."))
-    ! $convert_command
-
-
-
-Convert command:
-``omz_converter --name text-recognition-resnet-fc --precisions FP16 --download_dir model --output_dir model``
-
-
-
-Converting text-recognition-resnet-fc…
-
-
-.. parsed-literal::
-
-    ========== Converting text-recognition-resnet-fc to ONNX
-    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/text-recognition-resnet-fc --model-path=model/public/text-recognition-resnet-fc --model-name=get_model --import-module=model '--model-param=file_config=r"model/public/text-recognition-resnet-fc/vedastr/configs/resnet_fc.py"' '--model-param=weights=r"model/public/text-recognition-resnet-fc/vedastr/ckpt/resnet_fc.pth"' --input-shape=1,1,32,100 --input-names=input --output-names=output --output-file=model/public/text-recognition-resnet-fc/resnet_fc.onnx
-
-
-
-.. parsed-literal::
-
-    ONNX check passed successfully.
-
-
-.. parsed-literal::
-
-
-    ========== Converting text-recognition-resnet-fc to IR (FP16)
-    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/text-recognition-resnet-fc/FP16 --model_name=text-recognition-resnet-fc --input=input '--mean_values=input[127.5]' '--scale_values=input[127.5]' --output=output --input_model=model/public/text-recognition-resnet-fc/resnet_fc.onnx '--layout=input(NCHW)' '--input_shape=[1, 1, 32, 100]' --compress_to_fp16=True
-
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-
-
-.. parsed-literal::
-
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/208-optical-character-recognition/model/public/text-recognition-resnet-fc/FP16/text-recognition-resnet-fc.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/208-optical-character-recognition/model/public/text-recognition-resnet-fc/FP16/text-recognition-resnet-fc.bin
-
-
-
-Select inference device
------------------------
-
-
-
-Select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
-        disabled=False,
-    )
-
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-Object Detection
-----------------
-
-
-
-Load a detection model, load an image, do inference and get the
-detection inference result.
-
-Load a Detection Model
-~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    detection_model = core.read_model(
-        model=detection_model_path, weights=detection_model_path.with_suffix(".bin")
-    )
-    detection_compiled_model = core.compile_model(model=detection_model, device_name=device.value)
-
-    detection_input_layer = detection_compiled_model.input(0)
-
-Load an Image
-~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    # The `image_file` variable can point to a URL or a local image.
-    image_file = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg"
-
-    image = load_image(image_file)
-
-    # N,C,H,W = batch size, number of channels, height, width.
-    N, C, H, W = detection_input_layer.shape
-
-    # Resize the image to meet network expected input sizes.
-    resized_image = cv2.resize(image, (W, H))
-
-    # Reshape to the network input shape.
-    input_image = np.expand_dims(resized_image.transpose(2, 0, 1), 0)
-
-    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB));
-
-
-
-.. image:: 208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_16_0.png
-
-
-Do Inference
-~~~~~~~~~~~~
-
-
-
-Text boxes are detected in the images and returned as blobs of data in
-the shape of ``[100, 5]``. Each description of detection has the
-``[x_min, y_min, x_max, y_max, conf]`` format.
-
-.. code:: ipython3
-
-    output_key = detection_compiled_model.output("boxes")
-    boxes = detection_compiled_model([input_image])[output_key]
-
-    # Remove zero only boxes.
-    boxes = boxes[~np.all(boxes == 0, axis=1)]
-
-Get Detection Results
-~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    def multiply_by_ratio(ratio_x, ratio_y, box):
-        return [
-            max(shape * ratio_y, 10) if idx % 2 else shape * ratio_x
-            for idx, shape in enumerate(box[:-1])
-        ]
-
-
-    def run_preprocesing_on_crop(crop, net_shape):
-        temp_img = cv2.resize(crop, net_shape)
-        temp_img = temp_img.reshape((1,) * 2 + temp_img.shape)
-        return temp_img
-
-
-    def convert_result_to_image(bgr_image, resized_image, boxes, threshold=0.3, conf_labels=True):
-        # Define colors for boxes and descriptions.
-        colors = {"red": (255, 0, 0), "green": (0, 255, 0), "white": (255, 255, 255)}
-
-        # Fetch image shapes to calculate a ratio.
-        (real_y, real_x), (resized_y, resized_x) = image.shape[:2], resized_image.shape[:2]
-        ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
-
-        # Convert the base image from BGR to RGB format.
-        rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
-
-        # Iterate through non-zero boxes.
-        for box, annotation in boxes:
-            # Pick a confidence factor from the last place in an array.
-            conf = box[-1]
-            if conf > threshold:
-                # Convert float to int and multiply position of each box by x and y ratio.
-                (x_min, y_min, x_max, y_max) = map(int, multiply_by_ratio(ratio_x, ratio_y, box))
-
-                # Draw a box based on the position. Parameters in the `rectangle` function are: image, start_point, end_point, color, thickness.
-                cv2.rectangle(rgb_image, (x_min, y_min), (x_max, y_max), colors["green"], 3)
-
-                # Add a text to an image based on the position and confidence. Parameters in the `putText` function are: image, text, bottomleft_corner_textfield, font, font_scale, color, thickness, line_type
-                if conf_labels:
-                    # Create a background box based on annotation length.
-                    (text_w, text_h), _ = cv2.getTextSize(
-                        f"{annotation}", cv2.FONT_HERSHEY_TRIPLEX, 0.8, 1
-                    )
-                    image_copy = rgb_image.copy()
-                    cv2.rectangle(
-                        image_copy,
-                        (x_min, y_min - text_h - 10),
-                        (x_min + text_w, y_min - 10),
-                        colors["white"],
-                        -1,
-                    )
-                    # Add weighted image copy with white boxes under a text.
-                    cv2.addWeighted(image_copy, 0.4, rgb_image, 0.6, 0, rgb_image)
-                    cv2.putText(
-                        rgb_image,
-                        f"{annotation}",
-                        (x_min, y_min - 10),
-                        cv2.FONT_HERSHEY_SIMPLEX,
-                        0.8,
-                        colors["red"],
-                        1,
-                        cv2.LINE_AA,
-                    )
-
-        return rgb_image
-
-Text Recognition
-----------------
-
-
-
-Load the text recognition model and do inference on the detected boxes
-from the detection model.
-
-Load Text Recognition Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    recognition_model = core.read_model(
-        model=recognition_model_path, weights=recognition_model_path.with_suffix(".bin")
-    )
-
-    recognition_compiled_model = core.compile_model(model=recognition_model, device_name=device.value)
-
-    recognition_output_layer = recognition_compiled_model.output(0)
-    recognition_input_layer = recognition_compiled_model.input(0)
-
-    # Get the height and width of the input layer.
-    _, _, H, W = recognition_input_layer.shape
-
-Do Inference
-~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    # Calculate scale for image resizing.
-    (real_y, real_x), (resized_y, resized_x) = image.shape[:2], resized_image.shape[:2]
-    ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
-
-    # Convert the image to grayscale for the text recognition model.
-    grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-    # Get a dictionary to encode output, based on the model documentation.
-    letters = "~0123456789abcdefghijklmnopqrstuvwxyz"
-
-    # Prepare an empty list for annotations.
-    annotations = list()
-    cropped_images = list()
-    # fig, ax = plt.subplots(len(boxes), 1, figsize=(5,15), sharex=True, sharey=True)
-    # Get annotations for each crop, based on boxes given by the detection model.
-    for i, crop in enumerate(boxes):
-        # Get coordinates on corners of a crop.
-        (x_min, y_min, x_max, y_max) = map(int, multiply_by_ratio(ratio_x, ratio_y, crop))
-        image_crop = run_preprocesing_on_crop(grayscale_image[y_min:y_max, x_min:x_max], (W, H))
-
-        # Run inference with the recognition model.
-        result = recognition_compiled_model([image_crop])[recognition_output_layer]
-
-        # Squeeze the output to remove unnecessary dimension.
-        recognition_results_test = np.squeeze(result)
-
-        # Read an annotation based on probabilities from the output layer.
-        annotation = list()
-        for letter in recognition_results_test:
-            parsed_letter = letters[letter.argmax()]
-
-            # Returning 0 index from `argmax` signalizes an end of a string.
-            if parsed_letter == letters[0]:
-                break
-            annotation.append(parsed_letter)
-        annotations.append("".join(annotation))
-        cropped_image = Image.fromarray(image[y_min:y_max, x_min:x_max])
-        cropped_images.append(cropped_image)
-
-    boxes_with_annotations = list(zip(boxes, annotations))
-
-Show Results
-------------
-
-
-
-Show Detected Text Boxes and OCR Results for the Image
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Visualize the result by drawing boxes around recognized text and showing
-the OCR result from the text recognition model.
-
-.. code:: ipython3
-
-    plt.figure(figsize=(12, 12))
-    plt.imshow(convert_result_to_image(image, resized_image, boxes_with_annotations, conf_labels=True));
-
-
-
-.. image:: 208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_26_0.png
-
-
-Show the OCR Result per Bounding Box
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Depending on the image, the OCR result may not be readable in the image
-with boxes, as displayed in the cell above. Use the code below to
-display the extracted boxes and the OCR result per box.
-
-.. code:: ipython3
-
-    for cropped_image, annotation in zip(cropped_images, annotations):
-        display(cropped_image, Markdown("".join(annotation)))
-
-
-
-.. image:: 208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_28_0.png
-
-
-
-building
-
-
-
-.. image:: 208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_28_2.png
-
-
-
-noyce
-
-
-
-.. image:: 208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_28_4.png
-
-
-
-2200
-
-
-
-.. image:: 208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_28_6.png
-
-
-
-n
-
-
-
-.. image:: 208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_28_8.png
-
-
-
-center
-
-
-
-.. image:: 208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_28_10.png
-
-
-
-robert
-
-
-Print Annotations in Plain Text Format
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Print annotations for detected text based on their position in the input
-image, starting from the upper left corner.
-
-.. code:: ipython3
-
-    [
-        annotation
-        for _, annotation in sorted(zip(boxes, annotations), key=lambda x: x[0][0] ** 2 + x[0][1] ** 2)
-    ]
-
-
-
-
-.. parsed-literal::
-
-    ['robert', 'n', 'noyce', 'building', '2200', 'center']
-
-
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+++ /dev/null
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-The attention center model with OpenVINO™
-=========================================
-
-This notebook demonstrates how to use the `attention center
-model `__ with
-OpenVINO. This model is in the `TensorFlow Lite
-format `__, which is supported in
-OpenVINO now by TFLite frontend.
-
-Eye tracking is commonly used in visual neuroscience and cognitive
-science to answer related questions such as visual attention and
-decision making. Computational models that predict where to look have
-direct applications to a variety of computer vision tasks. The attention
-center model takes an RGB image as input and return a 2D point as
-output. This 2D point is the predicted center of human attention on the
-image i.e. the most salient part of images, on which people pay
-attention fist to. This allows find the most visually salient regions
-and handle it as early as possible. For example, it could be used for
-the latest generation image format (such as `JPEG
-XL `__), which supports encoding the
-parts that you pay attention to fist. It can help to improve user
-experience, image will appear to load faster.
-
-Attention center model architecture is: > The attention center model is
-a deep neural net, which takes an image as input, and uses a pre-trained
-classification network, e.g, ResNet, MobileNet, etc., as the backbone.
-Several intermediate layers that output from the backbone network are
-used as input for the attention center prediction module. These
-different intermediate layers contain different information e.g.,
-shallow layers often contain low level information like
-intensity/color/texture, while deeper layers usually contain higher and
-more semantic information like shape/object. All are useful for the
-attention prediction. The attention center prediction applies
-convolution, deconvolution and/or resizing operator together with
-aggregation and sigmoid function to generate a weighting map for the
-attention center. And then an operator (the Einstein summation operator
-in our case) can be applied to compute the (gravity) center from the
-weighting map. An L2 norm between the predicted attention center and the
-ground-truth attention center can be computed as the training loss.
-Source: `Google AI blog
-post `__.
-
-.. figure:: https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxLCDJHzJNjB_von-vFlq8TJJFA41aB85T-QE3ZNxW8kshAf3HOEyIEJ4uggXjbJmZhsdj7j6i6mvvmXtyaxXJPm3JHuKILNRTPfX9KvICbFBRD8KNuDVmLABzYuhQci3BT2BqV-wM54IxaoAV1YDBbnpJC92UZfEBGvakLusiqND2AaPpWPr2gJV1/s1600/image4.png
-   :alt: drawing
-
-   drawing
-
-The attention center model has been trained with images from the `COCO
-dataset `__ annotated with saliency from
-the `SALICON dataset `__.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Imports <#imports>`__
--  `Download the attention-center
-   model <#download-the-attention-center-model>`__
-
-   -  `Convert Tensorflow Lite model to OpenVINO IR
-      format <#convert-tensorflow-lite-model-to-openvino-ir-format>`__
-
--  `Select inference device <#select-inference-device>`__
--  `Prepare image to use with attention-center
-   model <#prepare-image-to-use-with-attention-center-model>`__
--  `Load input image <#load-input-image>`__
--  `Get result with OpenVINO IR
-   model <#get-result-with-openvino-ir-model>`__
-
-.. code:: ipython3
-
-    import platform
-    
-    %pip install "openvino>=2023.2.0"
-    
-    if platform.system() != "Windows":
-        %pip install -q "matplotlib>=3.4"
-    else:
-        %pip install -q "matplotlib>=3.4,<3.7"
-
-
-.. parsed-literal::
-
-    Requirement already satisfied: openvino>=2023.2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.0.0)
-    Requirement already satisfied: numpy>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.2.0) (1.23.5)
-    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.2.0) (2023.2.1)
-    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.2.0) (24.0)
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Imports
--------
-
-
-
-.. code:: ipython3
-
-    import cv2
-    
-    import numpy as np
-    import tensorflow as tf
-    from pathlib import Path
-    import matplotlib.pyplot as plt
-    
-    import openvino as ov
-
-
-.. parsed-literal::
-
-    2024-03-12 23:28:02.634827: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 23:28:02.669284: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-
-
-.. parsed-literal::
-
-    2024-03-12 23:28:03.239575: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
-Download the attention-center model
------------------------------------
-
-
-
-Download the model as part of `attention-center
-repo `__. The repo
-include model in folder ``./model``.
-
-.. code:: ipython3
-
-    if not Path('./attention-center').exists():
-        ! git clone https://github.com/google/attention-center
-
-
-.. parsed-literal::
-
-    Cloning into 'attention-center'...
-
-
-.. parsed-literal::
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-Receiving objects:  89% (150/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects:  90% (152/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects:  91% (153/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects:  92% (155/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects:  93% (157/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects:  94% (158/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects:  95% (160/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects:  96% (162/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects:  97% (163/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects:  98% (165/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects:  99% (167/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects: 100% (168/168), 22.22 MiB | 14.29 MiB/s
-Receiving objects: 100% (168/168), 26.22 MiB | 15.49 MiB/s, done.
-    Resolving deltas:   0% (0/73)
-Resolving deltas:   1% (1/73)
-Resolving deltas:  13% (10/73)
-Resolving deltas:  21% (16/73)
-Resolving deltas:  31% (23/73)
-Resolving deltas:  45% (33/73)
-Resolving deltas:  60% (44/73)
-Resolving deltas:  61% (45/73)
-Resolving deltas:  63% (46/73)
-Resolving deltas:  68% (50/73)
-Resolving deltas:  71% (52/73)
-Resolving deltas:  72% (53/73)
-Resolving deltas:  73% (54/73)
-Resolving deltas:  79% (58/73)
-Resolving deltas:  84% (62/73)
-Resolving deltas:  98% (72/73)
-Resolving deltas: 100% (73/73)
-Resolving deltas: 100% (73/73), done.
-
-
-Convert Tensorflow Lite model to OpenVINO IR format
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-The attention-center model is pre-trained model in TensorFlow Lite
-format. In this Notebook the model will be converted to OpenVINO IR
-format with model conversion API. For more information about model
-conversion, see this
-`page `__.
-This step is also skipped if the model is already converted.
-
-Also TFLite models format is supported in OpenVINO by TFLite frontend,
-so the model can be passed directly to ``core.read_model()``. You can
-find example in
-`002-openvino-api `__.
-
-.. code:: ipython3
-
-    tflite_model_path = Path("./attention-center/model/center.tflite")
-    
-    ir_model_path = Path("./model/ir_center_model.xml")
-    
-    core = ov.Core()
-    
-    if not ir_model_path.exists():
-        model = ov.convert_model(tflite_model_path, input=[('image:0', [1,480,640,3], ov.Type.f32)])
-        ov.save_model(model, ir_model_path)
-        print("IR model saved to {}".format(ir_model_path))
-    else:
-        print("Read IR model from {}".format(ir_model_path))
-        model = core.read_model(ir_model_path)
-
-
-.. parsed-literal::
-
-    IR model saved to model/ir_center_model.xml
-
-
-Select inference device
------------------------
-
-
-
-select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-    if "GPU" in device.value:
-        core.set_property(device_name=device.value, properties={'INFERENCE_PRECISION_HINT': ov.Type.f32})
-    compiled_model = core.compile_model(model=model, device_name=device.value)
-
-Prepare image to use with attention-center model
-------------------------------------------------
-
-
-
-The attention-center model takes an RGB image with shape (480, 640) as
-input.
-
-.. code:: ipython3
-
-    class Image():
-        def __init__(self, model_input_image_shape, image_path=None, image=None):
-            self.model_input_image_shape = model_input_image_shape
-            self.image = None
-            self.real_input_image_shape = None
-    
-            if image_path is not None:
-                self.image = cv2.imread(str(image_path))
-                self.real_input_image_shape = self.image.shape
-            elif image is not None:
-                self.image = image
-                self.real_input_image_shape = self.image.shape
-            else:
-                raise Exception("Sorry, image can't be found, please, specify image_path or image")
-    
-        def prepare_image_tensor(self):
-            rgb_image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
-            resized_image = cv2.resize(rgb_image, (self.model_input_image_shape[1], self.model_input_image_shape[0]))
-    
-            image_tensor = tf.constant(np.expand_dims(resized_image, axis=0),
-                                       dtype=tf.float32)
-            return image_tensor
-    
-        def scalt_center_to_real_image_shape(self, predicted_center):
-            new_center_y = round(predicted_center[0] * self.real_input_image_shape[1] / self.model_input_image_shape[1])
-            new_center_x = round(predicted_center[1] * self.real_input_image_shape[0] / self.model_input_image_shape[0])
-            return (int(new_center_y), int(new_center_x))
-    
-        def draw_attention_center_point(self, predicted_center):
-            image_with_circle = cv2.circle(self.image,
-                                           predicted_center,
-                                           radius=10,
-                                           color=(3, 3, 255),
-                                           thickness=-1)
-            return image_with_circle
-    
-        def print_image(self, predicted_center=None):
-            image_to_print = self.image
-            if predicted_center is not None:
-                image_to_print = self.draw_attention_center_point(predicted_center)
-    
-            plt.imshow(cv2.cvtColor(image_to_print, cv2.COLOR_BGR2RGB))
-
-Load input image
-----------------
-
-
-
-Upload input image using file loading button
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    load_file_widget = widgets.FileUpload(
-        accept="image/*", multiple=False, description="Image file",
-    )
-    
-    load_file_widget
-
-
-
-
-.. parsed-literal::
-
-    FileUpload(value=(), accept='image/*', description='Image file')
-
-
-
-.. code:: ipython3
-
-    import io
-    import PIL
-    from urllib.request import urlretrieve
-    
-    img_path = Path("data/coco.jpg")
-    img_path.parent.mkdir(parents=True, exist_ok=True)
-    urlretrieve(
-        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
-        img_path,
-    )
-    
-    # read uploaded image
-    image = PIL.Image.open(io.BytesIO(list(load_file_widget.value.values())[-1]['content'])) if load_file_widget.value else PIL.Image.open(img_path)
-    image.convert("RGB")
-    
-    input_image = Image((480, 640), image=(np.ascontiguousarray(image)[:, :, ::-1]).astype(np.uint8))
-    image_tensor = input_image.prepare_image_tensor()
-    input_image.print_image()
-
-
-.. parsed-literal::
-
-    2024-03-12 23:28:11.205498: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-03-12 23:28:11.205530: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
-    2024-03-12 23:28:11.205534: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
-    2024-03-12 23:28:11.205690: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
-    2024-03-12 23:28:11.205706: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
-    2024-03-12 23:28:11.205709: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
-
-
-
-.. image:: 216-attention-center-with-output_files/216-attention-center-with-output_15_1.png
-
-
-Get result with OpenVINO IR model
----------------------------------
-
-
-
-.. code:: ipython3
-
-    output_layer = compiled_model.output(0)
-    
-    # make inference, get result in input image resolution
-    res = compiled_model([image_tensor])[output_layer]
-    # scale point to original image resulution
-    predicted_center = input_image.scalt_center_to_real_image_shape(res[0])
-    print(f'Prediction attention center point {predicted_center}')
-    input_image.print_image(predicted_center)
-
-
-.. parsed-literal::
-
-    Prediction attention center point (292, 277)
-
-
-
-.. image:: 216-attention-center-with-output_files/216-attention-center-with-output_17_1.png
-
diff --git a/docs/notebooks/216-attention-center-with-output_files/216-attention-center-with-output_15_1.png b/docs/notebooks/216-attention-center-with-output_files/216-attention-center-with-output_15_1.png
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diff --git a/docs/notebooks/222-vision-image-colorization-with-output.rst b/docs/notebooks/222-vision-image-colorization-with-output.rst
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-Image Colorization with OpenVINO
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This notebook demonstrates how to colorize images with OpenVINO using
-the Colorization model
-`colorization-v2 `__
-or
-`colorization-siggraph `__
-from `Open Model
-Zoo `__
-based on the paper `Colorful Image
-Colorization `__ models from Open
-Model Zoo.
-
-.. figure:: https://user-images.githubusercontent.com/18904157/180923280-9caefaf1-742b-4d2f-8943-5d4a6126e2fc.png
-   :alt: Let there be color
-
-   Let there be color
-
-Given a grayscale image as input, the model generates colorized version
-of the image as the output.
-
-About Colorization-v2
-^^^^^^^^^^^^^^^^^^^^^
-
--  The colorization-v2 model is one of the colorization group of models
-   designed to perform image colorization.
--  Model trained on the ImageNet dataset.
--  Model consumes L-channel of LAB-image as input and produces predict
-   A- and B-channels of LAB-image as output.
-
-About Colorization-siggraph
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
--  The colorization-siggraph model is one of the colorization group of
-   models designed to real-time user-guided image colorization.
--  Model trained on the ImageNet dataset with synthetically generated
-   user interaction.
--  Model consumes L-channel of LAB-image as input and produces predict
-   A- and B-channels of LAB-image as output.
-
-See the `colorization `__
-repository for more details.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Imports <#imports>`__
--  `Configurations <#configurations>`__
-
-   -  `Select inference device <#select-inference-device>`__
-
--  `Download the model <#download-the-model>`__
--  `Convert the model to OpenVINO
-   IR <#convert-the-model-to-openvino-ir>`__
--  `Loading the Model <#loading-the-model>`__
--  `Utility Functions <#utility-functions>`__
--  `Load the Image <#load-the-image>`__
--  `Display Colorized Image <#display-colorized-image>`__
-
-.. code:: ipython3
-
-    %pip install "openvino-dev>=2024.0.0"
-
-
-.. parsed-literal::
-
-    Requirement already satisfied: openvino-dev>=2024.0.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.0.0)
-    Requirement already satisfied: defusedxml>=0.7.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (0.7.1)
-    Requirement already satisfied: networkx<=3.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (2.8.8)
-    Requirement already satisfied: numpy>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (1.23.5)
-    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (2023.2.1)
-    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (24.0)
-    Requirement already satisfied: pyyaml>=5.4.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (6.0.1)
-    Requirement already satisfied: requests>=2.25.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (2.31.0)
-    Requirement already satisfied: openvino==2024.0.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (2024.0.0)
-
-
-.. parsed-literal::
-
-    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (3.3.2)
-    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (3.6)
-    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (2.2.1)
-    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (2024.2.2)
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Imports
--------
-
-
-
-.. code:: ipython3
-
-    import os
-    import sys
-    from pathlib import Path
-
-    import cv2
-    import matplotlib.pyplot as plt
-    import numpy as np
-    import openvino as ov
-
-    sys.path.append("../utils")
-    import notebook_utils as utils
-
-Configurations
---------------
-
-
-
--  ``PRECISION`` - {FP16, FP32}, default: FP16.
--  ``MODEL_DIR`` - directory where the model is to be stored, default:
-   public.
--  ``MODEL_NAME`` - name of the model used for inference, default:
-   colorization-v2.
--  ``DATA_DIR`` - directory where test images are stored, default: data.
-
-.. code:: ipython3
-
-    PRECISION = "FP16"
-    MODEL_DIR = "models"
-    MODEL_NAME = "colorization-v2"
-    # MODEL_NAME="colorization-siggraph"
-    MODEL_PATH = f"{MODEL_DIR}/public/{MODEL_NAME}/{PRECISION}/{MODEL_NAME}.xml"
-    DATA_DIR = "data"
-
-Select inference device
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-
-    core = ov.Core()
-
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
-        disabled=False,
-    )
-
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-Download the model
-------------------
-
-
-
-``omz_downloader`` downloads model files from online sources and, if
-necessary, patches them to make them more usable with Model Converter.
-
-In this case, ``omz_downloader`` downloads the checkpoint and pytorch
-model of
-`colorization-v2 `__
-or
-`colorization-siggraph `__
-from `Open Model
-Zoo `__
-and saves it under ``MODEL_DIR``, as specified in the configuration
-above.
-
-.. code:: ipython3
-
-    download_command = (
-        f"omz_downloader "
-        f"--name {MODEL_NAME} "
-        f"--output_dir {MODEL_DIR} "
-        f"--cache_dir {MODEL_DIR}"
-    )
-    ! $download_command
-
-
-.. parsed-literal::
-
-    ################|| Downloading colorization-v2 ||################
-
-    ========== Downloading models/public/colorization-v2/ckpt/colorization-v2-eccv16.pth
-
-
-.. parsed-literal::
-
-    ... 0%, 32 KB, 932 KB/s, 0 seconds passed
-
-.. parsed-literal::
-
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-
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-.. parsed-literal::
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-
-    ========== Downloading models/public/colorization-v2/model/__init__.py
-
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-    ========== Downloading models/public/colorization-v2/model/base_color.py
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-
-    ========== Downloading models/public/colorization-v2/model/eccv16.py
-
-
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-
-
-    ========== Replacing text in models/public/colorization-v2/model/__init__.py
-    ========== Replacing text in models/public/colorization-v2/model/__init__.py
-    ========== Replacing text in models/public/colorization-v2/model/eccv16.py
-
-
-
-Convert the model to OpenVINO IR
---------------------------------
-
-
-
-``omz_converter`` converts the models that are not in the OpenVINO™ IR
-format into that format using model conversion API.
-
-The downloaded pytorch model is not in OpenVINO IR format which is
-required for inference with OpenVINO runtime. ``omz_converter`` is used
-to convert the downloaded pytorch model into ONNX and OpenVINO IR format
-respectively
-
-.. code:: ipython3
-
-    if not os.path.exists(MODEL_PATH):
-        convert_command = (
-            f"omz_converter "
-            f"--name {MODEL_NAME} "
-            f"--download_dir {MODEL_DIR} "
-            f"--precisions {PRECISION}"
-        )
-        ! $convert_command
-
-
-.. parsed-literal::
-
-    ========== Converting colorization-v2 to ONNX
-    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=models/public/colorization-v2 --model-name=ECCVGenerator --weights=models/public/colorization-v2/ckpt/colorization-v2-eccv16.pth --import-module=model --input-shape=1,1,256,256 --output-file=models/public/colorization-v2/colorization-v2-eccv16.onnx --input-names=data_l --output-names=color_ab
-
-
-
-.. parsed-literal::
-
-    ONNX check passed successfully.
-
-
-.. parsed-literal::
-
-
-    ========== Converting colorization-v2 to IR (FP16)
-    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=models/public/colorization-v2/FP16 --model_name=colorization-v2 --input=data_l --output=color_ab --input_model=models/public/colorization-v2/colorization-v2-eccv16.onnx '--layout=data_l(NCHW)' '--input_shape=[1, 1, 256, 256]' --compress_to_fp16=True
-
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-
-
-.. parsed-literal::
-
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/222-vision-image-colorization/models/public/colorization-v2/FP16/colorization-v2.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/222-vision-image-colorization/models/public/colorization-v2/FP16/colorization-v2.bin
-
-
-
-Loading the Model
------------------
-
-
-
-Load the model in OpenVINO Runtime with ``ie.read_model`` and compile it
-for the specified device with ``ie.compile_model``.
-
-.. code:: ipython3
-
-    core = ov.Core()
-    model = core.read_model(model=MODEL_PATH)
-    compiled_model = core.compile_model(model=model, device_name=device.value)
-    input_layer = compiled_model.input(0)
-    output_layer = compiled_model.output(0)
-    N, C, H, W = list(input_layer.shape)
-
-Utility Functions
------------------
-
-
-
-.. code:: ipython3
-
-    def read_image(impath: str) -> np.ndarray:
-        """
-        Returns an image as ndarra, given path to an image reads the
-        (BGR) image using opencv's imread() API.
-
-            Parameter:
-                impath (string): Path of the image to be read and returned.
-
-            Returns:
-                image (ndarray): Numpy array representing the read image.
-        """
-
-        raw_image = cv2.imread(impath)
-        if raw_image.shape[2] > 1:
-            image = cv2.cvtColor(
-                cv2.cvtColor(raw_image, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2RGB
-            )
-        else:
-            image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
-
-        return image
-
-
-    def plot_image(image: np.ndarray, title: str = "") -> None:
-        """
-        Given a image as ndarray and title as string, display it using
-        matplotlib.
-
-            Parameters:
-                image (ndarray): Numpy array representing the image to be
-                                 displayed.
-                title (string): String representing the title of the plot.
-
-            Returns:
-                None
-
-        """
-
-        plt.imshow(image)
-        plt.title(title)
-        plt.axis("off")
-        plt.show()
-
-
-    def plot_output(gray_img: np.ndarray, color_img: np.ndarray) -> None:
-        """
-        Plots the original (bw or grayscale) image and colorized image
-        on different column axes for comparing side by side.
-
-            Parameters:
-                gray_image (ndarray): Numpy array representing the original image.
-                color_image (ndarray): Numpy array representing the model output.
-
-            Returns:
-                None
-        """
-
-        fig = plt.figure(figsize=(12, 12))
-
-        ax1 = fig.add_subplot(1, 2, 1)
-        plt.title("Input", fontsize=20)
-        ax1.axis("off")
-
-        ax2 = fig.add_subplot(1, 2, 2)
-        plt.title("Colorized", fontsize=20)
-        ax2.axis("off")
-
-        ax1.imshow(gray_img)
-        ax2.imshow(color_img)
-
-        plt.show()
-
-Load the Image
---------------
-
-
-
-.. code:: ipython3
-
-    img_url_0 = "https://user-images.githubusercontent.com/18904157/180923287-20339d01-b1bf-493f-9a0d-55eff997aff1.jpg"
-    img_url_1 = "https://user-images.githubusercontent.com/18904157/180923289-0bb71e09-25e1-46a6-aaf1-e8f666b62d26.jpg"
-
-    image_file_0 = utils.download_file(
-        img_url_0, filename="test_0.jpg", directory="data", show_progress=False, silent=True, timeout=30
-    )
-    assert Path(image_file_0).exists()
-
-    image_file_1 = utils.download_file(
-        img_url_1, filename="test_1.jpg", directory="data", show_progress=False, silent=True, timeout=30
-    )
-    assert Path(image_file_1).exists()
-
-    test_img_0 = read_image("data/test_0.jpg")
-    test_img_1 = read_image("data/test_1.jpg")
-
-.. code:: ipython3
-
-    def colorize(gray_img: np.ndarray) -> np.ndarray:
-
-        """
-        Given an image as ndarray for inference convert the image into LAB image,
-        the model consumes as input L-Channel of LAB image and provides output
-        A & B - Channels of LAB image. i.e returns a colorized image
-
-            Parameters:
-                gray_img (ndarray): Numpy array representing the original
-                                    image.
-
-            Returns:
-                colorize_image (ndarray): Numpy arrray depicting the
-                                          colorized version of the original
-                                          image.
-        """
-
-        # Preprocess
-        h_in, w_in, _ = gray_img.shape
-        img_rgb = gray_img.astype(np.float32) / 255
-        img_lab = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2Lab)
-        img_l_rs = cv2.resize(img_lab.copy(), (W, H))[:, :, 0]
-
-        # Inference
-        inputs = np.expand_dims(img_l_rs, axis=[0, 1])
-        res = compiled_model([inputs])[output_layer]
-        update_res = np.squeeze(res)
-
-        # Post-process
-        out = update_res.transpose((1, 2, 0))
-        out = cv2.resize(out, (w_in, h_in))
-        img_lab_out = np.concatenate((img_lab[:, :, 0][:, :, np.newaxis],
-                                      out), axis=2)
-        img_bgr_out = np.clip(cv2.cvtColor(img_lab_out, cv2.COLOR_Lab2RGB), 0, 1)
-        colorized_image = (cv2.resize(img_bgr_out, (w_in, h_in))
-                           * 255).astype(np.uint8)
-        return colorized_image
-
-.. code:: ipython3
-
-    color_img_0 = colorize(test_img_0)
-    color_img_1 = colorize(test_img_1)
-
-Display Colorized Image
------------------------
-
-
-
-.. code:: ipython3
-
-    plot_output(test_img_0, color_img_0)
-
-
-
-.. image:: 222-vision-image-colorization-with-output_files/222-vision-image-colorization-with-output_21_0.png
-
-
-.. code:: ipython3
-
-    plot_output(test_img_1, color_img_1)
-
-
-
-.. image:: 222-vision-image-colorization-with-output_files/222-vision-image-colorization-with-output_22_0.png
-
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-Post-Training Quantization of ImageBind model with NNCF
-=======================================================
-
-The goal of this tutorial is to demonstrate how to speed up the model by
-applying 8-bit post-training quantization from
-`NNCF `__ (Neural Network
-Compression Framework) and infer quantized model via OpenVINO™ Toolkit.
-
-The optimization process contains the following steps: 1. Quantize
-converted OpenVINO model from
-`notebook <239-image-bind-convert-with-output.html>`__ with NNCF. 2. Compare
-probability matrices between converted and quantized models on input
-data examples. 3. Compare model size of converted and quantized models.
-4. Compare performance of converted and quantized models.
-
-   **NOTE**: you should run
-   `239-image-bind-convert <239-image-bind-convert-with-output.html>`__ notebook
-   first to generate OpenVINO IR models that are used for quantization.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Prerequisites <#prerequisites>`__
--  `Create and initialize
-   quantization <#create-and-initialize-quantization>`__
-
-   -  `Prepare datasets <#prepare-datasets>`__
-
--  `Compare results for the OpenVINO FP16 model and the quantized
-   model <#compare-results-for-the-openvino-fp16-model-and-the-quantized-model>`__
-
-   -  `Select inference device <#select-inference-device>`__
-
-      -  `Compare File Size <#compare-file-size>`__
-      -  `Compare inference time of the FP16 IR and quantized
-         models <#compare-inference-time-of-the-fp16-ir-and-quantized-models>`__
-
-Prerequisites
--------------
-
-
-
-.. code:: ipython3
-
-    %pip install -q datasets librosa soundfile "openvino>=2023.1.0" "nncf"
-
-
-.. parsed-literal::
-
-    DEPRECATION: omegaconf 2.0.6 has a non-standard dependency specifier PyYAML>=5.1.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of omegaconf or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-    [notice] A new release of pip is available: 23.2.1 -> 23.3.1
-    [notice] To update, run: pip install --upgrade pip
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. code:: ipython3
-
-    from pathlib import Path
-    
-    repo_dir = Path("ImageBind")
-    
-    if not repo_dir.exists():
-        raise RuntimeError('This notebook should be run after 239-image-bind-convert notebook')
-    
-    %cd {repo_dir}
-
-
-.. parsed-literal::
-
-    /home/ea/work/openvino_notebooks/notebooks/239-image-bind/ImageBind
-
-
-Create and initialize quantization
-----------------------------------
-
-
-
-`NNCF `__ enables
-post-training quantization by adding the quantization layers into the
-model graph and then using a subset of the training dataset to
-initialize the parameters of these additional quantization layers. The
-framework is designed so that modifications to your original training
-code are minor. Quantization is the simplest scenario and requires a few
-modifications.
-
-The optimization process contains the following steps: 1. Create a
-Dataset for quantization. 2. Run ``nncf.quantize`` for getting a
-quantized model. 3. Serialize the ``INT8`` model using
-``openvino.save_model`` function.
-
-.. code:: ipython3
-
-    from pathlib import Path
-    from imagebind.models.imagebind_model import ModalityType
-    
-    modalities = [ModalityType.TEXT, ModalityType.VISION, ModalityType.AUDIO]
-    fp_model_paths = {modality: Path(f"image-bind-{modality}") / f"image-bind-{modality}.xml" for modality in modalities}
-    int8_model_paths = {modality: Path(f"image-bind-{modality}") / f"image-bind-{modality}_int8.xml" for modality in modalities}
-
-
-.. parsed-literal::
-
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.
-      warnings.warn(
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
-      warnings.warn(
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
-      warnings.warn(
-
-
-Prepare datasets
-~~~~~~~~~~~~~~~~
-
-
-
-The `Conceptual
-Captions `__ dataset
-consisting of ~3.3M images annotated with captions. Dataset is used to
-quantize image and text models.
-
-.. code:: ipython3
-
-    import imagebind.data as data
-    import os
-    import requests
-    import tempfile
-    
-    from requests.packages.urllib3.exceptions import InsecureRequestWarning
-    requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
-    
-    def check_text_data(data):
-        """
-        Check if the given data is text-based.
-        """
-        if isinstance(data, str):
-            return True
-        if isinstance(data, list):
-            return all(isinstance(x, str) for x in data)
-        return False
-    
-    def collate_fn(examples, image_column="image_url", text_column="caption"):
-        """
-        Collates examples into a batch for processing.
-        Preprocesses each example by loading and transforming image and text data.
-        Checks if the text data in the example is valid by calling the `check_text_data` function.
-        Downloads the image specified by the URL in the image_column of the example dictionary.
-        Constructs and returns a dictionary representing the collated batch with the following keys:
-           - "pixel_values": The pixel values of the preprocessed example.
-           - "input_ids": The transformed text data of the preprocessed example.
-        """
-        assert len(examples) == 1
-        example = examples[0]
-        if not check_text_data(example[text_column]):
-            raise ValueError("Text data is not valid")
-    
-        url = example[image_column]
-        with tempfile.TemporaryDirectory() as tempdir:
-            f_name = os.path.join(tempdir, 'image.jpg')
-            try:
-                response = requests.get(url, verify=False, timeout=20)
-                with open(f_name, "wb") as file:
-                    file.write(response.content)
-                pixel_values = data.load_and_transform_vision_data([f_name], "cpu")
-            except Exception:
-                print(f"Can't load image from url: {url}")
-                return None
-    
-        text = data.load_and_transform_text([example[text_column]], "cpu")
-    
-        return {
-            "pixel_values": pixel_values,
-            "input_ids": text
-        }
-
-.. code:: ipython3
-
-    from datasets import load_dataset
-    import itertools
-    import torch
-    from tqdm.notebook import tqdm
-    
-    def collect_vision_text_data(dataloader, init_steps):
-        """
-        This function collects vision and text data from a dataloader for a specified number of initialization steps.
-        It iterates over the dataloader, fetching batches and storing the relevant vision and text data.
-        Returns a tuple containing the collected vision_data and text_data lists.
-        """
-        text_data = []
-        vision_data = []
-        print(f"Fetching {init_steps} for the initialization...")
-        counter = 0
-        for batch in tqdm(dataloader):
-            if counter == init_steps:
-                break
-            with torch.no_grad():
-                if batch:
-                    counter += 1
-                    text_data.append(batch["input_ids"].to("cpu"))
-                    vision_data.append(batch["pixel_values"].to("cpu"))
-        return vision_data, text_data
-    
-    def prepare_vision_text_dataset(opt_init_steps=300):
-        """
-        Prepares a vision-text dataset for quantization by collecting vision and text data.
-        """
-        dataset = load_dataset("conceptual_captions", streaming=True)
-        train_dataset = dataset["train"].shuffle(seed=0)
-        dataloader = torch.utils.data.DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1)
-        vision_data, text_data = collect_vision_text_data(dataloader, opt_init_steps)
-        return vision_data, text_data
-
-The `ESC-50 `__ dataset is
-used to quantize the audio modality of the ImageBind model. Dataset is a
-labeled collection of 2000 environmental audio recordings suitable for
-benchmarking methods of environmental sound classification. The dataset
-consists of 5-second-long recordings organized into 50 semantic classes.
-
-.. code:: ipython3
-
-    import numpy as np
-    import torchaudio
-    
-    def collect_audio_data(dataloader, init_steps=300):
-        """
-        This function collects audio data from a dataloader for a specified number of initialization steps.
-        It iterates over the dataloader, fetching batches and storing them in a list.
-        """
-        audio_data = []
-        for _, batch in tqdm(zip(range(init_steps), itertools.islice(dataloader, 0, init_steps))):
-            with torch.no_grad():
-                audio_data.append(batch)
-        return audio_data
-    
-    def prepare_audio_dataset():
-        """
-        Prepares an "ashraq/esc50" audio dataset for quantization by collecting audio data.
-        Collects audio data from the dataloader by calling the `collect_audio_data` function.
-        Returns a list containing the collected calibration audio data batches.
-        """
-        audio_dataset = load_dataset("ashraq/esc50", streaming=True)
-        train_dataset = audio_dataset["train"].shuffle(seed=42, buffer_size=1000)
-    
-        def collate_fn(examples):
-            assert len(examples) == 1
-            with tempfile.TemporaryDirectory() as tempdir:
-                f_name = os.path.join(tempdir, 'audio.wav')
-                audio_data = examples[0]['audio']['array']
-                sample_rate = examples[0]['audio']["sampling_rate"]
-                audio_data = torch.from_numpy(audio_data).to(torch.float32).unsqueeze(0)
-                torchaudio.save(f_name, audio_data, sample_rate)
-                return data.load_and_transform_audio_data([f_name], "cpu")
-    
-        dataloader = torch.utils.data.DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1)
-        calibration_data = collect_audio_data(dataloader)
-        return calibration_data
-
-Create a quantized model from the pre-trained ``FP16`` model.
-
-.. code:: ipython3
-
-    vision_data, text_data = prepare_vision_text_dataset()
-
-
-.. parsed-literal::
-
-    Fetching 300 for the initialization...
-
-
-
-.. parsed-literal::
-
-    0it [00:00, ?it/s]
-
-
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-
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-    Can't load image from url: http://www.nextavenue.org/wp-content/uploads/2017/05/image-3-w1024-750x485.jpg
-    Can't load image from url: http://nnimgt-a.akamaihd.net/transform/v1/crop/frm/342N54ExNnUCDyWzghgYbSC/cd538c73-466c-4e05-8202-0892dceb8a44.jpg/r401_321_5388_3369_w1200_h678_fmax.jpg
-    Can't load image from url: https://www.universetoday.com/wp-content/uploads/2016/05/Earth-magnetosphere-ESA-Medialab.jpg
-    Can't load image from url: https://c5eeb468edc90bcfda59-8477d1500ace5389b08f6bb1cc2fee82.ssl.cf5.rackcdn.com/837712-residential-x722qn-o.jpg
-    Can't load image from url: https://ak3.picdn.net/shutterstock/videos/7414963/thumb/1.jpg
-
-
-.. code:: ipython3
-
-    import logging
-    import nncf
-    import openvino as ov
-    
-    nncf.set_log_level(logging.ERROR)
-    
-    core = ov.Core()
-    
-    def quantize_openvino_model(modality, calibration_data):
-        model_path = fp_model_paths[modality]
-        if not os.path.exists(model_path):
-            raise RuntimeError(f"Model: {model_path} not found. \
-                                 First run 239-image-bind-convert notebook to convert model to OpenVINO IR.")
-    
-        model = core.read_model(model_path)
-        quantized_model = nncf.quantize(
-            model=model,
-            calibration_dataset=calibration_data,
-            model_type=nncf.ModelType.TRANSFORMER,
-            # remove ignored_scope for nncf>=2.6.0 (PR with fix https://github.com/openvinotoolkit/nncf/pull/1953)
-            ignored_scope=nncf.IgnoredScope(types=["ReduceL2"])
-        )
-        ov.save_model(quantized_model, int8_model_paths[modality])
-        return quantized_model
-
-
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-Quantize ImageBind model for vision modality.
-
-   **NOTE**: Quantization is time and memory consuming operation.
-   Running quantization code below may take a long time.
-
-.. code:: ipython3
-
-    if len(vision_data) == 0:
-        raise RuntimeError(
-            'Calibration dataset is empty. Please check internet connection and try to download images manually from the URLs above.'
-        )
-    
-    vision_dataset = nncf.Dataset(vision_data)
-    vision_quantized_model = quantize_openvino_model(modality=ModalityType.VISION, calibration_data=vision_dataset)
-
-
-.. parsed-literal::
-
-    2023-10-26 13:34:25.166422: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2023-10-26 13:34:25.203294: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2023-10-26 13:34:26.097309: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    Statistics collection: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [01:18<00:00,  3.81it/s]
-    Applying Smooth Quant: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 129/129 [00:13<00:00,  9.69it/s]
-    Statistics collection: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [03:03<00:00,  1.64it/s]
-    Applying Fast Bias correction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 128/128 [00:23<00:00,  5.54it/s]
-
-
-Quantize ImageBind model for text modality
-
-.. code:: ipython3
-
-    text_dataset = nncf.Dataset(text_data)
-    text_quantized_model = quantize_openvino_model(modality=ModalityType.TEXT, calibration_data=text_dataset)
-
-
-.. parsed-literal::
-
-    Statistics collection: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [00:17<00:00, 16.82it/s]
-    Applying Smooth Quant: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 97/97 [00:06<00:00, 15.41it/s]
-    Statistics collection: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [00:50<00:00,  5.97it/s]
-    Applying Fast Bias correction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 120/120 [00:10<00:00, 11.06it/s]
-
-
-Quantize ImageBind model for audio modality
-
-.. code:: ipython3
-
-    audio_calibration_data = prepare_audio_dataset()
-    audio_dataset = nncf.Dataset(audio_calibration_data)
-    audio_quantized_model = quantize_openvino_model(modality=ModalityType.AUDIO, calibration_data=audio_dataset)
-
-
-.. parsed-literal::
-
-    Repo card metadata block was not found. Setting CardData to empty.
-
-
-
-.. parsed-literal::
-
-    0it [00:00, ?it/s]
-
-
-.. parsed-literal::
-
-    Statistics collection: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [01:10<00:00,  4.26it/s]
-    Applying Smooth Quant: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 49/49 [00:01<00:00, 27.79it/s]
-    Statistics collection: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [01:21<00:00,  3.69it/s]
-    Applying Fast Bias correction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 48/48 [00:03<00:00, 12.18it/s]
-
-
-NNCF also supports quantization-aware training, and other algorithms
-than quantization. See the `NNCF
-documentation `__
-in the NNCF repository for more information.
-
-Compare results for the OpenVINO FP16 model and the quantized model
--------------------------------------------------------------------
-
-
-
-Compare the probability matrices for ``FP16`` and ``INT8`` models. More
-details about probability matrix can be found in
-`notebook <239-image-bind-convert-with-output.html>`__.
-
-.. code:: ipython3
-
-    # Prepare inputs
-    from imagebind.models.imagebind_model import ModalityType
-    
-    text_list = ["A car", "A bird", "A dog"]
-    image_paths = [".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
-    audio_paths = [".assets/dog_audio.wav", ".assets/bird_audio.wav", ".assets/car_audio.wav"]
-    
-    inputs = {
-        ModalityType.TEXT: data.load_and_transform_text(text_list, "cpu"),
-        ModalityType.VISION: data.load_and_transform_vision_data(image_paths, "cpu"),
-        ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, "cpu"),
-    }
-
-Select inference device
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
-Get embeddings of original models
-
-.. code:: ipython3
-
-    embeddings = {}
-    for modality in modalities:
-        ov_model = core.compile_model(fp_model_paths[modality], device.value)
-        embeddings[modality] = ov_model(inputs[modality])[ov_model.output(0)]
-
-Get embeddings of quantized models
-
-.. code:: ipython3
-
-    quantized_embeddings = {}
-    for modality in modalities:
-        model = core.compile_model(int8_model_paths[modality], device.value)
-        quantized_embeddings[modality] = model(inputs[modality])[model.output(0)]
-
-.. code:: ipython3
-
-    import matplotlib.pyplot as plt
-    from scipy.special import softmax
-    
-    def visualize_prob_matrices(fp_matrix, int_matrix, x_label, y_label):
-        fig, ax = plt.subplots(1, 2)
-        for i, matrix in enumerate([fp_matrix, int_matrix]):
-            ax[i].matshow(matrix, cmap='winter')
-    
-            for (k, j), z in np.ndenumerate(matrix):
-                ax[i].title.set_text('FP16 probs' if i == 0 else 'INT8 probs')
-                ax[i].text(j, k, '{:0.3f}'.format(z), ha='center', va='center')
-                ax[i].set_xticks(range(len(x_label)), x_label)
-                ax[i].set_yticks(range(len(y_label)), y_label)
-        fig.tight_layout()
-    
-    image_list = [img.split('/')[-1] for img in image_paths]
-    audio_list = [audio.split('/')[-1] for audio in audio_paths]
-
-.. code:: ipython3
-
-    fp_text_vision_scores = softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, axis=-1)
-    int_text_vision_scores = softmax(quantized_embeddings[ModalityType.VISION] @ quantized_embeddings[ModalityType.TEXT].T, axis=-1)
-    
-    visualize_prob_matrices(fp_text_vision_scores, int_text_vision_scores, text_list, image_list)
-
-
-
-.. image:: 239-image-bind-quantize-with-output_files/239-image-bind-quantize-with-output_32_0.png
-
-
-.. code:: ipython3
-
-    fp_text_audio_scores = softmax(embeddings[ModalityType.AUDIO] @ embeddings[ModalityType.TEXT].T, axis=-1)
-    int_text_audio_scores = softmax(quantized_embeddings[ModalityType.AUDIO] @ quantized_embeddings[ModalityType.TEXT].T, axis=-1)
-    
-    visualize_prob_matrices(fp_text_audio_scores, int_text_audio_scores, text_list, image_list)
-
-
-
-.. image:: 239-image-bind-quantize-with-output_files/239-image-bind-quantize-with-output_33_0.png
-
-
-.. code:: ipython3
-
-    fp_audio_vision_scores = softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, axis=-1)
-    int_audio_vision_scores = softmax(quantized_embeddings[ModalityType.VISION] @ quantized_embeddings[ModalityType.AUDIO].T, axis=-1)
-    
-    visualize_prob_matrices(fp_audio_vision_scores, int_audio_vision_scores, text_list, image_list)
-
-
-
-.. image:: 239-image-bind-quantize-with-output_files/239-image-bind-quantize-with-output_34_0.png
-
-
-Compare File Size
-^^^^^^^^^^^^^^^^^
-
-
-
-.. code:: ipython3
-
-    def calculate_compression_rate(modality):
-        fp16_ir_model_size = Path(fp_model_paths[modality]).with_suffix(".bin").stat().st_size / 1024
-        quantized_model_size = Path(int8_model_paths[modality]).with_suffix(".bin").stat().st_size / 1024
-        print(f'Modality: {modality}')
-        print(f"    * FP16 IR model size: {fp16_ir_model_size:.2f} KB")
-        print(f"    * INT8 model size: {quantized_model_size:.2f} KB")
-        print(f"    * Model compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
-
-.. code:: ipython3
-
-    for modality in modalities:
-        calculate_compression_rate(modality)
-
-
-.. parsed-literal::
-
-    Modality: text
-        * FP16 IR model size: 691481.77 KB
-        * INT8 model size: 347007.35 KB
-        * Model compression rate: 1.993
-    Modality: vision
-        * FP16 IR model size: 1235995.26 KB
-        * INT8 model size: 620133.72 KB
-        * Model compression rate: 1.993
-    Modality: audio
-        * FP16 IR model size: 168429.22 KB
-        * INT8 model size: 84818.78 KB
-        * Model compression rate: 1.986
-
-
-Compare inference time of the FP16 IR and quantized models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-
-
-To measure the inference performance of the ``FP16`` and ``INT8``
-models, we use median inference time on calibration dataset. So we can
-approximately estimate the speed up of the dynamic quantized models.
-
-   **NOTE**: For the most accurate performance estimation, it is
-   recommended to run ``benchmark_app`` in a terminal/command prompt
-   after closing other applications with static shapes.
-
-.. code:: ipython3
-
-    import time
-    
-    def calculate_inference_time(model_path, calibration_data):
-        model = core.compile_model(model_path)
-        output_layer = model.output(0)
-        inference_time = []
-        for batch in calibration_data:
-            start = time.perf_counter()
-            _ = model(batch)[output_layer]
-            end = time.perf_counter()
-            delta = end - start
-            inference_time.append(delta)
-        return np.median(inference_time)
-
-Vision model
-
-.. code:: ipython3
-
-    fp16_latency = calculate_inference_time(fp_model_paths[ModalityType.VISION], vision_data)
-    int8_latency = calculate_inference_time(int8_model_paths[ModalityType.VISION], vision_data)
-    print(f"Performance speed up: {fp16_latency / int8_latency:.3f}")
-
-
-.. parsed-literal::
-
-    Performance speed up: 2.040
-
-
-Text model
-
-.. code:: ipython3
-
-    fp16_latency = calculate_inference_time(fp_model_paths[ModalityType.TEXT], text_data)
-    int8_latency = calculate_inference_time(int8_model_paths[ModalityType.TEXT], text_data)
-    print(f"Performance speed up: {fp16_latency / int8_latency:.3f}")
-
-
-.. parsed-literal::
-
-    Performance speed up: 1.404
-
-
-Audio model
-
-.. code:: ipython3
-
-    fp16_latency = calculate_inference_time(fp_model_paths[ModalityType.AUDIO], audio_calibration_data)
-    int8_latency = calculate_inference_time(int8_model_paths[ModalityType.AUDIO], audio_calibration_data)
-    print(f"Performance speed up: {fp16_latency / int8_latency:.3f}")
-
-
-.. parsed-literal::
-
-    Performance speed up: 5.713
-
diff --git a/docs/notebooks/239-image-bind-quantize-with-output_files/239-image-bind-quantize-with-output_32_0.png b/docs/notebooks/239-image-bind-quantize-with-output_files/239-image-bind-quantize-with-output_32_0.png
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diff --git a/docs/notebooks/242-freevc-voice-conversion-with-output.rst b/docs/notebooks/242-freevc-voice-conversion-with-output.rst
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+++ /dev/null
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-High-Quality Text-Free One-Shot Voice Conversion with FreeVC and OpenVINO™
-==========================================================================
-
-`FreeVC `__ allows alter the voice of
-a source speaker to a target style, while keeping the linguistic content
-unchanged, without text annotation.
-
-Figure bellow illustrates model architecture of FreeVC for inference. In
-this notebook we concentrate only on inference part. There are three
-main parts: Prior Encoder, Speaker Encoder and Decoder. The prior
-encoder contains a WavLM model, a bottleneck extractor and a normalizing
-flow. Detailed information is available in this
-`paper `__.
-
-.. figure:: https://github.com/OlaWod/FreeVC/blob/main/resources/infer.png?raw=true
-   :alt: Inference
-
-   Inference
-
-`\**image_source\* `__
-
-FreeVC suggests only command line interface to use and only with CUDA.
-In this notebook it shows how to use FreeVC in Python and without CUDA
-devices. It consists of the following steps:
-
--  Download and prepare models.
--  Inference.
--  Convert models to OpenVINO Intermediate Representation.
--  Inference using only OpenVINO’s IR models.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Pre-requisites <#pre-requisites>`__
--  `Imports and settings <#imports-and-settings>`__
--  `Convert Modes to OpenVINO Intermediate
-   Representation <#convert-modes-to-openvino-intermediate-representation>`__
-
-   -  `Convert Prior Encoder. <#convert-prior-encoder->`__
-   -  `Convert SpeakerEncoder <#convert-speakerencoder>`__
-   -  `Convert Decoder <#convert-decoder>`__
-
-Pre-requisites
---------------
-
-
-
-This steps can be done manually or will be performed automatically
-during the execution of the notebook, but in minimum necessary scope. 1.
-Clone this repo: git clone https://github.com/OlaWod/FreeVC.git. 2.
-Download
-`WavLM-Large `__
-and put it under directory ``FreeVC/wavlm/``. 3. You can download the
-`VCTK `__ dataset. For
-this example we download only two of them from `Hugging Face FreeVC
-example `__. 4.
-Download `pretrained
-models `__
-and put it under directory ‘checkpoints’ (for current example only
-``freevc.pth`` are required).
-
-Install extra requirements
-
-.. code:: ipython3
-
-    %pip install -q "openvino>=2023.3.0" "librosa>=0.8.1" "webrtcvad==2.0.10" gradio "torch>=2.1" torchvision --extra-index-url https://download.pytorch.org/whl/cpu
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Check if FreeVC is installed and append its path to ``sys.path``
-
-.. code:: ipython3
-
-    from pathlib import Path
-    import sys
-
-
-    free_vc_repo = 'FreeVC'
-    if not Path(free_vc_repo).exists():
-        !git clone https://github.com/OlaWod/FreeVC.git
-
-    sys.path.append(free_vc_repo)
-
-
-.. parsed-literal::
-
-    Cloning into 'FreeVC'...
-
-
-.. parsed-literal::
-
-    remote: Enumerating objects: 131, done.
-    remote: Counting objects:   1% (1/65)
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-
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-
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-
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-    Resolving deltas:   0% (0/43)
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-
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-
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-
-
-.. code:: ipython3
-
-    sys.path.append("../utils")
-    from notebook_utils import download_file
-
-    wavlm_large_dir_path = Path('FreeVC/wavlm')
-    wavlm_large_path = wavlm_large_dir_path / 'WavLM-Large.pt'
-
-    if not wavlm_large_path.exists():
-        download_file(
-            'https://valle.blob.core.windows.net/share/wavlm/WavLM-Large.pt?sv=2020-08-04&st=2023-03-01T07%3A51%3A05Z&se=2033-03-02T07%3A51%3A00Z&sr=c&sp=rl&sig=QJXmSJG9DbMKf48UDIU1MfzIro8HQOf3sqlNXiflY1I%3D',
-            directory=wavlm_large_dir_path
-        )
-
-
-
-.. parsed-literal::
-
-    FreeVC/wavlm/WavLM-Large.pt:   0%|          | 0.00/1.18G [00:00`__.
-
-Convert Prior Encoder.
-~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-First we convert WavLM model, as a part of Convert Prior Encoder to
-OpenVINO’s IR format. We keep the original name of the model in code:
-``cmodel``.
-
-.. code:: ipython3
-
-    # define forward as extract_features for compatibility
-    cmodel.forward = cmodel.extract_features
-
-.. code:: ipython3
-
-    OUTPUT_DIR = Path("output")
-    BASE_MODEL_NAME = "cmodel"
-
-    OUTPUT_DIR.mkdir(exist_ok=True)
-
-    ir_cmodel_path = Path(OUTPUT_DIR / (BASE_MODEL_NAME + "_ir")).with_suffix(".xml")
-
-    length = 32000
-
-    dummy_input = torch.randn(1, length)
-
-Converting to OpenVINO’s IR format.
-
-.. code:: ipython3
-
-    core = ov.Core()
-
-    class ModelWrapper(torch.nn.Module):
-        def __init__(self, model):
-            super().__init__()
-            self.model = model
-
-        def forward(self, input):
-            return self.model(input)[0]
-
-    if not ir_cmodel_path.exists():
-        ir_cmodel = ov.convert_model(ModelWrapper(cmodel), example_input=dummy_input)
-        ov.save_model(ir_cmodel, ir_cmodel_path)
-    else:
-        ir_cmodel = core.read_model(ir_cmodel_path)
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/242-freevc-voice-conversion/FreeVC/wavlm/modules.py:495: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert embed_dim == self.embed_dim
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/242-freevc-voice-conversion/FreeVC/wavlm/modules.py:496: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert list(query.size()) == [tgt_len, bsz, embed_dim]
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/242-freevc-voice-conversion/FreeVC/wavlm/modules.py:500: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert key_bsz == bsz
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/242-freevc-voice-conversion/FreeVC/wavlm/modules.py:502: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert src_len, bsz == value.shape[:2]
-
-
-Select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
-        disabled=False,
-    )
-
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-
-    compiled_cmodel = core.compile_model(ir_cmodel, device.value)
-
-Convert ``SpeakerEncoder``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    OUTPUT_DIR = Path("output")
-    BASE_MODEL_NAME = "smodel"
-
-    OUTPUT_DIR.mkdir(exist_ok=True)
-
-    ir_smodel_path = Path(OUTPUT_DIR / (BASE_MODEL_NAME + "ir")).with_suffix(".xml")
-
-
-    length = 32000
-
-    dummy_input = torch.randn(1, length, 40)
-
-    if not ir_smodel_path.exists():
-        ir_smodel = ov.convert_model(smodel, example_input=dummy_input)
-        ov.save_model(ir_smodel, ir_smodel_path)
-    else:
-        ir_smodel = core.read_model(ir_smodel_path)
-
-For preparing input for inference, we should define helper functions
-based on ``speaker_encoder.voice_encoder.SpeakerEncoder`` class methods
-
-.. code:: ipython3
-
-    from speaker_encoder.hparams import sampling_rate, mel_window_step, partials_n_frames
-    from speaker_encoder import audio
-
-
-    def compute_partial_slices(n_samples: int, rate, min_coverage):
-        """
-        Computes where to split an utterance waveform and its corresponding mel spectrogram to
-        obtain partial utterances of  each. Both the waveform and the
-        mel spectrogram slices are returned, so as to make each partial utterance waveform
-        correspond to its spectrogram.
-
-        The returned ranges may be indexing further than the length of the waveform. It is
-        recommended that you pad the waveform with zeros up to wav_slices[-1].stop.
-
-        :param n_samples: the number of samples in the waveform
-        :param rate: how many partial utterances should occur per second. Partial utterances must
-        cover the span of the entire utterance, thus the rate should not be lower than the inverse
-        of the duration of a partial utterance. By default, partial utterances are 1.6s long and
-        the minimum rate is thus 0.625.
-        :param min_coverage: when reaching the last partial utterance, it may or may not have
-        enough frames. If at least  of  are present,
-        then the last partial utterance will be considered by zero-padding the audio. Otherwise,
-        it will be discarded. If there aren't enough frames for one partial utterance,
-        this parameter is ignored so that the function always returns at least one slice.
-        :return: the waveform slices and mel spectrogram slices as lists of array slices. Index
-        respectively the waveform and the mel spectrogram with these slices to obtain the partial
-        utterances.
-        """
-        assert 0 < min_coverage <= 1
-
-        # Compute how many frames separate two partial utterances
-        samples_per_frame = int((sampling_rate * mel_window_step / 1000))
-        n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
-        frame_step = int(np.round((sampling_rate / rate) / samples_per_frame))
-        assert 0 < frame_step, "The rate is too high"
-        assert frame_step <= partials_n_frames, "The rate is too low, it should be %f at least" % \
-            (sampling_rate / (samples_per_frame * partials_n_frames))
-
-        # Compute the slices
-        wav_slices, mel_slices = [], []
-        steps = max(1, n_frames - partials_n_frames + frame_step + 1)
-        for i in range(0, steps, frame_step):
-            mel_range = np.array([i, i + partials_n_frames])
-            wav_range = mel_range * samples_per_frame
-            mel_slices.append(slice(*mel_range))
-            wav_slices.append(slice(*wav_range))
-
-        # Evaluate whether extra padding is warranted or not
-        last_wav_range = wav_slices[-1]
-        coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start)
-        if coverage < min_coverage and len(mel_slices) > 1:
-            mel_slices = mel_slices[:-1]
-            wav_slices = wav_slices[:-1]
-
-        return wav_slices, mel_slices
-
-
-    def embed_utterance(wav: np.ndarray, smodel: ov.CompiledModel, return_partials=False, rate=1.3, min_coverage=0.75):
-        """
-        Computes an embedding for a single utterance. The utterance is divided in partial
-        utterances and an embedding is computed for each. The complete utterance embedding is the
-        L2-normed average embedding of the partial utterances.
-
-        :param wav: a preprocessed utterance waveform as a numpy array of float32
-        :param smodel: compiled speaker encoder model.
-        :param return_partials: if True, the partial embeddings will also be returned along with
-        the wav slices corresponding to each partial utterance.
-        :param rate: how many partial utterances should occur per second. Partial utterances must
-        cover the span of the entire utterance, thus the rate should not be lower than the inverse
-        of the duration of a partial utterance. By default, partial utterances are 1.6s long and
-        the minimum rate is thus 0.625.
-        :param min_coverage: when reaching the last partial utterance, it may or may not have
-        enough frames. If at least  of  are present,
-        then the last partial utterance will be considered by zero-padding the audio. Otherwise,
-        it will be discarded. If there aren't enough frames for one partial utterance,
-        this parameter is ignored so that the function always returns at least one slice.
-        :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If
-         is True, the partial utterances as a numpy array of float32 of shape
-        (n_partials, model_embedding_size) and the wav partials as a list of slices will also be
-        returned.
-        """
-        # Compute where to split the utterance into partials and pad the waveform with zeros if
-        # the partial utterances cover a larger range.
-        wav_slices, mel_slices = compute_partial_slices(len(wav), rate, min_coverage)
-        max_wave_length = wav_slices[-1].stop
-        if max_wave_length >= len(wav):
-            wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
-
-        # Split the utterance into partials and forward them through the model
-        mel = audio.wav_to_mel_spectrogram(wav)
-        mels = np.array([mel[s] for s in mel_slices])
-        with torch.no_grad():
-            mels = torch.from_numpy(mels).to(torch.device('cpu'))
-            output_layer = smodel.output(0)
-            partial_embeds = smodel(mels)[output_layer]
-
-        # Compute the utterance embedding from the partial embeddings
-        raw_embed = np.mean(partial_embeds, axis=0)
-        embed = raw_embed / np.linalg.norm(raw_embed, 2)
-
-        if return_partials:
-            return embed, partial_embeds, wav_slices
-        return embed
-
-Select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-Then compile model.
-
-.. code:: ipython3
-
-    compiled_smodel = core.compile_model(ir_smodel, device.value)
-
-Convert Decoder
-~~~~~~~~~~~~~~~
-
-
-
-In the same way export ``SynthesizerTrn`` model, that implements decoder
-function to OpenVINO IR format.
-
-.. code:: ipython3
-
-    OUTPUT_DIR = Path("output")
-    BASE_MODEL_NAME = "net_g"
-    onnx_net_g_path = Path(OUTPUT_DIR / (BASE_MODEL_NAME + "_fp32")).with_suffix(".onnx")
-    ir_net_g_path = Path(OUTPUT_DIR / (BASE_MODEL_NAME + "ir")).with_suffix(".xml")
-
-    dummy_input_1 = torch.randn(1, 1024, 81)
-    dummy_input_2 = torch.randn(1, 256)
-
-    # define forward as infer
-    net_g.forward = net_g.infer
-
-
-    if not ir_net_g_path.exists():
-        ir_net_g_model = ov.convert_model(net_g, example_input=(dummy_input_1, dummy_input_2))
-        ov.save_model(ir_net_g_model, ir_net_g_path)
-    else:
-        ir_net_g_model = core.read_model(ir_net_g_path)
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
-    Tensor-likes are not close!
-
-    Mismatched elements: 25900 / 25920 (99.9%)
-    Greatest absolute difference: 0.2173359915614128 at index (0, 0, 13783) (up to 1e-05 allowed)
-    Greatest relative difference: 96072.65179063361 at index (0, 0, 1833) (up to 1e-05 allowed)
-      _check_trace(
-
-
-Select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    compiled_ir_net_g_model = core.compile_model(ir_net_g_model, device.value)
-
-Define function for synthesizing.
-
-.. code:: ipython3
-
-    def synthesize_audio(src, tgt):
-        wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
-        wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
-
-        g_tgt = embed_utterance(wav_tgt, compiled_smodel)
-        g_tgt = torch.from_numpy(g_tgt).unsqueeze(0)
-
-        # src
-        wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
-        wav_src = np.expand_dims(wav_src, axis=0)
-
-        output_layer = compiled_cmodel.output(0)
-        c = compiled_cmodel(wav_src)[output_layer]
-        c = c.transpose((0, 2, 1))
-
-        output_layer = compiled_ir_net_g_model.output(0)
-        tgt_audio = compiled_ir_net_g_model((c, g_tgt))[output_layer]
-        tgt_audio = tgt_audio[0][0]
-
-        return tgt_audio
-
-And now we can check inference using only IR models.
-
-.. code:: ipython3
-
-    result_wav_names = []
-
-    with torch.no_grad():
-        for line in tqdm(zip(srcs, tgts)):
-            src, tgt = line
-
-            output_audio = synthesize_audio(src, tgt)
-
-            timestamp = time.strftime("%m-%d_%H-%M", time.localtime())
-            result_name = f'{timestamp}.wav'
-            result_wav_names.append(result_name)
-            write(
-                os.path.join('outputs/freevc', result_name),
-                hps.data.sampling_rate,
-                output_audio
-            )
-
-
-.. parsed-literal::
-
-
-   0it [00:00, ?it/s]
-
-.. parsed-literal::
-
-
-   1it [00:00,  1.68it/s]
-
-.. parsed-literal::
-
-
-   2it [00:01,  1.19it/s]
-
-.. parsed-literal::
-
-
-   2it [00:01,  1.25it/s]
-
-
-Result audio files should be available in ‘outputs/freevc’ and you can
-check them and compare with generated earlier. Below one of the results
-presents.
-
-Source audio (source of text):
-
-.. code:: ipython3
-
-    import IPython.display as ipd
-    ipd.Audio(srcs[0])
-
-
-
-
-.. raw:: html
-
-
-    
-
-
-
-
-Target audio (source of voice):
-
-.. code:: ipython3
-
-    ipd.Audio(tgts[0])
-
-
-
-
-.. raw:: html
-
-
-    
-
-
-
-
-Result audio:
-
-.. code:: ipython3
-
-    ipd.Audio(f'outputs/freevc/{result_wav_names[0]}')
-
-
-
-
-.. raw:: html
-
-
-    
-
-
-
-
-Also, you can use your own audio file. Just upload them and use for
-inference. Use rate corresponding to the value of
-``hps.data.sampling_rate``.
-
-.. code:: ipython3
-
-    import gradio as gr
-
-
-    audio1 = gr.Audio(label="Source Audio", type='filepath')
-    audio2 = gr.Audio(label="Reference Audio", type='filepath')
-    outputs = gr.Audio(label="Output Audio", type='filepath')
-    examples = [[audio1_name, audio2_name]]
-
-    title = 'FreeVC with Gradio'
-    description = 'Gradio Demo for FreeVC and OpenVINO™. Upload a source audio and a reference audio, then click the "Submit" button to inference.'
-
-
-    def infer(src, tgt):
-        output_audio = synthesize_audio(src, tgt)
-
-        timestamp = time.strftime("%m-%d_%H-%M", time.localtime())
-        result_name = f'{timestamp}.wav'
-        write(result_name, hps.data.sampling_rate, output_audio)
-
-        return result_name
-
-
-    iface = gr.Interface(infer, [audio1, audio2], outputs, title=title, description=description, examples=examples)
-    iface.launch()
-    # if you are launching remotely, specify server_name and server_port
-    # iface.launch(server_name='your server name', server_port='server port in int')
-    # if you have any issue to launch on your platform, you can pass share=True to launch method:
-    # iface.launch(share=True)
-    # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
-
-
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7860
-
-    To create a public link, set `share=True` in `launch()`.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. code:: ipython3
-
-    iface.close()
-
-
-.. parsed-literal::
-
-    Closing server running on port: 7860
-
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diff --git a/docs/notebooks/244-named-entity-recognition-with-output.rst b/docs/notebooks/244-named-entity-recognition-with-output.rst
deleted file mode 100644
index d2f570dcd11..00000000000
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+++ /dev/null
@@ -1,514 +0,0 @@
-Named entity recognition with OpenVINO™
-=======================================
-
-The Named Entity Recognition(NER) is a natural language processing
-method that involves the detecting of key information in the
-unstructured text and categorizing it into pre-defined categories. These
-categories or named entities refer to the key subjects of text, such as
-names, locations, companies and etc.
-
-NER is a good method for the situations when a high-level overview of a
-large amount of text is needed. NER can be helpful with such task as
-analyzing key information in unstructured text or automates the
-information extraction of large amounts of data.
-
-This tutorial shows how to perform named entity recognition using
-OpenVINO. We will use the pre-trained model
-`elastic/distilbert-base-cased-finetuned-conll03-english `__.
-It is DistilBERT based model, trained on
-`conll03 english dataset `__.
-The model can recognize four named entities in text: persons, locations,
-organizations and names of miscellaneous entities that do not belong to
-the previous three groups. The model is sensitive to capital letters.
-
-To simplify the user experience, the `Hugging Face
-Optimum `__ library is used to
-convert the model to OpenVINO™ IR format and quantize it.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Prerequisites <#prerequisites>`__
--  `Download the NER model <#download-the-ner-model>`__
--  `Quantize the model, using Hugging Face Optimum
-   API <#quantize-the-model-using-hugging-face-optimum-api>`__
--  `Compare the Original and Quantized
-   Models <#compare-the-original-and-quantized-models>`__
-
-   -  `Compare performance <#compare-performance>`__
-   -  `Compare size of the models <#compare-size-of-the-models>`__
-
--  `Prepare demo for Named Entity Recognition OpenVINO
-   Runtime <#prepare-demo-for-named-entity-recognition-openvino-runtime>`__
-
-Prerequisites
--------------
-
-
-
-.. code:: ipython3
-
-    %pip install -q "diffusers>=0.17.1" "openvino>=2023.1.0" "nncf>=2.5.0" "gradio" "onnx>=1.11.0" "transformers>=4.33.0" --extra-index-url https://download.pytorch.org/whl/cpu
-    %pip install -q "git+https://github.com/huggingface/optimum-intel.git"
-
-
-.. parsed-literal::
-
-
-    [notice] A new release of pip is available: 23.3.2 -> 24.0
-    [notice] To update, run: pip install --upgrade pip
-    Note: you may need to restart the kernel to use updated packages.
-
-    [notice] A new release of pip is available: 23.3.2 -> 24.0
-    [notice] To update, run: pip install --upgrade pip
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Download the NER model
-----------------------
-
-
-
-We load the
-`distilbert-base-cased-finetuned-conll03-english `__
-model from the `Hugging Face Hub `__ with
-`Hugging Face Transformers
-library `__.
-
-Model class initialization starts with calling ``from_pretrained``
-method. To easily save the model, you can use the ``save_pretrained()``
-method.
-
-.. code:: ipython3
-
-    from transformers import AutoTokenizer, AutoModelForTokenClassification
-
-    model_id = "elastic/distilbert-base-cased-finetuned-conll03-english"
-    model = AutoModelForTokenClassification.from_pretrained(model_id)
-
-    original_ner_model_dir = 'original_ner_model'
-    model.save_pretrained(original_ner_model_dir)
-
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-Quantize the model, using Hugging Face Optimum API
---------------------------------------------------
-
-
-
-Post-training static quantization introduces an additional calibration
-step where data is fed through the network in order to compute the
-activations quantization parameters. For quantization it will be used
-`Hugging Face Optimum Intel
-API `__.
-
-To handle the NNCF quantization process we use class
-`OVQuantizer `__.
-The quantization with Hugging Face Optimum Intel API contains the next
-steps: \* Model class initialization starts with calling
-``from_pretrained()`` method. \* Next we create calibration dataset with
-``get_calibration_dataset()`` to use for the post-training static
-quantization calibration step. \* After we quantize a model and save the
-resulting model in the OpenVINO IR format to save_directory with
-``quantize()`` method. \* Then we load the quantized model. The Optimum
-Inference models are API compatible with Hugging Face Transformers
-models and we can just replace ``AutoModelForXxx`` class with the
-corresponding ``OVModelForXxx`` class. So we use
-``OVModelForTokenClassification`` to load the model.
-
-.. code:: ipython3
-
-    from functools import partial
-    from optimum.intel import OVQuantizer
-
-    from optimum.intel import OVModelForTokenClassification
-
-    def preprocess_fn(data, tokenizer):
-        examples = []
-        for data_chunk in data["tokens"]:
-            examples.append(' '.join(data_chunk))
-
-        return tokenizer(
-            examples, padding=True, truncation=True, max_length=128
-        )
-
-    quantizer = OVQuantizer.from_pretrained(model)
-    calibration_dataset = quantizer.get_calibration_dataset(
-        "conll2003",
-        preprocess_function=partial(preprocess_fn, tokenizer=tokenizer),
-        num_samples=100,
-        dataset_split="train",
-        preprocess_batch=True,
-    )
-
-    # The directory where the quantized model will be saved
-    quantized_ner_model_dir = "quantized_ner_model"
-
-    # Apply static quantization and save the resulting model in the OpenVINO IR format
-    quantizer.quantize(calibration_dataset=calibration_dataset, save_directory=quantized_ner_model_dir)
-
-
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    2024-02-22 10:51:17.449018: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-02-22 10:51:17.450787: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-02-22 10:51:17.485744: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-02-22 10:51:18.196389: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
-        PyTorch 2.1.0+cu121 with CUDA 1201 (you have 2.2.0+cu121)
-        Python  3.8.18 (you have 3.8.10)
-      Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
-      Memory-efficient attention, SwiGLU, sparse and more won't be available.
-      Set XFORMERS_MORE_DETAILS=1 for more details
-    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-      torch.utils._pytree._register_pytree_node(
-    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/datasets/load.py:2483: FutureWarning: 'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.
-    You can remove this warning by passing 'token=' instead.
-      warnings.warn(
-
-
-
-.. parsed-literal::
-
-    Downloading data:   0%|          | 0.00/1.23M [00:00`__
-model with quantized and converted to OpenVINO IR format models to see
-the difference.
-
-Compare performance
-~~~~~~~~~~~~~~~~~~~
-
-
-
-As the Optimum Inference models are API compatible with Hugging Face
-Transformers models, we can just use ``pipleine()`` from `Hugging Face
-Transformers API `__ for
-inference.
-
-.. code:: ipython3
-
-    from transformers import pipeline
-
-    ner_pipeline_optimized = pipeline("token-classification", model=optimized_model, tokenizer=tokenizer)
-
-    ner_pipeline_original = pipeline("token-classification", model=model, tokenizer=tokenizer)
-
-
-.. parsed-literal::
-
-    device must be of type  but got  instead
-
-
-.. code:: ipython3
-
-    import time
-    import numpy as np
-
-    def calc_perf(ner_pipeline):
-        inference_times = []
-
-        for data in calibration_dataset:
-            text = ' '.join(data['tokens'])
-            start = time.perf_counter()
-            ner_pipeline(text)
-            end = time.perf_counter()
-            inference_times.append(end - start)
-
-        return np.median(inference_times)
-
-
-    print(
-        f"Median inference time of quantized model: {calc_perf(ner_pipeline_optimized)} "
-    )
-
-    print(
-        f"Median inference time of original model: {calc_perf(ner_pipeline_original)} "
-    )
-
-
-.. parsed-literal::
-
-    Median inference time of quantized model: 0.007757613499961735
-    Median inference time of original model: 0.09963577150028868
-
-
-Compare size of the models
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    from pathlib import Path
-
-    pytorch_model_file = Path(original_ner_model_dir) / "pytorch_model.bin"
-    if not pytorch_model_file.exists():
-        pytorch_model_file = pytorch_model_file.parent / "model.safetensors"
-    print(f'Size of original model in Bytes is {pytorch_model_file.stat().st_size}')
-    print(f'Size of quantized model in Bytes is {Path(quantized_ner_model_dir, "openvino_model.bin").stat().st_size}')
-
-
-.. parsed-literal::
-
-    Size of original model in Bytes is 260803668
-    Size of quantized model in Bytes is 133539000
-
-
-Prepare demo for Named Entity Recognition OpenVINO Runtime
-----------------------------------------------------------
-
-
-
-Now, you can try NER model on own text. Put your sentence to input text
-box, click Submit button, the model label the recognized entities in the
-text.
-
-.. code:: ipython3
-
-    import gradio as gr
-
-    examples = [
-        "My name is Wolfgang and I live in Berlin.",
-    ]
-
-    def run_ner(text):
-        output = ner_pipeline_optimized(text)
-        return {"text": text, "entities": output}
-
-    demo = gr.Interface(run_ner,
-                        gr.Textbox(placeholder="Enter sentence here...", label="Input Text"),
-                        gr.HighlightedText(label="Output Text"),
-                        examples=examples,
-                        allow_flagging="never")
-
-    if __name__ == "__main__":
-        try:
-            demo.launch(debug=False)
-        except Exception:
-            demo.launch(share=True, debug=False)
-    # if you are launching remotely, specify server_name and server_port
-    # demo.launch(server_name='your server name', server_port='server port in int')
-    # Read more in the docs: https://gradio.app/docs/
-
-
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7860
-
-    To create a public link, set `share=True` in `launch()`.
-
-
-
-
-
-
-
-
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-Controllable Music Generation with MusicGen and OpenVINO
-========================================================
-
-MusicGen is a single-stage auto-regressive Transformer model capable of
-generating high-quality music samples conditioned on text descriptions
-or audio prompts. The text prompt is passed to a text encoder model (T5)
-to obtain a sequence of hidden-state representations. These hidden
-states are fed to MusicGen, which predicts discrete audio tokens (audio
-codes). Finally, audio tokens are then decoded using an audio
-compression model (EnCodec) to recover the audio waveform.
-
-.. figure:: https://user-images.githubusercontent.com/76463150/260439306-81c81c8d-1f9c-41d0-b881-9491766def8e.png
-   :alt: pipeline
-
-   pipeline
-
-`The MusicGen model `__ does not
-require a self-supervised semantic representation of the text/audio
-prompts; it operates over several streams of compressed discrete music
-representation with efficient token interleaving patterns, thus
-eliminating the need to cascade multiple models to predict a set of
-codebooks (e.g. hierarchically or upsampling). Unlike prior models
-addressing music generation, it is able to generate all the codebooks in
-a single forward pass.
-
-In this tutorial, we consider how to run the MusicGen model using
-OpenVINO.
-
-We will use a model implementation from the `Hugging Face
-Transformers `__
-library.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Prerequisites <#prerequisites>`__
-
-   -  `Install requirements <#install-requirements>`__
-   -  `Imports <#imports>`__
-
--  `MusicGen in HF Transformers <#musicgen-in-hf-transformers>`__
-
-   -  `Original Pipeline Inference <#original-pipeline-inference>`__
-
--  `Convert models to OpenVINO Intermediate representation (IR)
-   format <#convert-models-to-openvino-intermediate-representation-ir-format>`__
-
-   -  `0. Set Up Variables <#0--set-up-variables>`__
-   -  `1. Convert Text Encoder <#1--convert-text-encoder>`__
-   -  `2. Convert MusicGen Language
-      Model <#2--convert-musicgen-language-model>`__
-   -  `3. Convert Audio Decoder <#3--convert-audio-decoder>`__
-
--  `Embedding the converted models into the original
-   pipeline <#embedding-the-converted-models-into-the-original-pipeline>`__
-
-   -  `Select inference device <#select-inference-device>`__
-   -  `Adapt OpenVINO models to the original
-      pipeline <#adapt-openvino-models-to-the-original-pipeline>`__
-
--  `Try out the converted pipeline <#try-out-the-converted-pipeline>`__
-
-Prerequisites
--------------
-
-
-
-Install requirements
-~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    %pip install -q "openvino>=2023.3.0"
-    %pip install -q torch gradio transformers --extra-index-url https://download.pytorch.org/whl/cpu
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Imports
-~~~~~~~
-
-
-
-.. code:: ipython3
-
-    from collections import namedtuple
-    from functools import partial
-    import gc
-    from pathlib import Path
-    from typing import Optional, Tuple
-    import warnings
-    
-    from IPython.display import Audio
-    import openvino as ov
-    import numpy as np
-    import torch
-    from torch.jit import TracerWarning
-    from transformers import AutoProcessor, MusicgenForConditionalGeneration
-    from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
-    
-    # Ignore tracing warnings
-    warnings.filterwarnings("ignore", category=TracerWarning)
-
-
-.. parsed-literal::
-
-    2024-03-13 00:01:00.322267: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:01:00.356009: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-
-
-.. parsed-literal::
-
-    2024-03-13 00:01:00.944072: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
-MusicGen in HF Transformers
----------------------------
-
-
-
-To work with
-`MusicGen `__ by Meta
-AI, we will use `Hugging Face Transformers
-package `__. Transformers
-package exposes the ``MusicgenForConditionalGeneration`` class,
-simplifying the model instantiation and weights loading. The code below
-demonstrates how to create a ``MusicgenForConditionalGeneration`` and
-generate a text-conditioned music sample.
-
-.. code:: ipython3
-
-    # Load the pipeline
-    model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small", torchscript=True, return_dict=False)
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
-      warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
-
-
-In the cell below user is free to change the desired music sample
-length.
-
-.. code:: ipython3
-
-    sample_length = 8  # seconds
-    
-    n_tokens = sample_length * model.config.audio_encoder.frame_rate + 3
-    sampling_rate = model.config.audio_encoder.sampling_rate
-    print('Sampling rate is', sampling_rate, 'Hz')
-    
-    model.to("cpu")
-    model.eval();
-
-
-.. parsed-literal::
-
-    Sampling rate is 32000 Hz
-
-
-Original Pipeline Inference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Text Preprocessing prepares the text prompt to be fed into the model,
-the ``processor`` object abstracts this step for us. Text tokenization
-is performed under the hood, it assigning tokens or IDs to the words; in
-other words, token IDs are just indices of the words in the model
-vocabulary. It helps the model understand the context of a sentence.
-
-.. code:: ipython3
-
-    processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
-    
-    inputs = processor(
-        text=["80s pop track with bassy drums and synth"],
-        return_tensors="pt",
-    )
-    
-    audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=n_tokens)
-    
-    Audio(audio_values[0].cpu().numpy(), rate=sampling_rate)
-
-
-
-
-.. raw:: html
-
-    
-    
-
-
-
-
-Convert models to OpenVINO Intermediate representation (IR) format
-------------------------------------------------------------------
-
-
-
-Model conversion API enables direct conversion of PyTorch models. We
-will utilize the ``openvino.convert_model`` method to acquire OpenVINO
-IR versions of the models. The method requires a model object and
-example input for model tracing. Under the hood, the converter will use
-the PyTorch JIT compiler, to build a frozen model graph.
-
-The pipeline consists of three important parts:
-
--  The `T5 text encoder `__
-   that translates user prompts into vectors in the latent space that
-   the next model - the MusicGen decoder can utilize.
--  The `MusicGen Language
-   Model `__
-   that auto-regressively generates audio tokens (codes).
--  The `EnCodec model `__
-   (we will use only the decoder part of it) is used to decode the audio
-   waveform from the audio tokens predicted by the MusicGen Language
-   Model.
-
-Let us convert each model step by step.
-
-0. Set Up Variables
-~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    models_dir = Path("./models")
-    t5_ir_path = models_dir / "t5.xml"
-    musicgen_0_ir_path = models_dir / "mg_0.xml"
-    musicgen_ir_path = models_dir / "mg.xml"
-    audio_decoder_ir_path = models_dir / "encodec.xml"
-
-1. Convert Text Encoder
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-The text encoder is responsible for converting the input prompt, such as
-“90s rock song with loud guitars and heavy drums” into an embedding
-space that can be fed to the next model. Typically, it is a
-transformer-based encoder that maps a sequence of input tokens to a
-sequence of text embeddings.
-
-The input for the text encoder consists of a tensor ``input_ids``, which
-contains token indices from the text processed by the tokenizer and
-``attention_mask`` that we will ignore as we will process one prompt at
-a time and this vector will just consist of ones.
-
-We use OpenVINO Converter (OVC) below to convert the PyTorch model to
-the OpenVINO Intermediate Representation format (IR), which you can
-infer later with `OpenVINO
-runtime `__
-
-.. code:: ipython3
-
-    if not t5_ir_path.exists():
-        t5_ov = ov.convert_model(model.text_encoder, example_input={'input_ids': inputs['input_ids']})
-    
-        ov.save_model(t5_ov, t5_ir_path)
-        del t5_ov
-        gc.collect()
-
-
-.. parsed-literal::
-
-    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
-
-
-.. parsed-literal::
-
-    [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
-      warnings.warn(
-
-
-2. Convert MusicGen Language Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-This model is the central part of the whole pipeline, it takes the
-embedded text representation and generates audio codes that can be then
-decoded into actual music. The model outputs several streams of audio
-codes - tokens sampled from the pre-trained codebooks representing music
-efficiently with a lower frame rate. The model employs innovative codes
-intervaling strategy, that makes single-stage generation possible.
-
-On the 0th generation step the model accepts ``input_ids`` representing
-the indices of audio codes, ``encoder_hidden_states`` and
-``encoder_attention_mask`` that were provided by the text encoder.
-
-.. code:: ipython3
-
-    # Set model config `torchscript` to True, so the model returns a tuple as output
-    model.decoder.config.torchscript = True
-    
-    if not musicgen_0_ir_path.exists():
-        decoder_input = {
-            'input_ids': torch.ones(8, 1, dtype=torch.int64),
-            'encoder_hidden_states': torch.ones(2, 12, 1024, dtype=torch.float32),
-            'encoder_attention_mask': torch.ones(2, 12, dtype=torch.int64),
-        }
-        mg_ov_0_step = ov.convert_model(model.decoder, example_input=decoder_input)
-    
-        ov.save_model(mg_ov_0_step, musicgen_0_ir_path)
-        del mg_ov_0_step
-        gc.collect()
-
-On further iterations, the model is also provided with a
-``past_key_values`` argument that contains previous outputs of the
-attention block, it allows us to save on computations. But for us, it
-means that the signature of the model’s ``forward`` method changed.
-Models in OpenVINO IR have frozen calculation graphs and do not allow
-optional arguments, that is why the MusicGen model must be converted a
-second time, with an increased number of inputs.
-
-.. code:: ipython3
-
-    # Add additional argument to the example_input dict
-    if not musicgen_ir_path.exists():
-        # Add `past_key_values` to the converted model signature  
-        decoder_input['past_key_values'] = tuple(
-            [(
-                torch.ones(2, 16, 1, 64, dtype=torch.float32),
-                torch.ones(2, 16, 1, 64, dtype=torch.float32),
-                torch.ones(2, 16, 12, 64, dtype=torch.float32),
-                torch.ones(2, 16, 12, 64, dtype=torch.float32),
-            )] * 24
-        )
-    
-        mg_ov = ov.convert_model(model.decoder, example_input=decoder_input)
-        for input in mg_ov.inputs[3:]:
-            input.get_node().set_partial_shape(ov.PartialShape([-1, 16, -1, 64]))
-            input.get_node().set_element_type(ov.Type.f32)
-        
-        mg_ov.validate_nodes_and_infer_types()
-    
-        ov.save_model(mg_ov, musicgen_ir_path)
-        del mg_ov
-        gc.collect()
-
-3. Convert Audio Decoder
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-The audio decoder which is a part of the EnCodec model is used to
-recover the audio waveform from the audio tokens predicted by the
-MusicGen decoder. To learn more about the model please refer to the
-corresponding `OpenVINO
-example `__.
-
-.. code:: ipython3
-
-    if not audio_decoder_ir_path.exists():
-        class AudioDecoder(torch.nn.Module):
-            def __init__(self, model):
-                super().__init__()
-                self.model = model
-        
-            def forward(self, output_ids):
-                return self.model.decode(output_ids, [None])
-    
-        audio_decoder_input = {'output_ids': torch.ones((1, 1, 4, n_tokens - 3), dtype=torch.int64)}
-    
-        with torch.no_grad():
-            audio_decoder_ov = ov.convert_model(AudioDecoder(model.audio_encoder), example_input=audio_decoder_input)
-        ov.save_model(audio_decoder_ov, audio_decoder_ir_path)
-        del audio_decoder_ov
-        gc.collect()
-
-Embedding the converted models into the original pipeline
----------------------------------------------------------
-
-
-
-OpenVINO™ Runtime Python API is used to compile the model in OpenVINO IR
-format. The
-`Core `__
-class provides access to the OpenVINO Runtime API. The ``core`` object,
-which is an instance of the ``Core`` class represents the API and it is
-used to compile the model.
-
-.. code:: ipython3
-
-    core = ov.Core()
-
-Select inference device
-^^^^^^^^^^^^^^^^^^^^^^^
-
-
-
-Select device that will be used to do models inference using OpenVINO
-from the dropdown list:
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-Adapt OpenVINO models to the original pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Here we create wrapper classes for all three OpenVINO models that we
-want to embed in the original inference pipeline. Here are some of the
-things to consider when adapting an OV model: - Make sure that
-parameters passed by the original pipeline are forwarded to the compiled
-OV model properly; sometimes the OV model uses only a portion of the
-input arguments and some are ignored, sometimes you need to convert the
-argument to another data type or unwrap some data structures such as
-tuples or dictionaries. - Guarantee that the wrapper class returns
-results to the pipeline in an expected format. In the example below you
-can see how we pack OV model outputs into special classes declared in
-the HF repo. - Pay attention to the model method used in the original
-pipeline for calling the model - it may be not the ``forward`` method!
-Refer to the ``AudioDecoderWrapper`` to see how we wrap OV model
-inference into the ``decode`` method.
-
-.. code:: ipython3
-
-    class TextEncoderWrapper(torch.nn.Module):
-        def __init__(self, encoder_ir, config):
-            super().__init__()
-            self.encoder = core.compile_model(encoder_ir, device.value)
-            self.config = config
-    
-        def forward(self, input_ids, **kwargs):
-            last_hidden_state = self.encoder(input_ids)[self.encoder.outputs[0]]
-            last_hidden_state = torch.tensor(last_hidden_state)
-            return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=last_hidden_state)
-    
-    
-    class MusicGenWrapper(torch.nn.Module):
-        def __init__(self, music_gen_lm_0_ir, music_gen_lm_ir, config, num_codebooks, build_delay_pattern_mask,
-                     apply_delay_pattern_mask):
-            super().__init__()
-            self.music_gen_lm_0 = core.compile_model(music_gen_lm_0_ir, device.value)
-            self.music_gen_lm = core.compile_model(music_gen_lm_ir, device.value)
-            self.config = config
-            self.num_codebooks = num_codebooks
-            self.build_delay_pattern_mask = build_delay_pattern_mask
-            self.apply_delay_pattern_mask = apply_delay_pattern_mask
-    
-        def forward(
-            self,
-            input_ids: torch.LongTensor = None,
-            encoder_hidden_states: torch.FloatTensor = None,
-            encoder_attention_mask: torch.LongTensor = None,
-            past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
-            **kwargs
-        ):
-            if past_key_values is None:
-                model = self.music_gen_lm_0
-                arguments = (input_ids, encoder_hidden_states, encoder_attention_mask)
-            else:
-                model = self.music_gen_lm
-                arguments = (input_ids, encoder_hidden_states, encoder_attention_mask, *past_key_values)
-                
-            output = model(arguments)
-            return CausalLMOutputWithCrossAttentions(
-                logits=torch.tensor(output[model.outputs[0]]),
-                past_key_values=tuple([output[model.outputs[i]] for i in range(1, 97)]),
-            )
-    
-    class AudioDecoderWrapper(torch.nn.Module):
-        def __init__(self, decoder_ir, config):
-            super().__init__()
-            self.decoder = core.compile_model(decoder_ir, device.value)
-            self.config = config
-            self.output_type = namedtuple("AudioDecoderOutput", ["audio_values"])
-    
-        def decode(self, output_ids, audio_scales):
-            output = self.decoder(output_ids)[self.decoder.outputs[0]]
-            return self.output_type(audio_values=torch.tensor(output))
-
-Now we initialize the wrapper objects and load them to the HF pipeline
-
-.. code:: ipython3
-
-    text_encode_ov = TextEncoderWrapper(t5_ir_path, model.text_encoder.config)
-    musicgen_decoder_ov = MusicGenWrapper(
-        musicgen_0_ir_path,
-        musicgen_ir_path,
-        model.decoder.config,
-        model.decoder.num_codebooks,
-        model.decoder.build_delay_pattern_mask,
-        model.decoder.apply_delay_pattern_mask
-    )
-    audio_encoder_ov = AudioDecoderWrapper(audio_decoder_ir_path, model.audio_encoder.config)
-    
-    del model.text_encoder
-    del model.decoder
-    del model.audio_encoder
-    gc.collect()
-    
-    model.text_encoder = text_encode_ov
-    model.decoder = musicgen_decoder_ov
-    model.audio_encoder = audio_encoder_ov
-    
-    def prepare_inputs_for_generation(
-        self,
-        decoder_input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        head_mask=None,
-        decoder_attention_mask=None,
-        decoder_head_mask=None,
-        cross_attn_head_mask=None,
-        use_cache=None,
-        encoder_outputs=None,
-        decoder_delay_pattern_mask=None,
-        guidance_scale=None,
-        **kwargs,
-    ):
-        if decoder_delay_pattern_mask is None:
-            decoder_input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
-                decoder_input_ids,
-                self.generation_config.pad_token_id,
-                max_length=self.generation_config.max_length,
-            )
-    
-        # apply the delay pattern mask
-        decoder_input_ids = self.decoder.apply_delay_pattern_mask(decoder_input_ids, decoder_delay_pattern_mask)
-    
-        if guidance_scale is not None and guidance_scale > 1:
-            # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these
-            # before sampling)
-            decoder_input_ids = decoder_input_ids.repeat((2, 1))
-            if decoder_attention_mask is not None:
-                decoder_attention_mask = decoder_attention_mask.repeat((2, 1))
-    
-        if past_key_values is not None:
-            # cut decoder_input_ids if past is used
-            decoder_input_ids = decoder_input_ids[:, -1:]
-    
-        return {
-            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
-            "encoder_outputs": encoder_outputs,
-            "past_key_values": past_key_values,
-            "decoder_input_ids": decoder_input_ids,
-            "attention_mask": attention_mask,
-            "decoder_attention_mask": decoder_attention_mask,
-            "head_mask": head_mask,
-            "decoder_head_mask": decoder_head_mask,
-            "cross_attn_head_mask": cross_attn_head_mask,
-            "use_cache": use_cache,
-        }
-    
-    model.prepare_inputs_for_generation = partial(prepare_inputs_for_generation, model)
-
-We can now infer the pipeline backed by OpenVINO models.
-
-.. code:: ipython3
-
-    processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
-    
-    inputs = processor(
-        text=["80s pop track with bassy drums and synth"],
-        return_tensors="pt",
-    )
-    
-    audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=n_tokens)
-    
-    Audio(audio_values[0].cpu().numpy(), rate=sampling_rate)
-
-
-
-
-.. raw:: html
-
-    
-    
-
-
-
-
-Try out the converted pipeline
-------------------------------
-
-
-
-The demo app below is created using `Gradio
-package `__
-
-.. code:: ipython3
-
-    def _generate(prompt):
-        inputs = processor(
-            text=[prompt,],
-            padding=True,
-            return_tensors="pt",
-        )
-        audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=n_tokens)
-        waveform = audio_values[0].cpu().squeeze() * 2**15
-        return (sampling_rate, waveform.numpy().astype(np.int16))
-
-.. code:: ipython3
-
-    import gradio as gr
-    
-    demo = gr.Interface(
-        _generate,
-        inputs=[
-            gr.Textbox(label="Text Prompt"),
-        ],
-        outputs=[
-            "audio"
-        ],
-        examples=[
-            ["80s pop track with bassy drums and synth"],
-            ["Earthy tones, environmentally conscious, ukulele-infused, harmonic, breezy, easygoing, organic instrumentation, gentle grooves"],
-            ["90s rock song with loud guitars and heavy drums"],
-            ["Heartful EDM with beautiful synths and chords"],
-        ],
-    )
-    try:
-        demo.launch(debug=False)
-    except Exception:
-        demo.launch(share=True, debug=False)
-    
-    # If you are launching remotely, specify server_name and server_port
-    # EXAMPLE: `demo.launch(server_name='your server name', server_port='server port in int')`
-    # To learn more please refer to the Gradio docs: https://gradio.app/docs/
-
-
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7860
-    
-    To create a public link, set `share=True` in `launch()`.
-
-
-
-
-
-
-
diff --git a/docs/notebooks/254-rag-chatbot-with-output.rst b/docs/notebooks/254-rag-chatbot-with-output.rst
deleted file mode 100644
index 05bf78e6c2b..00000000000
--- a/docs/notebooks/254-rag-chatbot-with-output.rst
+++ /dev/null
@@ -1,1543 +0,0 @@
-Create an LLM-powered RAG system using OpenVINO
-===============================================
-
-**Retrieval-augmented generation (RAG)** is a technique for augmenting
-LLM knowledge with additional, often private or real-time, data. LLMs
-can reason about wide-ranging topics, but their knowledge is limited to
-the public data up to a specific point in time that they were trained
-on. If you want to build AI applications that can reason about private
-data or data introduced after a model’s cutoff date, you need to augment
-the knowledge of the model with the specific information it needs. The
-process of bringing the appropriate information and inserting it into
-the model prompt is known as Retrieval Augmented Generation (RAG).
-
-`LangChain `__
-is a framework for developing applications powered by language models.
-It has a number of components specifically designed to help build RAG
-applications. In this tutorial, we’ll build a simple question-answering
-application over a Markdown or CSV data source.
-
-The tutorial consists of the following steps:
-
--  Install prerequisites
--  Download and convert the model from a public source using the
-   `OpenVINO integration with Hugging Face
-   Optimum `__.
--  Compress model weights to 4-bit or 8-bit data types using
-   `NNCF `__
--  Create a RAG chain pipeline
--  Run chat pipeline
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Prerequisites <#prerequisites>`__
--  `Select model for inference <#select-model-for-inference>`__
--  `login to huggingfacehub to get access to pretrained
-   model <#login-to-huggingfacehub-to-get-access-to-pretrained-model>`__
--  `Convert model <#convert-model>`__
-
-   -  `Convert LLM model <#convert-llm-model>`__
-
--  `Compress model weights <#compress-model-weights>`__
-
-   -  `Weights Compression using Optimum
-      Intel <#weights-compression-using-optimum-intel>`__
-   -  `Weights Compression using
-      NNCF <#weights-compression-using-nncf>`__
-   -  `Convert embedding model <#convert-embedding-model>`__
-
--  `Select device for inference and model
-   variant <#select-device-for-inference-and-model-variant>`__
-
-   -  `Select device for embedding model
-      inference <#select-device-for-embedding-model-inference>`__
-   -  `Select device for LLM model
-      inference <#select-device-for-llm-model-inference>`__
-
--  `Load model <#load-model>`__
-
-   -  `Load embedding model <#load-embedding-model>`__
-   -  `Load LLM model <#load-llm-model>`__
-
--  `Run QA over Document <#run-qa-over-document>`__
-
-Prerequisites
--------------
-
-
-
-Install required dependencies
-
-.. code:: ipython3
-
-    %pip uninstall -q -y openvino-dev openvino openvino-nightly optimum optimum-intel
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
-    "git+https://github.com/huggingface/optimum-intel.git"\
-    "git+https://github.com/openvinotoolkit/nncf.git"\
-    "datasets"\
-    "accelerate"\
-    "openvino-nightly"\
-    "gradio"\
-    "onnx" "chromadb" "sentence_transformers" "langchain>=0.1.7" "langchainhub" "transformers>=4.37.0" "unstructured" "scikit-learn" "python-docx" "pdfminer.six" "bitsandbytes"
-
-
-.. parsed-literal::
-
-    WARNING: Skipping openvino-dev as it is not installed.
-    WARNING: Skipping openvino as it is not installed.
-    Note: you may need to restart the kernel to use updated packages.
-    
-    [notice] A new release of pip is available: 23.3.2 -> 24.0
-    [notice] To update, run: pip install --upgrade pip
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Select model for inference
---------------------------
-
-
-
-The tutorial supports different models, you can select one from the
-provided options to compare the quality of open source LLM solutions.
->\ **Note**: conversion of some models can require additional actions
-from user side and at least 64GB RAM for conversion.
-
-The available embedding model options are:
-
--  **all-mpnet-base-v2(All)** - This is a
-   `sentence-transformers `__
-   model: It maps sentences & paragraphs to a 768 dimensional dense
-   vector space and can be used for tasks like clustering or semantic
-   search. More details about model can be found in `model
-   card `__
--  **text2vec-large-chinese(Chinese)** - This is a
-   `CoSENT `__ model. It can be used
-   for tasks like sentence embeddings, text matching or semantic search.
-   More details about model can be found in `model
-   card `__
-
-The available LLM model options are:
-
--  **tiny-llama-1b-chat** - This is the chat model finetuned on top of
-   `TinyLlama/TinyLlama-1.1B-intermediate-step-955k-2T `__.
-   The TinyLlama project aims to pretrain a 1.1B Llama model on 3
-   trillion tokens with the adoption of the same architecture and
-   tokenizer as Llama 2. This means TinyLlama can be plugged and played
-   in many open-source projects built upon Llama. Besides, TinyLlama is
-   compact with only 1.1B parameters. This compactness allows it to
-   cater to a multitude of applications demanding a restricted
-   computation and memory footprint. More details about model can be
-   found in `model
-   card `__
--  **gemma-2b-it** - Gemma is a family of lightweight, state-of-the-art
-   open models from Google, built from the same research and technology
-   used to create the Gemini models. They are text-to-text, decoder-only
-   large language models, available in English, with open weights,
-   pre-trained variants, and instruction-tuned variants. Gemma models
-   are well-suited for a variety of text generation tasks, including
-   question answering, summarization, and reasoning. This model is
-   instruction-tuned version of 2B parameters model. More details about
-   model can be found in `model
-   card `__. >\ **Note**: run
-   model with demo, you will need to accept license agreement. >You must
-   be a registered user in Hugging Face Hub. Please visit `HuggingFace
-   model card `__, carefully
-   read terms of usage and click accept button. You will need to use an
-   access token for the code below to run. For more information on
-   access tokens, refer to `this section of the
-   documentation `__.
-   >You can login on Hugging Face Hub in notebook environment, using
-   following code:
-
-.. code:: python
-
-       ## login to huggingfacehub to get access to pretrained model 
-
-       from huggingface_hub import notebook_login, whoami
-
-       try:
-           whoami()
-           print('Authorization token already provided')
-       except OSError:
-           notebook_login()
-
--  **mini-cpm-2b-dpo** - MiniCPM is an End-Size LLM developed by
-   ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding
-   embeddings. After Direct Preference Optimization (DPO) fine-tuning,
-   MiniCPM outperforms many popular 7b, 13b and 70b models. More details
-   can be found in
-   `model_card `__.
--  **red-pajama-3b-chat** - A 2.8B parameter pre-trained language model
-   based on GPT-NEOX architecture. It was developed by Together Computer
-   and leaders from the open-source AI community. The model is
-   fine-tuned on OASST1 and Dolly2 datasets to enhance chatting ability.
-   More details about model can be found in `HuggingFace model
-   card `__.
--  **gemma-7b-it** - Gemma is a family of lightweight, state-of-the-art
-   open models from Google, built from the same research and technology
-   used to create the Gemini models. They are text-to-text, decoder-only
-   large language models, available in English, with open weights,
-   pre-trained variants, and instruction-tuned variants. Gemma models
-   are well-suited for a variety of text generation tasks, including
-   question answering, summarization, and reasoning. This model is
-   instruction-tuned version of 7B parameters model. More details about
-   model can be found in `model
-   card `__. >\ **Note**: run
-   model with demo, you will need to accept license agreement. >You must
-   be a registered user in Hugging Face Hub. Please visit `HuggingFace
-   model card `__, carefully
-   read terms of usage and click accept button. You will need to use an
-   access token for the code below to run. For more information on
-   access tokens, refer to `this section of the
-   documentation `__.
-   >You can login on Hugging Face Hub in notebook environment, using
-   following code:
-
-.. code:: python
-
-       ## login to huggingfacehub to get access to pretrained model 
-
-       from huggingface_hub import notebook_login, whoami
-
-       try:
-           whoami()
-           print('Authorization token already provided')
-       except OSError:
-           notebook_login()
-
--  **llama-2-7b-chat** - LLama 2 is the second generation of LLama
-   models developed by Meta. Llama 2 is a collection of pre-trained and
-   fine-tuned generative text models ranging in scale from 7 billion to
-   70 billion parameters. llama-2-7b-chat is 7 billions parameters
-   version of LLama 2 finetuned and optimized for dialogue use case.
-   More details about model can be found in the
-   `paper `__,
-   `repository `__ and
-   `HuggingFace model
-   card `__.
-   >\ **Note**: run model with demo, you will need to accept license
-   agreement. >You must be a registered user in Hugging Face Hub.
-   Please visit `HuggingFace model
-   card `__,
-   carefully read terms of usage and click accept button. You will need
-   to use an access token for the code below to run. For more
-   information on access tokens, refer to `this section of the
-   documentation `__.
-   >You can login on Hugging Face Hub in notebook environment, using
-   following code:
-
-.. code:: python
-
-       ## login to huggingfacehub to get access to pretrained model 
-
-       from huggingface_hub import notebook_login, whoami
-
-       try:
-           whoami()
-           print('Authorization token already provided')
-       except OSError:
-           notebook_login()
-
--  **qwen1.5-0.5b-chat/qwen1.5-1.8b-chat/qwen1.5-7b-chat** - Qwen1.5 is
-   the beta version of Qwen2, a transformer-based decoder-only language
-   model pretrained on a large amount of data. Qwen1.5 is a language
-   model series including decoder language models of different model
-   sizes. It is based on the Transformer architecture with SwiGLU
-   activation, attention QKV bias, group query attention, mixture of
-   sliding window attention and full attention. You can find more
-   details about model in the `model
-   repository `__.
--  **qwen-7b-chat** - Qwen-7B is the 7B-parameter version of the large
-   language model series, Qwen (abbr. Tongyi Qianwen), proposed by
-   Alibaba Cloud. Qwen-7B is a Transformer-based large language model,
-   which is pretrained on a large volume of data, including web texts,
-   books, codes, etc. For more details about Qwen, please refer to the
-   `GitHub `__ code repository.
--  **mpt-7b-chat** - MPT-7B is part of the family of
-   MosaicPretrainedTransformer (MPT) models, which use a modified
-   transformer architecture optimized for efficient training and
-   inference. These architectural changes include performance-optimized
-   layer implementations and the elimination of context length limits by
-   replacing positional embeddings with Attention with Linear Biases
-   (`ALiBi `__). Thanks to these
-   modifications, MPT models can be trained with high throughput
-   efficiency and stable convergence. MPT-7B-chat is a chatbot-like
-   model for dialogue generation. It was built by finetuning MPT-7B on
-   the
-   `ShareGPT-Vicuna `__,
-   `HC3 `__,
-   `Alpaca `__,
-   `HH-RLHF `__, and
-   `Evol-Instruct `__
-   datasets. More details about the model can be found in `blog
-   post `__,
-   `repository `__ and
-   `HuggingFace model
-   card `__.
--  **chatglm3-6b** - ChatGLM3-6B is the latest open-source model in the
-   ChatGLM series. While retaining many excellent features such as
-   smooth dialogue and low deployment threshold from the previous two
-   generations, ChatGLM3-6B employs a more diverse training dataset,
-   more sufficient training steps, and a more reasonable training
-   strategy. ChatGLM3-6B adopts a newly designed `Prompt
-   format `__,
-   in addition to the normal multi-turn dialogue. You can find more
-   details about model in the `model
-   card `__
--  **mistral-7b** - The Mistral-7B-v0.1 Large Language Model (LLM) is a
-   pretrained generative text model with 7 billion parameters. You can
-   find more details about model in the `model
-   card `__,
-   `paper `__ and `release blog
-   post `__.
--  **zephyr-7b-beta** - Zephyr is a series of language models that are
-   trained to act as helpful assistants. Zephyr-7B-beta is the second
-   model in the series, and is a fine-tuned version of
-   `mistralai/Mistral-7B-v0.1 `__
-   that was trained on on a mix of publicly available, synthetic
-   datasets using `Direct Preference Optimization
-   (DPO) `__. You can find more
-   details about model in `technical
-   report `__ and `HuggingFace model
-   card `__.
--  **neural-chat-7b-v3-1** - Mistral-7b model fine-tuned using Intel
-   Gaudi. The model fine-tuned on the open source dataset
-   `Open-Orca/SlimOrca `__
-   and aligned with `Direct Preference Optimization (DPO)
-   algorithm `__. More details can be
-   found in `model
-   card `__ and `blog
-   post `__.
--  **notus-7b-v1** - Notus is a collection of fine-tuned models using
-   `Direct Preference Optimization
-   (DPO) `__. and related
-   `RLHF `__ techniques. This model is
-   the first version, fine-tuned with DPO over zephyr-7b-sft. Following
-   a data-first approach, the only difference between Notus-7B-v1 and
-   Zephyr-7B-beta is the preference dataset used for dDPO. Proposed
-   approach for dataset creation helps to effectively fine-tune Notus-7b
-   that surpasses Zephyr-7B-beta and Claude 2 on
-   `AlpacaEval `__. More
-   details about model can be found in `model
-   card `__.
--  **youri-7b-chat** - Youri-7b-chat is a Llama2 based model. `Rinna
-   Co., Ltd. `__ conducted further pre-training
-   for the Llama2 model with a mixture of English and Japanese datasets
-   to improve Japanese task capability. The model is publicly released
-   on Hugging Face hub. You can find detailed information at the
-   `rinna/youri-7b-chat project
-   page `__.
--  **baichuan2-7b-chat** - Baichuan 2 is the new generation of
-   large-scale open-source language models launched by `Baichuan
-   Intelligence inc `__. It is trained
-   on a high-quality corpus with 2.6 trillion tokens and has achieved
-   the best performance in authoritative Chinese and English benchmarks
-   of the same size.
--  **internlm2-chat-1.8b** - InternLM2 is the second generation InternLM
-   series. Compared to the previous generation model, it shows
-   significant improvements in various capabilities, including
-   reasoning, mathematics, and coding. More details about model can be
-   found in `model repository `__.
-
-.. code:: ipython3
-
-    from pathlib import Path
-    from optimum.intel.openvino import OVModelForCausalLM
-    import openvino as ov
-    import torch
-    import nncf
-    import logging
-    import shutil
-    import gc
-    import ipywidgets as widgets
-    from transformers import (
-        AutoModelForCausalLM,
-        AutoModel,
-        AutoTokenizer,
-        AutoConfig,
-        TextIteratorStreamer,
-        pipeline,
-        StoppingCriteria,
-        StoppingCriteriaList,
-    )
-
-
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    2024-03-06 07:05:19.617312: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-06 07:05:19.620814: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-03-06 07:05:19.663621: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
-    2024-03-06 07:05:19.663653: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
-    2024-03-06 07:05:19.663683: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
-    2024-03-06 07:05:19.671963: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-03-06 07:05:19.673938: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-03-06 07:05:20.726709: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
-Convert model
--------------
-
-
-
-Convert LLM model
-~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    from config import SUPPORTED_EMBEDDING_MODELS, SUPPORTED_LLM_MODELS
-    
-    model_languages = list(SUPPORTED_LLM_MODELS)
-    
-    model_language = widgets.Dropdown(
-        options=model_languages,
-        value=model_languages[0],
-        description="Model Language:",
-        disabled=False,
-    )
-    
-    model_language
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Model Language:', options=('English', 'Chinese', 'Japanese'), value='English')
-
-
-
-.. code:: ipython3
-
-    llm_model_ids = list(SUPPORTED_LLM_MODELS[model_language.value])
-    
-    llm_model_id = widgets.Dropdown(
-        options=llm_model_ids,
-        value=llm_model_ids[4],
-        description="Model:",
-        disabled=False,
-    )
-    
-    llm_model_id
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Model:', options=('tiny-llama-1b-chat', 'gemma-2b-it', 'red-pajama-3b-chat', 'gemma-7b-i…
-
-
-
-.. code:: ipython3
-
-    llm_model_configuration = SUPPORTED_LLM_MODELS[model_language.value][llm_model_id.value]
-    print(f"Selected LLM model {llm_model_id.value}")
-
-
-.. parsed-literal::
-
-    Selected LLM model tiny-llama-1b-chat
-
-
-Optimum Intel can be used to load optimized models from the `Hugging
-Face Hub `__ and
-create pipelines to run an inference with OpenVINO Runtime using Hugging
-Face APIs. The Optimum Inference models are API compatible with Hugging
-Face Transformers models. This means we just need to replace
-``AutoModelForXxx`` class with the corresponding ``OVModelForXxx``
-class.
-
-Below is an example of the RedPajama model
-
-.. code:: diff
-
-   -from transformers import AutoModelForCausalLM
-   +from optimum.intel.openvino import OVModelForCausalLM
-   from transformers import AutoTokenizer, pipeline
-
-   model_id = "togethercomputer/RedPajama-INCITE-Chat-3B-v1"
-   -model = AutoModelForCausalLM.from_pretrained(model_id)
-   +model = OVModelForCausalLM.from_pretrained(model_id, export=True)
-
-Model class initialization starts with calling ``from_pretrained``
-method. When downloading and converting Transformers model, the
-parameter ``export=True`` should be added. We can save the converted
-model for the next usage with the ``save_pretrained`` method. Tokenizer
-class and pipelines API are compatible with Optimum models.
-
-To optimize the generation process and use memory more efficiently, the
-``use_cache=True`` option is enabled. Since the output side is
-auto-regressive, an output token hidden state remains the same once
-computed for every further generation step. Therefore, recomputing it
-every time you want to generate a new token seems wasteful. With the
-cache, the model saves the hidden state once it has been computed. The
-model only computes the one for the most recently generated output token
-at each time step, re-using the saved ones for hidden tokens. This
-reduces the generation complexity from :math:`O(n^3)` to :math:`O(n^2)`
-for a transformer model. More details about how it works can be found in
-this
-`article `__.
-With this option, the model gets the previous step’s hidden states
-(cached attention keys and values) as input and additionally provides
-hidden states for the current step as output. It means for all next
-iterations, it is enough to provide only a new token obtained from the
-previous step and cached key values to get the next token prediction.
-
-In our case, MPT, Qwen and ChatGLM model currently is not covered by
-Optimum Intel, we will convert it manually and create wrapper compatible
-with Optimum Intel.
-
-Compress model weights
-----------------------
-
-
-
-The Weights Compression algorithm is aimed at compressing the weights of
-the models and can be used to optimize the model footprint and
-performance of large models where the size of weights is relatively
-larger than the size of activations, for example, Large Language Models
-(LLM). Compared to INT8 compression, INT4 compression improves
-performance even more, but introduces a minor drop in prediction
-quality.
-
-Weights Compression using Optimum Intel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Optimum Intel supports weight compression via NNCF out of the box. For
-8-bit compression we pass ``load_in_8bit=True`` to ``from_pretrained()``
-method of ``OVModelForCausalLM``. For 4 bit compression we provide
-``quantization_config=OVWeightQuantizationConfig(bits=4, ...)`` argument
-containing number of bits and other compression parameters. We will
-consider how to do it on RedPajama, LLAMA and Zephyr examples.
-
-   **Note**: There may be no speedup for INT4/INT8 compressed models on
-   dGPU.
-
-Weights Compression using NNCF
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-You also can perform weights compression for OpenVINO models using NNCF
-directly. ``nncf.compress_weights`` function accepts OpenVINO model
-instance and compresses its weights for Linear and Embedding layers. We
-will consider this variant based on MPT model.
-
-   **Note**: This tutorial involves conversion model for FP16 and
-   INT4/INT8 weights compression scenarios. It may be memory and
-   time-consuming in the first run. You can manually control the
-   compression precision below.
-
-.. code:: ipython3
-
-    from IPython.display import display
-    
-    prepare_int4_model = widgets.Checkbox(
-        value=True,
-        description="Prepare INT4 model",
-        disabled=False,
-    )
-    prepare_int8_model = widgets.Checkbox(
-        value=False,
-        description="Prepare INT8 model",
-        disabled=False,
-    )
-    prepare_fp16_model = widgets.Checkbox(
-        value=False,
-        description="Prepare FP16 model",
-        disabled=False,
-    )
-    
-    display(prepare_int4_model)
-    display(prepare_int8_model)
-    display(prepare_fp16_model)
-
-
-
-.. parsed-literal::
-
-    Checkbox(value=True, description='Prepare INT4 model')
-
-
-
-.. parsed-literal::
-
-    Checkbox(value=False, description='Prepare INT8 model')
-
-
-
-.. parsed-literal::
-
-    Checkbox(value=False, description='Prepare FP16 model')
-
-
-.. code:: ipython3
-
-    from optimum.intel import OVWeightQuantizationConfig
-    from converter import converters, register_configs
-    
-    register_configs()
-    
-    nncf.set_log_level(logging.ERROR)
-    
-    pt_model_id = llm_model_configuration["model_id"]
-    pt_model_name = llm_model_id.value.split("-")[0]
-    model_type = AutoConfig.from_pretrained(pt_model_id, trust_remote_code=True).model_type
-    fp16_model_dir = Path(llm_model_id.value) / "FP16"
-    int8_model_dir = Path(llm_model_id.value) / "INT8_compressed_weights"
-    int4_model_dir = Path(llm_model_id.value) / "INT4_compressed_weights"
-    
-    
-    def convert_to_fp16():
-        if (fp16_model_dir / "openvino_model.xml").exists():
-            return
-        if not llm_model_configuration["remote"]:
-            remote_code = llm_model_configuration.get("remote_code", False)
-            model_kwargs = {}
-            if remote_code:
-                model_kwargs = {
-                    "trust_remote_code": True,
-                    "config": AutoConfig.from_pretrained(pt_model_id, trust_remote_code=True)
-                }
-            ov_model = OVModelForCausalLM.from_pretrained(
-                pt_model_id, export=True, compile=False, load_in_8bit=False, **model_kwargs
-            )
-            ov_model.half()
-            ov_model.save_pretrained(fp16_model_dir)
-            del ov_model
-        else:
-            model_kwargs = {}
-            if "revision" in llm_model_configuration:
-                model_kwargs["revision"] = llm_model_configuration["revision"]
-            model = AutoModelForCausalLM.from_pretrained(
-                llm_model_configuration["model_id"],
-                torch_dtype=torch.float32,
-                trust_remote_code=True,
-                **model_kwargs
-            )
-            converters[pt_model_name](model, fp16_model_dir)
-            del model
-        gc.collect()
-    
-    
-    def convert_to_int8():
-        if (int8_model_dir / "openvino_model.xml").exists():
-            return
-        int8_model_dir.mkdir(parents=True, exist_ok=True)
-        if not llm_model_configuration["remote"]:
-            remote_code = llm_model_configuration.get("remote_code", False)
-            model_kwargs = {}
-            if remote_code:
-                model_kwargs = {
-                    "trust_remote_code": True,
-                    "config": AutoConfig.from_pretrained(pt_model_id, trust_remote_code=True)
-                }
-            ov_model = OVModelForCausalLM.from_pretrained(
-                pt_model_id, export=True, compile=False, load_in_8bit=True, **model_kwargs
-            )
-            ov_model.save_pretrained(int8_model_dir)
-            del ov_model
-        else:
-            convert_to_fp16()
-            ov_model = ov.Core().read_model(fp16_model_dir / "openvino_model.xml")
-            shutil.copy(fp16_model_dir / "config.json", int8_model_dir / "config.json")
-            configuration_file = fp16_model_dir / f"configuration_{model_type}.py"
-            if configuration_file.exists():
-                shutil.copy(
-                    configuration_file, int8_model_dir / f"configuration_{model_type}.py"
-                )
-            compressed_model = nncf.compress_weights(ov_model)
-            ov.save_model(compressed_model, int8_model_dir / "openvino_model.xml")
-            del ov_model
-            del compressed_model
-        gc.collect()
-    
-    
-    def convert_to_int4():
-        compression_configs = {
-            "zephyr-7b-beta": {
-                "sym": True,
-                "group_size": 64,
-                "ratio": 0.6,
-            },
-            "mistral-7b": {
-                "sym": True,
-                "group_size": 64,
-                "ratio": 0.6,
-            },
-            "minicpm-2b-dpo": {
-                "sym": True,
-                "group_size": 64,
-                "ratio": 0.6,
-            },
-            "notus-7b-v1": {
-                "sym": True,
-                "group_size": 64,
-                "ratio": 0.6,
-            },
-            "neural-chat-7b-v3-1": {
-                "sym": True,
-                "group_size": 64,
-                "ratio": 0.6,
-            },
-            "llama-2-chat-7b": {
-                "sym": True,
-                "group_size": 128,
-                "ratio": 0.8,
-            },
-            "chatglm2-6b": {
-                "sym": True,
-                "group_size": 128,
-                "ratio": 0.72,
-            },
-            "qwen-7b-chat": {
-                "sym": True, 
-                "group_size": 128, 
-                "ratio": 0.6
-            },
-            'red-pajama-3b-chat': {
-                "sym": False,
-                "group_size": 128,
-                "ratio": 0.5,
-            },
-            "default": {
-                "sym": False,
-                "group_size": 128,
-                "ratio": 0.8,
-            },
-        }
-    
-        model_compression_params = compression_configs.get(
-            llm_model_id.value, compression_configs["default"]
-        )
-        if (int4_model_dir / "openvino_model.xml").exists():
-            return
-        int4_model_dir.mkdir(parents=True, exist_ok=True)
-        if not llm_model_configuration["remote"]:
-            remote_code = llm_model_configuration.get("remote_code", False)
-            model_kwargs = {}
-            if remote_code:
-                model_kwargs = {
-                    "trust_remote_code" : True,
-                    "config": AutoConfig.from_pretrained(pt_model_id, trust_remote_code=True)
-                }
-            ov_model = OVModelForCausalLM.from_pretrained(
-                pt_model_id, export=True, compile=False,
-                quantization_config=OVWeightQuantizationConfig(bits=4, **model_compression_params),
-                **model_kwargs
-            )
-            ov_model.save_pretrained(int4_model_dir)
-            del ov_model
-    
-        else:
-            convert_to_fp16()
-            ov_model = ov.Core().read_model(fp16_model_dir / "openvino_model.xml")
-            shutil.copy(fp16_model_dir / "config.json", int4_model_dir / "config.json")
-            configuration_file = fp16_model_dir / f"configuration_{model_type}.py"
-            if configuration_file.exists():
-                shutil.copy(
-                    configuration_file, int4_model_dir / f"configuration_{model_type}.py"
-                )
-            mode = nncf.CompressWeightsMode.INT4_SYM if model_compression_params["sym"] else \
-                nncf.CompressWeightsMode.INT4_ASYM
-            del model_compression_params["sym"]
-            compressed_model = nncf.compress_weights(ov_model, mode=mode, **model_compression_params)
-            ov.save_model(compressed_model, int4_model_dir / "openvino_model.xml")
-            del ov_model
-            del compressed_model
-        gc.collect()
-    
-    
-    if prepare_fp16_model.value:
-        convert_to_fp16()
-    if prepare_int8_model.value:
-        convert_to_int8()
-    if prepare_int4_model.value:
-        convert_to_int4()
-
-Let’s compare model size for different compression types
-
-.. code:: ipython3
-
-    fp16_weights = fp16_model_dir / "openvino_model.bin"
-    int8_weights = int8_model_dir / "openvino_model.bin"
-    int4_weights = int4_model_dir / "openvino_model.bin"
-    
-    if fp16_weights.exists():
-        print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
-    for precision, compressed_weights in zip([8, 4], [int8_weights, int4_weights]):
-        if compressed_weights.exists():
-            print(
-                f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB"
-            )
-        if compressed_weights.exists() and fp16_weights.exists():
-            print(
-                f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}"
-            )
-
-
-.. parsed-literal::
-
-    Size of model with INT4 compressed weights is 1837.58 MB
-
-
-Convert embedding model
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Since some embedding models can only support limited languages, we can
-filter them out according the LLM you selected.
-
-.. code:: ipython3
-
-    embedding_model_id = list(SUPPORTED_EMBEDDING_MODELS)
-    
-    if model_language.value != "Chinese":
-        embedding_model_id = [x for x in embedding_model_id if "chinese" not in x]
-    
-    embedding_model_id = widgets.Dropdown(
-        options=embedding_model_id,
-        value=embedding_model_id[0],
-        description="Embedding Model:",
-        disabled=False,
-    )
-    
-    embedding_model_id
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Embedding Model:', options=('all-mpnet-base-v2',), value='all-mpnet-base-v2')
-
-
-
-.. code:: ipython3
-
-    embedding_model_configuration = SUPPORTED_EMBEDDING_MODELS[embedding_model_id.value]
-    print(f"Selected {embedding_model_id.value} model")
-
-
-.. parsed-literal::
-
-    Selected all-mpnet-base-v2 model
-
-
-.. code:: ipython3
-
-    embedding_model_dir = Path(embedding_model_id.value)
-    
-    if not (embedding_model_dir / "openvino_model.xml").exists():
-        model = AutoModel.from_pretrained(embedding_model_configuration["model_id"])
-        converters[embedding_model_id.value](model, embedding_model_dir)
-        tokenizer = AutoTokenizer.from_pretrained(embedding_model_configuration["model_id"])
-        tokenizer.save_pretrained(embedding_model_dir)
-        del model
-
-Select device for inference and model variant
----------------------------------------------
-
-
-
-   **Note**: There may be no speedup for INT4/INT8 compressed models on
-   dGPU.
-
-Select device for embedding model inference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    core = ov.Core()
-    embedding_device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="CPU",
-        description="Device:",
-        disabled=False,
-    )
-    
-    embedding_device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')
-
-
-
-.. code:: ipython3
-
-    print(f"Embedding model will be loaded to {embedding_device.value} device for response generation")
-
-
-.. parsed-literal::
-
-    Embedding model will be loaded to CPU device for response generation
-
-
-Select device for LLM model inference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    llm_device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="CPU",
-        description="Device:",
-        disabled=False,
-    )
-    
-    llm_device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')
-
-
-
-.. code:: ipython3
-
-    print(f"LLM model will be loaded to {llm_device.value} device for response generation")
-
-
-.. parsed-literal::
-
-    LLM model will be loaded to CPU device for response generation
-
-
-Load model
-----------
-
-
-
-Load embedding model
-~~~~~~~~~~~~~~~~~~~~
-
-
-
-Wrapper around a text embedding model for LangChain, used for converting
-text to embeddings.
-
-.. code:: ipython3
-
-    from ov_embedding_model import OVEmbeddings
-    
-    embedding = OVEmbeddings.from_model_id(
-        embedding_model_dir,
-        do_norm=embedding_model_configuration["do_norm"],
-        ov_config={
-            "device_name": embedding_device.value,
-            "config": {"PERFORMANCE_HINT": "THROUGHPUT"},
-        },
-        model_kwargs={
-            "model_max_length": 512,
-        },
-    )
-
-Load LLM model
-~~~~~~~~~~~~~~
-
-
-
-The cell below create ``OVMPTModel`` and ``OVCHATGLM2Model`` wrapper
-based on ``OVModelForCausalLM`` model.
-
-.. code:: ipython3
-
-    from ov_llm_model import model_classes
-
-.. code:: ipython3
-
-    available_models = []
-    if int4_model_dir.exists():
-        available_models.append("INT4")
-    if int8_model_dir.exists():
-        available_models.append("INT8")
-    if fp16_model_dir.exists():
-        available_models.append("FP16")
-    
-    model_to_run = widgets.Dropdown(
-        options=available_models,
-        value=available_models[0],
-        description="Model to run:",
-        disabled=False,
-    )
-    
-    model_to_run
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Model to run:', options=('FP16',), value='FP16')
-
-
-
-.. code:: ipython3
-
-    from langchain.llms import HuggingFacePipeline
-    
-    if model_to_run.value == "INT4":
-        model_dir = int4_model_dir
-    elif model_to_run.value == "INT8":
-        model_dir = int8_model_dir
-    else:
-        model_dir = fp16_model_dir
-    print(f"Loading model from {model_dir}")
-    
-    ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
-    
-    # On a GPU device a model is executed in FP16 precision. For red-pajama-3b-chat model there known accuracy
-    # issues caused by this, which we avoid by setting precision hint to "f32".
-    if llm_model_id.value == "red-pajama-3b-chat" and "GPU" in core.available_devices and llm_device.value in ["GPU", "AUTO"]:
-        ov_config["INFERENCE_PRECISION_HINT"] = "f32"
-    
-    model_name = llm_model_configuration["model_id"]
-    stop_tokens = llm_model_configuration.get("stop_tokens")
-    class_key = llm_model_id.value.split("-")[0]
-    tok = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-    
-    class StopOnTokens(StoppingCriteria):
-        def __init__(self, token_ids):
-            self.token_ids = token_ids
-    
-        def __call__(
-            self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
-        ) -> bool:
-            for stop_id in self.token_ids:
-                if input_ids[0][-1] == stop_id:
-                    return True
-            return False
-    
-    if stop_tokens is not None:
-        if isinstance(stop_tokens[0], str):
-            stop_tokens = tok.convert_tokens_to_ids(stop_tokens)
-    
-        stop_tokens = [StopOnTokens(stop_tokens)]
-    
-    model_class = (
-        OVModelForCausalLM
-        if not llm_model_configuration["remote"]
-        else model_classes[class_key]
-    )
-    ov_model = model_class.from_pretrained(
-        model_dir,
-        device=llm_device.value,
-        ov_config=ov_config,
-        config=AutoConfig.from_pretrained(model_dir, trust_remote_code=True),
-        trust_remote_code=True,
-    )
-
-
-.. parsed-literal::
-
-    Loading model from chatglm3-6b/FP16
-
-
-
-.. parsed-literal::
-
-    tokenizer_config.json:   0%|          | 0.00/1.41k [00:00`__.
-
-A typical RAG application has two main components:
-
--  **Indexing**: a pipeline for ingesting data from a source and
-   indexing it. This usually happen offline.
-
--  **Retrieval and generation**: the actual RAG chain, which takes the
-   user query at run time and retrieves the relevant data from the
-   index, then passes that to the model.
-
-The most common full sequence from raw data to answer looks like:
-
-**Indexing** 1. ``Load``: First we need to load our data. We’ll use
-DocumentLoaders for this. 2. ``Split``: Text splitters break large
-Documents into smaller chunks. This is useful both for indexing data and
-for passing it in to a model, since large chunks are harder to search
-over and won’t in a model’s finite context window. 3. ``Store``: We need
-somewhere to store and index our splits, so that they can later be
-searched over. This is often done using a VectorStore and Embeddings
-model.
-
-.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/dfed2ba3-0c3a-4e0e-a2a7-01638730486a
-   :alt: Indexing pipeline
-
-   Indexing pipeline
-
-**Retrieval and generation** 1. ``Retrieve``: Given a user input,
-relevant splits are retrieved from storage using a Retriever. 2.
-``Generate``: A LLM produces an answer using a prompt that includes the
-question and the retrieved data.
-
-.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/f0545ddc-c0cd-4569-8c86-9879fdab105a
-   :alt: Retrieval and generation pipeline
-
-   Retrieval and generation pipeline
-
-.. code:: ipython3
-
-    from typing import List
-    from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter, MarkdownTextSplitter
-    from langchain.document_loaders import (
-        CSVLoader,
-        EverNoteLoader,
-        PDFMinerLoader,
-        TextLoader,
-        UnstructuredEPubLoader,
-        UnstructuredHTMLLoader,
-        UnstructuredMarkdownLoader,
-        UnstructuredODTLoader,
-        UnstructuredPowerPointLoader,
-        UnstructuredWordDocumentLoader, )
-    
-    
-    class ChineseTextSplitter(CharacterTextSplitter):
-        def __init__(self, pdf: bool = False, **kwargs):
-            super().__init__(**kwargs)
-            self.pdf = pdf
-    
-        def split_text(self, text: str) -> List[str]:
-            if self.pdf:
-                text = re.sub(r"\n{3,}", "\n", text)
-                text = text.replace("\n\n", "")
-            sent_sep_pattern = re.compile(
-                '([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))')
-            sent_list = []
-            for ele in sent_sep_pattern.split(text):
-                if sent_sep_pattern.match(ele) and sent_list:
-                    sent_list[-1] += ele
-                elif ele:
-                    sent_list.append(ele)
-            return sent_list
-    
-    
-    TEXT_SPLITERS = {
-        "Character": CharacterTextSplitter,
-        "RecursiveCharacter": RecursiveCharacterTextSplitter,
-        "Markdown": MarkdownTextSplitter,
-        "Chinese": ChineseTextSplitter,
-    }
-    
-    
-    LOADERS = {
-        ".csv": (CSVLoader, {}),
-        ".doc": (UnstructuredWordDocumentLoader, {}),
-        ".docx": (UnstructuredWordDocumentLoader, {}),
-        ".enex": (EverNoteLoader, {}),
-        ".epub": (UnstructuredEPubLoader, {}),
-        ".html": (UnstructuredHTMLLoader, {}),
-        ".md": (UnstructuredMarkdownLoader, {}),
-        ".odt": (UnstructuredODTLoader, {}),
-        ".pdf": (PDFMinerLoader, {}),
-        ".ppt": (UnstructuredPowerPointLoader, {}),
-        ".pptx": (UnstructuredPowerPointLoader, {}),
-        ".txt": (TextLoader, {"encoding": "utf8"}),
-    }
-
-To reuse RAG pipeline of LangChain, we can wrapper around a LLM/chat
-model for LangChain, used for generating the response text. An OpenVINO
-compiled model can be run locally through the ``HuggingFacePipeline``
-class.
-
-.. code:: ipython3
-
-    from langchain.prompts import PromptTemplate
-    from langchain.vectorstores import Chroma
-    from langchain.chains import RetrievalQA
-    from langchain.docstore.document import Document
-    from threading import Event, Thread
-    import gradio as gr
-    import re
-    from uuid import uuid4
-    
-    
-    def load_single_document(file_path: str) -> List[Document]:
-        """
-        helper for loading a single document
-    
-        Params:
-          file_path: document path
-        Returns:
-          documents loaded
-    
-        """
-        ext = "." + file_path.rsplit(".", 1)[-1]
-        if ext in LOADERS:
-            loader_class, loader_args = LOADERS[ext]
-            loader = loader_class(file_path, **loader_args)
-            return loader.load()
-    
-        raise ValueError(f"File does not exist '{ext}'")
-    
-    
-    def default_partial_text_processor(partial_text: str, new_text: str):
-        """
-        helper for updating partially generated answer, used by default
-    
-        Params:
-          partial_text: text buffer for storing previosly generated text
-          new_text: text update for the current step
-        Returns:
-          updated text string
-    
-        """
-        partial_text += new_text
-        return partial_text
-    
-    
-    text_processor = llm_model_configuration.get(
-        "partial_text_processor", default_partial_text_processor
-    )
-    
-    
-    def build_retriever(docs, spliter_name, chunk_size, chunk_overlap, vector_search_top_k):
-        """
-        Initialize a vector database
-    
-        Params:
-          doc: orignal documents provided by user
-          chunk_size:  size of a single sentence chunk
-          chunk_overlap: overlap size between 2 chunks
-          vector_search_top_k: Vector search top k
-    
-        """
-        documents = []
-        for doc in docs:
-            documents.extend(load_single_document(doc.name))
-    
-        text_splitter = TEXT_SPLITERS[spliter_name](
-            chunk_size=chunk_size, chunk_overlap=chunk_overlap
-        )
-    
-        texts = text_splitter.split_documents(documents)
-    
-        db = Chroma.from_documents(texts, embedding)
-    
-        global retriever
-        retriever = db.as_retriever(search_kwargs={"k": vector_search_top_k})
-    
-        return "Retriever is Ready"
-    
-    
-    def user(message, history):
-        """
-        callback function for updating user messages in interface on submit button click
-    
-        Params:
-          message: current message
-          history: conversation history
-        Returns:
-          None
-        """
-        # Append the user's message to the conversation history
-        return "", history + [[message, ""]]
-    
-    
-    def bot(history, temperature, top_p, top_k, repetition_penalty, conversation_id):
-        """
-        callback function for running chatbot on submit button click
-    
-        Params:
-          history: conversation history
-          temperature:  parameter for control the level of creativity in AI-generated text.
-                        By adjusting the `temperature`, you can influence the AI model's probability distribution, making the text more focused or diverse.
-          top_p: parameter for control the range of tokens considered by the AI model based on their cumulative probability.
-          top_k: parameter for control the range of tokens considered by the AI model based on their cumulative probability, selecting number of tokens with highest probability.
-          repetition_penalty: parameter for penalizing tokens based on how frequently they occur in the text.
-          conversation_id: unique conversation identifier.
-    
-        """
-        streamer = TextIteratorStreamer(
-            tok, timeout=60.0, skip_prompt=True, skip_special_tokens=True
-        )
-        generate_kwargs = dict(
-            model=ov_model,
-            tokenizer=tok,
-            max_new_tokens=256,
-            temperature=temperature,
-            do_sample=temperature > 0.0,
-            top_p=top_p,
-            top_k=top_k,
-            repetition_penalty=repetition_penalty,
-            streamer=streamer,
-        )
-        if stop_tokens is not None:
-            generate_kwargs["stopping_criteria"] = StoppingCriteriaList(stop_tokens)
-            
-        pipe = pipeline("text-generation", **generate_kwargs)
-        llm = HuggingFacePipeline(pipeline=pipe)
-        
-        prompt = PromptTemplate.from_template(llm_model_configuration["rag_prompt_template"])
-        chain_type_kwargs = {"prompt": prompt}
-        rag_chain = RetrievalQA.from_chain_type(
-            llm=llm,
-            chain_type="stuff",
-            retriever=retriever,
-            chain_type_kwargs=chain_type_kwargs,
-        )
-        
-        stream_complete = Event()
-    
-        def infer(question):
-            rag_chain.invoke(question)
-            stream_complete.set()
-    
-        t1 = Thread(target=infer, args=(history[-1][0],))
-        t1.start()
-    
-        # Initialize an empty string to store the generated text
-        partial_text = ""
-        for new_text in streamer:
-            partial_text = text_processor(partial_text, new_text)
-            history[-1][1] = partial_text
-            yield history
-    
-    
-    def request_cancel():
-        ov_model.request.cancel()
-    
-    
-    def get_uuid():
-        """
-        universal unique identifier for thread
-        """
-        return str(uuid4())
-    
-    
-    with gr.Blocks(
-        theme=gr.themes.Soft(),
-        css=".disclaimer {font-variant-caps: all-small-caps;}",
-    ) as demo:
-        conversation_id = gr.State(get_uuid)
-        gr.Markdown("""
QA over Document
""")
-        gr.Markdown(f"""
Powered by OpenVINO and {llm_model_id.value} 
""")
-        with gr.Row():
-            with gr.Column(scale=1):
-                docs = gr.File(
-                    label="Load text files",
-                    file_count="multiple",
-                    file_types=[
-                        ".csv",
-                        ".doc",
-                        ".docx",
-                        ".enex",
-                        ".epub",
-                        ".html",
-                        ".md",
-                        ".odt",
-                        ".pdf",
-                        ".ppt",
-                        ".pptx",
-                        ".txt",
-                    ],
-                )
-                load_docs = gr.Button("Build Retriever")
-                retriever_argument = gr.Accordion("Retriever Configuration", open=False)
-                with retriever_argument:
-                    spliter = gr.Dropdown(
-                        ["Character", "RecursiveCharacter", "Markdown", "Chinese"],
-                        value="RecursiveCharacter",
-                        label="Text Spliter",
-                        info="Method used to splite the documents",
-                        multiselect=False,
-                    )
-    
-                    chunk_size = gr.Slider(
-                        label="Chunk size",
-                        value=1000,
-                        minimum=100,
-                        maximum=2000,
-                        step=50,
-                        interactive=True,
-                        info="Size of sentence chunk",
-                    )
-    
-                    chunk_overlap = gr.Slider(
-                        label="Chunk overlap",
-                        value=200,
-                        minimum=0,
-                        maximum=400,
-                        step=10,
-                        interactive=True,
-                        info=("Overlap between 2 chunks"),
-                    )
-    
-                    vector_search_top_k = gr.Slider(
-                        1,
-                        10,
-                        value=4,
-                        step=1,
-                        label="Vector search top k",
-                        interactive=True,
-                    )
-                langchain_status = gr.Textbox(
-                    label="Status", value="Retriever is Not ready", interactive=False
-                )
-                with gr.Accordion("Generation Configuration", open=False):
-                    with gr.Row():
-                        with gr.Column():
-                            with gr.Row():
-                                temperature = gr.Slider(
-                                    label="Temperature",
-                                    value=0.1,
-                                    minimum=0.0,
-                                    maximum=1.0,
-                                    step=0.1,
-                                    interactive=True,
-                                    info="Higher values produce more diverse outputs",
-                                )
-                        with gr.Column():
-                            with gr.Row():
-                                top_p = gr.Slider(
-                                    label="Top-p (nucleus sampling)",
-                                    value=1.0,
-                                    minimum=0.0,
-                                    maximum=1,
-                                    step=0.01,
-                                    interactive=True,
-                                    info=(
-                                        "Sample from the smallest possible set of tokens whose cumulative probability "
-                                        "exceeds top_p. Set to 1 to disable and sample from all tokens."
-                                    ),
-                                )
-                        with gr.Column():
-                            with gr.Row():
-                                top_k = gr.Slider(
-                                    label="Top-k",
-                                    value=50,
-                                    minimum=0.0,
-                                    maximum=200,
-                                    step=1,
-                                    interactive=True,
-                                    info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
-                                )
-                        with gr.Column():
-                            with gr.Row():
-                                repetition_penalty = gr.Slider(
-                                    label="Repetition Penalty",
-                                    value=1.1,
-                                    minimum=1.0,
-                                    maximum=2.0,
-                                    step=0.1,
-                                    interactive=True,
-                                    info="Penalize repetition — 1.0 to disable.",
-                                )
-            with gr.Column(scale=4):
-                chatbot = gr.Chatbot(height=600)
-                with gr.Row():
-                    with gr.Column():
-                        msg = gr.Textbox(
-                            label="Chat Message Box",
-                            placeholder="Chat Message Box",
-                            show_label=False,
-                            container=False,
-                        )
-                    with gr.Column():
-                        with gr.Row():
-                            submit = gr.Button("Submit")
-                            stop = gr.Button("Stop")
-                            clear = gr.Button("Clear")
-        load_docs.click(
-            build_retriever,
-            inputs=[docs, spliter, chunk_size, chunk_overlap, vector_search_top_k],
-            outputs=[langchain_status],
-            queue=False,
-        )
-        submit_event = msg.submit(
-            user, [msg, chatbot], [msg, chatbot], queue=False
-        ).then(bot, [chatbot, temperature, top_p, top_k, repetition_penalty, conversation_id], chatbot, queue=True)
-        submit_click_event = submit.click(
-            user, [msg, chatbot], [msg, chatbot], queue=False
-        ).then(bot, [chatbot, temperature, top_p, top_k, repetition_penalty, conversation_id], chatbot, queue=True)
-        stop.click(
-            fn=request_cancel,
-            inputs=None,
-            outputs=None,
-            cancels=[submit_event, submit_click_event],
-            queue=False,
-        )
-        clear.click(lambda: None, None, chatbot, queue=False)
-    
-    demo.queue()
-    # if you are launching remotely, specify server_name and server_port
-    #  demo.launch(server_name='your server name', server_port='server port in int')
-    # if you have any issue to launch on your platform, you can pass share=True to launch method:
-    # demo.launch(share=True)
-    # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
-    demo.launch()
-
-.. code:: ipython3
-
-    # please run this cell for stopping gradio interface
-    demo.close()
-
-
-.. parsed-literal::
-
-    Closing server running on port: 5579
-
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-Text Generation via Speculative Sampling, KV Caching, and OpenVINO™
-===================================================================
-
-As model sizes grow, Generative AI implementations require significant
-inference resources. This not only increases the cost per generation
-from a prompt, but also increases the power consumption used to serve
-such requests.
-
-Inference optimizations for text generation are essential for reducing
-costs and power consumption. When optimizing the inference process, the
-amount of time and energy required to generate text can be significantly
-reduced. This can lead to cost savings in terms of hardware and
-software, as well as reduced power consumption. Additionally, inference
-optimizations can help improve the accuracy of text generation as well
-as the speed at which it can be generated. This can lead to an improved
-user experience and increased efficiency in text-generation tasks. In
-summary, inference optimizations for text generation are essential to
-reduce costs and power consumption, while also improving the accuracy
-and speed of text generation.
-
-Another necessary condition is that the optimizations are compatible
-with each other. That is, implementing a certain optimization should not
-preclude other optimizations. There are several levels of optimizations
-that can provide significant speedup without “bumping into each other”
-in a way that will compromise overall efficiency.
-
-For details on this method, please refer to the paper by Chen et al,
-http://arxiv.org/abs/2302.01318. Additionally, there’s an interesting
-proof of correctness of speculative sampling (showing that the original
-distribution is preserved) by Leviathan et al,
-http://arxiv.org/abs/2211.17192
-
-Our blog article describing this implementation with OpenVino is
-available at openvino.ai
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Prerequisites <#prerequisites>`__
-
-   -  `Select inference device <#select-inference-device>`__
-
--  `Create autoregressive and speculative forms of sampling with KV
-   Cache
-   support <#create-autoregressive-and-speculative-forms-of-sampling-with-kv-cache-support>`__
-
-   -  `Setup imports <#setup-imports>`__
-   -  `Prepare autoregressive
-      sampling <#prepare-autoregressive-sampling>`__
-   -  `Prepare speculative sampling <#prepare-speculative-sampling>`__
-
--  `Main generation function <#main-generation-function>`__
-
-   -  `Download and Convert Model <#download-and-convert-model>`__
-
-Prerequisites
--------------
-
-
-
-First, we should install the `Hugging Face
-Optimum `__ library
-accelerated by OpenVINO integration. The Hugging Face Optimum Intel API
-is a high-level API that enables us to convert and quantize models from
-the Hugging Face Transformers library to the OpenVINO™ IR format. For
-more details, refer to the `Hugging Face Optimum Intel
-documentation `__.
-
-We will also need to install transformers (HuggingFace) and some other
-useful modules.
-
-.. code:: ipython3
-
-    %pip install -q --upgrade pip
-    %pip install -q --upgrade transformers torch gradio openvino accelerate onnx ipywidgets "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
-    %pip install -q "git+https://github.com/huggingface/optimum-intel.git"
-
-Select inference device
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Select the device from dropdown list for running inference using
-OpenVINO.
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    from openvino.runtime import Core
-    
-    core = Core()
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value='CPU',
-        description='Device:',
-        disabled=False,
-    )
-    
-    device
-
-Create autoregressive and speculative forms of sampling with KV Cache support
------------------------------------------------------------------------------
-
-
-
-Text generation is often done in an autoregressive fashion. We will all
-support a KV cache (aka Past Value Cache) in the code. Note that we are
-using greedy sampling. We do not adjust other text generation parameters
-(e.g. temperature) so keep this illustration of speculative sampling as
-simple and understandable as possible.
-
-Setup imports
-~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    import time
-    import numpy as np
-    import torch
-    import gradio as gr
-
-Prepare autoregressive sampling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    def max_fn(x):
-        x_max = torch.where(x > 0, x, torch.zeros_like(x))
-        return x_max / torch.sum(x_max)
-    
-    def autoregressive_sampling_with_pkv(x, model, N):
-        n = len(x)
-        T = n + N
-        input = x
-        past_kv = None
-    
-        while n < T:
-            res = model(input, attention_mask=torch.ones(input.size(), dtype=torch.long), past_key_values=past_kv)
-            model_out = torch.softmax(res.logits, dim=2)
-            past_kv = res.past_key_values
-            next_token = torch.reshape(torch.argmax(model_out[-1][-1]), (1, 1))
-            x = torch.cat((x, next_token), dim=1)
-            n += 1
-            input = next_token
-    
-        return x
-
-Prepare speculative sampling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
--  Step 1: With speculative sampling, we first generate K samples from
-   the draft model (in an autoregressive manner).
--  Step 2: These are now candidates to examine using the target model
-   (step 2) using a batch size of K.
--  Step 3: We now determine if the K candidates from the draft model are
-   acceptable based on the logits generated from the target model in
-   step 2.
--  Step 4: We can sample another token with no additional cost (assuming
-   that all the candidates were accepted).
-
-Regarding the acceptance criterion for step 3, we need to compare logits
-from the target model and compare with the draft model. If the ratio is
-high enough, it’s likely to be accepted (using a random number).
-
-.. code:: ipython3
-
-    def speculative_sampling_with_pkv(x, draft_model, target_model, N, K):
-        n = x.size(1)
-        T = n + N
-        target_past_kv = None
-        while n < T:
-            # Step 1: autoregressive decode of K candidate tokens from
-            # the draft model and get final p for this batch of candidates
-            x_draft = None
-            draft_past_kv = None
-            x_draft_input = x
-            p_cum = None
-            for _ in range(K):
-                res_draft = draft_model(x_draft_input, attention_mask=torch.ones(x_draft_input.size(), dtype=torch.long), past_key_values=draft_past_kv, use_cache=True)
-                p = res_draft.logits
-                p = torch.softmax(p, dim=2)
-                draft_past_kv = res_draft.past_key_values
-                next_token = torch.reshape(torch.argmax(p[-1][-1]), (1, 1))
-                x_draft_input = next_token
-                if p_cum is None:
-                    p_cum = p[:, -1].unsqueeze(1)
-                    x_draft = next_token
-                else:
-                    p_cum = torch.cat((p_cum, p), dim=1)
-                    x_draft = torch.cat((x_draft, next_token), dim=1)
-            # Step 2: target model forward passes on x_draft
-            if target_past_kv is None:
-                x_draft_target_input = torch.cat((x, x_draft), dim=1)
-            else:
-                x_draft_target_input = x_draft
-    
-            res = target_model(x_draft_target_input, attention_mask=torch.ones(x_draft_target_input.size(), dtype=torch.long), use_cache=False)
-            q = res.logits
-    
-            target_new_past_kv = res.past_key_values
-            # Step 3: append draft tokens based on acceptance-rejection criterion and resample a token on rejection
-            all_accepted = True
-            for k in range(K):
-                j = x_draft[0][k].item()
-    
-                q_item = q[-1][k][j].detach().numpy()
-                p_item = p_cum[-1][k][j].detach().numpy()
-    
-                if np.random.random() < min(1, (q_item / p_item)):  # accepted
-                    x = torch.cat((x, torch.tensor(j).reshape(1,1)), dim=1)
-                    n += 1
-                else:                                               # rejected
-                    q_p = max_fn(q[0][k] - p_cum[0][k])
-                    resampled_output = torch.argmax(q_p)      
-                    resampled_output = torch.reshape(resampled_output, (1,1))
-                    x = torch.cat((x, resampled_output), dim=1)
-                    n += 1
-                    all_accepted = False
-                    break
-                
-            target_past_kv = target_new_past_kv
-            # Step 4: if all draft tokens were accepted, sample a final token
-            if all_accepted:
-                x = torch.cat((x, torch.reshape(torch.argmax(q[-1][-1]), (1,1))), dim=1)
-                n += 1
-    
-        return x
-
-Main generation function
-------------------------
-
-
-
-Download and Convert Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Optimum Intel can be used to load optimized models from the `Hugging
-Face Hub `__ and
-create pipelines to run an inference with OpenVINO Runtime using Hugging
-Face APIs. The Optimum Inference models are API compatible with Hugging
-Face Transformers models. This means we just need to replace
-``AutoModelForXxx`` class with the corresponding ``OVModelForXxx``
-class.
-
-Below is an example of the Dolly model
-
-.. code:: diff
-
-   -from transformers import AutoModelForCausalLM
-   +from optimum.intel.openvino import OVModelForCausalLM
-   from transformers import AutoTokenizer, pipeline
-
-   model_id = "databricks/dolly-v2-3b"
-   -model = AutoModelForCausalLM.from_pretrained(model_id)
-   +model = OVModelForCausalLM.from_pretrained(model_id, from_transformers=True)
-
-Model class initialization starts with calling ``from_pretrained``
-method. When downloading and converting Transformers model, the
-parameter ``from_transformers=True`` should be added. We can save the
-converted model for the next usage with the ``save_pretrained`` method.
-Tokenizer class and pipelines API are compatible with Optimum models.
-
-.. code:: ipython3
-
-    from pathlib import Path
-    from transformers import AutoTokenizer
-    from optimum.intel.openvino import OVModelForCausalLM
-    
-    #  If you are on a large system with lots of memory, you can run a larger model like DollyV2
-    # draft_model_id = "databricks/dolly-v2-3b"
-    # draft_model_path = Path("dolly-v2-3b")
-    # target_model_id = "databricks/dolly-v2-12b"
-    # target_model_path = Path("dolly-v2-12b")
-    #  If you are on a system with limited memory, you can try the smaller GPT2 models
-    draft_model_id = "gpt2"
-    draft_model_path = Path("gpt2-local")
-    target_model_id = "gpt2-xl"
-    target_model_path = Path("gpt2-xl-local")
-    
-    target_tokenizer = AutoTokenizer.from_pretrained(target_model_id)
-    
-    current_device = device.value
-    
-    # Save local copies for subsequent runs
-    if draft_model_path.exists():
-        draft_ov_model = OVModelForCausalLM.from_pretrained(draft_model_path, device=current_device)
-    else:
-        draft_ov_model = OVModelForCausalLM.from_pretrained(draft_model_id, device=current_device, from_transformers=True)
-        draft_ov_model.save_pretrained(draft_model_path)
-    if target_model_path.exists():
-        target_ov_model = OVModelForCausalLM.from_pretrained(target_model_path, device=current_device)
-    else:
-        target_ov_model = OVModelForCausalLM.from_pretrained(target_model_id, device=current_device, from_transformers=True)
-        target_ov_model.save_pretrained(target_model_path)
-
-
-.. code:: ipython3
-
-    def main(
-        prompt: str = "Explain the difference between fission and fusion",
-        n_tokens_to_generate: int = 100,
-        K: int = 5,
-        seed: int = 5555,
-    ):
-        # seed numpy rng
-        np.random.seed(seed)
-        draft_model = draft_ov_model
-        target_model = target_ov_model
-        
-    
-        input_ids = target_tokenizer(prompt, return_tensors="pt")['input_ids']
-    
-        def run_autoregressive_sampling_fn(decode_fn, input_ids, **kwargs):
-            start = time.perf_counter()
-            output_ids = decode_fn(x=input_ids, **kwargs)
-            text = target_tokenizer.decode(output_ids[0], skip_special_tokens=True)
-            elapsed_time = time.perf_counter() - start
-            return text, elapsed_time
-    
-        def run_speculative_sampling_fn(decode_fn, input_ids, **kwargs):
-            start = time.perf_counter()
-            output_ids = decode_fn(x=input_ids, **kwargs)
-            text = target_tokenizer.decode(output_ids[0], skip_special_tokens=True)
-            elapsed_time = time.perf_counter() - start
-            return text, elapsed_time
-    
-        autoregressive_text, autoregressive_time = run_autoregressive_sampling_fn(
-            autoregressive_sampling_with_pkv,
-            input_ids,
-            model=target_model,
-            N=n_tokens_to_generate,
-        )
-    
-        speculative_text, speculative_time = run_speculative_sampling_fn(
-            speculative_sampling_with_pkv,
-            input_ids,
-            target_model=target_model,
-            draft_model=draft_model,
-            N=n_tokens_to_generate,
-            K=K,
-        )
-    
-    #   Format results for output in gradio
-        out = "\n" + "Autoregressive Decode" + "\n" + "---------------------" + "\n"
-        out = out + f"Time = {autoregressive_time:.2f}s" + "\n" + f"Text = {autoregressive_text}" + "\n"
-        out = out + "\n" + "Speculative Decode" + "\n" + "------------------" + "\n"
-        out = out + f"Time = {speculative_time:.2f}s" + "\n" + f"Text = {speculative_text}"
-        return out
-    
-    if __name__ == "__main__":
-        with gr.Blocks() as demo:
-            gr.Markdown(
-                """
-                # Speculative Sampling Demo
-                ## The output will show a comparison of Autoregressive Sampling vs Speculative Sampling
-                - Target Model: Dolly V2 12B
-                - Draft Model: Dolly V2 3B
-                - K = 5
-                > Some improvements can be made to acceptance criterion and adjusting temperature to improve text quality.
-                """)
-            with gr.Row():
-                inp = gr.Textbox(placeholder="THIS CANNOT BE EMPTY", label="Input Prompt")
-                out = gr.Textbox(label="Output")
-            btn = gr.Button("Run")
-            btn.click(fn=main, inputs=inp, outputs=out)
-        demo.launch()
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diff --git a/docs/notebooks/283-photo-maker-with-output.rst b/docs/notebooks/283-photo-maker-with-output.rst
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+++ /dev/null
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-Text-to-image generation using PhotoMaker and OpenVINO
-======================================================
-
-PhotoMaker is an efficient personalized text-to-image generation method,
-which mainly encodes an arbitrary number of input ID images into a stack
-ID embedding for preserving ID information. Such an embedding, serving
-as a unified ID representation, can not only encapsulate the
-characteristics of the same input ID comprehensively, but also
-accommodate the characteristics of different IDs for subsequent
-integration. This paves the way for more intriguing and practically
-valuable applications. Users can input one or a few face photos, along
-with a text prompt, to receive a customized photo or painting (no
-training required!). Additionally, this model can be adapted to any base
-model based on ``SDXL`` or used in conjunction with other ``LoRA``
-modules.More details about PhotoMaker can be found in the `technical
-report `__.
-
-This notebook explores how to speed up PhotoMaker pipeline using
-OpenVINO.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `PhotoMaker pipeline
-   introduction <#photomaker-pipeline-introduction>`__
--  `Prerequisites <#prerequisites>`__
--  `Load original pipeline and prepare models for
-   conversion <#load-original-pipeline-and-prepare-models-for-conversion>`__
--  `Convert models to OpenVINO Intermediate representation (IR)
-   format <#convert-models-to-openvino-intermediate-representation-ir-format>`__
-
-   -  `ID Encoder <#id-encoder>`__
-   -  `Text Encoder <#text-encoder>`__
-   -  `U-Net <#u-net>`__
-   -  `VAE Decoder <#vae-decoder>`__
-
--  `Prepare Inference pipeline <#prepare-inference-pipeline>`__
-
-   -  `Select inference device for Stable Diffusion
-      pipeline <#select-inference-device-for-stable-diffusion-pipeline>`__
-   -  `Compile models and create their Wrappers for
-      inference <#compile-models-and-create-their-wrappers-for-inference>`__
-
--  `Running Text-to-Image Generation with
-   OpenVINO <#running-text-to-image-generation-with-openvino>`__
--  `Interactive Demo <#interactive-demo>`__
-
-PhotoMaker pipeline introduction
---------------------------------
-
-
-
-For the proposed PhotoMaker, we first obtain the text embedding and
-image embeddings from ``text encoder(s)`` and ``image(ID) encoder``,
-respectively. Then, we extract the fused embedding by merging the
-corresponding class embedding (e.g., man and woman) and each image
-embedding. Next, we concatenate all fused embeddings along the length
-dimension to form the stacked ID embedding. Finally, we feed the stacked
-ID embedding to all cross-attention layers for adaptively merging the ID
-content in the ``diffusion model``. Note that although we use images of
-the same ID with the masked background during training, we can directly
-input images of different IDs without background distortion to create a
-new ID during inference.
-
-Prerequisites
--------------
-
-
-
-Clone PhotoMaker repository
-
-.. code:: ipython3
-
-    from pathlib import Path
-    
-    if not Path("PhotoMaker").exists():
-        !git clone https://github.com/TencentARC/PhotoMaker.git
-
-
-.. parsed-literal::
-
-    Cloning into 'PhotoMaker'...
-
-
-.. parsed-literal::
-
-    remote: Enumerating objects: 236, done.
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-remote: Counting objects: 100% (145/145), done.
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-
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-
-
-Install required packages
-
-.. code:: ipython3
-
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
-    transformers diffusers gradio "openvino>=2024.0.0" torchvision
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Prepare PyTorch models
-
-.. code:: ipython3
-
-    adapter_id = "TencentARC/PhotoMaker"
-    base_model_id = "SG161222/RealVisXL_V3.0"
-    
-    TEXT_ENCODER_OV_PATH = Path("model/text_encoder.xml")
-    TEXT_ENCODER_2_OV_PATH = Path("model/text_encoder_2.xml")
-    UNET_OV_PATH = Path("model/unet.xml")
-    ID_ENCODER_OV_PATH = Path("model/id_encoder.xml")
-    VAE_DECODER_OV_PATH = Path("model/vae_decoder.xml")
-
-Load original pipeline and prepare models for conversion
---------------------------------------------------------
-
-
-
-For exporting each PyTorch model, we will download the ``ID encoder``
-weight, ``LoRa`` weight from HuggingFace hub, then using the
-``PhotoMakerStableDiffusionXLPipeline`` object from repository of
-PhotoMaker to generate the original PhotoMaker pipeline.
-
-.. code:: ipython3
-
-    import torch
-    import numpy as np
-    import os
-    from PIL import Image
-    from pathlib import Path
-    from PhotoMaker.photomaker.model import PhotoMakerIDEncoder
-    from PhotoMaker.photomaker.pipeline import PhotoMakerStableDiffusionXLPipeline
-    from diffusers import EulerDiscreteScheduler
-    import gc
-    
-    trigger_word = "img"
-    
-    def load_original_pytorch_pipeline_components(photomaker_path: str, base_model_id: str):
-        # Load base model
-        pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
-            base_model_id, use_safetensors=True
-        ).to("cpu")
-    
-        # Load PhotoMaker checkpoint
-        pipe.load_photomaker_adapter(
-            os.path.dirname(photomaker_path),
-            subfolder="",
-            weight_name=os.path.basename(photomaker_path),
-            trigger_word=trigger_word,
-        )
-        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
-        pipe.fuse_lora()
-        gc.collect()
-        return pipe
-
-
-.. parsed-literal::
-
-    2024-03-13 00:39:33.433623: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:39:33.468677: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-
-
-.. parsed-literal::
-
-    2024-03-13 00:39:34.115959: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-      torch.utils._pytree._register_pytree_node(
-
-
-.. code:: ipython3
-
-    from huggingface_hub import hf_hub_download
-    
-    photomaker_path = hf_hub_download(
-        repo_id=adapter_id, filename="photomaker-v1.bin", repo_type="model"
-    )
-    
-    pipe = load_original_pytorch_pipeline_components(
-        photomaker_path, base_model_id
-    )
-
-
-
-.. parsed-literal::
-
-    Loading pipeline components...:   0%|          | 0/7 [00:00 1 or self.sliding_window is not None:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if past_key_values_length > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:289: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
-
-
-
-
-.. parsed-literal::
-
-    3376
-
-
-
-U-Net
-~~~~~
-
-
-
-The process of U-Net model conversion remains the same, like for
-original Stable Diffusion XL model.
-
-.. code:: ipython3
-
-    unet = pipe.unet
-    unet.eval()
-    
-    class UnetWrapper(torch.nn.Module):
-        def __init__(self, unet):
-            super().__init__()
-            self.unet = unet
-    
-        def forward(
-            self,
-            sample=None,
-            timestep=None,
-            encoder_hidden_states=None,
-            text_embeds=None,
-            time_ids=None,
-        ):
-            return self.unet.forward(
-                sample,
-                timestep,
-                encoder_hidden_states,
-                added_cond_kwargs={"text_embeds": text_embeds, "time_ids": time_ids},
-            )
-    
-    
-    inputs = {
-        "sample": torch.rand([2, 4, 128, 128], dtype=torch.float32),
-        "timestep": torch.from_numpy(np.array(1, dtype=float)),
-        "encoder_hidden_states": torch.rand([2, 77, 2048], dtype=torch.float32),
-        "text_embeds": torch.rand([2, 1280], dtype=torch.float32),
-        "time_ids": torch.rand([2, 6], dtype=torch.float32),
-    }
-    
-    input_info = prepare_input_info(inputs)
-    
-    w_unet = UnetWrapper(unet)
-    convert(w_unet, UNET_OV_PATH, inputs, input_info)
-    
-    del w_unet, unet
-    gc.collect()
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/unets/unet_2d_condition.py:924: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if dim % default_overall_up_factor != 0:
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:135: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:144: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert hidden_states.shape[1] == self.channels
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:149: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:165: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if hidden_states.shape[0] >= 64:
-
-
-
-
-.. parsed-literal::
-
-    11629
-
-
-
-VAE Decoder
-~~~~~~~~~~~
-
-
-
-The VAE model has two parts, an encoder and a decoder. The encoder is
-used to convert the image into a low dimensional latent representation,
-which will serve as the input to the U-Net model. The decoder,
-conversely, transforms the latent representation back into an image.
-
-When running Text-to-Image pipeline, we will see that we only need the
-VAE decoder.
-
-.. code:: ipython3
-
-    vae_decoder = pipe.vae
-    vae_decoder.eval()
-    
-    class VAEDecoderWrapper(torch.nn.Module):
-        def __init__(self, vae_decoder):
-            super().__init__()
-            self.vae = vae_decoder
-    
-        def forward(self, latents):
-            return self.vae.decode(latents)
-    
-    
-    w_vae_decoder = VAEDecoderWrapper(vae_decoder)
-    inputs = torch.zeros((1, 4, 128, 128))
-    
-    convert(w_vae_decoder, VAE_DECODER_OV_PATH, inputs, input_info=[1, 4, 128, 128])
-    
-    del w_vae_decoder, vae_decoder
-    gc.collect()
-
-
-
-
-.. parsed-literal::
-
-    1534
-
-
-
-Prepare Inference pipeline
---------------------------
-
-
-
-In this example, we will reuse ``PhotoMakerStableDiffusionXLPipeline``
-pipeline to generate the image with OpenVINO, so each model’s object in
-this pipeline should be replaced with new OpenVINO model object.
-
-Select inference device for Stable Diffusion pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    core = ov.Core()
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="CPU",
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')
-
-
-
-Compile models and create their Wrappers for inference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-To access original PhotoMaker workflow, we have to create a new wrapper
-for each OpenVINO compiled model. For matching original pipeline, part
-of OpenVINO model wrapper’s attributes should be reused from original
-model objects and inference output must be converted from numpy to
-``torch.tensor``.
-
-
-
-.. code:: ipython3
-
-    compiled_id_encoder = core.compile_model(ID_ENCODER_OV_PATH, device.value)
-    compiled_unet = core.compile_model(UNET_OV_PATH, device.value)
-    compiled_text_encoder = core.compile_model(TEXT_ENCODER_OV_PATH, device.value)
-    compiled_text_encoder_2 = core.compile_model(TEXT_ENCODER_2_OV_PATH, device.value)
-    compiled_vae_decoder = core.compile_model(VAE_DECODER_OV_PATH, device.value)
-
-.. code:: ipython3
-
-    from collections import namedtuple
-    
-    
-    class OVIDEncoderWrapper(PhotoMakerIDEncoder):
-        dtype = torch.float32  # accessed in the original workflow
-    
-        def __init__(self, id_encoder, orig_id_encoder):
-            super().__init__()
-            self.id_encoder = id_encoder
-            self.modules = orig_id_encoder.modules  # accessed in the original workflow
-            self.config = orig_id_encoder.config  # accessed in the original workflow
-    
-        def __call__(
-            self,
-            *args,
-        ):
-            id_pixel_values, prompt_embeds, class_tokens_mask = args
-            inputs = {
-                "id_pixel_values": id_pixel_values,
-                "prompt_embeds": prompt_embeds,
-                "class_tokens_mask": class_tokens_mask,
-            }
-            output = self.id_encoder(inputs)[0]
-            return torch.from_numpy(output)
-
-.. code:: ipython3
-
-    class OVTextEncoderWrapper:
-        dtype = torch.float32  # accessed in the original workflow
-    
-        def __init__(self, text_encoder, orig_text_encoder):
-            self.text_encoder = text_encoder
-            self.modules = orig_text_encoder.modules  # accessed in the original workflow
-            self.config = orig_text_encoder.config  # accessed in the original workflow
-    
-        def __call__(self, input_ids, **kwargs):
-            inputs = {"input_ids": input_ids}
-            output = self.text_encoder(inputs)
-    
-            hidden_states = []
-            hidden_states_len = len(output)
-            for i in range(1, hidden_states_len):
-                hidden_states.append(torch.from_numpy(output[i]))
-    
-            BaseModelOutputWithPooling = namedtuple(
-                "BaseModelOutputWithPooling", "last_hidden_state hidden_states"
-            )
-            output = BaseModelOutputWithPooling(torch.from_numpy(output[0]), hidden_states)
-            return output
-
-.. code:: ipython3
-
-    class OVUnetWrapper:
-        def __init__(self, unet, unet_orig):
-            self.unet = unet
-            self.config = unet_orig.config  # accessed in the original workflow
-            self.add_embedding = (
-                unet_orig.add_embedding
-            )  # accessed in the original workflow
-    
-        def __call__(self, *args, **kwargs):
-            latent_model_input, t = args
-            inputs = {
-                "sample": latent_model_input,
-                "timestep": t,
-                "encoder_hidden_states": kwargs["encoder_hidden_states"],
-                "text_embeds": kwargs["added_cond_kwargs"]["text_embeds"],
-                "time_ids": kwargs["added_cond_kwargs"]["time_ids"],
-            }
-    
-            output = self.unet(inputs)
-    
-            return [torch.from_numpy(output[0])]
-
-.. code:: ipython3
-
-    class OVVAEDecoderWrapper:
-        dtype = torch.float32  # accessed in the original workflow
-    
-        def __init__(self, vae, vae_orig):
-            self.vae = vae
-            self.config = vae_orig.config  # accessed in the original workflow
-    
-        def decode(self, latents, return_dict=False):
-            output = self.vae(latents)[0]
-            output = torch.from_numpy(output)
-    
-            return [output]
-
-Replace the PyTorch model objects in original pipeline with OpenVINO
-models
-
-.. code:: ipython3
-
-    pipe.id_encoder = OVIDEncoderWrapper(compiled_id_encoder, pipe.id_encoder)
-    pipe.unet = OVUnetWrapper(compiled_unet, pipe.unet)
-    pipe.text_encoder = OVTextEncoderWrapper(compiled_text_encoder, pipe.text_encoder)
-    pipe.text_encoder_2 = OVTextEncoderWrapper(compiled_text_encoder_2, pipe.text_encoder_2)
-    pipe.vae = OVVAEDecoderWrapper(compiled_vae_decoder, pipe.vae)
-
-Running Text-to-Image Generation with OpenVINO
-----------------------------------------------
-
-
-
-.. code:: ipython3
-
-    from diffusers.utils import load_image
-    
-    prompt = "sci-fi, closeup portrait photo of a man img in Iron man suit, face"
-    negative_prompt = "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth"
-    generator = torch.Generator("cpu").manual_seed(42)
-    
-    input_id_images = []
-    original_image = load_image("./PhotoMaker/examples/newton_man/newton_0.jpg")
-    input_id_images.append(original_image)
-    
-    ## Parameter setting
-    num_steps = 20
-    style_strength_ratio = 20
-    start_merge_step = int(float(style_strength_ratio) / 100 * num_steps)
-    if start_merge_step > 30:
-        start_merge_step = 30
-    
-    images = pipe(
-        prompt=prompt,
-        input_id_images=input_id_images,
-        negative_prompt=negative_prompt,
-        num_images_per_prompt=1,
-        num_inference_steps=num_steps,
-        start_merge_step=start_merge_step,
-        generator=generator,
-    ).images
-
-
-
-.. parsed-literal::
-
-      0%|          | 0/20 [00:00 30:
-            start_merge_step = 30
-        result = pipe(
-            text_promt,
-            input_id_images=input_image,
-            negative_prompt=neg_prompt,
-            num_inference_steps=num_steps,
-            num_images_per_prompt=1,
-            start_merge_step=start_merge_step,
-            generator=torch.Generator().manual_seed(seed),
-            height=1024,
-            width=1024,
-        ).images[0]
-    
-        return result
-    
-    
-    with gr.Blocks() as demo:
-        with gr.Column():
-            with gr.Row():
-                input_image = gr.Image(label="Your image", sources=[
-                                       "upload"], type="pil")
-                output_image = gr.Image(label="Generated Images", type="pil")
-            positive_input = gr.Textbox(
-                label=f"Text prompt, Trigger words is '{trigger_word}'")
-            neg_input = gr.Textbox(label="Negative prompt")
-            with gr.Row():
-                seed_input = gr.Slider(0, 10_000_000, value=42, label="Seed")
-                steps_input = gr.Slider(
-                    label="Steps", value=10, minimum=5, maximum=50, step=1
-                )
-                style_strength_ratio_input = gr.Slider(
-                    label="Style strength ratio", value=20, minimum=5, maximum=100, step=5
-                )
-                btn = gr.Button()
-            btn.click(
-                generate_from_text,
-                [
-                    positive_input,
-                    input_image,
-                    neg_input,
-                    seed_input,
-                    steps_input,
-                    style_strength_ratio_input,
-                ],
-                output_image,
-            )
-            gr.Examples(
-                [
-                    [prompt, negative_prompt],
-                    [
-                        "A woman img wearing a Christmas hat",
-                        negative_prompt,
-                    ],
-                    [
-                        "A man img in a helmet and vest riding a motorcycle",
-                        negative_prompt,
-                    ],
-                    [
-                        "photo of a middle-aged man img sitting on a plush leather couch, and watching television show",
-                        negative_prompt,
-                    ],
-                    [
-                        "photo of a skilled doctor img in a pristine white lab coat enjoying a delicious meal in a sophisticated dining room",
-                        negative_prompt,
-                    ],
-                    [
-                        "photo of superman img flying through a vibrant sunset sky, with his cape billowing in the wind",
-                        negative_prompt,
-                    ],
-                ],
-                [positive_input, neg_input],
-            )
-    
-    
-    demo.queue().launch()
-    # if you are launching remotely, specify server_name and server_port
-    # demo.launch(server_name='your server name', server_port='server port in int')
-    # Read more in the docs: https://gradio.app/docs/
-
-
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7860
-    
-    To create a public link, set `share=True` in `launch()`.
-
-
-
-
-
-
-
-
-
-
-
-
-    
-
-
-
-.. code:: ipython3
-
-    demo.close()
-
-
-.. parsed-literal::
-
-    Closing server running on port: 7860
-
diff --git a/docs/notebooks/283-photo-maker-with-output_files/283-photo-maker-with-output_33_0.png b/docs/notebooks/283-photo-maker-with-output_files/283-photo-maker-with-output_33_0.png
deleted file mode 100644
index f5fc221fbbe..00000000000
--- a/docs/notebooks/283-photo-maker-with-output_files/283-photo-maker-with-output_33_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8727ce5f57d91a85a1d27a08ca58ada5f465f4ca1cfaee31a08104a3b64e69f4
-size 357214
diff --git a/docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_36_0.png b/docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_36_0.png
deleted file mode 100644
index fb0e77db578..00000000000
--- a/docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_36_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f6d7eb79cd44933b7ff1595904860d180193172e94aba297a48e1fdc99f94491
-size 495875
diff --git a/docs/notebooks/288-grounded-segment-anything-with-output.rst b/docs/notebooks/288-grounded-segment-anything-with-output.rst
deleted file mode 100644
index 3f5bd18bdab..00000000000
--- a/docs/notebooks/288-grounded-segment-anything-with-output.rst
+++ /dev/null
@@ -1,1762 +0,0 @@
-Object detection and masking from prompts with GroundedSAM (GroundingDINO + SAM) and OpenVINO
-=============================================================================================
-
-In this notebook, we provide the OpenVINO™ optimization for the
-combination of GroundingDINO + SAM =
-`GroundedSAM `__
-on Intel® platforms.
-
-GroundedSAM aims to detect and segment anything with text inputs.
-GroundingDINO is a language-guided query selection module to enhance
-object detection using input text. It selects relevant features from
-image and text inputs and returns predicted boxes with detections. The
-Segment Anything Model (SAM) produces high quality object masks from
-input prompts such as points or boxes, and it can be used to generate
-masks for all objects in an image. We use box predictions from
-GroundingDINO to mask the original image.
-
-More details about the model can be found in the
-`paper `__, and the official
-`repository `__.
-
-.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/5703039/3c19063a-c60a-4d5d-b534-e1305a854180
-   :alt: image
-
-   image
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Clone repository and install
-   requirements <#clone-repository-and-install-requirements>`__
--  `Download checkpoints and load PyTorch
-   model <#download-checkpoints-and-load-pytorch-model>`__
--  `Convert GroundingDINO to OpenVINO IR
-   format <#convert-groundingdino-to-openvino-ir-format>`__
--  `Run OpenVINO optimized
-   GroundingDINO <#run-openvino-optimized-groundingdino>`__
--  `Convert SAM to OpenVINO IR <#convert-sam-to-openvino-ir>`__
--  `Combine GroundingDINO + SAM
-   (GroundedSAM) <#combine-groundingdino--sam-groundedsam>`__
--  `Interactive GroundedSAM <#interactive-groundedsam>`__
--  `Cleanup <#cleanup>`__
-
-Clone repositories and install requirements
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    %pip install -q timm --extra-index-url https://download.pytorch.org/whl/cpu  # is needed for torch
-    %pip install -q "openvino>=2024.0" opencv-python supervision transformers yapf pycocotools addict gradio
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-
-
-.. parsed-literal::
-
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    paddleclas 2.5.1 requires faiss-cpu==1.7.1.post2, but you have faiss-cpu 1.8.0 which is incompatible.
-    paddleclas 2.5.1 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.
-    ppgan 2.1.0 requires imageio==2.9.0, but you have imageio 2.34.0 which is incompatible.
-    ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.
-    ppgan 2.1.0 requires opencv-python<=4.6.0.66, but you have opencv-python 4.9.0.80 which is incompatible.
-    pyannote-audio 2.0.1 requires torchaudio<1.0,>=0.10, but you have torchaudio 2.2.1+cpu which is incompatible.
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-For faster computation and to limit RAM by default we use
-``EfficientSAM`` for segmentation, but if you wish more accurate
-segmentation you can select vanilla ``SAM``.
-
-.. code:: ipython3
-
-    import ipywidgets
-
-    sam_type_widget = ipywidgets.Dropdown(
-        options=['EfficientSAM', 'SAM'],
-        value='EfficientSAM',
-        description='Segment Anything type:',
-    )
-    sam_type_widget
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Segment Anything type:', options=('EfficientSAM', 'SAM'), value='EfficientSAM')
-
-
-
-.. code:: ipython3
-
-    use_efficient_sam = sam_type_widget.value == 'EfficientSAM'
-
-.. code:: ipython3
-
-    from pathlib import Path
-    import sys
-    import os
-
-    repo_dir = Path("Grounded-Segment-Anything")
-    ground_dino_dir = Path('GroundingDINO')
-    efficient_sam_dir = Path('EfficientSAM')
-
-    # we use grounding dino from a fork which contains modifications that allow conversion to OpenVINO IR format
-    if not ground_dino_dir.exists():
-        !git clone https://github.com/wenyi5608/GroundingDINO/
-    if use_efficient_sam and not efficient_sam_dir.exists():
-        !git clone https://github.com/yformer/EfficientSAM
-    if not use_efficient_sam and not repo_dir.exists():
-        !git clone https://github.com/IDEA-Research/Grounded-Segment-Anything
-
-    # append to sys.path so that modules from the repo could be imported
-    sys.path.append(str(ground_dino_dir))
-    sys.path.append(str('EfficientSAM' if use_efficient_sam else repo_dir / 'segment_anything'))
-
-
-.. parsed-literal::
-
-    Cloning into 'GroundingDINO'...
-
-
-.. parsed-literal::
-
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-remote: Counting objects: 100% (177/177), done.
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-remote: Compressing objects: 100% (64/64), done.
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-
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-
-.. parsed-literal::
-
-    Receiving objects:  50% (190/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  51% (194/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  52% (198/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  53% (201/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  54% (205/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  55% (209/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  56% (213/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  57% (217/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  58% (220/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  59% (224/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  60% (228/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  61% (232/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  62% (235/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  63% (239/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  64% (243/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  65% (247/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  66% (251/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  67% (254/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  68% (258/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  69% (262/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  70% (266/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  71% (270/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  72% (273/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  73% (277/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  74% (281/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  75% (285/379), 11.64 MiB | 21.59 MiB/s
-remote: Total 379 (delta 137), reused 113 (delta 113), pack-reused 202
-    Receiving objects:  76% (289/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  77% (292/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  78% (296/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  79% (300/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  80% (304/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  81% (307/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  82% (311/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  83% (315/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  84% (319/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  85% (323/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  86% (326/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  87% (330/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  88% (334/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  89% (338/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  90% (342/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  91% (345/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  92% (349/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  93% (353/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  94% (357/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  95% (361/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  96% (364/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  97% (368/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  98% (372/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects:  99% (376/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects: 100% (379/379), 11.64 MiB | 21.59 MiB/s
-Receiving objects: 100% (379/379), 14.03 MiB | 21.38 MiB/s, done.
-    Resolving deltas:   0% (0/195)
-Resolving deltas:   1% (2/195)
-Resolving deltas:   3% (7/195)
-Resolving deltas:   7% (14/195)
-Resolving deltas:   8% (16/195)
-Resolving deltas:  16% (32/195)
-Resolving deltas:  17% (35/195)
-Resolving deltas:  18% (37/195)
-Resolving deltas:  19% (38/195)
-Resolving deltas:  20% (40/195)
-Resolving deltas:  21% (41/195)
-Resolving deltas:  22% (43/195)
-Resolving deltas:  27% (53/195)
-Resolving deltas:  42% (83/195)
-Resolving deltas:  44% (87/195)
-Resolving deltas:  47% (93/195)
-Resolving deltas:  55% (108/195)
-Resolving deltas:  56% (111/195)
-Resolving deltas:  57% (112/195)
-Resolving deltas:  58% (114/195)
-Resolving deltas:  60% (117/195)
-Resolving deltas:  61% (119/195)
-Resolving deltas:  63% (123/195)
-Resolving deltas:  65% (127/195)
-Resolving deltas:  69% (135/195)
-Resolving deltas:  70% (138/195)
-Resolving deltas:  71% (139/195)
-Resolving deltas:  72% (142/195)
-Resolving deltas:  75% (148/195)
-Resolving deltas:  76% (149/195)
-Resolving deltas:  78% (153/195)
-Resolving deltas:  80% (157/195)
-Resolving deltas:  82% (160/195)
-Resolving deltas: 100% (195/195)
-Resolving deltas: 100% (195/195), done.
-
-
-.. parsed-literal::
-
-    Cloning into 'EfficientSAM'...
-
-
-.. parsed-literal::
-
-    remote: Enumerating objects: 424, done.
-    remote: Counting objects:   0% (1/140)
-remote: Counting objects:   1% (2/140)
-remote: Counting objects:   2% (3/140)
-remote: Counting objects:   3% (5/140)
-remote: Counting objects:   4% (6/140)
-remote: Counting objects:   5% (7/140)
-remote: Counting objects:   6% (9/140)
-remote: Counting objects:   7% (10/140)
-remote: Counting objects:   8% (12/140)
-remote: Counting objects:   9% (13/140)
-remote: Counting objects:  10% (14/140)
-remote: Counting objects:  11% (16/140)
-remote: Counting objects:  12% (17/140)
-remote: Counting objects:  13% (19/140)
-remote: Counting objects:  14% (20/140)
-remote: Counting objects:  15% (21/140)
-remote: Counting objects:  16% (23/140)
-remote: Counting objects:  17% (24/140)
-remote: Counting objects:  18% (26/140)
-remote: Counting objects:  19% (27/140)
-remote: Counting objects:  20% (28/140)
-remote: Counting objects:  21% (30/140)
-remote: Counting objects:  22% (31/140)
-remote: Counting objects:  23% (33/140)
-remote: Counting objects:  24% (34/140)
-remote: Counting objects:  25% (35/140)
-remote: Counting objects:  26% (37/140)
-remote: Counting objects:  27% (38/140)
-remote: Counting objects:  28% (40/140)
-remote: Counting objects:  29% (41/140)
-remote: Counting objects:  30% (42/140)
-remote: Counting objects:  31% (44/140)
-remote: Counting objects:  32% (45/140)
-remote: Counting objects:  33% (47/140)
-remote: Counting objects:  34% (48/140)
-remote: Counting objects:  35% (49/140)
-remote: Counting objects:  36% (51/140)
-remote: Counting objects:  37% (52/140)
-remote: Counting objects:  38% (54/140)
-remote: Counting objects:  39% (55/140)
-remote: Counting objects:  40% (56/140)
-remote: Counting objects:  41% (58/140)
-remote: Counting objects:  42% (59/140)
-remote: Counting objects:  43% (61/140)
-remote: Counting objects:  44% (62/140)
-remote: Counting objects:  45% (63/140)
-remote: Counting objects:  46% (65/140)
-remote: Counting objects:  47% (66/140)
-remote: Counting objects:  48% (68/140)
-remote: Counting objects:  49% (69/140)
-remote: Counting objects:  50% (70/140)
-remote: Counting objects:  51% (72/140)
-remote: Counting objects:  52% (73/140)
-remote: Counting objects:  53% (75/140)
-remote: Counting objects:  54% (76/140)
-remote: Counting objects:  55% (77/140)
-remote: Counting objects:  56% (79/140)
-remote: Counting objects:  57% (80/140)
-remote: Counting objects:  58% (82/140)
-remote: Counting objects:  59% (83/140)
-remote: Counting objects:  60% (84/140)
-remote: Counting objects:  61% (86/140)
-remote: Counting objects:  62% (87/140)
-remote: Counting objects:  63% (89/140)
-remote: Counting objects:  64% (90/140)
-remote: Counting objects:  65% (91/140)
-remote: Counting objects:  66% (93/140)
-remote: Counting objects:  67% (94/140)
-remote: Counting objects:  68% (96/140)
-remote: Counting objects:  69% (97/140)
-remote: Counting objects:  70% (98/140)
-remote: Counting objects:  71% (100/140)
-remote: Counting objects:  72% (101/140)
-remote: Counting objects:  73% (103/140)
-remote: Counting objects:  74% (104/140)
-remote: Counting objects:  75% (105/140)
-remote: Counting objects:  76% (107/140)
-remote: Counting objects:  77% (108/140)
-remote: Counting objects:  78% (110/140)
-remote: Counting objects:  79% (111/140)
-remote: Counting objects:  80% (112/140)
-remote: Counting objects:  81% (114/140)
-remote: Counting objects:  82% (115/140)
-remote: Counting objects:  83% (117/140)
-remote: Counting objects:  84% (118/140)
-remote: Counting objects:  85% (119/140)
-remote: Counting objects:  86% (121/140)
-remote: Counting objects:  87% (122/140)
-
-.. parsed-literal::
-
-    remote: Counting objects:  88% (124/140)
-remote: Counting objects:  89% (125/140)
-remote: Counting objects:  90% (126/140)
-remote: Counting objects:  91% (128/140)
-remote: Counting objects:  92% (129/140)
-remote: Counting objects:  93% (131/140)
-remote: Counting objects:  94% (132/140)
-remote: Counting objects:  95% (133/140)
-remote: Counting objects:  96% (135/140)
-remote: Counting objects:  97% (136/140)
-remote: Counting objects:  98% (138/140)
-remote: Counting objects:  99% (139/140)
-remote: Counting objects: 100% (140/140)
-remote: Counting objects: 100% (140/140), done.
-    remote: Compressing objects:   1% (1/85)
-remote: Compressing objects:   2% (2/85)
-remote: Compressing objects:   3% (3/85)
-remote: Compressing objects:   4% (4/85)
-remote: Compressing objects:   5% (5/85)
-remote: Compressing objects:   7% (6/85)
-remote: Compressing objects:   8% (7/85)
-remote: Compressing objects:   9% (8/85)
-remote: Compressing objects:  10% (9/85)
-remote: Compressing objects:  11% (10/85)
-
-.. parsed-literal::
-
-    remote: Compressing objects:  12% (11/85)
-remote: Compressing objects:  14% (12/85)
-remote: Compressing objects:  15% (13/85)
-remote: Compressing objects:  16% (14/85)
-remote: Compressing objects:  17% (15/85)
-remote: Compressing objects:  18% (16/85)
-remote: Compressing objects:  20% (17/85)
-remote: Compressing objects:  21% (18/85)
-remote: Compressing objects:  22% (19/85)
-remote: Compressing objects:  23% (20/85)
-remote: Compressing objects:  24% (21/85)
-remote: Compressing objects:  25% (22/85)
-remote: Compressing objects:  27% (23/85)
-remote: Compressing objects:  28% (24/85)
-remote: Compressing objects:  29% (25/85)
-remote: Compressing objects:  30% (26/85)
-remote: Compressing objects:  31% (27/85)
-remote: Compressing objects:  32% (28/85)
-remote: Compressing objects:  34% (29/85)
-remote: Compressing objects:  35% (30/85)
-remote: Compressing objects:  36% (31/85)
-remote: Compressing objects:  37% (32/85)
-remote: Compressing objects:  38% (33/85)
-remote: Compressing objects:  40% (34/85)
-remote: Compressing objects:  41% (35/85)
-remote: Compressing objects:  42% (36/85)
-remote: Compressing objects:  43% (37/85)
-remote: Compressing objects:  44% (38/85)
-remote: Compressing objects:  45% (39/85)
-remote: Compressing objects:  47% (40/85)
-remote: Compressing objects:  48% (41/85)
-remote: Compressing objects:  49% (42/85)
-remote: Compressing objects:  50% (43/85)
-remote: Compressing objects:  51% (44/85)
-remote: Compressing objects:  52% (45/85)
-remote: Compressing objects:  54% (46/85)
-remote: Compressing objects:  55% (47/85)
-remote: Compressing objects:  56% (48/85)
-remote: Compressing objects:  57% (49/85)
-remote: Compressing objects:  58% (50/85)
-remote: Compressing objects:  60% (51/85)
-remote: Compressing objects:  61% (52/85)
-remote: Compressing objects:  62% (53/85)
-remote: Compressing objects:  63% (54/85)
-remote: Compressing objects:  64% (55/85)
-remote: Compressing objects:  65% (56/85)
-remote: Compressing objects:  67% (57/85)
-remote: Compressing objects:  68% (58/85)
-remote: Compressing objects:  69% (59/85)
-remote: Compressing objects:  70% (60/85)
-remote: Compressing objects:  71% (61/85)
-remote: Compressing objects:  72% (62/85)
-remote: Compressing objects:  74% (63/85)
-remote: Compressing objects:  75% (64/85)
-remote: Compressing objects:  76% (65/85)
-remote: Compressing objects:  77% (66/85)
-remote: Compressing objects:  78% (67/85)
-remote: Compressing objects:  80% (68/85)
-remote: Compressing objects:  81% (69/85)
-remote: Compressing objects:  82% (70/85)
-remote: Compressing objects:  83% (71/85)
-remote: Compressing objects:  84% (72/85)
-remote: Compressing objects:  85% (73/85)
-remote: Compressing objects:  87% (74/85)
-remote: Compressing objects:  88% (75/85)
-remote: Compressing objects:  89% (76/85)
-remote: Compressing objects:  90% (77/85)
-remote: Compressing objects:  91% (78/85)
-remote: Compressing objects:  92% (79/85)
-remote: Compressing objects:  94% (80/85)
-remote: Compressing objects:  95% (81/85)
-remote: Compressing objects:  96% (82/85)
-remote: Compressing objects:  97% (83/85)
-remote: Compressing objects:  98% (84/85)
-remote: Compressing objects: 100% (85/85)
-remote: Compressing objects: 100% (85/85), done.
-    Receiving objects:   0% (1/424)
-
-.. parsed-literal::
-
-    Receiving objects:   1% (5/424)
-Receiving objects:   2% (9/424)
-Receiving objects:   3% (13/424)
-Receiving objects:   4% (17/424)
-
-.. parsed-literal::
-
-    Receiving objects:   5% (22/424)
-Receiving objects:   6% (26/424)
-
-.. parsed-literal::
-
-    Receiving objects:   6% (26/424), 20.47 MiB | 20.46 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:   6% (29/424), 42.52 MiB | 21.15 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:   6% (29/424), 64.88 MiB | 21.43 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:   7% (30/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:   8% (34/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:   9% (39/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  10% (43/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  11% (47/424), 72.89 MiB | 20.50 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  12% (51/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  13% (56/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  14% (60/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  15% (64/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  16% (68/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  17% (73/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  18% (77/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  19% (81/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  20% (85/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  21% (90/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  22% (94/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  23% (98/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  24% (102/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  25% (106/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  26% (111/424), 72.89 MiB | 20.50 MiB/s
-Receiving objects:  27% (115/424), 72.89 MiB | 20.50 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  27% (115/424), 83.68 MiB | 20.63 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  27% (115/424), 107.54 MiB | 21.41 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  27% (115/424), 131.54 MiB | 21.79 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  27% (115/424), 156.82 MiB | 22.49 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  28% (119/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  29% (123/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  30% (128/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  31% (132/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  32% (136/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  33% (140/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  34% (145/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  35% (149/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  36% (153/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  37% (157/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  38% (162/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  39% (166/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  40% (170/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  41% (174/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  42% (179/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  43% (183/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  44% (187/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  45% (191/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  46% (196/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  47% (200/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  48% (204/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  49% (208/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  50% (212/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  51% (217/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  52% (221/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  53% (225/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  54% (229/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  55% (234/424), 169.11 MiB | 22.60 MiB/s
-Receiving objects:  56% (238/424), 169.11 MiB | 22.60 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  56% (240/424), 181.91 MiB | 23.78 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  56% (241/424), 195.30 MiB | 24.35 MiB/s
-Receiving objects:  57% (242/424), 195.30 MiB | 24.35 MiB/s
-Receiving objects:  58% (246/424), 195.30 MiB | 24.35 MiB/s
-Receiving objects:  59% (251/424), 195.30 MiB | 24.35 MiB/s
-Receiving objects:  60% (255/424), 195.30 MiB | 24.35 MiB/s
-Receiving objects:  61% (259/424), 195.30 MiB | 24.35 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  62% (263/424), 209.77 MiB | 25.00 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  63% (268/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  64% (272/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  65% (276/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  66% (280/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  67% (285/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  68% (289/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  69% (293/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  70% (297/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  71% (302/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  72% (306/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  73% (310/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  74% (314/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  75% (318/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  76% (323/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  77% (327/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  78% (331/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  79% (335/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  80% (340/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  81% (344/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  82% (348/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  83% (352/424), 209.77 MiB | 25.00 MiB/s
-Receiving objects:  84% (357/424), 209.77 MiB | 25.00 MiB/s
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-remote: Total 424 (delta 84), reused 99 (delta 55), pack-reused 284
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-Receiving objects: 100% (424/424), 334.57 MiB | 25.17 MiB/s, done.
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-
-
-.. code:: ipython3
-
-    import torch
-    import numpy as np
-    import supervision as sv
-    import openvino as ov
-    from PIL import Image, ImageDraw, ImageFont
-    from typing import Union, List
-    import transformers
-
-    core = ov.Core()
-
-Download checkpoints and load PyTorch models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    IRS_PATH = Path('openvino_irs')
-    CKPT_BASE_PATH = Path('checkpoints')
-    os.makedirs(IRS_PATH, exist_ok=True)
-    os.makedirs(CKPT_BASE_PATH, exist_ok=True)
-
-    PT_DEVICE = 'cpu'
-    ov_dino_name = 'openvino_grounding_dino'
-    ov_sam_name = 'openvino_segment_anything'
-
-    ground_dino_img_size = (1024, 1280)
-
-    # GroundingDINO config and checkpoint
-    GROUNDING_DINO_CONFIG_PATH = f"{ground_dino_dir}/groundingdino/config/GroundingDINO_SwinT_OGC.py"
-    GROUNDING_DINO_CHECKPOINT_PATH = CKPT_BASE_PATH / "groundingdino_swint_ogc.pth"
-
-    # Segment Anything checkpoint
-    SAM_CHECKPOINT_PATH = CKPT_BASE_PATH / "sam_vit_h_4b8939.pth"
-
-    # Efficient Segment Anything checkpoint
-    EFFICIENT_SAM_CHECKPOINT_PATH = efficient_sam_dir / "weights/efficient_sam_vitt.pt"
-
-.. code:: ipython3
-
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
-    )
-    from notebook_utils import download_file
-
-    download_file("https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth", directory=CKPT_BASE_PATH)
-    if not use_efficient_sam:
-        download_file("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth", directory=CKPT_BASE_PATH)
-
-
-
-.. parsed-literal::
-
-    checkpoints/groundingdino_swint_ogc.pth:   0%|          | 0.00/662M [00:00 self.max_text_len:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/util/misc.py:506: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
-      torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/util/misc.py:517: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
-      for img in tensor_list:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py:486: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if W % self.patch_size[1] != 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py:488: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if H % self.patch_size[0] != 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py:417: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      Hp = int(np.ceil(H / self.window_size)) * self.window_size
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py:418: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      Wp = int(np.ceil(W / self.window_size)) * self.window_size
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py:247: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert L == H * W, "input feature has wrong size"
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py:71: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      B = int(windows.shape[0] / (H * W / window_size / window_size))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py:289: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if pad_r > 0 or pad_b > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py:321: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert L == H * W, "input feature has wrong size"
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py:326: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      pad_input = (H % 2 == 1) or (W % 2 == 1)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py:327: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if pad_input:
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py:244: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
-      spatial_shapes = torch.as_tensor(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py:468: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
-      for lvl, (H_, W_) in enumerate(spatial_shapes):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py:176: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py:227: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py:232: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py:109: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py:287: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py:309: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if reference_points.shape[-1] == 2:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py:102: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
-      value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py:105: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
-      for level, (H_, W_) in enumerate(value_spatial_shapes):
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/utils.py:72: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
-      for lvl, (H_, W_) in enumerate(spatial_shapes):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py:667: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if reference_points.shape[-1] == 4:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/utils.py:216: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if pos_tensor.size(-1) == 2:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/utils.py:218: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      elif pos_tensor.size(-1) == 4:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py:315: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      elif reference_points.shape[-1] == 4:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py:704: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if output.isnan().any() | output.isinf().any():
-
-
-Run OpenVINO optimized GroundingDINO
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    device_widget = ipywidgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
-    )
-    device_widget
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-In order to run inference ``ov_dino_model`` should be compiled.
-Resulting ``ov.CompiledModel`` object receives the same arguments as
-pytorch ``forward``/``__call__`` methods.
-
-.. code:: ipython3
-
-    device = device_widget.value
-    ov_compiled_grounded_dino = core.compile_model(ov_dino_model, device)
-
-We will reuse only tokenizer from the original GroundingDINO model
-class, but the inference will be done using OpenVINO optimized model.
-
-.. code:: ipython3
-
-    def transform_image(pil_image: Image.Image) -> torch.Tensor:
-        import groundingdino.datasets.transforms as T
-        transform = T.Compose(
-            [
-                T.RandomResize([800], max_size=1333),
-                T.ToTensor(),
-                T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-            ]
-        )
-        image, _ = transform(pil_image, None)  # 3, h, w
-        return image
-
-    # detects boxes usding openvino optimized grounding dino model
-    def get_ov_grounding_output(
-        model: ov.CompiledModel,
-        pil_image: Image.Image,
-        caption: Union[str, List[str]],
-        box_threshold: float,
-        text_threshold: float,
-        dino_tokenizer: transformers.PreTrainedTokenizerBase = dino_tokenizer,
-        max_text_len: int = max_text_len
-    ) -> (torch.Tensor, List[str], torch.Tensor):
-        #  for text prompt pre-processing we reuse existing routines from GroundignDINO repo
-        if isinstance(caption, list):
-            caption = '. '.join(caption)
-        caption = caption.lower()
-        caption = caption.strip()
-        if not caption.endswith("."):
-            caption = caption + "."
-        captions = [caption]
-
-        tokenized = dino_tokenizer(captions, padding="longest", return_tensors="pt")
-        specical_tokens = dino_tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
-
-        (
-            text_self_attention_masks,
-            position_ids,
-            cate_to_token_mask_list,
-        ) = generate_masks_with_special_tokens_and_transfer_map(
-            tokenized, specical_tokens, dino_tokenizer)
-
-        if text_self_attention_masks.shape[1] > max_text_len:
-            text_self_attention_masks = text_self_attention_masks[
-                :, : max_text_len, : max_text_len]
-
-            position_ids = position_ids[:, : max_text_len]
-            tokenized["input_ids"] = tokenized["input_ids"][:, : max_text_len]
-            tokenized["attention_mask"] = tokenized["attention_mask"][:, : max_text_len]
-            tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : max_text_len]
-
-        # inputs dictionary which will be fed into the ov.CompiledModel for inference
-        inputs = {}
-        inputs["attention_mask.1"] = tokenized["attention_mask"]
-        inputs["text_self_attention_masks"] = text_self_attention_masks
-        inputs["input_ids"] = tokenized["input_ids"]
-        inputs["position_ids"] = position_ids
-        inputs["token_type_ids"] = tokenized["token_type_ids"]
-
-        # GroundingDINO fails to run with input shapes different than one used for conversion.
-        # As a workaround we resize input_image to the size used for conversion. Model does not rely
-        # on image resolution to know object sizes therefore no need to resize box_predictions
-        from torchvision.transforms.functional import resize, InterpolationMode
-        input_img = resize(transform_image(pil_image), ground_dino_img_size, interpolation=InterpolationMode.BICUBIC)[None, ...]
-        inputs["samples"] = input_img
-
-        # OpenVINO inference
-        request = model.create_infer_request()
-        request.start_async(inputs, share_inputs=False)
-        request.wait()
-
-        def sig(x):
-            return 1 / (1 + np.exp(-x))
-
-        logits = torch.from_numpy(sig(np.squeeze(request.get_tensor("pred_logits").data, 0)))
-        boxes = torch.from_numpy(np.squeeze(request.get_tensor("pred_boxes").data, 0))
-
-        # filter output
-        filt_mask = logits.max(dim=1)[0] > box_threshold
-        logits, boxes = logits[filt_mask], boxes[filt_mask]
-
-        # get phrase and build predictions
-        tokenized = dino_tokenizer(caption)
-        pred_phrases = []
-        for logit in logits:
-            pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, dino_tokenizer)
-            pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
-
-        return boxes, pred_phrases, logits.max(dim=1)[0]
-
-.. code:: ipython3
-
-    SOURCE_IMAGE_PATH = f"{ground_dino_dir}/.asset/demo7.jpg"
-    BOX_THRESHOLD = 0.3
-    TEXT_THRESHOLD = 0.25
-    NMS_THRESHOLD = 0.8
-
-    pil_image = Image.open(SOURCE_IMAGE_PATH)
-    classes_prompt = ["Horse", "Cloud"]
-
-.. code:: ipython3
-
-    boxes_filt, pred_phrases, logits_filt = get_ov_grounding_output(
-        ov_compiled_grounded_dino,
-        pil_image,
-        classes_prompt,
-        BOX_THRESHOLD, TEXT_THRESHOLD
-    )
-
-
-.. parsed-literal::
-
-    2024-03-13 00:58:26.223873: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:58:26.261830: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-
-
-.. parsed-literal::
-
-    2024-03-13 00:58:26.819015: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
-Convert predicted boxes to supervision box detections format
-
-.. code:: ipython3
-
-    source_w, source_h = pil_image.size
-    detections = Model.post_process_result(
-        source_h=source_h,
-        source_w=source_w,
-        boxes=boxes_filt,
-        logits=logits_filt)
-
-    class_id = Model.phrases2classes(phrases=pred_phrases, classes=list(map(str.lower, classes_prompt)))
-    detections.class_id = class_id
-
-Draw box detections
-
-.. code:: ipython3
-
-    box_annotator = sv.BoxAnnotator()
-    labels = [
-        f"{classes_prompt[class_id] if class_id is not None else 'None'} {confidence:0.2f}"
-        for _, _, confidence, class_id, _, _
-        in detections]
-    annotated_frame = box_annotator.annotate(scene=np.array(pil_image).copy(), detections=detections, labels=labels)
-
-    Image.fromarray(annotated_frame)
-
-
-
-
-.. image:: 288-grounded-segment-anything-with-output_files/288-grounded-segment-anything-with-output_29_0.png
-
-
-
-Great! All clouds and horses are detected. Feel free to play around and
-specify other objects you wish to detect.
-
-Convert SAM to OpenVINO IR
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-And now let’s feed those detection to ``SAM`` model. We will use
-``EfficiendSAM`` for faster computation and to save ram, but feel free
-to select vanilla ``SAM`` if you wish more detailed and precise
-segmentation. First of all let’s convert ``SAM`` model to OpenVINO IR.
-
-.. code:: ipython3
-
-    ov_efficient_sam_name = 'openvino_efficient_sam'
-    ov_efficient_sam_path = IRS_PATH / f'{ov_efficient_sam_name}.xml'
-
-    # convert EfficientSAM to OpenVINO IR format
-    if not ov_efficient_sam_path.exists() and use_efficient_sam:
-        random_input_image = np.random.rand(1, 3, *pil_image.size[::-1]).astype(np.float32)
-        bounding_box = np.array([900, 100, 1000, 200]).reshape([1, 1, 2, 2])
-        bbox_labels = np.array([2, 3]).reshape([1, 1, 2])
-        efficient_sam_dummy_input = tuple(torch.from_numpy(x) for x in (random_input_image, bounding_box, bbox_labels))
-
-        ov_efficient_sam = ov.convert_model(efficient_sam_model, example_input=efficient_sam_dummy_input)
-        ov.save_model(ov_efficient_sam, ov_efficient_sam_path)
-    elif use_efficient_sam:
-        ov_efficient_sam = core.read_model(ov_efficient_sam_path)
-
-
-.. parsed-literal::
-
-    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/EfficientSAM/efficient_sam/efficient_sam.py:220: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if (
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/EfficientSAM/efficient_sam/efficient_sam_encoder.py:241: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert (
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/EfficientSAM/efficient_sam/efficient_sam_encoder.py:163: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      size = int(math.sqrt(xy_num))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/EfficientSAM/efficient_sam/efficient_sam_encoder.py:164: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert size * size == xy_num
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/EfficientSAM/efficient_sam/efficient_sam_encoder.py:166: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if size != h or size != w:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/EfficientSAM/efficient_sam/efficient_sam_encoder.py:251: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert x.shape[2] == num_patches
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/EfficientSAM/efficient_sam/efficient_sam.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if num_pts > self.decoder_max_num_input_points:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/EfficientSAM/efficient_sam/efficient_sam.py:92: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      elif num_pts < self.decoder_max_num_input_points:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/288-grounded-segment-anything/EfficientSAM/efficient_sam/efficient_sam.py:126: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if output_w > 0 and output_h > 0:
-
-
-Below is conversion of vanilla ``SAM``. This code is not used when
-``EfficientSAM`` is selected for segmentation.
-
-.. code:: ipython3
-
-    # In order to convert to OpenVINO IR neeed to patch forward method or the torch.nn.Module for SAM
-    class SamMaskFromBoxes(torch.nn.Module):
-        def __init__(
-            self,
-            sam_predictor,
-        ) -> None:
-            super().__init__()
-            self.model = sam_predictor
-
-        @torch.no_grad()
-        def forward(
-            self,
-            input_image: torch.Tensor,
-            transformed_boxes: torch.Tensor,
-            multimask_output: bool = False,
-            hq_token_only: bool = False,
-        ):
-            pre_processed_image = self.model.model.preprocess(input_image)
-            image_embeddings, interm_features = self.model.model.image_encoder(pre_processed_image)
-
-            # Embed prompts
-            sparse_embeddings, dense_embeddings = self.model.model.prompt_encoder(
-                points=None,
-                boxes=transformed_boxes,
-                masks=None,
-            )
-
-            # Predict masks
-            low_res_masks, iou_predictions = self.model.model.mask_decoder(
-                image_embeddings=image_embeddings,
-                image_pe=self.model.model.prompt_encoder.get_dense_pe(),
-                sparse_prompt_embeddings=sparse_embeddings,
-                dense_prompt_embeddings=dense_embeddings,
-                multimask_output=multimask_output,
-                hq_token_only=hq_token_only,
-                interm_embeddings=interm_features,
-            )
-
-            return low_res_masks, iou_predictions
-
-.. code:: ipython3
-
-    ov_sam_path = IRS_PATH / f'{ov_sam_name}.xml'
-
-    # example input for vanilla SAM
-    input_image_torch = torch.randint(0, 255, size=[1, 3, 683, 1024], dtype=torch.uint8)
-    dummy_transformed_boxes = torch.rand(1, 4, dtype=torch.float32) * 200
-
-    # convert vanilla SAM to OpenVINO IR format
-    if not ov_sam_path.exists() and not use_efficient_sam:
-        # Load pytorch model object and prepare example input for conversion
-        exportable = SamMaskFromBoxes(sam_predictor)
-        exportable.model.model.eval()
-        for par in exportable.model.model.parameters():
-            par.requires_grad = False
-
-        traced = torch.jit.trace(exportable, example_inputs=(input_image_torch, dummy_transformed_boxes))
-        ov_sam = ov.convert_model(traced, example_input=(input_image_torch, dummy_transformed_boxes))
-        ov.save_model(ov_sam, ov_sam_path)
-    elif not use_efficient_sam:
-        ov_sam = core.read_model(ov_sam_path)
-
-.. code:: ipython3
-
-    if use_efficient_sam:
-        compiled_efficient_sam = core.compile_model(ov_efficient_sam, device_name=device)
-    else:
-        compiled_vanilla_sam = core.compile_model(ov_sam, device_name=device)
-
-Combine GroundingDINO + SAM (GroundedSAM)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-We have OpenVINO IRs for both GroundingDINO and SAM models. Lets run the
-segmentation using predictions from GroundingDINO. Same as above, use
-``EfficientSAM`` by default.
-
-.. code:: ipython3
-
-    def predict_efficient_sam_mask(compiled_efficient_sam: ov.CompiledModel, image: Image.Image, bbox: torch.Tensor):
-        # input image is scaled so that none of the sizes is greater than 1024, same as in 274-efficient-sam notebook
-        input_size = 1024
-        w, h = image.size[:2]
-        scale = input_size / max(w, h)
-        new_w = int(w * scale)
-        new_h = int(h * scale)
-        image = image.resize((new_w, new_h))
-
-        numpy_image = np.array(image, dtype=np.float32) / 255.0
-        numpy_image = np.transpose(numpy_image, (2, 0, 1))[None, ...]
-
-        scaled_points = bbox * scale
-
-        bounding_box = scaled_points.reshape([1, 1, 2, 2])
-        bbox_labels = np.reshape(np.array([2, 3]), [1, 1, 2])
-
-        res = compiled_efficient_sam((numpy_image, bounding_box, bbox_labels))
-
-        predicted_logits, predicted_iou = res[0], res[1]
-
-        all_masks = torch.ge(torch.sigmoid(torch.from_numpy(predicted_logits[0, 0, :, :, :])), 0.5).numpy()
-        predicted_iou = predicted_iou[0, 0, ...]
-
-        # select the mask with the greatest IOU
-        max_predicted_iou = -1
-        selected_mask_using_predicted_iou = None
-        for m in range(all_masks.shape[0]):
-            curr_predicted_iou = predicted_iou[m]
-            if (
-                curr_predicted_iou > max_predicted_iou
-                or selected_mask_using_predicted_iou is None
-            ):
-                max_predicted_iou = curr_predicted_iou
-                selected_mask_using_predicted_iou = all_masks[m]
-        return selected_mask_using_predicted_iou
-
-    # If several detections are fed to EfficientSAM, it merges them to a single mask. Therefore, we call it one by one for each detection.
-    def predict_efficient_sam_masks(compiled_efficient_sam: ov.CompiledModel, pil_image: Image.Image, transformed_boxes) -> torch.Tensor:
-        masks = []
-        for bbox in transformed_boxes:
-            mask = predict_efficient_sam_mask(compiled_efficient_sam, pil_image, bbox)
-            mask = Image.fromarray(mask).resize(pil_image.size)
-            masks.append(np.array(mask))
-        masks = torch.from_numpy(np.array(masks))
-        return masks
-
-.. code:: ipython3
-
-    def transform_boxes(sam_predictor: torch.nn.Module, boxes: torch.Tensor, size: tuple) -> torch.Tensor:
-        H, W = size[0], size[1]
-        for i in range(boxes.size(0)):
-            boxes[i] = boxes[i] * torch.Tensor([W, H, W, H])
-            boxes[i][:2] -= boxes[i][2:] / 2
-            boxes[i][2:] += boxes[i][:2]
-
-        return sam_predictor.transform.apply_boxes_torch(boxes, size).to(PT_DEVICE)
-
-    def predict_vanilla_sam_masks(compiled_vanilla_sam: ov.CompiledModel, image: np.ndarray, transformed_boxes: torch.Tensor) -> torch.Tensor:
-        transfromed_image = exportable.model.transform.apply_image(image)
-        input_image_torch = torch.as_tensor(transfromed_image, device=PT_DEVICE)
-        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
-
-        original_size = tuple(image.shape[:2])
-        input_size = tuple(input_image_torch.shape[-2:])
-
-        low_res_masks = compiled_vanilla_sam((input_image_torch, transformed_boxes))[0]
-
-        # Upscale the masks to the original image resolution
-        masks = exportable.model.model.postprocess_masks(torch.from_numpy(low_res_masks), input_size, original_size)
-        masks = masks > exportable.model.model.mask_threshold
-        return masks
-
-Run SAM model for the same image with the detected boxes from
-GroundingDINO.
-
-Please note that vanilla SAM and EfficientSAM have slightly different
-detection formats. But inputs for both of them originate from
-``boxes_filt`` which is result of the ``get_ov_grounding_output``. For
-EfficientSAM we use ``detections.xyxy`` boxes obtained after
-``boxes_filt`` is fed to ``Model.post_process_result``. While vanilla
-SAM has it’s own preprocessing function ``transform_boxes``.
-
-.. code:: ipython3
-
-    if use_efficient_sam:
-        masks = predict_efficient_sam_masks(compiled_efficient_sam, pil_image, detections.xyxy)
-        detections.mask = masks.numpy()
-    else:
-        transformed_boxes = transform_boxes(sam_predictor, boxes_filt, pil_image.size[::-1])
-        masks = predict_vanilla_sam_masks(compiled_vanilla_sam, np.array(pil_image), transformed_boxes)
-        detections.mask = masks[:, 0].numpy()
-
-Combine both boxes and segmentation masks and draw them.
-
-.. code:: ipython3
-
-    box_annotator = sv.BoxAnnotator()
-    mask_annotator = sv.MaskAnnotator()
-
-    annotated_image = np.array(pil_image)
-    annotated_image = mask_annotator.annotate(scene=np.array(pil_image).copy(), detections=detections)
-    annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)
-
-    Image.fromarray(annotated_image)
-
-
-
-
-.. image:: 288-grounded-segment-anything-with-output_files/288-grounded-segment-anything-with-output_45_0.png
-
-
-
-Great! All detected horses and clouds are segmented as well.
-
-Interactive GroundedSAM
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Now, you can try apply grounding sam on your own images using
-interactive demo. The code below provides helper functions used in
-demonstration.
-
-.. code:: ipython3
-
-    def draw_mask(mask, draw, random_color=False):
-        import random
-        if random_color:
-            color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 153)
-        else:
-            color = (30, 144, 255, 153)
-
-        nonzero_coords = np.transpose(np.nonzero(mask))
-
-        for coord in nonzero_coords:
-            draw.point(coord[::-1], fill=color)
-
-    def draw_box(box, draw, label):
-        # random color
-        color = tuple(np.random.randint(0, 255, size=3).tolist())
-
-        draw.rectangle(((box[0], box[1]), (box[2], box[3])), outline=color, width=4)
-
-        if label:
-            font = ImageFont.load_default(18)
-            if hasattr(font, "getbbox"):
-                bbox = draw.textbbox((box[0], box[1]), str(label), font, anchor='ld')
-            else:
-                w, h = draw.textsize(str(label), font)
-                bbox = (box[0], box[1], box[0] + w, box[1] + h)
-            draw.rectangle(bbox, fill=color)
-            draw.text((box[0], box[1]), str(label), fill="white", anchor='ld', font=font)
-
-.. code:: ipython3
-
-    """"
-    run_grounding_sam is called every time "Submit" button is clicked
-    """
-    def run_grounding_sam(image, task_type, text_prompt, box_threshold, text_threshold):
-        pil_image = Image.fromarray(image)
-        size = image.shape[1], image.shape[0]  # size is WH image.shape HWC
-
-        boxes_filt, scores, pred_phrases = get_ov_grounding_output(
-            ov_compiled_grounded_dino,
-            pil_image,
-            text_prompt,
-            box_threshold,
-            text_threshold
-        )
-
-        # process boxes
-        H, W = size[1], size[0]
-        for i in range(boxes_filt.size(0)):
-            boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
-            boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
-            boxes_filt[i][2:] += boxes_filt[i][:2]
-
-        if task_type == 'seg':
-            if use_efficient_sam:
-                masks = predict_efficient_sam_masks(compiled_efficient_sam, pil_image, boxes_filt.numpy())
-            else:
-                transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(PT_DEVICE)
-                masks = predict_vanilla_sam_masks(compiled_vanilla_sam, image, transformed_boxes)[:, 0]
-
-            mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0))
-            mask_draw = ImageDraw.Draw(mask_image)
-            for mask in masks:
-                draw_mask(mask.numpy(), mask_draw, random_color=True)
-
-            image_draw = ImageDraw.Draw(pil_image)
-            for box, label in zip(boxes_filt, pred_phrases):
-                draw_box(box, image_draw, label)
-
-            pil_image = pil_image.convert('RGBA')
-            pil_image.alpha_composite(mask_image)
-
-            return [pil_image, mask_image]
-        if task_type == 'det':
-            image_draw = ImageDraw.Draw(pil_image)
-            for box, label in zip(boxes_filt, pred_phrases):
-                draw_box(box, image_draw, label)
-            return [pil_image]
-        else:
-            gr.Warning(f"task_type:{task_type} error!")
-
-You can run interactive app with your own image and text prompts. To
-define prompt specify comma (or conjunction) separated names of objects
-you wish to segment. For demonstration, this demo already has two
-predefined examples. If many object are crowded and overlapping please
-increase threshold values in ``Advanced options``.
-
-.. code:: ipython3
-
-    import gradio as gr
-
-    with gr.Accordion("Advanced options", open=False) as advanced:
-        box_threshold = gr.Slider(label="Box Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.05)
-        text_threshold = gr.Slider(label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.05)
-
-    demo = gr.Interface(
-        run_grounding_sam,
-        [
-            gr.Image(),
-            gr.Dropdown(["det", "seg"], value="seg", label="task_type"),
-            gr.Textbox(value='bears', label="Text Prompt"),
-        ],
-        additional_inputs=[
-            box_threshold,
-            text_threshold,
-        ],
-        outputs=gr.Gallery(preview=True, object_fit="scale-down"),
-        examples=[[f"{ground_dino_dir}/.asset/demo2.jpg", "seg", 'dog, forest'], [f"{ground_dino_dir}/.asset/demo7.jpg", "seg", 'horses and clouds']],
-        additional_inputs_accordion=advanced,
-    )
-
-    try:
-        demo.launch(server_name='0.0.0.0', debug=False, height=1000)
-    except Exception:
-        demo.launch(share=True, debug=False, height=1000)
-    # if you are launching remotely, specify server_name and server_port
-    # demo.launch(server_name='your server name', server_port='server port in int')
-    # Read more in the docs: https://gradio.app/docs/
-
-
-.. parsed-literal::
-
-    Running on local URL:  http://0.0.0.0:7860
-
-    To create a public link, set `share=True` in `launch()`.
-
-
-
-
-
-
-
-
-Cleanup
-~~~~~~~
-
-
-
-.. code:: ipython3
-
-    # import shutil
-    # shutil.rmtree(CKPT_BASE_PATH)
-    # shutil.rmtree(IRS_PATH)
diff --git a/docs/notebooks/289-mobileclip-video-search-with-output_files/289-mobileclip-video-search-with-output_10_4.png b/docs/notebooks/289-mobileclip-video-search-with-output_files/289-mobileclip-video-search-with-output_10_4.png
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+Live 3D Human Pose Estimation with OpenVINO
+===========================================
+
+This notebook demonstrates live 3D Human Pose Estimation with OpenVINO
+via a webcam. We utilize the model
+`human-pose-estimation-3d-0001 `__
+from `Open Model
+Zoo `__. At the end
+of this notebook, you will see live inference results from your webcam
+(if available). Alternatively, you can also upload a video file to test
+out the algorithms. **Make sure you have properly installed
+the**\ `Jupyter
+extension `__\ **and
+been using JupyterLab to run the demo as suggested in the
+``README.md``**
+
+   **NOTE**: *To use a webcam, you must run this Jupyter notebook on a
+   computer with a webcam. If you run on a remote server, the webcam
+   will not work. However, you can still do inference on a video file in
+   the final step. This demo utilizes the Python interface in
+   ``Three.js`` integrated with WebGL to process data from the model
+   inference. These results are processed and displayed in the
+   notebook.*
+
+*To ensure that the results are displayed correctly, run the code in a
+recommended browser on one of the following operating systems:* *Ubuntu,
+Windows: Chrome* *macOS: Safari*
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+-  `Imports <#imports>`__
+-  `The model <#the-model>`__
+
+   -  `Download the model <#download-the-model>`__
+   -  `Convert Model to OpenVINO IR
+      format <#convert-model-to-openvino-ir-format>`__
+   -  `Select inference device <#select-inference-device>`__
+   -  `Load the model <#load-the-model>`__
+
+-  `Processing <#processing>`__
+
+   -  `Model Inference <#model-inference>`__
+   -  `Draw 2D Pose Overlays <#draw-2d-pose-overlays>`__
+   -  `Main Processing Function <#main-processing-function>`__
+
+-  `Run <#run>`__
+
+Prerequisites
+-------------
+
+
+
+**The ``pythreejs`` extension may not display properly when using the
+latest Jupyter Notebook release (2.4.1). Therefore, it is recommended to
+use Jupyter Lab instead.**
+
+.. code:: ipython3
+
+    %pip install pythreejs "openvino-dev>=2024.0.0" "opencv-python" "torch" "onnx" --extra-index-url https://download.pytorch.org/whl/cpu
+
+
+.. parsed-literal::
+
+    Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cpu
+
+
+.. parsed-literal::
+
+    Collecting pythreejs
+      Using cached pythreejs-2.4.2-py3-none-any.whl.metadata (5.4 kB)
+
+
+.. parsed-literal::
+
+    Collecting openvino-dev>=2024.0.0
+      Using cached openvino_dev-2024.0.0-14509-py3-none-any.whl.metadata (16 kB)
+
+
+.. parsed-literal::
+
+    Collecting opencv-python
+      Using cached opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)
+
+
+.. parsed-literal::
+
+    Collecting torch
+
+
+.. parsed-literal::
+
+      Using cached https://download.pytorch.org/whl/cpu/torch-2.2.2%2Bcpu-cp38-cp38-linux_x86_64.whl (186.8 MB)
+
+
+.. parsed-literal::
+
+    Collecting onnx
+      Using cached onnx-1.16.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (16 kB)
+    Requirement already satisfied: ipywidgets>=7.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pythreejs) (8.1.2)
+
+
+.. parsed-literal::
+
+    Collecting ipydatawidgets>=1.1.1 (from pythreejs)
+      Using cached ipydatawidgets-4.3.5-py2.py3-none-any.whl.metadata (1.4 kB)
+
+
+.. parsed-literal::
+
+    Collecting numpy (from pythreejs)
+      Using cached numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB)
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+
+
+.. parsed-literal::
+
+    Collecting networkx<=3.1.0 (from openvino-dev>=2024.0.0)
+      Using cached networkx-3.1-py3-none-any.whl.metadata (5.3 kB)
+
+
+.. parsed-literal::
+
+    Collecting openvino-telemetry>=2023.2.1 (from openvino-dev>=2024.0.0)
+
+
+.. parsed-literal::
+
+      Using cached openvino_telemetry-2024.1.0-py3-none-any.whl.metadata (2.3 kB)
+    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (24.0)
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+
+
+.. parsed-literal::
+
+    Collecting openvino==2024.0.0 (from openvino-dev>=2024.0.0)
+      Using cached openvino-2024.0.0-14509-cp38-cp38-manylinux2014_x86_64.whl.metadata (8.7 kB)
+
+
+.. parsed-literal::
+
+    Collecting filelock (from torch)
+      Using cached filelock-3.13.4-py3-none-any.whl.metadata (2.8 kB)
+    Requirement already satisfied: typing-extensions>=4.8.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch) (4.11.0)
+
+
+.. parsed-literal::
+
+    Collecting sympy (from torch)
+      Using cached https://download.pytorch.org/whl/sympy-1.12-py3-none-any.whl (5.7 MB)
+
+
+.. parsed-literal::
+
+    Requirement already satisfied: jinja2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch) (3.1.3)
+
+
+.. parsed-literal::
+
+    Collecting fsspec (from torch)
+      Using cached fsspec-2024.3.1-py3-none-any.whl.metadata (6.8 kB)
+
+
+.. parsed-literal::
+
+    Collecting protobuf>=3.20.2 (from onnx)
+      Using cached protobuf-5.26.1-cp37-abi3-manylinux2014_x86_64.whl.metadata (592 bytes)
+
+
+.. parsed-literal::
+
+    Collecting traittypes>=0.2.0 (from ipydatawidgets>=1.1.1->pythreejs)
+      Using cached traittypes-0.2.1-py2.py3-none-any.whl.metadata (1.0 kB)
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+
+
+.. parsed-literal::
+
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+
+
+.. parsed-literal::
+
+    Collecting mpmath>=0.19 (from sympy->torch)
+
+
+.. parsed-literal::
+
+      Using cached https://download.pytorch.org/whl/mpmath-1.3.0-py3-none-any.whl (536 kB)
+
+
+.. parsed-literal::
+
+    Requirement already satisfied: backcall in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (0.2.0)
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+    Requirement already satisfied: pygments>=2.4.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (2.17.2)
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+
+
+.. parsed-literal::
+
+    Requirement already satisfied: parso<0.9.0,>=0.8.3 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (0.8.4)
+    Requirement already satisfied: ptyprocess>=0.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (0.7.0)
+    Requirement already satisfied: wcwidth in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (0.2.13)
+    Requirement already satisfied: executing>=1.2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (2.0.1)
+    Requirement already satisfied: asttokens>=2.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (2.4.1)
+    Requirement already satisfied: pure-eval in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (0.2.2)
+    Requirement already satisfied: six>=1.12.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (1.16.0)
+
+
+.. parsed-literal::
+
+    Using cached pythreejs-2.4.2-py3-none-any.whl (3.4 MB)
+    Using cached openvino_dev-2024.0.0-14509-py3-none-any.whl (4.7 MB)
+    Using cached openvino-2024.0.0-14509-cp38-cp38-manylinux2014_x86_64.whl (38.1 MB)
+
+
+.. parsed-literal::
+
+    Using cached opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (62.2 MB)
+
+
+.. parsed-literal::
+
+    Using cached onnx-1.16.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.9 MB)
+    Using cached ipydatawidgets-4.3.5-py2.py3-none-any.whl (271 kB)
+    Using cached networkx-3.1-py3-none-any.whl (2.1 MB)
+
+
+.. parsed-literal::
+
+    Using cached numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)
+    Using cached openvino_telemetry-2024.1.0-py3-none-any.whl (23 kB)
+    Using cached protobuf-5.26.1-cp37-abi3-manylinux2014_x86_64.whl (302 kB)
+    Using cached filelock-3.13.4-py3-none-any.whl (11 kB)
+    Using cached fsspec-2024.3.1-py3-none-any.whl (171 kB)
+    Using cached traittypes-0.2.1-py2.py3-none-any.whl (8.6 kB)
+
+
+.. parsed-literal::
+
+    Installing collected packages: openvino-telemetry, mpmath, traittypes, sympy, protobuf, numpy, networkx, fsspec, filelock, torch, openvino, opencv-python, onnx, openvino-dev, ipydatawidgets, pythreejs
+
+
+.. parsed-literal::
+
+    Successfully installed filelock-3.13.4 fsspec-2024.3.1 ipydatawidgets-4.3.5 mpmath-1.3.0 networkx-3.1 numpy-1.24.4 onnx-1.16.0 opencv-python-4.9.0.80 openvino-2024.0.0 openvino-dev-2024.0.0 openvino-telemetry-2024.1.0 protobuf-5.26.1 pythreejs-2.4.2 sympy-1.12 torch-2.2.2+cpu traittypes-0.2.1
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Imports
+-------
+
+
+
+.. code:: ipython3
+
+    import collections
+    import sys
+    import time
+    from pathlib import Path
+
+    import cv2
+    import ipywidgets as widgets
+    import numpy as np
+    from IPython.display import clear_output, display
+    import openvino as ov
+
+    # Fetch `notebook_utils` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    open("notebook_utils.py", "w").write(r.text)
+    import notebook_utils as utils
+
+    sys.path.append("./engine")
+    import engine.engine3js as engine
+    from engine.parse_poses import parse_poses
+
+The model
+---------
+
+
+
+Download the model
+~~~~~~~~~~~~~~~~~~
+
+
+
+We use ``omz_downloader``, which is a command line tool from the
+``openvino-dev`` package. ``omz_downloader`` automatically creates a
+directory structure and downloads the selected model.
+
+.. code:: ipython3
+
+    # directory where model will be downloaded
+    base_model_dir = "model"
+
+    # model name as named in Open Model Zoo
+    model_name = "human-pose-estimation-3d-0001"
+    # selected precision (FP32, FP16)
+    precision = "FP32"
+
+    BASE_MODEL_NAME = f"{base_model_dir}/public/{model_name}/{model_name}"
+    model_path = Path(BASE_MODEL_NAME).with_suffix(".pth")
+    onnx_path = Path(BASE_MODEL_NAME).with_suffix(".onnx")
+
+    ir_model_path = f"model/public/{model_name}/{precision}/{model_name}.xml"
+    model_weights_path = f"model/public/{model_name}/{precision}/{model_name}.bin"
+
+    if not model_path.exists():
+        download_command = f"omz_downloader " f"--name {model_name} " f"--output_dir {base_model_dir}"
+        ! $download_command
+
+
+.. parsed-literal::
+
+    ################|| Downloading human-pose-estimation-3d-0001 ||################
+
+    ========== Downloading model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.tar.gz
+
+
+.. parsed-literal::
+
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+.. parsed-literal::
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+.. parsed-literal::
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+
+.. parsed-literal::
+
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+
+.. parsed-literal::
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+
+.. parsed-literal::
+
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+
+
+    ========== Unpacking model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.tar.gz
+
+
+
+Convert Model to OpenVINO IR format
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+The selected model comes from the public directory, which means it must
+be converted into OpenVINO Intermediate Representation (OpenVINO IR). We
+use ``omz_converter`` to convert the ONNX format model to the OpenVINO
+IR format.
+
+.. code:: ipython3
+
+    if not onnx_path.exists():
+        convert_command = (
+            f"omz_converter " f"--name {model_name} " f"--precisions {precision} " f"--download_dir {base_model_dir} " f"--output_dir {base_model_dir}"
+        )
+        ! $convert_command
+
+
+.. parsed-literal::
+
+    ========== Converting human-pose-estimation-3d-0001 to ONNX
+    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=model/public/human-pose-estimation-3d-0001 --model-name=PoseEstimationWithMobileNet --model-param=is_convertible_by_mo=True --import-module=model --weights=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.pth --input-shape=1,3,256,448 --input-names=data --output-names=features,heatmaps,pafs --output-file=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx
+
+
+
+.. parsed-literal::
+
+    ONNX check passed successfully.
+
+
+.. parsed-literal::
+
+
+    ========== Converting human-pose-estimation-3d-0001 to IR (FP32)
+    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/human-pose-estimation-3d-0001/FP32 --model_name=human-pose-estimation-3d-0001 --input=data '--mean_values=data[128.0,128.0,128.0]' '--scale_values=data[255.0,255.0,255.0]' --output=features,heatmaps,pafs --input_model=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 256, 448]' --compress_to_fp16=False
+
+
+
+.. parsed-literal::
+
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
+    [ SUCCESS ] Generated IR version 11 model.
+    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.xml
+    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.bin
+
+
+
+
+Select inference device
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+select device from dropdown list for running inference using OpenVINO
+
+.. code:: ipython3
+
+    core = ov.Core()
+
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+Load the model
+~~~~~~~~~~~~~~
+
+
+
+Converted models are located in a fixed structure, which indicates
+vendor, model name and precision.
+
+First, initialize the inference engine, OpenVINO Runtime. Then, read the
+network architecture and model weights from the ``.bin`` and ``.xml``
+files to compile for the desired device. An inference request is then
+created to infer the compiled model.
+
+.. code:: ipython3
+
+    # initialize inference engine
+    core = ov.Core()
+    # read the network and corresponding weights from file
+    model = core.read_model(model=ir_model_path, weights=model_weights_path)
+    # load the model on the specified device
+    compiled_model = core.compile_model(model=model, device_name=device.value)
+    infer_request = compiled_model.create_infer_request()
+    input_tensor_name = model.inputs[0].get_any_name()
+
+    # get input and output names of nodes
+    input_layer = compiled_model.input(0)
+    output_layers = list(compiled_model.outputs)
+
+The input for the model is data from the input image and the outputs are
+heat maps, PAF (part affinity fields) and features.
+
+.. code:: ipython3
+
+    input_layer.any_name, [o.any_name for o in output_layers]
+
+
+
+
+.. parsed-literal::
+
+    ('data', ['features', 'heatmaps', 'pafs'])
+
+
+
+Processing
+----------
+
+
+
+Model Inference
+~~~~~~~~~~~~~~~
+
+
+
+Frames captured from video files or the live webcam are used as the
+input for the 3D model. This is how you obtain the output heat maps, PAF
+(part affinity fields) and features.
+
+.. code:: ipython3
+
+    def model_infer(scaled_img, stride):
+        """
+        Run model inference on the input image
+
+        Parameters:
+            scaled_img: resized image according to the input size of the model
+            stride: int, the stride of the window
+        """
+
+        # Remove excess space from the picture
+        img = scaled_img[
+            0 : scaled_img.shape[0] - (scaled_img.shape[0] % stride),
+            0 : scaled_img.shape[1] - (scaled_img.shape[1] % stride),
+        ]
+
+        img = np.transpose(img, (2, 0, 1))[None,]
+        infer_request.infer({input_tensor_name: img})
+        # A set of three inference results is obtained
+        results = {name: infer_request.get_tensor(name).data[:] for name in {"features", "heatmaps", "pafs"}}
+        # Get the results
+        results = (results["features"][0], results["heatmaps"][0], results["pafs"][0])
+
+        return results
+
+Draw 2D Pose Overlays
+~~~~~~~~~~~~~~~~~~~~~
+
+
+
+We need to define some connections between the joints in advance, so
+that we can draw the structure of the human body in the resulting image
+after obtaining the inference results. Joints are drawn as circles and
+limbs are drawn as lines. The code is based on the `3D Human Pose
+Estimation
+Demo `__
+from Open Model Zoo.
+
+.. code:: ipython3
+
+    # 3D edge index array
+    body_edges = np.array(
+        [
+            [0, 1],
+            [0, 9],
+            [9, 10],
+            [10, 11],  # neck - r_shoulder - r_elbow - r_wrist
+            [0, 3],
+            [3, 4],
+            [4, 5],  # neck - l_shoulder - l_elbow - l_wrist
+            [1, 15],
+            [15, 16],  # nose - l_eye - l_ear
+            [1, 17],
+            [17, 18],  # nose - r_eye - r_ear
+            [0, 6],
+            [6, 7],
+            [7, 8],  # neck - l_hip - l_knee - l_ankle
+            [0, 12],
+            [12, 13],
+            [13, 14],  # neck - r_hip - r_knee - r_ankle
+        ]
+    )
+
+
+    body_edges_2d = np.array(
+        [
+            [0, 1],  # neck - nose
+            [1, 16],
+            [16, 18],  # nose - l_eye - l_ear
+            [1, 15],
+            [15, 17],  # nose - r_eye - r_ear
+            [0, 3],
+            [3, 4],
+            [4, 5],  # neck - l_shoulder - l_elbow - l_wrist
+            [0, 9],
+            [9, 10],
+            [10, 11],  # neck - r_shoulder - r_elbow - r_wrist
+            [0, 6],
+            [6, 7],
+            [7, 8],  # neck - l_hip - l_knee - l_ankle
+            [0, 12],
+            [12, 13],
+            [13, 14],  # neck - r_hip - r_knee - r_ankle
+        ]
+    )
+
+
+    def draw_poses(frame, poses_2d, scaled_img, use_popup):
+        """
+        Draw 2D pose overlays on the image to visualize estimated poses.
+        Joints are drawn as circles and limbs are drawn as lines.
+
+        :param frame: the input image
+        :param poses_2d: array of human joint pairs
+        """
+        for pose in poses_2d:
+            pose = np.array(pose[0:-1]).reshape((-1, 3)).transpose()
+            was_found = pose[2] > 0
+
+            pose[0], pose[1] = (
+                pose[0] * frame.shape[1] / scaled_img.shape[1],
+                pose[1] * frame.shape[0] / scaled_img.shape[0],
+            )
+
+            # Draw joints.
+            for edge in body_edges_2d:
+                if was_found[edge[0]] and was_found[edge[1]]:
+                    cv2.line(
+                        frame,
+                        tuple(pose[0:2, edge[0]].astype(np.int32)),
+                        tuple(pose[0:2, edge[1]].astype(np.int32)),
+                        (255, 255, 0),
+                        4,
+                        cv2.LINE_AA,
+                    )
+            # Draw limbs.
+            for kpt_id in range(pose.shape[1]):
+                if pose[2, kpt_id] != -1:
+                    cv2.circle(
+                        frame,
+                        tuple(pose[0:2, kpt_id].astype(np.int32)),
+                        3,
+                        (0, 255, 255),
+                        -1,
+                        cv2.LINE_AA,
+                    )
+
+        return frame
+
+Main Processing Function
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Run 3D pose estimation on the specified source. It could be either a
+webcam feed or a video file.
+
+.. code:: ipython3
+
+    def run_pose_estimation(source=0, flip=False, use_popup=False, skip_frames=0):
+        """
+        2D image as input, using OpenVINO as inference backend,
+        get joints 3D coordinates, and draw 3D human skeleton in the scene
+
+        :param source:      The webcam number to feed the video stream with primary webcam set to "0", or the video path.
+        :param flip:        To be used by VideoPlayer function for flipping capture image.
+        :param use_popup:   False for showing encoded frames over this notebook, True for creating a popup window.
+        :param skip_frames: Number of frames to skip at the beginning of the video.
+        """
+
+        focal_length = -1  # default
+        stride = 8
+        player = None
+        skeleton_set = None
+
+        try:
+            # create video player to play with target fps  video_path
+            # get the frame from camera
+            # You can skip first N frames to fast forward video. change 'skip_first_frames'
+            player = utils.VideoPlayer(source, flip=flip, fps=30, skip_first_frames=skip_frames)
+            # start capturing
+            player.start()
+
+            input_image = player.next()
+            # set the window size
+            resize_scale = 450 / input_image.shape[1]
+            windows_width = int(input_image.shape[1] * resize_scale)
+            windows_height = int(input_image.shape[0] * resize_scale)
+
+            # use visualization library
+            engine3D = engine.Engine3js(grid=True, axis=True, view_width=windows_width, view_height=windows_height)
+
+            if use_popup:
+                # display the 3D human pose in this notebook, and origin frame in popup window
+                display(engine3D.renderer)
+                title = "Press ESC to Exit"
+                cv2.namedWindow(title, cv2.WINDOW_KEEPRATIO | cv2.WINDOW_AUTOSIZE)
+            else:
+                # set the 2D image box, show both human pose and image in the notebook
+                imgbox = widgets.Image(format="jpg", height=windows_height, width=windows_width)
+                display(widgets.HBox([engine3D.renderer, imgbox]))
+
+            skeleton = engine.Skeleton(body_edges=body_edges)
+
+            processing_times = collections.deque()
+
+            while True:
+                # grab the frame
+                frame = player.next()
+                if frame is None:
+                    print("Source ended")
+                    break
+
+                # resize image and change dims to fit neural network input
+                # (see https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/human-pose-estimation-3d-0001)
+                scaled_img = cv2.resize(frame, dsize=(model.inputs[0].shape[3], model.inputs[0].shape[2]))
+
+                if focal_length < 0:  # Focal length is unknown
+                    focal_length = np.float32(0.8 * scaled_img.shape[1])
+
+                # inference start
+                start_time = time.time()
+                # get results
+                inference_result = model_infer(scaled_img, stride)
+
+                # inference stop
+                stop_time = time.time()
+                processing_times.append(stop_time - start_time)
+                # Process the point to point coordinates of the data
+                poses_3d, poses_2d = parse_poses(inference_result, 1, stride, focal_length, True)
+
+                # use processing times from last 200 frames
+                if len(processing_times) > 200:
+                    processing_times.popleft()
+
+                processing_time = np.mean(processing_times) * 1000
+                fps = 1000 / processing_time
+
+                if len(poses_3d) > 0:
+                    # From here, you can rotate the 3D point positions using the function "draw_poses",
+                    # or you can directly make the correct mapping below to properly display the object image on the screen
+                    poses_3d_copy = poses_3d.copy()
+                    x = poses_3d_copy[:, 0::4]
+                    y = poses_3d_copy[:, 1::4]
+                    z = poses_3d_copy[:, 2::4]
+                    poses_3d[:, 0::4], poses_3d[:, 1::4], poses_3d[:, 2::4] = (
+                        -z + np.ones(poses_3d[:, 2::4].shape) * 200,
+                        -y + np.ones(poses_3d[:, 2::4].shape) * 100,
+                        -x,
+                    )
+
+                    poses_3d = poses_3d.reshape(poses_3d.shape[0], 19, -1)[:, :, 0:3]
+                    people = skeleton(poses_3d=poses_3d)
+
+                    try:
+                        engine3D.scene_remove(skeleton_set)
+                    except Exception:
+                        pass
+
+                    engine3D.scene_add(people)
+                    skeleton_set = people
+
+                    # draw 2D
+                    frame = draw_poses(frame, poses_2d, scaled_img, use_popup)
+
+                else:
+                    try:
+                        engine3D.scene_remove(skeleton_set)
+                        skeleton_set = None
+                    except Exception:
+                        pass
+
+                cv2.putText(
+                    frame,
+                    f"Inference time: {processing_time:.1f}ms ({fps:.1f} FPS)",
+                    (10, 30),
+                    cv2.FONT_HERSHEY_COMPLEX,
+                    0.7,
+                    (0, 0, 255),
+                    1,
+                    cv2.LINE_AA,
+                )
+
+                if use_popup:
+                    cv2.imshow(title, frame)
+                    key = cv2.waitKey(1)
+                    # escape = 27, use ESC to exit
+                    if key == 27:
+                        break
+                else:
+                    # encode numpy array to jpg
+                    imgbox.value = cv2.imencode(
+                        ".jpg",
+                        frame,
+                        params=[cv2.IMWRITE_JPEG_QUALITY, 90],
+                    )[1].tobytes()
+
+                engine3D.renderer.render(engine3D.scene, engine3D.cam)
+
+        except KeyboardInterrupt:
+            print("Interrupted")
+        except RuntimeError as e:
+            print(e)
+        finally:
+            clear_output()
+            if player is not None:
+                # stop capturing
+                player.stop()
+            if use_popup:
+                cv2.destroyAllWindows()
+            if skeleton_set:
+                engine3D.scene_remove(skeleton_set)
+
+Run
+---
+
+
+
+Run, using a webcam as the video input. By default, the primary webcam
+is set with ``source=0``. If you have multiple webcams, each one will be
+assigned a consecutive number starting at 0. Set ``flip=True`` when
+using a front-facing camera. Some web browsers, especially Mozilla
+Firefox, may cause flickering. If you experience flickering, set
+``use_popup=True``.
+
+   **NOTE**:
+
+   *1. To use this notebook with a webcam, you need to run the notebook
+   on a computer with a webcam. If you run the notebook on a server
+   (e.g. Binder), the webcam will not work.*
+
+   *2. Popup mode may not work if you run this notebook on a remote
+   computer (e.g. Binder).*
+
+If you do not have a webcam, you can still run this demo with a video
+file. Any `format supported by
+OpenCV `__
+will work.
+
+Using the following method, you can click and move your mouse over the
+picture on the left to interact.
+
+.. code:: ipython3
+
+    USE_WEBCAM = False
+
+    cam_id = 0
+    video_path = "https://github.com/intel-iot-devkit/sample-videos/raw/master/face-demographics-walking.mp4"
+
+    source = cam_id if USE_WEBCAM else video_path
+
+    run_pose_estimation(source=source, flip=isinstance(source, int), use_popup=False)
diff --git a/docs/notebooks/224-3D-segmentation-point-clouds-with-output.rst b/docs/notebooks/3D-segmentation-point-clouds-with-output.rst
similarity index 76%
rename from docs/notebooks/224-3D-segmentation-point-clouds-with-output.rst
rename to docs/notebooks/3D-segmentation-point-clouds-with-output.rst
index efdf2da82df..a527f33d1aa 100644
--- a/docs/notebooks/224-3D-segmentation-point-clouds-with-output.rst
+++ b/docs/notebooks/3D-segmentation-point-clouds-with-output.rst
@@ -37,7 +37,7 @@ Table of contents:
 
     import platform
     
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" "tqdm"
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -45,21 +45,11 @@ Table of contents:
         %pip install -q "matplotlib>=3.4,<3.7"
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -79,11 +69,12 @@ Imports
     import openvino as ov
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    open("notebook_utils.py", "w").write(r.text)
     
     from notebook_utils import download_file
 
@@ -102,7 +93,11 @@ find more point clouds examples
     # Set the data and model directories, model source URL and model filename
     MODEL_DIR = Path("model")
     MODEL_DIR.mkdir(exist_ok=True)
-    download_file("https://storage.googleapis.com/ailia-models/pointnet_pytorch/chair_100.onnx", directory=Path(MODEL_DIR), show_progress=False)
+    download_file(
+        "https://storage.googleapis.com/ailia-models/pointnet_pytorch/chair_100.onnx",
+        directory=Path(MODEL_DIR),
+        show_progress=False,
+    )
     onnx_model_path = MODEL_DIR / "chair_100.onnx"
 
 Convert the ONNX model to OpenVINO IR. An OpenVINO IR (Intermediate
@@ -130,7 +125,6 @@ API, see this
     else:
         # Read model
         model = core.read_model(model=ir_model_xml)
-        
 
 Data Processing Module
 ----------------------
@@ -153,13 +147,13 @@ Data Processing Module
     
         # normailization
         point_set = point_set - np.expand_dims(np.mean(point_set, axis=0), 0)  # center
-        dist = np.max(np.sqrt(np.sum(point_set ** 2, axis=1)), 0)
+        dist = np.max(np.sqrt(np.sum(point_set**2, axis=1)), 0)
         point_set = point_set / dist  # scale
     
         return point_set
     
     
-    def visualize(point_set:np.ndarray):
+    def visualize(point_set: np.ndarray):
         """
         Create a 3D view for data visualization
     
@@ -168,7 +162,7 @@ Data Processing Module
         """
     
         fig = plt.figure(dpi=192, figsize=(4, 4))
-        ax = fig.add_subplot(111, projection='3d')
+        ax = fig.add_subplot(111, projection="3d")
         X = point_set[:, 0]
         Y = point_set[:, 2]
         Z = point_set[:, 1]
@@ -181,11 +175,11 @@ Data Processing Module
         ax.set_xlim(mid_x - max_range, mid_x + max_range)
         ax.set_ylim(mid_y - max_range, mid_y + max_range)
         ax.set_zlim(mid_z - max_range, mid_z + max_range)
-        
+    
         plt.tick_params(labelsize=5)
-        ax.set_xlabel('X', fontsize=10)
-        ax.set_ylabel('Y', fontsize=10)
-        ax.set_zlabel('Z', fontsize=10)
+        ax.set_xlabel("X", fontsize=10)
+        ax.set_ylabel("Y", fontsize=10)
+        ax.set_zlabel("Z", fontsize=10)
     
         return ax
 
@@ -204,7 +198,7 @@ chair for example.
     # Download data from the openvino_notebooks storage
     point_data = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/pts/chair.pts",
-        directory="data"
+        directory="data",
     )
     
     points = load_data(str(point_data))
@@ -212,9 +206,9 @@ chair for example.
     Y = points[:, 2]
     Z = points[:, 1]
     ax = visualize(points)
-    ax.scatter3D(X, Y, Z, s=5, cmap="jet", marker="o", label='chair')
-    ax.set_title('3D Visualization')
-    plt.legend(loc='upper right', fontsize=8)
+    ax.scatter3D(X, Y, Z, s=5, cmap="jet", marker="o", label="chair")
+    ax.set_title("3D Visualization")
+    plt.legend(loc="upper right", fontsize=8)
     plt.show()
 
 
@@ -226,12 +220,12 @@ chair for example.
 
 .. parsed-literal::
 
-    /tmp/ipykernel_3065558/2347079344.py:12: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
-      ax.scatter3D(X, Y, Z, s=5, cmap="jet", marker="o", label='chair')
+    /tmp/ipykernel_3630/2434168836.py:12: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
+      ax.scatter3D(X, Y, Z, s=5, cmap="jet", marker="o", label="chair")
 
 
 
-.. image:: 224-3D-segmentation-point-clouds-with-output_files/224-3D-segmentation-point-clouds-with-output_11_2.png
+.. image:: 3D-segmentation-point-clouds-with-output_files/3D-segmentation-point-clouds-with-output_11_2.png
 
 
 Run inference
@@ -248,7 +242,7 @@ each input point.
 .. code:: ipython3
 
     # Parts of a chair
-    classes = ['back', 'seat', 'leg', 'arm']
+    classes = ["back", "seat", "leg", "arm"]
     
     # Preprocess the input data
     point = points.transpose(1, 0)
@@ -278,8 +272,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -320,17 +314,17 @@ select device from dropdown list for running inference using OpenVINO
         # add current point of the part
         ax.scatter(XCur, YCur, ZCur, s=5, cmap="jet", marker="o", label=classes[i])
     
-    ax.set_title('3D Segmentation Visualization')
-    plt.legend(loc='upper right', fontsize=8)
+    ax.set_title("3D Segmentation Visualization")
+    plt.legend(loc="upper right", fontsize=8)
     plt.show()
 
 
 .. parsed-literal::
 
-    /tmp/ipykernel_3065558/68687822.py:23: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
+    /tmp/ipykernel_3630/2804603389.py:23: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
       ax.scatter(XCur, YCur, ZCur, s=5, cmap="jet", marker="o", label=classes[i])
 
 
 
-.. image:: 224-3D-segmentation-point-clouds-with-output_files/224-3D-segmentation-point-clouds-with-output_16_1.png
+.. image:: 3D-segmentation-point-clouds-with-output_files/3D-segmentation-point-clouds-with-output_16_1.png
 
diff --git a/docs/notebooks/224-3D-segmentation-point-clouds-with-output_files/224-3D-segmentation-point-clouds-with-output_11_2.png b/docs/notebooks/3D-segmentation-point-clouds-with-output_files/3D-segmentation-point-clouds-with-output_11_2.png
similarity index 100%
rename from docs/notebooks/224-3D-segmentation-point-clouds-with-output_files/224-3D-segmentation-point-clouds-with-output_11_2.png
rename to docs/notebooks/3D-segmentation-point-clouds-with-output_files/3D-segmentation-point-clouds-with-output_11_2.png
diff --git a/docs/notebooks/224-3D-segmentation-point-clouds-with-output_files/224-3D-segmentation-point-clouds-with-output_16_1.png b/docs/notebooks/3D-segmentation-point-clouds-with-output_files/3D-segmentation-point-clouds-with-output_16_1.png
similarity index 100%
rename from docs/notebooks/224-3D-segmentation-point-clouds-with-output_files/224-3D-segmentation-point-clouds-with-output_16_1.png
rename to docs/notebooks/3D-segmentation-point-clouds-with-output_files/3D-segmentation-point-clouds-with-output_16_1.png
diff --git a/docs/notebooks/401-object-detection-with-output_files/401-object-detection-with-output_19_0.png b/docs/notebooks/401-object-detection-with-output_files/401-object-detection-with-output_19_0.png
deleted file mode 100644
index 9450055a626..00000000000
--- a/docs/notebooks/401-object-detection-with-output_files/401-object-detection-with-output_19_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/docs/notebooks/402-pose-estimation-with-output_files/402-pose-estimation-with-output_20_0.png b/docs/notebooks/402-pose-estimation-with-output_files/402-pose-estimation-with-output_20_0.png
deleted file mode 100644
index b36188cdcce..00000000000
--- a/docs/notebooks/402-pose-estimation-with-output_files/402-pose-estimation-with-output_20_0.png
+++ /dev/null
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diff --git a/docs/notebooks/403-action-recognition-webcam-with-output_files/403-action-recognition-webcam-with-output_22_0.png b/docs/notebooks/403-action-recognition-webcam-with-output_files/403-action-recognition-webcam-with-output_22_0.png
deleted file mode 100644
index ba6e9e09142..00000000000
--- a/docs/notebooks/403-action-recognition-webcam-with-output_files/403-action-recognition-webcam-with-output_22_0.png
+++ /dev/null
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diff --git a/docs/notebooks/405-paddle-ocr-webcam-with-output_files/405-paddle-ocr-webcam-with-output_30_0.png b/docs/notebooks/405-paddle-ocr-webcam-with-output_files/405-paddle-ocr-webcam-with-output_30_0.png
deleted file mode 100644
index e17e9c8c790..00000000000
--- a/docs/notebooks/405-paddle-ocr-webcam-with-output_files/405-paddle-ocr-webcam-with-output_30_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 590562
diff --git a/docs/notebooks/406-3D-pose-estimation-with-output.rst b/docs/notebooks/406-3D-pose-estimation-with-output.rst
deleted file mode 100644
index 58ce71f495b..00000000000
--- a/docs/notebooks/406-3D-pose-estimation-with-output.rst
+++ /dev/null
@@ -1,1279 +0,0 @@
-Live 3D Human Pose Estimation with OpenVINO
-===========================================
-
-This notebook demonstrates live 3D Human Pose Estimation with OpenVINO
-via a webcam. We utilize the model
-`human-pose-estimation-3d-0001 `__
-from `Open Model
-Zoo `__. At the end
-of this notebook, you will see live inference results from your webcam
-(if available). Alternatively, you can also upload a video file to test
-out the algorithms. **Make sure you have properly installed
-the**\ `Jupyter
-extension `__\ **and
-been using JupyterLab to run the demo as suggested in the
-``README.md``**
-
-   **NOTE**: *To use a webcam, you must run this Jupyter notebook on a
-   computer with a webcam. If you run on a remote server, the webcam
-   will not work. However, you can still do inference on a video file in
-   the final step. This demo utilizes the Python interface in
-   ``Three.js`` integrated with WebGL to process data from the model
-   inference. These results are processed and displayed in the
-   notebook.*
-
-*To ensure that the results are displayed correctly, run the code in a
-recommended browser on one of the following operating systems:* *Ubuntu,
-Windows: Chrome* *macOS: Safari*
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Prerequisites <#prerequisites>`__
--  `Imports <#imports>`__
--  `The model <#the-model>`__
-
-   -  `Download the model <#download-the-model>`__
-   -  `Convert Model to OpenVINO IR
-      format <#convert-model-to-openvino-ir-format>`__
-   -  `Select inference device <#select-inference-device>`__
-   -  `Load the model <#load-the-model>`__
-
--  `Processing <#processing>`__
-
-   -  `Model Inference <#model-inference>`__
-   -  `Draw 2D Pose Overlays <#draw-2d-pose-overlays>`__
-   -  `Main Processing Function <#main-processing-function>`__
-
--  `Run <#run>`__
-
-Prerequisites
--------------
-
-
-
-**The ``pythreejs`` extension may not display properly when using the
-latest Jupyter Notebook release (2.4.1). Therefore, it is recommended to
-use Jupyter Lab instead.**
-
-.. code:: ipython3
-
-    %pip install pythreejs "openvino-dev>=2024.0.0"
-
-
-.. parsed-literal::
-
-    Collecting pythreejs
-      Using cached pythreejs-2.4.2-py3-none-any.whl.metadata (5.4 kB)
-    Requirement already satisfied: openvino-dev>=2024.0.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.0.0)
-
-
-.. parsed-literal::
-
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-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
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-    Note: you may need to restart the kernel to use updated packages.
-
-
-Imports
--------
-
-
-
-.. code:: ipython3
-
-    import collections
-    import sys
-    import time
-    from pathlib import Path
-
-    import cv2
-    import ipywidgets as widgets
-    import numpy as np
-    from IPython.display import clear_output, display
-    import openvino as ov
-
-    sys.path.append("../utils")
-    import notebook_utils as utils
-
-    sys.path.append("./engine")
-    import engine.engine3js as engine
-    from engine.parse_poses import parse_poses
-
-The model
----------
-
-
-
-Download the model
-~~~~~~~~~~~~~~~~~~
-
-
-
-We use ``omz_downloader``, which is a command line tool from the
-``openvino-dev`` package. ``omz_downloader`` automatically creates a
-directory structure and downloads the selected model.
-
-.. code:: ipython3
-
-    # directory where model will be downloaded
-    base_model_dir = "model"
-
-    # model name as named in Open Model Zoo
-    model_name = "human-pose-estimation-3d-0001"
-    # selected precision (FP32, FP16)
-    precision = "FP32"
-
-    BASE_MODEL_NAME = f"{base_model_dir}/public/{model_name}/{model_name}"
-    model_path = Path(BASE_MODEL_NAME).with_suffix(".pth")
-    onnx_path = Path(BASE_MODEL_NAME).with_suffix(".onnx")
-
-    ir_model_path = f"model/public/{model_name}/{precision}/{model_name}.xml"
-    model_weights_path = f"model/public/{model_name}/{precision}/{model_name}.bin"
-
-    if not model_path.exists():
-        download_command = (
-            f"omz_downloader " f"--name {model_name} " f"--output_dir {base_model_dir}"
-        )
-        ! $download_command
-
-
-.. parsed-literal::
-
-    ################|| Downloading human-pose-estimation-3d-0001 ||################
-
-    ========== Downloading model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.tar.gz
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-.. parsed-literal::
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-
-
-.. parsed-literal::
-
-
-    ========== Unpacking model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.tar.gz
-
-
-Convert Model to OpenVINO IR format
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-The selected model comes from the public directory, which means it must
-be converted into OpenVINO Intermediate Representation (OpenVINO IR). We
-use ``omz_converter`` to convert the ONNX format model to the OpenVINO
-IR format.
-
-.. code:: ipython3
-
-    if not onnx_path.exists():
-        convert_command = (
-            f"omz_converter "
-            f"--name {model_name} "
-            f"--precisions {precision} "
-            f"--download_dir {base_model_dir} "
-            f"--output_dir {base_model_dir}"
-        )
-        ! $convert_command
-
-
-.. parsed-literal::
-
-    ========== Converting human-pose-estimation-3d-0001 to ONNX
-    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=model/public/human-pose-estimation-3d-0001 --model-name=PoseEstimationWithMobileNet --model-param=is_convertible_by_mo=True --import-module=model --weights=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.pth --input-shape=1,3,256,448 --input-names=data --output-names=features,heatmaps,pafs --output-file=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx
-
-
-
-.. parsed-literal::
-
-    ONNX check passed successfully.
-
-
-.. parsed-literal::
-
-
-    ========== Converting human-pose-estimation-3d-0001 to IR (FP32)
-    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/human-pose-estimation-3d-0001/FP32 --model_name=human-pose-estimation-3d-0001 --input=data '--mean_values=data[128.0,128.0,128.0]' '--scale_values=data[255.0,255.0,255.0]' --output=features,heatmaps,pafs --input_model=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 256, 448]' --compress_to_fp16=False
-
-
-
-.. parsed-literal::
-
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/406-3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/406-3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.bin
-
-
-Select inference device
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-Select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    core = ov.Core()
-
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
-        disabled=False,
-    )
-
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-Load the model
-~~~~~~~~~~~~~~
-
-
-
-Converted models are located in a fixed structure, which indicates
-vendor, model name and precision.
-
-First, initialize the inference engine, OpenVINO Runtime. Then, read the
-network architecture and model weights from the ``.bin`` and ``.xml``
-files to compile for the desired device. An inference request is then
-created to infer the compiled model.
-
-.. code:: ipython3
-
-    # initialize inference engine
-    core = ov.Core()
-    # read the network and corresponding weights from file
-    model = core.read_model(model=ir_model_path, weights=model_weights_path)
-    # load the model on the specified device
-    compiled_model = core.compile_model(model=model, device_name=device.value)
-    infer_request = compiled_model.create_infer_request()
-    input_tensor_name = model.inputs[0].get_any_name()
-
-    # get input and output names of nodes
-    input_layer = compiled_model.input(0)
-    output_layers = list(compiled_model.outputs)
-
-The input for the model is data from the input image and the outputs are
-heat maps, PAF (part affinity fields) and features.
-
-.. code:: ipython3
-
-    input_layer.any_name, [o.any_name for o in output_layers]
-
-
-
-
-.. parsed-literal::
-
-    ('data', ['features', 'heatmaps', 'pafs'])
-
-
-
-Processing
-----------
-
-
-
-Model Inference
-~~~~~~~~~~~~~~~
-
-
-
-Frames captured from video files or the live webcam are used as the
-input for the 3D model. This is how you obtain the output heat maps, PAF
-(part affinity fields) and features.
-
-.. code:: ipython3
-
-    def model_infer(scaled_img, stride):
-        """
-        Run model inference on the input image
-
-        Parameters:
-            scaled_img: resized image according to the input size of the model
-            stride: int, the stride of the window
-        """
-
-        # Remove excess space from the picture
-        img = scaled_img[
-            0 : scaled_img.shape[0] - (scaled_img.shape[0] % stride),
-            0 : scaled_img.shape[1] - (scaled_img.shape[1] % stride),
-        ]
-
-        img = np.transpose(img, (2, 0, 1))[
-            None,
-        ]
-        infer_request.infer({input_tensor_name: img})
-        # A set of three inference results is obtained
-        results = {
-            name: infer_request.get_tensor(name).data[:]
-            for name in {"features", "heatmaps", "pafs"}
-        }
-        # Get the results
-        results = (results["features"][0], results["heatmaps"][0], results["pafs"][0])
-
-        return results
-
-Draw 2D Pose Overlays
-~~~~~~~~~~~~~~~~~~~~~
-
-
-
-We need to define some connections between the joints in advance, so
-that we can draw the structure of the human body in the resulting image
-after obtaining the inference results. Joints are drawn as circles and
-limbs are drawn as lines. The code is based on the `3D Human Pose
-Estimation
-Demo `__
-from Open Model Zoo.
-
-.. code:: ipython3
-
-    # 3D edge index array
-    body_edges = np.array(
-        [
-            [0, 1],
-            [0, 9], [9, 10], [10, 11],    # neck - r_shoulder - r_elbow - r_wrist
-            [0, 3], [3, 4], [4, 5],       # neck - l_shoulder - l_elbow - l_wrist
-            [1, 15], [15, 16],            # nose - l_eye - l_ear
-            [1, 17], [17, 18],            # nose - r_eye - r_ear
-            [0, 6], [6, 7], [7, 8],       # neck - l_hip - l_knee - l_ankle
-            [0, 12], [12, 13], [13, 14],  # neck - r_hip - r_knee - r_ankle
-        ]
-    )
-
-
-    body_edges_2d = np.array(
-        [
-            [0, 1],                       # neck - nose
-            [1, 16], [16, 18],            # nose - l_eye - l_ear
-            [1, 15], [15, 17],            # nose - r_eye - r_ear
-            [0, 3], [3, 4], [4, 5],       # neck - l_shoulder - l_elbow - l_wrist
-            [0, 9], [9, 10], [10, 11],    # neck - r_shoulder - r_elbow - r_wrist
-            [0, 6], [6, 7], [7, 8],       # neck - l_hip - l_knee - l_ankle
-            [0, 12], [12, 13], [13, 14],  # neck - r_hip - r_knee - r_ankle
-        ]
-    )
-
-
-    def draw_poses(frame, poses_2d, scaled_img, use_popup):
-        """
-        Draw 2D pose overlays on the image to visualize estimated poses.
-        Joints are drawn as circles and limbs are drawn as lines.
-
-        :param frame: the input image
-        :param poses_2d: array of human joint pairs
-        """
-        for pose in poses_2d:
-            pose = np.array(pose[0:-1]).reshape((-1, 3)).transpose()
-            was_found = pose[2] > 0
-
-            pose[0], pose[1] = (
-                pose[0] * frame.shape[1] / scaled_img.shape[1],
-                pose[1] * frame.shape[0] / scaled_img.shape[0],
-            )
-
-            # Draw joints.
-            for edge in body_edges_2d:
-                if was_found[edge[0]] and was_found[edge[1]]:
-                    cv2.line(
-                        frame,
-                        tuple(pose[0:2, edge[0]].astype(np.int32)),
-                        tuple(pose[0:2, edge[1]].astype(np.int32)),
-                        (255, 255, 0),
-                        4,
-                        cv2.LINE_AA,
-                    )
-            # Draw limbs.
-            for kpt_id in range(pose.shape[1]):
-                if pose[2, kpt_id] != -1:
-                    cv2.circle(
-                        frame,
-                        tuple(pose[0:2, kpt_id].astype(np.int32)),
-                        3,
-                        (0, 255, 255),
-                        -1,
-                        cv2.LINE_AA,
-                    )
-
-        return frame
-
-Main Processing Function
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Run 3D pose estimation on the specified source. It could be either a
-webcam feed or a video file.
-
-.. code:: ipython3
-
-    def run_pose_estimation(source=0, flip=False, use_popup=False, skip_frames=0):
-        """
-        2D image as input, using OpenVINO as inference backend,
-        get joints 3D coordinates, and draw 3D human skeleton in the scene
-
-        :param source:      The webcam number to feed the video stream with primary webcam set to "0", or the video path.
-        :param flip:        To be used by VideoPlayer function for flipping capture image.
-        :param use_popup:   False for showing encoded frames over this notebook, True for creating a popup window.
-        :param skip_frames: Number of frames to skip at the beginning of the video.
-        """
-
-        focal_length = -1  # default
-        stride = 8
-        player = None
-        skeleton_set = None
-
-        try:
-            # create video player to play with target fps  video_path
-            # get the frame from camera
-            # You can skip first N frames to fast forward video. change 'skip_first_frames'
-            player = utils.VideoPlayer(source, flip=flip, fps=30, skip_first_frames=skip_frames)
-            # start capturing
-            player.start()
-
-            input_image = player.next()
-            # set the window size
-            resize_scale = 450 / input_image.shape[1]
-            windows_width = int(input_image.shape[1] * resize_scale)
-            windows_height = int(input_image.shape[0] * resize_scale)
-
-            # use visualization library
-            engine3D = engine.Engine3js(grid=True, axis=True, view_width=windows_width, view_height=windows_height)
-
-            if use_popup:
-                # display the 3D human pose in this notebook, and origin frame in popup window
-                display(engine3D.renderer)
-                title = "Press ESC to Exit"
-                cv2.namedWindow(title, cv2.WINDOW_KEEPRATIO | cv2.WINDOW_AUTOSIZE)
-            else:
-                # set the 2D image box, show both human pose and image in the notebook
-                imgbox = widgets.Image(
-                    format="jpg", height=windows_height, width=windows_width
-                )
-                display(widgets.HBox([engine3D.renderer, imgbox]))
-
-            skeleton = engine.Skeleton(body_edges=body_edges)
-
-            processing_times = collections.deque()
-
-            while True:
-                # grab the frame
-                frame = player.next()
-                if frame is None:
-                    print("Source ended")
-                    break
-
-                # resize image and change dims to fit neural network input
-                # (see https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/human-pose-estimation-3d-0001)
-                scaled_img = cv2.resize(frame, dsize=(model.inputs[0].shape[3], model.inputs[0].shape[2]))
-
-                if focal_length < 0:  # Focal length is unknown
-                    focal_length = np.float32(0.8 * scaled_img.shape[1])
-
-                # inference start
-                start_time = time.time()
-                # get results
-                inference_result = model_infer(scaled_img, stride)
-
-                # inference stop
-                stop_time = time.time()
-                processing_times.append(stop_time - start_time)
-                # Process the point to point coordinates of the data
-                poses_3d, poses_2d = parse_poses(inference_result, 1, stride, focal_length, True)
-
-                # use processing times from last 200 frames
-                if len(processing_times) > 200:
-                    processing_times.popleft()
-
-                processing_time = np.mean(processing_times) * 1000
-                fps = 1000 / processing_time
-
-                if len(poses_3d) > 0:
-                    # From here, you can rotate the 3D point positions using the function "draw_poses",
-                    # or you can directly make the correct mapping below to properly display the object image on the screen
-                    poses_3d_copy = poses_3d.copy()
-                    x = poses_3d_copy[:, 0::4]
-                    y = poses_3d_copy[:, 1::4]
-                    z = poses_3d_copy[:, 2::4]
-                    poses_3d[:, 0::4], poses_3d[:, 1::4], poses_3d[:, 2::4] = (
-                        -z + np.ones(poses_3d[:, 2::4].shape) * 200,
-                        -y + np.ones(poses_3d[:, 2::4].shape) * 100,
-                        -x,
-                    )
-
-                    poses_3d = poses_3d.reshape(poses_3d.shape[0], 19, -1)[:, :, 0:3]
-                    people = skeleton(poses_3d=poses_3d)
-
-                    try:
-                        engine3D.scene_remove(skeleton_set)
-                    except Exception:
-                        pass
-
-                    engine3D.scene_add(people)
-                    skeleton_set = people
-
-                    # draw 2D
-                    frame = draw_poses(frame, poses_2d, scaled_img, use_popup)
-
-                else:
-                    try:
-                        engine3D.scene_remove(skeleton_set)
-                        skeleton_set = None
-                    except Exception:
-                        pass
-
-                cv2.putText(
-                    frame,
-                    f"Inference time: {processing_time:.1f}ms ({fps:.1f} FPS)",
-                    (10, 30),
-                    cv2.FONT_HERSHEY_COMPLEX,
-                    0.7,
-                    (0, 0, 255),
-                    1,
-                    cv2.LINE_AA,
-                )
-
-                if use_popup:
-                    cv2.imshow(title, frame)
-                    key = cv2.waitKey(1)
-                    # escape = 27, use ESC to exit
-                    if key == 27:
-                        break
-                else:
-                    # encode numpy array to jpg
-                    imgbox.value = cv2.imencode(
-                        ".jpg",
-                        frame,
-                        params=[cv2.IMWRITE_JPEG_QUALITY, 90],
-                    )[1].tobytes()
-
-                engine3D.renderer.render(engine3D.scene, engine3D.cam)
-
-        except KeyboardInterrupt:
-            print("Interrupted")
-        except RuntimeError as e:
-            print(e)
-        finally:
-            clear_output()
-            if player is not None:
-                # stop capturing
-                player.stop()
-            if use_popup:
-                cv2.destroyAllWindows()
-            if skeleton_set:
-                engine3D.scene_remove(skeleton_set)
-
-Run
----
-
-
-
-Run, using a webcam as the video input. By default, the primary webcam
-is set with ``source=0``. If you have multiple webcams, each one will be
-assigned a consecutive number starting at 0. Set ``flip=True`` when
-using a front-facing camera. Some web browsers, especially Mozilla
-Firefox, may cause flickering. If you experience flickering, set
-``use_popup=True``.
-
-   **NOTE**:
-
-   *1. To use this notebook with a webcam, you need to run the notebook
-   on a computer with a webcam. If you run the notebook on a server
-   (e.g. Binder), the webcam will not work.*
-
-   *2. Popup mode may not work if you run this notebook on a remote
-   computer (e.g. Binder).*
-
-If you do not have a webcam, you can still run this demo with a video
-file. Any `format supported by
-OpenCV `__
-will work.
-
-Using the following method, you can click and move your mouse over the
-picture on the left to interact.
-
-.. code:: ipython3
-
-    USE_WEBCAM = False
-
-    cam_id = 0
-    video_path = "https://github.com/intel-iot-devkit/sample-videos/raw/master/face-demographics-walking.mp4"
-
-    source = cam_id if USE_WEBCAM else video_path
-
-    run_pose_estimation(source=source, flip=isinstance(source, int), use_popup=False)
diff --git a/docs/notebooks/407-person-tracking-with-output_files/407-person-tracking-with-output_17_3.png b/docs/notebooks/407-person-tracking-with-output_files/407-person-tracking-with-output_17_3.png
deleted file mode 100644
index 1be4ba9fa45..00000000000
--- a/docs/notebooks/407-person-tracking-with-output_files/407-person-tracking-with-output_17_3.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eacdcf6e619052ffe8bea1810c93678559cf210808d871e0e2b8a81939e1fd26
-size 106259
diff --git a/docs/notebooks/407-person-tracking-with-output_files/407-person-tracking-with-output_25_0.png b/docs/notebooks/407-person-tracking-with-output_files/407-person-tracking-with-output_25_0.png
deleted file mode 100644
index d8cf16f16e7..00000000000
--- a/docs/notebooks/407-person-tracking-with-output_files/407-person-tracking-with-output_25_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:effa513581f21c88361a2a8c7612f4d66f90d3f25ee9c52d007b33a6470d9b71
-size 219379
diff --git a/docs/notebooks/403-action-recognition-webcam-with-output.rst b/docs/notebooks/action-recognition-webcam-with-output.rst
similarity index 94%
rename from docs/notebooks/403-action-recognition-webcam-with-output.rst
rename to docs/notebooks/action-recognition-webcam-with-output.rst
index 6eeb203f20f..3b3455ba9b4 100644
--- a/docs/notebooks/403-action-recognition-webcam-with-output.rst
+++ b/docs/notebooks/action-recognition-webcam-with-output.rst
@@ -57,14 +57,9 @@ Table of contents:
 
 .. code:: ipython3
 
-    %pip install -q "openvino-dev>=2024.0.0"
+    %pip install -q "openvino>=2024.0.0" "opencv-python" "tqdm"
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -91,11 +86,12 @@ Imports
     from openvino.runtime.ie_api import CompiledModel
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    open("notebook_utils.py", "w").write(r.text)
     import notebook_utils as utils
 
 The models
@@ -132,12 +128,8 @@ and the system automatically downloads the two models
     model_name = "action-recognition-0001"
     # Selected precision (FP32, FP16, FP16-INT8).
     precision = "FP16"
-    model_path_decoder = (
-        f"model/intel/{model_name}/{model_name}-decoder/{precision}/{model_name}-decoder.xml"
-    )
-    model_path_encoder = (
-        f"model/intel/{model_name}/{model_name}-encoder/{precision}/{model_name}-encoder.xml"
-    )
+    model_path_decoder = f"model/intel/{model_name}/{model_name}-decoder/{precision}/{model_name}-decoder.xml"
+    model_path_encoder = f"model/intel/{model_name}/{model_name}-encoder/{precision}/{model_name}-encoder.xml"
     encoder_url = f"https://storage.openvinotoolkit.org/repositories/open_model_zoo/temp/{model_name}/{model_name}-encoder/{precision}/{model_name}-encoder.xml"
     decoder_url = f"https://storage.openvinotoolkit.org/repositories/open_model_zoo/temp/{model_name}/{model_name}-decoder/{precision}/{model_name}-decoder.xml"
     
@@ -177,10 +169,10 @@ also provides the text file embedded into this notebook.
     # Download the text from the openvino_notebooks storage
     vocab_file_path = utils.download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/text/kinetics.txt",
-        directory="data"
+        directory="data",
     )
     
-    with vocab_file_path.open(mode='r') as f:
+    with vocab_file_path.open(mode="r") as f:
         labels = [line.strip() for line in f]
     
     print(labels[0:9], np.shape(labels))
@@ -223,8 +215,8 @@ Select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -255,11 +247,11 @@ Model Initialization function
         Read the network and weights from a file, load the
         model on CPU and get input and output names of nodes
     
-        :param: 
+        :param:
                 model: model architecture path *.xml
                 device: inference device
         :retuns:
-                compiled_model: Compiled model 
+                compiled_model: Compiled model
                 input_key: Input node for model
                 output_key: Output node for model
         """
@@ -449,10 +441,7 @@ Following the pipeline above, you will use the next functions to:
         return preprocessed, roi
     
     
-    def encoder(
-        preprocessed: np.ndarray,
-        compiled_model: CompiledModel
-    ) -> List:
+    def encoder(preprocessed: np.ndarray, compiled_model: CompiledModel) -> List:
         """
         Encoder Inference per frame. This function calls the network previously
         configured for the encoder model (compiled_model), extracts the data
@@ -463,7 +452,7 @@ Following the pipeline above, you will use the next functions to:
         :returns: encoder_output: embedding layer that is appended with each arriving frame
         """
         output_key_en = compiled_model.output(0)
-        
+    
         # Get results on action-recognition-0001-encoder model
         infer_result_encoder = compiled_model([preprocessed])[output_key_en]
         return infer_result_encoder
@@ -692,11 +681,10 @@ multi-camera systems).
 
 
 
-.. image:: 403-action-recognition-webcam-with-output_files/403-action-recognition-webcam-with-output_22_0.png
+.. image:: action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png
 
 
 .. parsed-literal::
 
     Source ended
 
-
diff --git a/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png b/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png
new file mode 100644
index 00000000000..3fa1f8411a3
--- /dev/null
+++ b/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a88055dc2dbfefd2c65a09518b7c3d76951fee1961547834059ed7dadb633cc
+size 69011
diff --git a/docs/notebooks/all_notebooks_paths.txt b/docs/notebooks/all_notebooks_paths.txt
index 821fea0cd05..a9700daeaef 100644
--- a/docs/notebooks/all_notebooks_paths.txt
+++ b/docs/notebooks/all_notebooks_paths.txt
@@ -1,155 +1,161 @@
-notebooks/001-hello-world/001-hello-world.ipynb
-notebooks/002-openvino-api/002-openvino-api.ipynb
-notebooks/003-hello-segmentation/003-hello-segmentation.ipynb
-notebooks/004-hello-detection/004-hello-detection.ipynb
-notebooks/101-tensorflow-classification-to-openvino/101-tensorflow-classification-to-openvino.ipynb
-notebooks/102-pytorch-to-openvino/102-pytorch-onnx-to-openvino.ipynb
-notebooks/102-pytorch-to-openvino/102-pytorch-to-openvino.ipynb
-notebooks/103-paddle-to-openvino/103-paddle-to-openvino-classification.ipynb
-notebooks/104-model-tools/104-model-tools.ipynb
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+notebooks/triposr-3d-reconstruction/triposr-3d-reconstruction.ipynb
+notebooks/typo-detector/typo-detector.ipynb
+notebooks/vehicle-detection-and-recognition/vehicle-detection-and-recognition.ipynb
+notebooks/vision-background-removal/vision-background-removal.ipynb
+notebooks/vision-image-colorization/vision-image-colorization.ipynb
+notebooks/vision-monodepth/vision-monodepth.ipynb
+notebooks/vision-paddlegan-anime/vision-paddlegan-anime.ipynb
+notebooks/vision-paddlegan-superresolution/vision-paddlegan-superresolution.ipynb
+notebooks/vision-superresolution/vision-superresolution-image.ipynb
+notebooks/vision-superresolution/vision-superresolution-video.ipynb
+notebooks/whisper-subtitles-generation/whisper-convert.ipynb
+notebooks/whisper-subtitles-generation/whisper-nncf-quantize.ipynb
+notebooks/wuerstchen-image-generation/wuerstchen-image-generation.ipynb
+notebooks/yolov7-optimization/yolov7-optimization.ipynb
+notebooks/yolov8-optimization/yolov8-instance-segmentation.ipynb
+notebooks/yolov8-optimization/yolov8-keypoint-detection.ipynb
+notebooks/yolov8-optimization/yolov8-obb.ipynb
+notebooks/yolov8-optimization/yolov8-object-detection.ipynb
+notebooks/yolov9-optimization/yolov9-optimization.ipynb
+notebooks/zeroscope-text2video/zeroscope-text2video.ipynb
diff --git a/docs/notebooks/277-amused-lightweight-text-to-image-with-output.rst b/docs/notebooks/amused-lightweight-text-to-image-with-output.rst
similarity index 55%
rename from docs/notebooks/277-amused-lightweight-text-to-image-with-output.rst
rename to docs/notebooks/amused-lightweight-text-to-image-with-output.rst
index 191e143bd7d..551f9a0b313 100644
--- a/docs/notebooks/277-amused-lightweight-text-to-image-with-output.rst
+++ b/docs/notebooks/amused-lightweight-text-to-image-with-output.rst
@@ -30,6 +30,13 @@ Table of contents:
 
 -  `Compiling models and prepare
    pipeline <#compiling-models-and-prepare-pipeline>`__
+-  `Quantization <#quantization>`__
+
+   -  `Prepare calibration dataset <#prepare-calibration-dataset>`__
+   -  `Run model quantization <#run-model-quantization>`__
+   -  `Compute Inception Scores and inference
+      time <#compute-inception-scores-and-inference-time>`__
+
 -  `Interactive inference <#interactive-inference>`__
 
 Prerequisites
@@ -39,12 +46,13 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q "diffusers>=0.25.0" "openvino>=2023.2.0" "accelerate>=0.20.3" gradio torch --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q transformers "diffusers>=0.25.0" "openvino>=2023.2.0" "accelerate>=0.20.3" "gradio>=4.19" "torch>=2.1" "pillow" "torchmetrics" "torch-fidelity" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "nncf>=2.9.0" datasets
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    WARNING: typer 0.12.3 does not provide the extra 'all'
     
 
 .. parsed-literal::
@@ -52,6 +60,11 @@ Prerequisites
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
 Load and run the original pipeline
 ----------------------------------
 
@@ -68,16 +81,8 @@ Load and run the original pipeline
     )
     
     prompt = "kind smiling ghost"
-    image = pipe(prompt, generator=torch.Generator('cpu').manual_seed(8)).images[0]
-    image.save('text2image_256.png')
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-      torch.utils._pytree._register_pytree_node(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-      torch.utils._pytree._register_pytree_node(
+    image = pipe(prompt, generator=torch.Generator("cpu").manual_seed(8)).images[0]
+    image.save("text2image_256.png")
 
 
 
@@ -86,18 +91,6 @@ Load and run the original pipeline
     Loading pipeline components...:   0%|          | 0/5 [00:00 1 or self.sliding_window is not None:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if past_key_values_length > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:622: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:622: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       encoder_states = () if output_hidden_states else None
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:627: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:627: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if output_hidden_states:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:281: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:281: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:289: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:289: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:321: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:321: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:650: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:650: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if output_hidden_states:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:653: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:653: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if not return_dict:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:744: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:744: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if not return_dict:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:1229: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:1229: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if not return_dict:
 
 
@@ -265,28 +247,31 @@ Convert the U-ViT transformer
             super().__init__()
             self.transformer = transformer
     
-        def forward(self, latents=None, micro_conds=None, pooled_text_emb=None, encoder_hidden_states=None):
-            
+        def forward(
+            self,
+            latents=None,
+            micro_conds=None,
+            pooled_text_emb=None,
+            encoder_hidden_states=None,
+        ):
             return self.transformer(
                 latents,
-                micro_conds=micro_conds, 
-                pooled_text_emb=pooled_text_emb, 
+                micro_conds=micro_conds,
+                pooled_text_emb=pooled_text_emb,
                 encoder_hidden_states=encoder_hidden_states,
             )
     
     
     shape = (1, 16, 16)
-    latents = torch.full(
-        shape, pipe.scheduler.config.mask_token_id, dtype=torch.long
-    )
+    latents = torch.full(shape, pipe.scheduler.config.mask_token_id, dtype=torch.long)
     latents = torch.cat([latents] * 2)
     
     
     example_input = {
-        'latents': latents,
-        'micro_conds': torch.rand([2, 5], dtype=torch.float32),
-        'pooled_text_emb': torch.rand([2, 768], dtype=torch.float32),
-        'encoder_hidden_states': torch.rand([2, 77, 768], dtype=torch.float32), 
+        "latents": latents,
+        "micro_conds": torch.rand([2, 5], dtype=torch.float32),
+        "pooled_text_emb": torch.rand([2, 768], dtype=torch.float32),
+        "encoder_hidden_states": torch.rand([2, 77, 768], dtype=torch.float32),
     }
     
     
@@ -297,7 +282,7 @@ Convert the U-ViT transformer
 Convert VQ-GAN decoder (VQVAE)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Function ``get_latents`` is
+ Function ``get_latents`` is
 needed to return real latents for the conversion. Due to the VQVAE
 implementation autogenerated tensor of the required shape is not
 suitable. This function repeats part of ``AmusedPipeline``.
@@ -306,12 +291,9 @@ suitable. This function repeats part of ``AmusedPipeline``.
 
     def get_latents():
         shape = (1, 16, 16)
-        latents = torch.full(
-            shape, pipe.scheduler.config.mask_token_id, dtype=torch.long
-        )
+        latents = torch.full(shape, pipe.scheduler.config.mask_token_id, dtype=torch.long)
         model_input = torch.cat([latents] * 2)
-        
-        
+    
         model_output = pipe.transformer(
             model_input,
             micro_conds=torch.rand([2, 5], dtype=torch.float32),
@@ -321,8 +303,7 @@ suitable. This function repeats part of ``AmusedPipeline``.
         guidance_scale = 10.0
         uncond_logits, cond_logits = model_output.chunk(2)
         model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)
-        
-        
+    
         latents = pipe.scheduler.step(
             model_output=model_output,
             timestep=torch.tensor(0),
@@ -341,7 +322,7 @@ suitable. This function repeats part of ``AmusedPipeline``.
             outputs = self.vqvae.decode(
                 latents,
                 force_not_quantize=force_not_quantize,
-                shape=shape.tolist(), 
+                shape=shape.tolist(),
             )
     
             return outputs
@@ -349,9 +330,9 @@ suitable. This function repeats part of ``AmusedPipeline``.
     
     latents = get_latents()
     example_vqvae_input = {
-        'latents': latents,
-        'force_not_quantize': torch.tensor(True),
-        'shape': torch.tensor((1, 16, 16, 64))
+        "latents": latents,
+        "force_not_quantize": torch.tensor(True),
+        "shape": torch.tensor((1, 16, 16, 64)),
     }
     
     convert(VQVAEWrapper(pipe.vqvae), VQVAE_OV_PATH, example_vqvae_input)
@@ -359,13 +340,13 @@ suitable. This function repeats part of ``AmusedPipeline``.
 
 .. parsed-literal::
 
-    /tmp/ipykernel_3081835/249287788.py:38: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /tmp/ipykernel_4430/3779428577.py:34: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       shape=shape.tolist(),
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/vq_model.py:144: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/vq_model.py:144: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if not force_not_quantize:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:149: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:149: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:165: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:165: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if hidden_states.shape[0] >= 64:
 
 
@@ -384,8 +365,8 @@ Select device from dropdown list for running inference using OpenVINO.
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -423,19 +404,19 @@ wrapper classes return ``torch.Tensor``\ s instead of ``np.array``\ s.
     
         def forward(self, input_ids=None, return_dict=None, output_hidden_states=None):
             inputs = {
-                'input_ids': input_ids,
-                'return_dict': return_dict,
-                'output_hidden_states': output_hidden_states
+                "input_ids": input_ids,
+                "return_dict": return_dict,
+                "output_hidden_states": output_hidden_states,
             }
-            
+    
             outs = self.text_encoder(inputs)
     
-            outputs = namedtuple('CLIPTextModelOutput', ('text_embeds', 'last_hidden_state', 'hidden_states'))
-            
+            outputs = namedtuple("CLIPTextModelOutput", ("text_embeds", "last_hidden_state", "hidden_states"))
+    
             text_embeds = torch.from_numpy(outs[0])
             last_hidden_state = torch.from_numpy(outs[1])
             hidden_states = list(torch.from_numpy(out) for out in outs.values())[2:]
-            
+    
             return outputs(text_embeds, last_hidden_state, hidden_states)
 
 .. code:: ipython3
@@ -449,12 +430,12 @@ wrapper classes return ``torch.Tensor``\ s instead of ``np.array``\ s.
         def forward(self, latents=None, micro_conds=None, pooled_text_emb=None, encoder_hidden_states=None, **kwargs):
             outputs = self.transformer(
                 {
-                    'latents': latents,
-                    'micro_conds': micro_conds, 
-                    'pooled_text_emb': pooled_text_emb, 
-                    'encoder_hidden_states': encoder_hidden_states,
+                    "latents": latents,
+                    "micro_conds": micro_conds,
+                    "pooled_text_emb": pooled_text_emb,
+                    "encoder_hidden_states": encoder_hidden_states,
                 },
-                share_inputs=False
+                share_inputs=False,
             )
     
             return torch.from_numpy(outputs[0])
@@ -470,14 +451,14 @@ wrapper classes return ``torch.Tensor``\ s instead of ``np.array``\ s.
     
         def decode(self, latents=None, force_not_quantize=True, shape=None):
             inputs = {
-                'latents': latents,
-                'force_not_quantize': force_not_quantize,
-                'shape': torch.tensor(shape)
+                "latents": latents,
+                "force_not_quantize": force_not_quantize,
+                "shape": torch.tensor(shape),
             }
-            
+    
             outs = self.vqvae(inputs)
-            outs = namedtuple('VQVAE', 'sample')(torch.from_numpy(outs[0]))
-            
+            outs = namedtuple("VQVAE", "sample")(torch.from_numpy(outs[0]))
+    
             return outs
 
 And insert wrappers instances in the pipeline:
@@ -490,20 +471,20 @@ And insert wrappers instances in the pipeline:
     vqvae = pipe.vqvae
     text_encoder = pipe.text_encoder
     
-    pipe.__dict__["_internal_dict"]['_execution_device'] = pipe._execution_device  # this is to avoid some problem that can occur in the pipeline 
+    pipe.__dict__["_internal_dict"]["_execution_device"] = pipe._execution_device  # this is to avoid some problem that can occur in the pipeline
     pipe.register_modules(
         text_encoder=ConvTextEncoderWrapper(ov_text_encoder, text_encoder.config),
         transformer=ConvTransformerWrapper(ov_transformer, transformer.config),
         vqvae=ConvVQVAEWrapper(ov_vqvae, vqvae.dtype, vqvae.config),
     )
     
-    image = pipe(prompt, generator=torch.Generator('cpu').manual_seed(8)).images[0]
-    image.save('text2image_256.png')
+    image = pipe(prompt, generator=torch.Generator("cpu").manual_seed(8)).images[0]
+    image.save("text2image_256.png")
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
       deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
 
 
@@ -520,23 +501,308 @@ And insert wrappers instances in the pipeline:
 
 
 
-.. image:: 277-amused-lightweight-text-to-image-with-output_files/277-amused-lightweight-text-to-image-with-output_28_0.png
+.. image:: amused-lightweight-text-to-image-with-output_files/amused-lightweight-text-to-image-with-output_28_0.png
 
 
 
+Quantization
+------------
+
+
+
+`NNCF `__ enables
+post-training quantization by adding quantization layers into model
+graph and then using a subset of the training dataset to initialize the
+parameters of these additional quantization layers. Quantized operations
+are executed in ``INT8`` instead of ``FP32``/``FP16`` making model
+inference faster.
+
+According to ``Amused`` pipeline structure, the vision transformer model
+takes up significant portion of the overall pipeline execution time. Now
+we will show you how to optimize the UNet part using
+`NNCF `__ to reduce
+computation cost and speed up the pipeline. Quantizing the rest of the
+pipeline does not significantly improve inference performance but can
+lead to a substantial degradation of generations quality.
+
+We also estimate the quality of generations produced by optimized
+pipeline with `Inception
+Score `__ which is often
+used to measure quality of text-to-image generation systems.
+
+The steps are the following:
+
+1. Create a calibration dataset for quantization.
+2. Run ``nncf.quantize()`` on the model.
+3. Save the quantized model using ``openvino.save_model()`` function.
+4. Compare inference time and Inception score for original and quantized
+   pipelines.
+
+Please select below whether you would like to run quantization to
+improve model inference speed.
+
+   **NOTE**: Quantization is time and memory consuming operation.
+   Running quantization code below may take some time.
+
+.. code:: ipython3
+
+    QUANTIZED_TRANSFORMER_OV_PATH = Path(str(TRANSFORMER_OV_PATH).replace(".xml", "_quantized.xml"))
+    
+    to_quantize = widgets.Checkbox(
+        value=False,
+        description="Quantization",
+        disabled=False,
+    )
+    
+    to_quantize
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=False, description='Quantization')
+
+
+
+.. code:: ipython3
+
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
+    
+    %load_ext skip_kernel_extension
+
+Prepare calibration dataset
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+We use a portion of
+`conceptual_captions `__
+dataset from Hugging Face as calibration data. To collect intermediate
+model inputs for calibration we customize ``CompiledModel``.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    import datasets
+    from tqdm.auto import tqdm
+    from typing import Any, Dict, List
+    import pickle
+    import numpy as np
+    
+    
+    def disable_progress_bar(pipeline, disable=True):
+        if not hasattr(pipeline, "_progress_bar_config"):
+            pipeline._progress_bar_config = {'disable': disable}
+        else:
+            pipeline._progress_bar_config['disable'] = disable
+    
+    
+    class CompiledModelDecorator(ov.CompiledModel):
+        def __init__(self, compiled_model: ov.CompiledModel, data_cache: List[Any] = None, keep_prob: float = 0.5):
+            super().__init__(compiled_model)
+            self.data_cache = data_cache if data_cache is not None else []
+            self.keep_prob = keep_prob
+    
+        def __call__(self, *args, **kwargs):
+            if np.random.rand() <= self.keep_prob:
+                self.data_cache.append(*args)
+            return super().__call__(*args, **kwargs)
+    
+    
+    def collect_calibration_data(ov_transformer_model, calibration_dataset_size: int) -> List[Dict]:
+        calibration_dataset_filepath = Path(f"calibration_data/{calibration_dataset_size}.pkl")
+        if not calibration_dataset_filepath.exists():
+            calibration_data = []
+            pipe.transformer.transformer = CompiledModelDecorator(ov_transformer_model, calibration_data, keep_prob=1.0)
+            disable_progress_bar(pipe)
+        
+            dataset = datasets.load_dataset("conceptual_captions", split="train").shuffle(seed=42)
+        
+            # Run inference for data collection
+            pbar = tqdm(total=calibration_dataset_size)
+            for batch in dataset:
+                prompt = batch["caption"]
+                if len(prompt) > pipe.tokenizer.model_max_length:
+                    continue
+                pipe(prompt, generator=torch.Generator('cpu').manual_seed(0))
+                pbar.update(len(calibration_data) - pbar.n)
+                if pbar.n >= calibration_dataset_size:
+                    break
+        
+            pipe.transformer.transformer = ov_transformer_model
+            disable_progress_bar(pipe, disable=False)
+            
+            calibration_dataset_filepath.parent.mkdir(exist_ok=True, parents=True)
+            with open(calibration_dataset_filepath, 'wb') as f:
+                pickle.dump(calibration_data, f)
+                
+        with open(calibration_dataset_filepath, 'rb') as f:
+            calibration_data = pickle.load(f)
+        return calibration_data
+
+Run model quantization
+~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Run calibration data collection and quantize the vision transformer
+model.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters
+    import nncf
+    
+    CALIBRATION_DATASET_SIZE = 12 * 200
+    
+    if not QUANTIZED_TRANSFORMER_OV_PATH.exists():
+        calibration_data = collect_calibration_data(ov_transformer, CALIBRATION_DATASET_SIZE)
+        quantized_model = nncf.quantize(
+            core.read_model(TRANSFORMER_OV_PATH),
+            nncf.Dataset(calibration_data),
+            model_type=nncf.ModelType.TRANSFORMER,
+            subset_size=len(calibration_data),
+            # We ignore convolutions to improve quality of generations without significant drop in inference speed
+            ignored_scope=nncf.IgnoredScope(types=["Convolution"]),
+            # Value of 0.85 was obtained using grid search based on Inception Score computed below
+            advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alphas=AdvancedSmoothQuantParameters(matmul=0.85))
+        )
+        ov.save_model(quantized_model, QUANTIZED_TRANSFORMER_OV_PATH)
+
+Demo generation with quantized pipeline
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    original_ov_transformer_model = pipe.transformer.transformer
+    pipe.transformer.transformer = core.compile_model(QUANTIZED_TRANSFORMER_OV_PATH, device.value)
+    
+    image = pipe(prompt, generator=torch.Generator('cpu').manual_seed(8)).images[0]
+    image.save('text2image_256_quantized.png')
+    
+    pipe.transformer.transformer = original_ov_transformer_model
+    
+    display(image)
+
+Compute Inception Scores and inference time
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Below we compute `Inception
+Score `__ of original and
+quantized pipelines on a small subset of images. Images are generated
+from prompts of ``conceptual_captions`` validation set. We also measure
+the time it took to generate the images for comparison reasons.
+
+Please note that the validation dataset size is small and serves only as
+a rough estimate of generation quality.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    from torchmetrics.image.inception import InceptionScore
+    from torchvision import transforms as transforms
+    from itertools import islice
+    import time
+    
+    VALIDATION_DATASET_SIZE = 100
+    
+    def compute_inception_score(ov_transformer_model_path, validation_set_size, batch_size=100):
+        original_ov_transformer_model = pipe.transformer.transformer
+        pipe.transformer.transformer = core.compile_model(ov_transformer_model_path, device.value)
+        
+        disable_progress_bar(pipe)
+        dataset = datasets.load_dataset("conceptual_captions", "unlabeled", split="validation").shuffle(seed=42)
+        dataset = islice(dataset, validation_set_size)
+        
+        inception_score = InceptionScore(normalize=True, splits=1)
+        
+        images = []
+        infer_times = []
+        for batch in tqdm(dataset, total=validation_set_size, desc="Computing Inception Score"):
+            prompt = batch["caption"]
+            if len(prompt) > pipe.tokenizer.model_max_length:
+                continue
+            start_time = time.perf_counter()
+            image = pipe(prompt, generator=torch.Generator('cpu').manual_seed(0)).images[0]
+            infer_times.append(time.perf_counter() - start_time)
+            image = transforms.ToTensor()(image)
+            images.append(image)
+        
+        mean_perf_time = sum(infer_times) / len(infer_times)
+            
+        while len(images) > 0:
+            images_batch = torch.stack(images[-batch_size:])
+            images = images[:-batch_size]
+            inception_score.update(images_batch)
+        kl_mean, kl_std = inception_score.compute()
+        
+        pipe.transformer.transformer = original_ov_transformer_model
+        disable_progress_bar(pipe, disable=False)
+        
+        return kl_mean, mean_perf_time
+    
+    
+    original_inception_score, original_time = compute_inception_score(TRANSFORMER_OV_PATH, VALIDATION_DATASET_SIZE)
+    print(f"Original pipeline Inception Score: {original_inception_score}")
+    quantized_inception_score, quantized_time = compute_inception_score(QUANTIZED_TRANSFORMER_OV_PATH, VALIDATION_DATASET_SIZE)
+    print(f"Quantized pipeline Inception Score: {quantized_inception_score}")
+    print(f"Quantization speed-up: {original_time / quantized_time:.2f}x")
+
 Interactive inference
 ---------------------
 
 
 
+Below you can select which pipeline to run: original or quantized.
+
+.. code:: ipython3
+
+    quantized_model_present = QUANTIZED_TRANSFORMER_OV_PATH.exists()
+    
+    use_quantized_model = widgets.Checkbox(
+        value=True if quantized_model_present else False,
+        description="Use quantized pipeline",
+        disabled=not quantized_model_present,
+    )
+    
+    use_quantized_model
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=False, description='Use quantized pipeline', disabled=True)
+
+
+
 .. code:: ipython3
 
-    import numpy as np
     import gradio as gr
+    import numpy as np
+    
+    pipe.transformer.transformer = core.compile_model(
+        QUANTIZED_TRANSFORMER_OV_PATH if use_quantized_model.value else TRANSFORMER_OV_PATH,
+        device.value,
+    )
     
     
     def generate(prompt, seed, _=gr.Progress(track_tqdm=True)):
-        image = pipe(prompt, generator=torch.Generator('cpu').manual_seed(seed)).images[0]
+        image = pipe(prompt, generator=torch.Generator("cpu").manual_seed(seed)).images[0]
         return image
     
     
@@ -544,7 +810,7 @@ Interactive inference
         generate,
         [
             gr.Textbox(label="Prompt"),
-            gr.Slider(0, np.iinfo(np.int32).max, label="Seed")
+            gr.Slider(0, np.iinfo(np.int32).max, label="Seed", step=1),
         ],
         "image",
         examples=[
diff --git a/docs/notebooks/277-amused-lightweight-text-to-image-with-output_files/277-amused-lightweight-text-to-image-with-output_28_0.jpg b/docs/notebooks/amused-lightweight-text-to-image-with-output_files/amused-lightweight-text-to-image-with-output_28_0.jpg
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diff --git a/docs/notebooks/277-amused-lightweight-text-to-image-with-output_files/277-amused-lightweight-text-to-image-with-output_28_0.png b/docs/notebooks/amused-lightweight-text-to-image-with-output_files/amused-lightweight-text-to-image-with-output_28_0.png
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rename from docs/notebooks/277-amused-lightweight-text-to-image-with-output_files/277-amused-lightweight-text-to-image-with-output_28_0.png
rename to docs/notebooks/amused-lightweight-text-to-image-with-output_files/amused-lightweight-text-to-image-with-output_28_0.png
diff --git a/docs/notebooks/amused-lightweight-text-to-image-with-output_files/amused-lightweight-text-to-image-with-output_6_0.jpg b/docs/notebooks/amused-lightweight-text-to-image-with-output_files/amused-lightweight-text-to-image-with-output_6_0.jpg
new file mode 100644
index 00000000000..80cedfe3acc
--- /dev/null
+++ b/docs/notebooks/amused-lightweight-text-to-image-with-output_files/amused-lightweight-text-to-image-with-output_6_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbe25aa3e1c514977b3a1bee08e474eb12794757e551909e5ac05273d8c2861d
+size 5528
diff --git a/docs/notebooks/277-amused-lightweight-text-to-image-with-output_files/277-amused-lightweight-text-to-image-with-output_6_0.png b/docs/notebooks/amused-lightweight-text-to-image-with-output_files/amused-lightweight-text-to-image-with-output_6_0.png
similarity index 100%
rename from docs/notebooks/277-amused-lightweight-text-to-image-with-output_files/277-amused-lightweight-text-to-image-with-output_6_0.png
rename to docs/notebooks/amused-lightweight-text-to-image-with-output_files/amused-lightweight-text-to-image-with-output_6_0.png
diff --git a/docs/notebooks/animate-anyone-with-output.rst b/docs/notebooks/animate-anyone-with-output.rst
new file mode 100644
index 00000000000..08e2e4d1817
--- /dev/null
+++ b/docs/notebooks/animate-anyone-with-output.rst
@@ -0,0 +1,1266 @@
+Image-to-Video synthesis with AnimateAnyone and OpenVINO
+========================================================
+
+|image0|
+
+`AnimateAnyone `__ tackles the
+task of generating animation sequences from a single character image. It
+builds upon diffusion models pre-trained on vast character image
+datasets.
+
+The core of AnimateAnyone is a diffusion model pre-trained on a massive
+dataset of character images. This model learns the underlying character
+representation and distribution, allowing for realistic and diverse
+character animation. To capture the specific details and characteristics
+of the input character image, AnimateAnyone incorporates a ReferenceNet
+module. This module acts like an attention mechanism, focusing on the
+input image and guiding the animation process to stay consistent with
+the original character’s appearance. AnimateAnyone enables control over
+the character’s pose during animation. This might involve using
+techniques like parametric pose embedding or direct pose vector input,
+allowing for the creation of various character actions and movements. To
+ensure smooth transitions and temporal coherence throughout the
+animation sequence, AnimateAnyone incorporates temporal modeling
+techniques. This may involve recurrent architectures like LSTMs or
+transformers that capture the temporal dependencies between video
+frames.
+
+Overall, AnimateAnyone combines a powerful pre-trained diffusion model
+with a character-specific attention mechanism (ReferenceNet), pose
+guidance, and temporal modeling to achieve controllable, high-fidelity
+character animation from a single image.
+
+Learn more in `GitHub
+repo `__ and
+`paper `__.
+
+.. container:: alert alert-warning
+
+   ::
+
+      
! WARNING !

+      

+          This tutorial requires at least 96 GB of RAM for model conversion and 40 GB for inference. Changing the values of HEIGHT, WIDTH and VIDEO_LENGTH variables will change the memory consumption but will also affect accuracy.
+      

+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+-  `Prepare base model <#prepare-base-model>`__
+-  `Prepare image encoder <#prepare-image-encoder>`__
+-  `Download weights <#download-weights>`__
+-  `Initialize models <#initialize-models>`__
+-  `Load pretrained weights <#load-pretrained-weights>`__
+-  `Convert model to OpenVINO IR <#convert-model-to-openvino-ir>`__
+
+   -  `VAE <#vae>`__
+   -  `Reference UNet <#reference-unet>`__
+   -  `Denoising UNet <#denoising-unet>`__
+   -  `Pose Guider <#pose-guider>`__
+   -  `Image Encoder <#image-encoder>`__
+
+-  `Inference <#inference>`__
+-  `Video post-processing <#video-post-processing>`__
+-  `Interactive inference <#interactive-inference>`__
+
+.. |image0| image:: animate-anyone-with-output_files/animate-anyone.gif
+
+Prerequisites
+-------------
+
+
+
+.. code:: ipython3
+
+    from pathlib import Path
+    import requests
+
+
+    REPO_PATH = Path("Moore-AnimateAnyone")
+    if not REPO_PATH.exists():
+        !git clone -q "https://github.com/itrushkin/Moore-AnimateAnyone.git"
+    %pip install -q "torch>=2.1" torchvision einops omegaconf "diffusers<=0.24" transformers av accelerate "openvino>=2024.0" "nncf>=2.9.0" "gradio>=4.19" --extra-index-url "https://download.pytorch.org/whl/cpu"
+    import sys
+
+    sys.path.insert(0, str(REPO_PATH.resolve()))
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
+    %load_ext skip_kernel_extension
+
+
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Note that we clone a fork of original repo with tweaked forward methods.
+
+.. code:: ipython3
+
+    MODEL_DIR = Path("models")
+    VAE_ENCODER_PATH = MODEL_DIR / "vae_encoder.xml"
+    VAE_DECODER_PATH = MODEL_DIR / "vae_decoder.xml"
+    REFERENCE_UNET_PATH = MODEL_DIR / "reference_unet.xml"
+    DENOISING_UNET_PATH = MODEL_DIR / "denoising_unet.xml"
+    POSE_GUIDER_PATH = MODEL_DIR / "pose_guider.xml"
+    IMAGE_ENCODER_PATH = MODEL_DIR / "image_encoder.xml"
+
+    WIDTH = 448
+    HEIGHT = 512
+    VIDEO_LENGTH = 24
+
+    SHOULD_CONVERT = not all(
+        p.exists()
+        for p in [
+            VAE_ENCODER_PATH,
+            VAE_DECODER_PATH,
+            REFERENCE_UNET_PATH,
+            DENOISING_UNET_PATH,
+            POSE_GUIDER_PATH,
+            IMAGE_ENCODER_PATH,
+        ]
+    )
+
+.. code:: ipython3
+
+    from datetime import datetime
+    from typing import Optional, Union, List, Callable
+    import math
+
+    from PIL import Image
+    import openvino as ov
+    from torchvision import transforms
+    from einops import repeat
+    from tqdm.auto import tqdm
+    from einops import rearrange
+    from omegaconf import OmegaConf
+    from diffusers import DDIMScheduler
+    from diffusers.image_processor import VaeImageProcessor
+    from transformers import CLIPImageProcessor
+    import torch
+    import gradio as gr
+    import ipywidgets as widgets
+    import numpy as np
+
+    from src.pipelines.pipeline_pose2vid_long import Pose2VideoPipeline
+    from src.utils.util import get_fps, read_frames
+    from src.utils.util import save_videos_grid
+    from src.pipelines.context import get_context_scheduler
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
+
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    from pathlib import PurePosixPath
+    import gc
+    import warnings
+
+    from typing import Dict, Any
+    from diffusers import AutoencoderKL
+    from huggingface_hub import hf_hub_download, snapshot_download
+    from transformers import CLIPVisionModelWithProjection
+    import nncf
+
+    from src.models.unet_2d_condition import UNet2DConditionModel
+    from src.models.unet_3d import UNet3DConditionModel
+    from src.models.pose_guider import PoseGuider
+
+
+.. parsed-literal::
+
+    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, onnx, openvino
+
+
+Prepare base model
+------------------
+
+
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    local_dir = Path("./pretrained_weights/stable-diffusion-v1-5")
+    local_dir.mkdir(parents=True, exist_ok=True)
+    for hub_file in ["unet/config.json", "unet/diffusion_pytorch_model.bin"]:
+        saved_path = local_dir / hub_file
+        if saved_path.exists():
+            continue
+        hf_hub_download(
+            repo_id="runwayml/stable-diffusion-v1-5",
+            subfolder=PurePosixPath(saved_path.parent.name),
+            filename=PurePosixPath(saved_path.name),
+            local_dir=local_dir,
+        )
+
+Prepare image encoder
+---------------------
+
+
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    local_dir = Path("./pretrained_weights")
+    local_dir.mkdir(parents=True, exist_ok=True)
+    for hub_file in ["image_encoder/config.json", "image_encoder/pytorch_model.bin"]:
+        saved_path = local_dir / hub_file
+        if saved_path.exists():
+            continue
+        hf_hub_download(
+            repo_id="lambdalabs/sd-image-variations-diffusers",
+            subfolder=PurePosixPath(saved_path.parent.name),
+            filename=PurePosixPath(saved_path.name),
+            local_dir=local_dir,
+        )
+
+Download weights
+----------------
+
+
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    snapshot_download(
+        repo_id="stabilityai/sd-vae-ft-mse", local_dir="./pretrained_weights/sd-vae-ft-mse"
+    )
+    snapshot_download(
+        repo_id="patrolli/AnimateAnyone",
+        local_dir="./pretrained_weights",
+    )
+
+
+
+.. parsed-literal::
+
+    Fetching 5 files:   0%|          | 0/5 [00:00`__. Weight
+compression aims to reduce the memory footprint of a model. models,
+which require extensive memory to store the weights during inference,
+can benefit from weight compression in the following ways:
+
+-  enabling the inference of exceptionally large models that cannot be
+   accommodated in the memory of the device;
+
+-  improving the inference performance of the models by reducing the
+   latency of the memory access when computing the operations with
+   weights, for example, Linear layers.
+
+`Neural Network Compression Framework
+(NNCF) `__ provides 4-bit /
+8-bit mixed weight quantization as a compression method. The main
+difference between weights compression and full model quantization
+(post-training quantization) is that activations remain floating-point
+in the case of weights compression which leads to a better accuracy. In
+addition, weight compression is data-free and does not require a
+calibration dataset, making it easy to use.
+
+``nncf.compress_weights`` function can be used for performing weights
+compression. The function accepts an OpenVINO model and other
+compression parameters.
+
+More details about weights compression can be found in `OpenVINO
+documentation `__.
+
+.. |image01| image:: https://humanaigc.github.io/animate-anyone/static/images/f2_img.png
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    def cleanup_torchscript_cache():
+        """
+        Helper for removing cached model representation
+        """
+        torch._C._jit_clear_class_registry()
+        torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
+        torch.jit._state._clear_class_state()
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    warnings.simplefilter("ignore", torch.jit.TracerWarning)
+
+VAE
+~~~
+
+
+
+The VAE model has two parts, an encoder and a decoder. The encoder is
+used to convert the image into a low dimensional latent representation,
+which will serve as the input to the U-Net model. The decoder,
+conversely, transforms the latent representation back into an image.
+
+During latent diffusion training, the encoder is used to get the latent
+representations (latents) of the images for the forward diffusion
+process, which applies more and more noise at each step. During
+inference, the denoised latents generated by the reverse diffusion
+process are converted back into images using the VAE decoder.
+
+As the encoder and the decoder are used independently in different parts
+of the pipeline, it will be better to convert them to separate models.
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    if not VAE_ENCODER_PATH.exists():
+        class VaeEncoder(torch.nn.Module):
+            def __init__(self, vae):
+                super().__init__()
+                self.vae = vae
+
+            def forward(self, x):
+                return self.vae.encode(x).latent_dist.mean
+        vae.eval()
+        with torch.no_grad():
+            vae_encoder = ov.convert_model(VaeEncoder(vae), example_input=torch.zeros(1,3,512,448))
+        vae_encoder = nncf.compress_weights(vae_encoder)
+        ov.save_model(vae_encoder, VAE_ENCODER_PATH)
+        del vae_encoder
+        cleanup_torchscript_cache()
+
+
+.. parsed-literal::
+
+    WARNING:nncf:NNCF provides best results with torch==2.1.2, while current torch version is 2.2.2+cpu. If you encounter issues, consider switching to torch==2.1.2
+
+
+.. parsed-literal::
+
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (32 / 32)            | 100% (32 / 32)                    |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    if not VAE_DECODER_PATH.exists():
+        class VaeDecoder(torch.nn.Module):
+            def __init__(self, vae):
+                super().__init__()
+                self.vae = vae
+
+            def forward(self, z):
+                return self.vae.decode(z).sample
+        vae.eval()
+        with torch.no_grad():
+            vae_decoder = ov.convert_model(VaeDecoder(vae), example_input=torch.zeros(1,4,HEIGHT//8,WIDTH//8))
+        vae_decoder = nncf.compress_weights(vae_decoder)
+        ov.save_model(vae_decoder, VAE_DECODER_PATH)
+        del vae_decoder
+        cleanup_torchscript_cache()
+    del vae
+    gc.collect()
+
+
+.. parsed-literal::
+
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (40 / 40)            | 100% (40 / 40)                    |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+Reference UNet
+~~~~~~~~~~~~~~
+
+
+
+Pipeline extracts reference attention features from all transformer
+blocks inside Reference UNet model. We call the original forward pass to
+obtain shapes of the outputs as they will be used in the next pipeline
+step.
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    if not REFERENCE_UNET_PATH.exists():
+        class ReferenceUNetWrapper(torch.nn.Module):
+            def __init__(self, reference_unet):
+                super().__init__()
+                self.reference_unet = reference_unet
+
+            def forward(self, sample, timestep, encoder_hidden_states):
+                return self.reference_unet(sample, timestep, encoder_hidden_states, return_dict=False)[1]
+
+        sample = torch.zeros(2, 4, HEIGHT // 8, WIDTH // 8)
+        timestep = torch.tensor(0)
+        encoder_hidden_states = torch.zeros(2, 1, 768)
+        reference_unet.eval()
+        with torch.no_grad():
+            wrapper =  ReferenceUNetWrapper(reference_unet)
+            example_input = (sample, timestep, encoder_hidden_states)
+            ref_features_shapes = {k: v.shape for k, v in wrapper(*example_input).items()}
+            ov_reference_unet = ov.convert_model(
+                wrapper,
+                example_input=example_input,
+            )
+        ov_reference_unet = nncf.compress_weights(ov_reference_unet)
+        ov.save_model(ov_reference_unet, REFERENCE_UNET_PATH)
+        del ov_reference_unet
+        del wrapper
+        cleanup_torchscript_cache()
+    del reference_unet
+    gc.collect()
+
+
+.. parsed-literal::
+
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (270 / 270)          | 100% (270 / 270)                  |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+Denoising UNet
+~~~~~~~~~~~~~~
+
+
+
+Denoising UNet is the main part of all diffusion pipelines. This model
+consumes the majority of memory, so we need to reduce its size as much
+as possible.
+
+Here we make all shapes static meaning that the size of the video will
+be constant.
+
+Also, we use the ``ref_features`` input with the same tensor shapes as
+output of `Reference UNet <#reference-unet>`__ model on the previous
+step.
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    if not DENOISING_UNET_PATH.exists():
+        class DenoisingUNetWrapper(torch.nn.Module):
+            def __init__(self, denoising_unet):
+                super().__init__()
+                self.denoising_unet = denoising_unet
+
+            def forward(
+                self,
+                sample,
+                timestep,
+                encoder_hidden_states,
+                pose_cond_fea,
+                ref_features
+            ):
+                return self.denoising_unet(
+                    sample,
+                    timestep,
+                    encoder_hidden_states,
+                    ref_features,
+                    pose_cond_fea=pose_cond_fea,
+                    return_dict=False)
+
+        example_input = {
+            "sample": torch.zeros(2, 4, VIDEO_LENGTH, HEIGHT // 8, WIDTH // 8),
+            "timestep": torch.tensor(999),
+            "encoder_hidden_states": torch.zeros(2,1,768),
+            "pose_cond_fea": torch.zeros(2, 320, VIDEO_LENGTH, HEIGHT // 8, WIDTH // 8),
+            "ref_features": {k: torch.zeros(shape) for k, shape in ref_features_shapes.items()}
+        }
+
+        denoising_unet.eval()
+        with torch.no_grad():
+            ov_denoising_unet = ov.convert_model(
+                DenoisingUNetWrapper(denoising_unet),
+                example_input=tuple(example_input.values())
+            )
+        ov_denoising_unet.inputs[0].get_node().set_partial_shape(ov.PartialShape((2, 4, VIDEO_LENGTH, HEIGHT // 8, WIDTH // 8)))
+        ov_denoising_unet.inputs[2].get_node().set_partial_shape(ov.PartialShape((2, 1, 768)))
+        ov_denoising_unet.inputs[3].get_node().set_partial_shape(ov.PartialShape((2, 320, VIDEO_LENGTH, HEIGHT // 8, WIDTH // 8)))
+        for ov_input, shape in zip(ov_denoising_unet.inputs[4:], ref_features_shapes.values()):
+            ov_input.get_node().set_partial_shape(ov.PartialShape(shape))
+            ov_input.get_node().set_element_type(ov.Type.f32)
+        ov_denoising_unet.validate_nodes_and_infer_types()
+        ov_denoising_unet = nncf.compress_weights(ov_denoising_unet)
+        ov.save_model(ov_denoising_unet, DENOISING_UNET_PATH)
+        del ov_denoising_unet
+        cleanup_torchscript_cache()
+    del denoising_unet
+    gc.collect()
+
+
+.. parsed-literal::
+
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (534 / 534)          | 100% (534 / 534)                  |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+Pose Guider
+~~~~~~~~~~~
+
+
+
+To ensure pose controllability, a lightweight pose guider is devised to
+efficiently integrate pose control signals into the denoising process.
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    if not POSE_GUIDER_PATH.exists():
+        pose_guider.eval()
+        with torch.no_grad():
+            ov_pose_guider = ov.convert_model(pose_guider, example_input=torch.zeros(1, 3, VIDEO_LENGTH, HEIGHT, WIDTH))
+        ov_pose_guider = nncf.compress_weights(ov_pose_guider)
+        ov.save_model(ov_pose_guider, POSE_GUIDER_PATH)
+        del ov_pose_guider
+        cleanup_torchscript_cache()
+    del pose_guider
+    gc.collect()
+
+
+.. parsed-literal::
+
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (8 / 8)              | 100% (8 / 8)                      |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+Image Encoder
+~~~~~~~~~~~~~
+
+
+
+Pipeline uses CLIP image encoder to generate encoder hidden states
+required for both reference and denoising UNets.
+
+.. code:: ipython3
+
+    %%skip not $SHOULD_CONVERT
+    if not IMAGE_ENCODER_PATH.exists():
+        image_enc.eval()
+        with torch.no_grad():
+            ov_image_encoder = ov.convert_model(image_enc, example_input=torch.zeros(1, 3, 224, 224), input=(1, 3, 224, 224))
+        ov_image_encoder = nncf.compress_weights(ov_image_encoder)
+        ov.save_model(ov_image_encoder, IMAGE_ENCODER_PATH)
+        del ov_image_encoder
+        cleanup_torchscript_cache()
+    del image_enc
+    gc.collect()
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+      warnings.warn(
+
+
+.. parsed-literal::
+
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (146 / 146)          | 100% (146 / 146)                  |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+Inference
+---------
+
+
+
+We inherit from the original pipeline modifying the calls to our models
+to match OpenVINO format.
+
+.. code:: ipython3
+
+    core = ov.Core()
+
+Select inference device
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+For starting work, please select inference device from dropdown list.
+
+.. code:: ipython3
+
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+.. code:: ipython3
+
+    class OVPose2VideoPipeline(Pose2VideoPipeline):
+        def __init__(
+            self,
+            vae_encoder_path=VAE_ENCODER_PATH,
+            vae_decoder_path=VAE_DECODER_PATH,
+            image_encoder_path=IMAGE_ENCODER_PATH,
+            reference_unet_path=REFERENCE_UNET_PATH,
+            denoising_unet_path=DENOISING_UNET_PATH,
+            pose_guider_path=POSE_GUIDER_PATH,
+            device=device.value,
+        ):
+            self.vae_encoder = core.compile_model(vae_encoder_path, device)
+            self.vae_decoder = core.compile_model(vae_decoder_path, device)
+            self.image_encoder = core.compile_model(image_encoder_path, device)
+            self.reference_unet = core.compile_model(reference_unet_path, device)
+            self.denoising_unet = core.compile_model(denoising_unet_path, device)
+            self.pose_guider = core.compile_model(pose_guider_path, device)
+            self.scheduler = DDIMScheduler(**OmegaConf.to_container(infer_config.noise_scheduler_kwargs))
+
+            self.vae_scale_factor = 8
+            self.clip_image_processor = CLIPImageProcessor()
+            self.ref_image_processor = VaeImageProcessor(do_convert_rgb=True)
+            self.cond_image_processor = VaeImageProcessor(do_convert_rgb=True, do_normalize=False)
+
+        def decode_latents(self, latents):
+            video_length = latents.shape[2]
+            latents = 1 / 0.18215 * latents
+            latents = rearrange(latents, "b c f h w -> (b f) c h w")
+            # video = self.vae.decode(latents).sample
+            video = []
+            for frame_idx in tqdm(range(latents.shape[0])):
+                video.append(torch.from_numpy(self.vae_decoder(latents[frame_idx : frame_idx + 1])[0]))
+            video = torch.cat(video)
+            video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+            video = (video / 2 + 0.5).clamp(0, 1)
+            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+            video = video.cpu().float().numpy()
+            return video
+
+        def __call__(
+            self,
+            ref_image,
+            pose_images,
+            width,
+            height,
+            video_length,
+            num_inference_steps=30,
+            guidance_scale=3.5,
+            num_images_per_prompt=1,
+            eta: float = 0.0,
+            generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+            output_type: Optional[str] = "tensor",
+            callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+            callback_steps: Optional[int] = 1,
+            context_schedule="uniform",
+            context_frames=24,
+            context_stride=1,
+            context_overlap=4,
+            context_batch_size=1,
+            interpolation_factor=1,
+            **kwargs,
+        ):
+            do_classifier_free_guidance = guidance_scale > 1.0
+
+            # Prepare timesteps
+            self.scheduler.set_timesteps(num_inference_steps)
+            timesteps = self.scheduler.timesteps
+
+            batch_size = 1
+
+            # Prepare clip image embeds
+            clip_image = self.clip_image_processor.preprocess(ref_image.resize((224, 224)), return_tensors="pt").pixel_values
+            clip_image_embeds = self.image_encoder(clip_image)["image_embeds"]
+            clip_image_embeds = torch.from_numpy(clip_image_embeds)
+            encoder_hidden_states = clip_image_embeds.unsqueeze(1)
+            uncond_encoder_hidden_states = torch.zeros_like(encoder_hidden_states)
+
+            if do_classifier_free_guidance:
+                encoder_hidden_states = torch.cat([uncond_encoder_hidden_states, encoder_hidden_states], dim=0)
+
+            latents = self.prepare_latents(
+                batch_size * num_images_per_prompt,
+                4,
+                width,
+                height,
+                video_length,
+                clip_image_embeds.dtype,
+                torch.device("cpu"),
+                generator,
+            )
+
+            # Prepare extra step kwargs.
+            extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+            # Prepare ref image latents
+            ref_image_tensor = self.ref_image_processor.preprocess(ref_image, height=height, width=width)  # (bs, c, width, height)
+            ref_image_latents = self.vae_encoder(ref_image_tensor)[0]
+            ref_image_latents = ref_image_latents * 0.18215  # (b, 4, h, w)
+            ref_image_latents = torch.from_numpy(ref_image_latents)
+
+            # Prepare a list of pose condition images
+            pose_cond_tensor_list = []
+            for pose_image in pose_images:
+                pose_cond_tensor = self.cond_image_processor.preprocess(pose_image, height=height, width=width)
+                pose_cond_tensor = pose_cond_tensor.unsqueeze(2)  # (bs, c, 1, h, w)
+                pose_cond_tensor_list.append(pose_cond_tensor)
+            pose_cond_tensor = torch.cat(pose_cond_tensor_list, dim=2)  # (bs, c, t, h, w)
+            pose_fea = self.pose_guider(pose_cond_tensor)[0]
+            pose_fea = torch.from_numpy(pose_fea)
+
+            context_scheduler = get_context_scheduler(context_schedule)
+
+            # denoising loop
+            num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+            with self.progress_bar(total=num_inference_steps) as progress_bar:
+                for i, t in enumerate(timesteps):
+                    noise_pred = torch.zeros(
+                        (
+                            latents.shape[0] * (2 if do_classifier_free_guidance else 1),
+                            *latents.shape[1:],
+                        ),
+                        device=latents.device,
+                        dtype=latents.dtype,
+                    )
+                    counter = torch.zeros(
+                        (1, 1, latents.shape[2], 1, 1),
+                        device=latents.device,
+                        dtype=latents.dtype,
+                    )
+
+                    # 1. Forward reference image
+                    if i == 0:
+                        ref_features = self.reference_unet(
+                            (
+                                ref_image_latents.repeat((2 if do_classifier_free_guidance else 1), 1, 1, 1),
+                                torch.zeros_like(t),
+                                # t,
+                                encoder_hidden_states,
+                            )
+                        ).values()
+
+                    context_queue = list(
+                        context_scheduler(
+                            0,
+                            num_inference_steps,
+                            latents.shape[2],
+                            context_frames,
+                            context_stride,
+                            0,
+                        )
+                    )
+                    num_context_batches = math.ceil(len(context_queue) / context_batch_size)
+
+                    context_queue = list(
+                        context_scheduler(
+                            0,
+                            num_inference_steps,
+                            latents.shape[2],
+                            context_frames,
+                            context_stride,
+                            context_overlap,
+                        )
+                    )
+
+                    num_context_batches = math.ceil(len(context_queue) / context_batch_size)
+                    global_context = []
+                    for i in range(num_context_batches):
+                        global_context.append(context_queue[i * context_batch_size : (i + 1) * context_batch_size])
+
+                    for context in global_context:
+                        # 3.1 expand the latents if we are doing classifier free guidance
+                        latent_model_input = torch.cat([latents[:, :, c] for c in context]).repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
+                        latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                        b, c, f, h, w = latent_model_input.shape
+                        latent_pose_input = torch.cat([pose_fea[:, :, c] for c in context]).repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
+
+                        pred = self.denoising_unet(
+                            (
+                                latent_model_input,
+                                t,
+                                encoder_hidden_states[:b],
+                                latent_pose_input,
+                                *ref_features,
+                            )
+                        )[0]
+
+                        for j, c in enumerate(context):
+                            noise_pred[:, :, c] = noise_pred[:, :, c] + pred
+                            counter[:, :, c] = counter[:, :, c] + 1
+
+                    # perform guidance
+                    if do_classifier_free_guidance:
+                        noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2)
+                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                    if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                        progress_bar.update()
+                        if callback is not None and i % callback_steps == 0:
+                            step_idx = i // getattr(self.scheduler, "order", 1)
+                            callback(step_idx, t, latents)
+
+            if interpolation_factor > 0:
+                latents = self.interpolate_latents(latents, interpolation_factor, latents.device)
+            # Post-processing
+            images = self.decode_latents(latents)  # (b, c, f, h, w)
+
+            # Convert to tensor
+            if output_type == "tensor":
+                images = torch.from_numpy(images)
+
+            return images
+
+.. code:: ipython3
+
+    pipe = OVPose2VideoPipeline()
+
+.. code:: ipython3
+
+    pose_images = read_frames("Moore-AnimateAnyone/configs/inference/pose_videos/anyone-video-2_kps.mp4")
+    src_fps = get_fps("Moore-AnimateAnyone/configs/inference/pose_videos/anyone-video-2_kps.mp4")
+    ref_image = Image.open("Moore-AnimateAnyone/configs/inference/ref_images/anyone-5.png").convert("RGB")
+    pose_list = []
+    for pose_image_pil in pose_images[:VIDEO_LENGTH]:
+        pose_list.append(pose_image_pil)
+
+.. code:: ipython3
+
+    video = pipe(
+        ref_image,
+        pose_list,
+        width=WIDTH,
+        height=HEIGHT,
+        video_length=VIDEO_LENGTH,
+    )
+
+
+
+.. parsed-literal::
+
+      0%|          | 0/30 [00:00 b c (repeat f) h w", repeat=VIDEO_LENGTH)
+    pose_tensor = torch.stack(pose_tensor_list, dim=0)  # (f, c, h, w)
+    pose_tensor = pose_tensor.transpose(0, 1)
+    pose_tensor = pose_tensor.unsqueeze(0)
+    video = torch.cat([ref_image_tensor, pose_tensor, video], dim=0)
+
+    save_dir = Path("./output")
+    save_dir.mkdir(parents=True, exist_ok=True)
+    date_str = datetime.now().strftime("%Y%m%d")
+    time_str = datetime.now().strftime("%H%M")
+    out_path = save_dir / f"{date_str}T{time_str}.mp4"
+    save_videos_grid(
+        video,
+        str(out_path),
+        n_rows=3,
+        fps=src_fps,
+    )
+
+.. code:: ipython3
+
+    from IPython.display import Video
+
+    Video(out_path, embed=True)
+
+
+
+
+.. raw:: html
+
+    
+
+
+
+Interactive inference
+---------------------
+
+
+
+.. code:: ipython3
+
+    def generate(
+        img,
+        pose_vid,
+        seed,
+        guidance_scale,
+        num_inference_steps,
+        _=gr.Progress(track_tqdm=True),
+    ):
+        generator = torch.Generator().manual_seed(seed)
+        pose_list = read_frames(pose_vid)[:VIDEO_LENGTH]
+        video = pipe(
+            img,
+            pose_list,
+            width=WIDTH,
+            height=HEIGHT,
+            video_length=VIDEO_LENGTH,
+            generator=generator,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+        )
+        new_h, new_w = video.shape[-2:]
+        pose_transform = transforms.Compose([transforms.Resize((new_h, new_w)), transforms.ToTensor()])
+        pose_tensor_list = []
+        for pose_image_pil in pose_list:
+            pose_tensor_list.append(pose_transform(pose_image_pil))
+
+        ref_image_tensor = pose_transform(img)  # (c, h, w)
+        ref_image_tensor = ref_image_tensor.unsqueeze(1).unsqueeze(0)  # (1, c, 1, h, w)
+        ref_image_tensor = repeat(ref_image_tensor, "b c f h w -> b c (repeat f) h w", repeat=VIDEO_LENGTH)
+        pose_tensor = torch.stack(pose_tensor_list, dim=0)  # (f, c, h, w)
+        pose_tensor = pose_tensor.transpose(0, 1)
+        pose_tensor = pose_tensor.unsqueeze(0)
+        video = torch.cat([ref_image_tensor, pose_tensor, video], dim=0)
+
+        save_dir = Path("./output/gradio")
+        save_dir.mkdir(parents=True, exist_ok=True)
+        date_str = datetime.now().strftime("%Y%m%d")
+        time_str = datetime.now().strftime("%H%M")
+        out_path = save_dir / f"{date_str}T{time_str}.mp4"
+        save_videos_grid(
+            video,
+            str(out_path),
+            n_rows=3,
+            fps=12,
+        )
+        return out_path
+
+
+    demo = gr.Interface(
+        generate,
+        [
+            gr.Image(label="Reference Image", type="pil"),
+            gr.Video(label="Pose video"),
+            gr.Slider(
+                label="Seed",
+                value=42,
+                minimum=np.iinfo(np.int32).min,
+                maximum=np.iinfo(np.int32).max,
+            ),
+            gr.Slider(label="Guidance scale", value=3.5, minimum=1.1, maximum=10),
+            gr.Slider(label="Number of inference steps", value=30, minimum=15, maximum=100),
+        ],
+        "video",
+        examples=[
+            [
+                "Moore-AnimateAnyone/configs/inference/ref_images/anyone-2.png",
+                "Moore-AnimateAnyone/configs/inference/pose_videos/anyone-video-2_kps.mp4",
+            ],
+            [
+                "Moore-AnimateAnyone/configs/inference/ref_images/anyone-10.png",
+                "Moore-AnimateAnyone/configs/inference/pose_videos/anyone-video-1_kps.mp4",
+            ],
+            [
+                "Moore-AnimateAnyone/configs/inference/ref_images/anyone-11.png",
+                "Moore-AnimateAnyone/configs/inference/pose_videos/anyone-video-1_kps.mp4",
+            ],
+            [
+                "Moore-AnimateAnyone/configs/inference/ref_images/anyone-3.png",
+                "Moore-AnimateAnyone/configs/inference/pose_videos/anyone-video-2_kps.mp4",
+            ],
+            [
+                "Moore-AnimateAnyone/configs/inference/ref_images/anyone-5.png",
+                "Moore-AnimateAnyone/configs/inference/pose_videos/anyone-video-2_kps.mp4",
+            ],
+        ],
+        allow_flagging="never",
+    )
+    try:
+        demo.queue().launch(debug=False)
+    except Exception:
+        demo.queue().launch(debug=False, share=True)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/"
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
diff --git a/docs/notebooks/animate-anyone-with-output_files/animate-anyone.gif b/docs/notebooks/animate-anyone-with-output_files/animate-anyone.gif
new file mode 100644
index 00000000000..5cd205f4af3
--- /dev/null
+++ b/docs/notebooks/animate-anyone-with-output_files/animate-anyone.gif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d53c86b99c2ec0b3b3cfc9f9acc9e1a44643013b4cfdf7ba27231bb70150be7
+size 1456471
diff --git a/docs/notebooks/115-async-api-with-output.rst b/docs/notebooks/async-api-with-output.rst
similarity index 72%
rename from docs/notebooks/115-async-api-with-output.rst
rename to docs/notebooks/async-api-with-output.rst
index 3a63fb53d0a..19a832b6964 100644
--- a/docs/notebooks/115-async-api-with-output.rst
+++ b/docs/notebooks/async-api-with-output.rst
@@ -50,7 +50,7 @@ Imports
     import platform
     
     %pip install -q "openvino>=2023.1.0"
-    %pip install -q opencv-python 
+    %pip install -q opencv-python
     if platform.system() != "windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -82,11 +82,12 @@ Imports
     import matplotlib.pyplot as plt
     
     # Fetch the notebook utils script from the openvino_notebooks repo
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    open("notebook_utils.py", "w").write(r.text)
     
     import notebook_utils as utils
 
@@ -113,14 +114,8 @@ the person in each frame of the video.
     # model name as named in Open Model Zoo
     model_name = "person-detection-0202"
     precision = "FP16"
-    model_path = (
-        f"model/intel/{model_name}/{precision}/{model_name}.xml"
-    )
-    download_command = f"omz_downloader " \
-                       f"--name {model_name} " \
-                       f"--precision {precision} " \
-                       f"--output_dir {base_model_dir} " \
-                       f"--cache_dir {base_model_dir}"
+    model_path = f"model/intel/{model_name}/{precision}/{model_name}.xml"
+    download_command = f"omz_downloader " f"--name {model_name} " f"--precision {precision} " f"--output_dir {base_model_dir} " f"--cache_dir {base_model_dir}"
     ! $download_command
 
 
@@ -133,20 +128,17 @@ the person in each frame of the video.
 
 .. parsed-literal::
 
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+    ... 12%, 32 KB, 998 KB/s, 0 seconds passed
 
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-
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-
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+... 89%, 224 KB, 2206 KB/s, 0 seconds passed
+... 100%, 248 KB, 2423 KB/s, 0 seconds passed
 
     
     ========== Downloading model/intel/person-detection-0202/FP16/person-detection-0202.bin
@@ -154,129 +146,132 @@ the person in each frame of the video.
 
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+
+.. parsed-literal::
+
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@@ -293,8 +288,8 @@ Select inference device
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
     
@@ -341,7 +336,7 @@ Create functions for data processing
     def preprocess(image):
         """
         Define the preprocess function for input data
-        
+    
         :param: image: the orignal input frame
         :returns:
                 resized_image: the image processed
@@ -356,7 +351,7 @@ Create functions for data processing
     def postprocess(result, image, fps):
         """
         Define the postprocess function for output data
-        
+    
         :param: result: the inference results
                 image: the orignal input frame
                 fps: average throughput calculated for each frame
@@ -372,7 +367,15 @@ Create functions for data processing
                 xmax = int(min((xmax * image.shape[1]), image.shape[1] - 10))
                 ymax = int(min((ymax * image.shape[0]), image.shape[0] - 10))
                 cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
-                cv2.putText(image, str(round(fps, 2)) + " fps", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 3) 
+                cv2.putText(
+                    image,
+                    str(round(fps, 2)) + " fps",
+                    (5, 20),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.7,
+                    (0, 255, 0),
+                    3,
+                )
         return image
 
 Get the test video
@@ -382,7 +385,7 @@ Get the test video
 
 .. code:: ipython3
 
-    video_path = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/CEO%20Pat%20Gelsinger%20on%20Leading%20Intel.mp4'
+    video_path = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/CEO%20Pat%20Gelsinger%20on%20Leading%20Intel.mp4"
 
 How to improve the throughput of video processing
 -------------------------------------------------
@@ -423,7 +426,7 @@ immediately processed:
     def sync_api(source, flip, fps, use_popup, skip_first_frames):
         """
         Define the main function for video processing in sync mode
-        
+    
         :param: source: the video path or the ID of your webcam
         :returns:
                 sync_fps: the inference throughput in sync mode
@@ -447,13 +450,13 @@ immediately processed:
                     break
                 resized_frame = preprocess(frame)
                 infer_request.set_tensor(input_layer_ir, ov.Tensor(resized_frame))
-                # Start the inference request in synchronous mode 
+                # Start the inference request in synchronous mode
                 infer_request.infer()
                 res = infer_request.get_output_tensor(0).data
                 stop_time = time.time()
                 total_time = stop_time - start_time
                 frame_number = frame_number + 1
-                sync_fps = frame_number / total_time 
+                sync_fps = frame_number / total_time
                 frame = postprocess(res, frame, sync_fps)
                 # Display the results
                 if use_popup:
@@ -469,7 +472,7 @@ immediately processed:
                     i = display.Image(data=encoded_img)
                     # Display the image in this notebook
                     display.clear_output(wait=True)
-                    display.display(i)         
+                    display.display(i)
         # ctrl-c
         except KeyboardInterrupt:
             print("Interrupted")
@@ -496,13 +499,13 @@ Test performance in Sync Mode
 
 
 
-.. image:: 115-async-api-with-output_files/115-async-api-with-output_17_0.png
+.. image:: async-api-with-output_files/async-api-with-output_17_0.png
 
 
 .. parsed-literal::
 
     Source ended
-    average throuput in sync mode: 43.30 fps
+    average throuput in sync mode: 44.01 fps
 
 
 Async Mode
@@ -546,7 +549,7 @@ pipeline (decoding vs inference) and not by the sum of the stages.
     def async_api(source, flip, fps, use_popup, skip_first_frames):
         """
         Define the main function for video processing in async mode
-        
+    
         :param: source: the video path or the ID of your webcam
         :returns:
                 async_fps: the inference throughput in async mode
@@ -588,7 +591,7 @@ pipeline (decoding vs inference) and not by the sum of the stages.
                 stop_time = time.time()
                 total_time = stop_time - start_time
                 frame_number = frame_number + 1
-                async_fps = frame_number / total_time  
+                async_fps = frame_number / total_time
                 frame = postprocess(res, frame, async_fps)
                 # Display the results
                 if use_popup:
@@ -608,7 +611,7 @@ pipeline (decoding vs inference) and not by the sum of the stages.
                 # Swap CURRENT and NEXT frames
                 frame = next_frame
                 # Swap CURRENT and NEXT infer requests
-                curr_request, next_request = next_request, curr_request         
+                curr_request, next_request = next_request, curr_request
         # ctrl-c
         except KeyboardInterrupt:
             print("Interrupted")
@@ -635,13 +638,13 @@ Test the performance in Async Mode
 
 
 
-.. image:: 115-async-api-with-output_files/115-async-api-with-output_21_0.png
+.. image:: async-api-with-output_files/async-api-with-output_21_0.png
 
 
 .. parsed-literal::
 
     Source ended
-    average throuput in async mode: 73.14 fps
+    average throuput in async mode: 73.74 fps
 
 
 Compare the performance
@@ -654,24 +657,24 @@ Compare the performance
     width = 0.4
     fontsize = 14
     
-    plt.rc('font', size=fontsize)
+    plt.rc("font", size=fontsize)
     fig, ax = plt.subplots(1, 1, figsize=(10, 8))
     
-    rects1 = ax.bar([0], sync_fps, width, color='#557f2d')
+    rects1 = ax.bar([0], sync_fps, width, color="#557f2d")
     rects2 = ax.bar([width], async_fps, width)
     ax.set_ylabel("frames per second")
-    ax.set_xticks([0, width]) 
+    ax.set_xticks([0, width])
     ax.set_xticklabels(["Sync mode", "Async mode"])
     ax.set_xlabel("Higher is better")
     
-    fig.suptitle('Sync mode VS Async mode')
+    fig.suptitle("Sync mode VS Async mode")
     fig.tight_layout()
     
     plt.show()
 
 
 
-.. image:: 115-async-api-with-output_files/115-async-api-with-output_23_0.png
+.. image:: async-api-with-output_files/async-api-with-output_23_0.png
 
 
 ``AsyncInferQueue``
@@ -702,7 +705,7 @@ the possibility of passing runtime values.
     def callback(infer_request, info) -> None:
         """
         Define the callback function for postprocessing
-        
+    
         :param: infer_request: the infer_request object
                 info: a tuple includes original frame and starts time
         :returns:
@@ -716,7 +719,7 @@ the possibility of passing runtime values.
         total_time = stop_time - start_time
         frame_number = frame_number + 1
         inferqueue_fps = frame_number / total_time
-        
+    
         res = infer_request.get_output_tensor(0).data[0]
         frame = postprocess(res, frame, inferqueue_fps)
         # Encode numpy array to jpg
@@ -732,7 +735,7 @@ the possibility of passing runtime values.
     def inferqueue(source, flip, fps, skip_first_frames) -> None:
         """
         Define the main function for video processing with async infer queue
-        
+    
         :param: source: the video path or the ID of your webcam
         :retuns:
             None
@@ -754,7 +757,7 @@ the possibility of passing runtime values.
                     print("Source ended")
                     break
                 resized_frame = preprocess(frame)
-                # Start the inference request with async infer queue 
+                # Start the inference request with async infer queue
                 infer_queue.start_async({input_layer_ir.any_name: resized_frame}, (frame, start_time))
         except KeyboardInterrupt:
             print("Interrupted")
@@ -779,10 +782,10 @@ Test the performance with ``AsyncInferQueue``
 
 
 
-.. image:: 115-async-api-with-output_files/115-async-api-with-output_29_0.png
+.. image:: async-api-with-output_files/async-api-with-output_29_0.png
 
 
 .. parsed-literal::
 
-    average throughput in async mode with async infer queue: 112.94 fps
+    average throughput in async mode with async infer queue: 112.89 fps
 
diff --git a/docs/notebooks/115-async-api-with-output_files/115-async-api-with-output_17_0.png b/docs/notebooks/async-api-with-output_files/async-api-with-output_17_0.png
similarity index 100%
rename from docs/notebooks/115-async-api-with-output_files/115-async-api-with-output_17_0.png
rename to docs/notebooks/async-api-with-output_files/async-api-with-output_17_0.png
diff --git a/docs/notebooks/115-async-api-with-output_files/115-async-api-with-output_21_0.png b/docs/notebooks/async-api-with-output_files/async-api-with-output_21_0.png
similarity index 100%
rename from docs/notebooks/115-async-api-with-output_files/115-async-api-with-output_21_0.png
rename to docs/notebooks/async-api-with-output_files/async-api-with-output_21_0.png
diff --git a/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png b/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png
new file mode 100644
index 00000000000..b7a2dc965c6
--- /dev/null
+++ b/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ee108fdee6cf1a56efa4a2347f0bc0c4cd09b6b30b121d114cfdd2cd87a9152
+size 30443
diff --git a/docs/notebooks/115-async-api-with-output_files/115-async-api-with-output_29_0.png b/docs/notebooks/async-api-with-output_files/async-api-with-output_29_0.png
similarity index 100%
rename from docs/notebooks/115-async-api-with-output_files/115-async-api-with-output_29_0.png
rename to docs/notebooks/async-api-with-output_files/async-api-with-output_29_0.png
diff --git a/docs/notebooks/106-auto-device-with-output.rst b/docs/notebooks/auto-device-with-output.rst
similarity index 83%
rename from docs/notebooks/106-auto-device-with-output.rst
rename to docs/notebooks/auto-device-with-output.rst
index d3268cb229f..bfe8b33f916 100644
--- a/docs/notebooks/106-auto-device-with-output.rst
+++ b/docs/notebooks/auto-device-with-output.rst
@@ -68,8 +68,20 @@ Import modules and create Core
 
 .. code:: ipython3
 
-    # Install openvino package
-    %pip install -q "openvino>=2023.1.0"
+    import platform
+    
+    # Install required packages
+    %pip install -q "openvino>=2023.1.0" Pillow torch torchvision tqdm --extra-index-url https://download.pytorch.org/whl/cpu
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -89,7 +101,11 @@ Import modules and create Core
     core = ov.Core()
     
     if "GPU" not in core.available_devices:
-        display(Markdown('
Warning:  A GPU device is not available. This notebook requires GPU device to have meaningful results. 
'))
+        display(
+            Markdown(
+                '
Warning:  A GPU device is not available. This notebook requires GPU device to have meaningful results. 
'
+            )
+        )
 
 
 
@@ -134,7 +150,7 @@ For more information about model conversion API, see this
     
     if not model_path.exists():
         pt_model = torchvision.models.resnet50(weights="DEFAULT")
-        ov_model = ov.convert_model(pt_model, input=[[1,3,224,224]])
+        ov_model = ov.convert_model(pt_model, input=[[1, 3, 224, 224]])
         ov.save_model(ov_model, str(model_path))
         print("IR model saved to {}".format(model_path))
     else:
@@ -163,26 +179,26 @@ By default, ``compile_model`` API will select **AUTO** as
 .. code:: ipython3
 
     # Set LOG_LEVEL to LOG_INFO.
-    core.set_property("AUTO", {"LOG_LEVEL":"LOG_INFO"})
+    core.set_property("AUTO", {"LOG_LEVEL": "LOG_INFO"})
     
     # Load the model onto the target device.
     compiled_model = core.compile_model(ov_model)
     
     if isinstance(compiled_model, ov.CompiledModel):
-        print("Successfully compiled model without a device_name.")   
+        print("Successfully compiled model without a device_name.")
 
 
 .. parsed-literal::
 
-    [22:24:04.4814]I[plugin.cpp:418][AUTO] device:CPU, config:LOG_LEVEL=LOG_INFO
-    [22:24:04.4815]I[plugin.cpp:418][AUTO] device:CPU, config:PERFORMANCE_HINT=LATENCY
-    [22:24:04.4815]I[plugin.cpp:418][AUTO] device:CPU, config:PERFORMANCE_HINT_NUM_REQUESTS=0
-    [22:24:04.4815]I[plugin.cpp:418][AUTO] device:CPU, config:PERF_COUNT=NO
-    [22:24:04.4815]I[plugin.cpp:423][AUTO] device:CPU, priority:0
-    [22:24:04.4815]I[schedule.cpp:17][AUTO] scheduler starting
-    [22:24:04.4815]I[auto_schedule.cpp:131][AUTO] select device:CPU
-    [22:24:04.6260]I[auto_schedule.cpp:109][AUTO] device:CPU compiling model finished
-    [22:24:04.6262]I[plugin.cpp:451][AUTO] underlying hardware does not support hardware context
+    [23:04:35.7467]I[plugin.cpp:418][AUTO] device:CPU, config:LOG_LEVEL=LOG_INFO
+    [23:04:35.7467]I[plugin.cpp:418][AUTO] device:CPU, config:PERFORMANCE_HINT=LATENCY
+    [23:04:35.7467]I[plugin.cpp:418][AUTO] device:CPU, config:PERFORMANCE_HINT_NUM_REQUESTS=0
+    [23:04:35.7467]I[plugin.cpp:418][AUTO] device:CPU, config:PERF_COUNT=NO
+    [23:04:35.7467]I[plugin.cpp:423][AUTO] device:CPU, priority:0
+    [23:04:35.7467]I[schedule.cpp:17][AUTO] scheduler starting
+    [23:04:35.7468]I[auto_schedule.cpp:131][AUTO] select device:CPU
+    [23:04:35.9061]I[auto_schedule.cpp:109][AUTO] device:CPU compiling model finished
+    [23:04:35.9063]I[plugin.cpp:451][AUTO] underlying hardware does not support hardware context
     Successfully compiled model without a device_name.
 
 
@@ -195,8 +211,8 @@ By default, ``compile_model`` API will select **AUTO** as
 
 .. parsed-literal::
 
-    [22:24:04.6373]I[schedule.cpp:303][AUTO] scheduler ending
     Deleted compiled_model
+    [23:04:35.9172]I[schedule.cpp:303][AUTO] scheduler ending
 
 
 Explicitly pass AUTO as device_name to Core::compile_model API
@@ -210,7 +226,7 @@ improve readability of your code.
 .. code:: ipython3
 
     # Set LOG_LEVEL to LOG_NONE.
-    core.set_property("AUTO", {"LOG_LEVEL":"LOG_NONE"})
+    core.set_property("AUTO", {"LOG_LEVEL": "LOG_NONE"})
     
     compiled_model = core.compile_model(model=ov_model, device_name="AUTO")
     
@@ -262,11 +278,11 @@ function, we will reuse it for preparing input data.
 .. code:: ipython3
 
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
-    )
+    import requests
+    
+    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py")
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file
 
 .. code:: ipython3
@@ -276,7 +292,7 @@ function, we will reuse it for preparing input data.
     # Download the image from the openvino_notebooks storage
     image_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
-        directory="data"
+        directory="data",
     )
     
     image = Image.open(str(image_filename))
@@ -295,7 +311,7 @@ function, we will reuse it for preparing input data.
 
 
 
-.. image:: 106-auto-device-with-output_files/106-auto-device-with-output_14_1.png
+.. image:: auto-device-with-output_files/auto-device-with-output_14_1.png
 
 
 
@@ -308,7 +324,7 @@ Load the model to GPU device and perform inference
 
     if "GPU" not in core.available_devices:
         print(f"A GPU device is not available. Available devices are: {core.available_devices}")
-    else :       
+    else:
         # Start time.
         gpu_load_start_time = time.perf_counter()
         compiled_model = core.compile_model(model=ov_model, device_name="GPU")  # load to GPU
@@ -354,7 +370,7 @@ executed on CPU until GPU is ready.
 
 .. parsed-literal::
 
-    Time to load model using AUTO device and get first inference: 0.16 seconds.
+    Time to load model using AUTO device and get first inference: 0.15 seconds.
 
 
 .. code:: ipython3
@@ -398,6 +414,7 @@ Class and callback definition
         :member: latency_list: Record the latency of each inference execution over @interval seconds duration.
         :member: interval: The metrics will be updated every @interval seconds
         """
+    
         def __init__(self, interval):
             """
             Create and initilize one instance of class PerformanceMetrics.
@@ -407,11 +424,11 @@ Class and callback definition
             """
             self.fps = 0
             self.latency = 0
-            
+    
             self.start_time = time.perf_counter()
             self.latency_list = []
             self.interval = interval
-            
+    
         def update(self, infer_request: ov.InferRequest) -> bool:
             """
             Update the metrics if current ongoing @interval seconds duration is expired. Record the latency only if it is not expired.
@@ -431,17 +448,18 @@ Class and callback definition
                 sys.stdout.flush()
                 self.latency_list = []
                 return True
-            else :
+            else:
                 return False
     
     
     class InferContext:
         """
         Inference context. Record and update peforamnce metrics via @metrics, set @feed_inference to False once @remaining_update_num <=0
-        :member: metrics: instance of class PerformanceMetrics 
+        :member: metrics: instance of class PerformanceMetrics
         :member: remaining_update_num: the remaining times for peforamnce metrics updating.
         :member: feed_inference: if feed inference request is required or not.
         """
+    
         def __init__(self, update_interval, num):
             """
             Create and initilize one instance of class InferContext.
@@ -453,19 +471,19 @@ Class and callback definition
             self.metrics = PerformanceMetrics(update_interval)
             self.remaining_update_num = num
             self.feed_inference = True
-            
+    
         def update(self, infer_request: ov.InferRequest):
             """
             Update the context. Set @feed_inference to False if the number of remaining performance metric updates (@remaining_update_num) reaches 0
             :param: infer_request: InferRequest returned from inference callback, which includes the result of inference request.
             :returns: None
             """
-            if self.remaining_update_num <= 0 :
+            if self.remaining_update_num <= 0:
                 self.feed_inference = False
-                
-            if self.metrics.update(infer_request) :
+    
+            if self.metrics.update(infer_request):
                 self.remaining_update_num = self.remaining_update_num - 1
-                if self.remaining_update_num <= 0 :
+                if self.remaining_update_num <= 0:
                     self.feed_inference = False
     
     
@@ -498,7 +516,7 @@ Loop for inference and update the FPS/Latency every
     print("Compiling Model for AUTO device with THROUGHPUT hint")
     sys.stdout.flush()
     
-    compiled_model = core.compile_model(model=ov_model, config={"PERFORMANCE_HINT":"THROUGHPUT"})
+    compiled_model = core.compile_model(model=ov_model, config={"PERFORMANCE_HINT": "THROUGHPUT"})
     
     infer_queue = ov.AsyncInferQueue(compiled_model, 0)  # Setting to 0 will query optimal number by default.
     infer_queue.set_callback(completion_callback)
@@ -508,7 +526,7 @@ Loop for inference and update the FPS/Latency every
     
     while THROUGHPUT_hint_context.feed_inference:
         infer_queue.start_async(input_tensor, THROUGHPUT_hint_context)
-        
+    
     infer_queue.wait_all()
     
     # Take the FPS and latency of the latest period.
@@ -532,32 +550,32 @@ Loop for inference and update the FPS/Latency every
 
 .. parsed-literal::
 
-    throughput:  181.84fps, latency:  31.28ms, time interval: 10.00s
+    throughput:  179.12fps, latency:  31.83ms, time interval: 10.00s
 
 
 .. parsed-literal::
 
-    throughput:  183.53fps, latency:  31.88ms, time interval: 10.01s
+    throughput:  181.09fps, latency:  32.33ms, time interval: 10.01s
 
 
 .. parsed-literal::
 
-    throughput:  182.49fps, latency:  32.10ms, time interval: 10.00s
+    throughput:  179.44fps, latency:  32.62ms, time interval: 10.00s
 
 
 .. parsed-literal::
 
-    throughput:  183.45fps, latency:  31.92ms, time interval: 10.00s
+    throughput:  179.98fps, latency:  32.57ms, time interval: 10.00s
 
 
 .. parsed-literal::
 
-    throughput:  182.69fps, latency:  32.02ms, time interval: 10.00s
+    throughput:  179.55fps, latency:  32.61ms, time interval: 10.01s
 
 
 .. parsed-literal::
 
-    throughput:  181.11fps, latency:  32.34ms, time interval: 10.02s
+    throughput:  179.60fps, latency:  32.58ms, time interval: 10.00s
 
 
 .. parsed-literal::
@@ -580,7 +598,7 @@ Loop for inference and update the FPS/Latency for each
     print("Compiling Model for AUTO Device with LATENCY hint")
     sys.stdout.flush()
     
-    compiled_model = core.compile_model(model=ov_model, config={"PERFORMANCE_HINT":"LATENCY"})
+    compiled_model = core.compile_model(model=ov_model, config={"PERFORMANCE_HINT": "LATENCY"})
     
     # Setting to 0 will query optimal number by default.
     infer_queue = ov.AsyncInferQueue(compiled_model, 0)
@@ -591,7 +609,7 @@ Loop for inference and update the FPS/Latency for each
     
     while LATENCY_hint_context.feed_inference:
         infer_queue.start_async(input_tensor, LATENCY_hint_context)
-        
+    
     infer_queue.wait_all()
     
     # Take the FPS and latency of the latest period.
@@ -615,32 +633,32 @@ Loop for inference and update the FPS/Latency for each
 
 .. parsed-literal::
 
-    throughput:  138.83fps, latency:  6.64ms, time interval: 10.01s
+    throughput:  137.86fps, latency:  6.72ms, time interval: 10.00s
 
 
 .. parsed-literal::
 
-    throughput:  141.03fps, latency:  6.64ms, time interval: 10.00s
+    throughput:  140.86fps, latency:  6.72ms, time interval: 10.00s
 
 
 .. parsed-literal::
 
-    throughput:  140.77fps, latency:  6.64ms, time interval: 10.00s
+    throughput:  140.85fps, latency:  6.72ms, time interval: 10.00s
 
 
 .. parsed-literal::
 
-    throughput:  141.80fps, latency:  6.65ms, time interval: 10.01s
+    throughput:  140.28fps, latency:  6.69ms, time interval: 10.00s
 
 
 .. parsed-literal::
 
-    throughput:  142.26fps, latency:  6.66ms, time interval: 10.00s
+    throughput:  140.66fps, latency:  6.70ms, time interval: 10.00s
 
 
 .. parsed-literal::
 
-    throughput:  141.36fps, latency:  6.63ms, time interval: 10.00s
+    throughput:  140.48fps, latency:  6.68ms, time interval: 10.00s
 
 
 .. parsed-literal::
@@ -661,18 +679,29 @@ Difference in FPS and latency
     LAT = 1
     labels = ["THROUGHPUT hint", "LATENCY hint"]
     
-    fig1, ax1 = plt.subplots(1, 1) 
+    fig1, ax1 = plt.subplots(1, 1)
     fig1.patch.set_visible(False)
-    ax1.axis('tight') 
-    ax1.axis('off') 
+    ax1.axis("tight")
+    ax1.axis("off")
     
     cell_text = []
-    cell_text.append(['%.2f%s' % (THROUGHPUT_hint_fps," FPS"), '%.2f%s' % (THROUGHPUT_hint_latency, " ms")])
-    cell_text.append(['%.2f%s' % (LATENCY_hint_fps," FPS"), '%.2f%s' % (LATENCY_hint_latency, " ms")])
+    cell_text.append(
+        [
+            "%.2f%s" % (THROUGHPUT_hint_fps, " FPS"),
+            "%.2f%s" % (THROUGHPUT_hint_latency, " ms"),
+        ]
+    )
+    cell_text.append(["%.2f%s" % (LATENCY_hint_fps, " FPS"), "%.2f%s" % (LATENCY_hint_latency, " ms")])
     
-    table = ax1.table(cellText=cell_text, colLabels=["FPS (Higher is better)", "Latency (Lower is better)"], rowLabels=labels,  
-                      rowColours=["deepskyblue"] * 2, colColours=["deepskyblue"] * 2,
-                      cellLoc='center', loc='upper left')
+    table = ax1.table(
+        cellText=cell_text,
+        colLabels=["FPS (Higher is better)", "Latency (Lower is better)"],
+        rowLabels=labels,
+        rowColours=["deepskyblue"] * 2,
+        colColours=["deepskyblue"] * 2,
+        cellLoc="center",
+        loc="upper left",
+    )
     table.auto_set_font_size(False)
     table.set_fontsize(18)
     table.auto_set_column_width(0)
@@ -684,7 +713,7 @@ Difference in FPS and latency
 
 
 
-.. image:: 106-auto-device-with-output_files/106-auto-device-with-output_27_0.png
+.. image:: auto-device-with-output_files/auto-device-with-output_27_0.png
 
 
 .. code:: ipython3
@@ -693,24 +722,24 @@ Difference in FPS and latency
     width = 0.4
     fontsize = 14
     
-    plt.rc('font', size=fontsize)
-    fig, ax = plt.subplots(1,2, figsize=(10, 8))
+    plt.rc("font", size=fontsize)
+    fig, ax = plt.subplots(1, 2, figsize=(10, 8))
     
-    rects1 = ax[0].bar([0], THROUGHPUT_hint_fps, width, label=labels[TPUT], color='#557f2d')
+    rects1 = ax[0].bar([0], THROUGHPUT_hint_fps, width, label=labels[TPUT], color="#557f2d")
     rects2 = ax[0].bar([width], LATENCY_hint_fps, width, label=labels[LAT])
     ax[0].set_ylabel("frames per second")
-    ax[0].set_xticks([width / 2]) 
+    ax[0].set_xticks([width / 2])
     ax[0].set_xticklabels(["FPS"])
     ax[0].set_xlabel("Higher is better")
     
-    rects1 = ax[1].bar([0], THROUGHPUT_hint_latency, width, label=labels[TPUT], color='#557f2d')
+    rects1 = ax[1].bar([0], THROUGHPUT_hint_latency, width, label=labels[TPUT], color="#557f2d")
     rects2 = ax[1].bar([width], LATENCY_hint_latency, width, label=labels[LAT])
     ax[1].set_ylabel("milliseconds")
     ax[1].set_xticks([width / 2])
     ax[1].set_xticklabels(["Latency (ms)"])
     ax[1].set_xlabel("Lower is better")
     
-    fig.suptitle('Performance Hints')
+    fig.suptitle("Performance Hints")
     fig.legend(labels, fontsize=fontsize)
     fig.tight_layout()
     
@@ -718,5 +747,5 @@ Difference in FPS and latency
 
 
 
-.. image:: 106-auto-device-with-output_files/106-auto-device-with-output_28_0.png
+.. image:: auto-device-with-output_files/auto-device-with-output_28_0.png
 
diff --git a/docs/notebooks/106-auto-device-with-output_files/106-auto-device-with-output_14_1.jpg b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_14_1.jpg
similarity index 100%
rename from docs/notebooks/106-auto-device-with-output_files/106-auto-device-with-output_14_1.jpg
rename to docs/notebooks/auto-device-with-output_files/auto-device-with-output_14_1.jpg
diff --git a/docs/notebooks/106-auto-device-with-output_files/106-auto-device-with-output_14_1.png b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_14_1.png
similarity index 100%
rename from docs/notebooks/106-auto-device-with-output_files/106-auto-device-with-output_14_1.png
rename to docs/notebooks/auto-device-with-output_files/auto-device-with-output_14_1.png
diff --git a/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png
new file mode 100644
index 00000000000..a5e6e606528
--- /dev/null
+++ b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84c73fa64ec907ad0be08db15bebc96b35c8b26b29d29f657f8ceeb75aab5662
+size 27580
diff --git a/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png
new file mode 100644
index 00000000000..97eef2a1702
--- /dev/null
+++ b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61109ff91028e7d3caeed1fd125fd850785bb6341898e0dce5a87aece1166832
+size 39983
diff --git a/docs/notebooks/256-bark-text-to-audio-with-output.rst b/docs/notebooks/bark-text-to-audio-with-output.rst
similarity index 99%
rename from docs/notebooks/256-bark-text-to-audio-with-output.rst
rename to docs/notebooks/bark-text-to-audio-with-output.rst
index c5e30597f74..1c2c30cc4f5 100644
--- a/docs/notebooks/256-bark-text-to-audio-with-output.rst
+++ b/docs/notebooks/bark-text-to-audio-with-output.rst
@@ -78,14 +78,9 @@ Prerequisites
 
 .. code:: ipython3
 
-    import sys
-    
-    if sys.platform == "linux":
-        %pip install -q "torch" "torchvision" "torchaudio" --index-url https://download.pytorch.org/whl/cpu
-    else:
-        %pip install -q "torch" "torchvision" "torchaudio"
-    %pip install -q "openvino>=2023.1.0" gradio
-    %pip install -q "git+https://github.com/suno-ai/bark.git"
+    %pip install -q "torch" "torchvision" "torchaudio" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2023.1.0" "gradio>=4.19"
+    %pip install -q "git+https://github.com/suno-ai/bark.git" --extra-index-url https://download.pytorch.org/whl/cpu
 
 
 .. parsed-literal::
@@ -130,9 +125,7 @@ models for that.
 
     text_use_small = True
     
-    text_encoder = load_model(
-        model_type="text", use_gpu=False, use_small=text_use_small, force_reload=False
-    )
+    text_encoder = load_model(model_type="text", use_gpu=False, use_small=text_use_small, force_reload=False)
     
     text_encoder_model = text_encoder["model"]
     tokenizer = text_encoder["tokenizer"]
@@ -161,9 +154,7 @@ models for that.
     
     if not text_encoder_path0.exists() or not text_encoder_path1.exists():
         text_encoder_exportable = TextEncoderModel(text_encoder_model)
-        ov_model = ov.convert_model(
-            text_encoder_exportable, example_input=torch.ones((1, 513), dtype=torch.int64)
-        )
+        ov_model = ov.convert_model(text_encoder_exportable, example_input=torch.ones((1, 513), dtype=torch.int64))
         ov.save_model(ov_model, text_encoder_path0)
         logits, kv_cache = text_encoder_exportable(torch.ones((1, 513), dtype=torch.int64))
         ov_model = ov.convert_model(
@@ -280,21 +271,15 @@ provide empty tensor on the first step.
                     dim=-1,
                 )
     
-            y = torch.nn.functional.scaled_dot_product_attention(
-                q, k, v, dropout_p=self.dropout, attn_mask=full_attention_mask
-            )
+            y = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout, attn_mask=full_attention_mask)
         else:
             # manual implementation of attention
             att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-            att = att.masked_fill(
-                self.bias[:, :, FULL_T - T : FULL_T, :FULL_T] == 0, float("-inf")
-            )
+            att = att.masked_fill(self.bias[:, :, FULL_T - T : FULL_T, :FULL_T] == 0, float("-inf"))
             att = F.softmax(att, dim=-1)
             att = self.attn_dropout(att)
             y = att @ v  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
-        y = (
-            y.transpose(1, 2).contiguous().view(B, T, C)
-        )  # re-assemble all head outputs side by side
+        y = y.transpose(1, 2).contiguous().view(B, T, C)  # re-assemble all head outputs side by side
     
         # output projection
         y = self.resid_dropout(self.c_proj(y))
@@ -305,9 +290,7 @@ provide empty tensor on the first step.
         coarse_encoder_exportable = CoarseEncoderModel(coarse_model)
         for block in coarse_encoder_exportable.encoder.transformer.h:
             block.attn.forward = types.MethodType(casual_self_attention_forward, block.attn)
-        logits, kv_cache = coarse_encoder_exportable(
-            torch.ones((1, 886), dtype=torch.int64)
-        )
+        logits, kv_cache = coarse_encoder_exportable(torch.ones((1, 886), dtype=torch.int64))
         ov_model = ov.convert_model(
             coarse_encoder_exportable,
             example_input=(torch.ones((1, 1), dtype=torch.int64), kv_cache),
@@ -328,9 +311,7 @@ provide empty tensor on the first step.
 
     fine_use_small = False
     
-    fine_model = load_model(
-        model_type="fine", use_gpu=False, use_small=fine_use_small, force_reload=False
-    )
+    fine_model = load_model(model_type="fine", use_gpu=False, use_small=fine_use_small, force_reload=False)
     
     fine_model_suffix = "_small" if fine_use_small else ""
     fine_model_dir = models_dir / f"fine_model{fine_model_suffix}"
@@ -348,14 +329,9 @@ provide empty tensor on the first step.
             pos = torch.arange(0, t, dtype=torch.long).unsqueeze(0)  # shape (1, t)
     
             # forward the GPT model itself
-            tok_embs = [
-                wte(idx[:, :, i]).unsqueeze(-1)
-                for i, wte in enumerate(self.model.transformer.wtes)
-            ]  # token embeddings of shape (b, t, n_embd)
+            tok_embs = [wte(idx[:, :, i]).unsqueeze(-1) for i, wte in enumerate(self.model.transformer.wtes)]  # token embeddings of shape (b, t, n_embd)
             tok_emb = torch.cat(tok_embs, dim=-1)
-            pos_emb = self.model.transformer.wpe(
-                pos
-            )  # position embeddings of shape (1, t, n_embd)
+            pos_emb = self.model.transformer.wpe(pos)  # position embeddings of shape (1, t, n_embd)
             x = tok_emb[:, :, :, : pred_idx + 1].sum(dim=-1)
             x = self.model.transformer.drop(x + pos_emb)
             for block in self.model.transformer.h:
@@ -380,9 +356,7 @@ codebooks embeddings obtained using Coarse encoder.
     if not fine_feature_extractor_path.exists():
         lm_heads = fine_model.lm_heads
         fine_feature_extractor = FineModel(fine_model)
-        feature_extractor_out = fine_feature_extractor(
-            3, torch.zeros((1, 1024, 8), dtype=torch.int32)
-        )
+        feature_extractor_out = fine_feature_extractor(3, torch.zeros((1, 1024, 8), dtype=torch.int32))
         ov_model = ov.convert_model(
             fine_feature_extractor,
             example_input=(
@@ -420,11 +394,7 @@ For better usability, classes for working with models provided below.
             return logits, kv_cache
     
         def postprocess_outputs(self, outs, is_first_stage):
-            net_outs = (
-                self.compiled_model1.outputs
-                if is_first_stage
-                else self.compiled_model2.outputs
-            )
+            net_outs = self.compiled_model1.outputs if is_first_stage else self.compiled_model2.outputs
             logits = outs[net_outs[0]]
             kv_cache = []
             for out_tensor in net_outs[1:]:
@@ -462,21 +432,15 @@ For better usability, classes for working with models provided below.
     
     class OVBarkFineEncoder:
         def __init__(self, core, device, model_dir, num_lm_heads=7):
-            self.feats_compiled_model = core.compile_model(
-                model_dir / "bark_fine_feature_extractor.xml", device
-            )
+            self.feats_compiled_model = core.compile_model(model_dir / "bark_fine_feature_extractor.xml", device)
             self.feats_out = self.feats_compiled_model.output(0)
             lm_heads = []
             for i in range(num_lm_heads):
-                lm_heads.append(
-                    core.compile_model(model_dir / f"bark_fine_lm_{i}.xml", device)
-                )
+                lm_heads.append(core.compile_model(model_dir / f"bark_fine_lm_{i}.xml", device))
             self.lm_heads = lm_heads
     
         def __call__(self, pred_idx, idx):
-            feats = self.feats_compiled_model([ov.Tensor(pred_idx), ov.Tensor(idx)])[
-                self.feats_out
-            ]
+            feats = self.feats_compiled_model([ov.Tensor(pred_idx), ov.Tensor(idx)])[self.feats_out]
             lm_id = pred_idx - 1
             logits = self.lm_heads[int(lm_id)](feats)[0]
             return logits
@@ -621,9 +585,7 @@ consists from several steps, illustrated on the diagram below:
             semantic_history = history_prompt["semantic_prompt"]
         else:
             semantic_history = None
-        encoded_text = (
-            np.ascontiguousarray(_tokenize(tokenizer, text)) + TEXT_ENCODING_OFFSET
-        )
+        encoded_text = np.ascontiguousarray(_tokenize(tokenizer, text)) + TEXT_ENCODING_OFFSET
         if len(encoded_text) > 256:
             p = round((len(encoded_text) - 256) / len(encoded_text) * 100, 1)
             logger.warning(f"warning, text too long, lopping of last {p}%")
@@ -646,9 +608,7 @@ consists from several steps, illustrated on the diagram below:
             )
         else:
             semantic_history = np.array([SEMANTIC_PAD_TOKEN] * 256)
-        x = np.hstack(
-            [encoded_text, semantic_history, np.array([SEMANTIC_INFER_TOKEN])]
-        ).astype(np.int64)[None]
+        x = np.hstack([encoded_text, semantic_history, np.array([SEMANTIC_INFER_TOKEN])]).astype(np.int64)[None]
         assert x.shape[1] == 256 + 256 + 1
         n_tot_steps = 768
         # custom tqdm updates since we don't know when eos will occur
@@ -664,9 +624,7 @@ consists from several steps, illustrated on the diagram below:
             logits, kv_cache = ov_text_model(ov.Tensor(x_input), kv_cache)
             relevant_logits = logits[0, 0, :SEMANTIC_VOCAB_SIZE]
             if allow_early_stop:
-                relevant_logits = np.hstack(
-                    (relevant_logits, logits[0, 0, [SEMANTIC_PAD_TOKEN]])
-                )  # eos
+                relevant_logits = np.hstack((relevant_logits, logits[0, 0, [SEMANTIC_PAD_TOKEN]]))  # eos
             if top_p is not None:
                 sorted_indices = np.argsort(relevant_logits)[::-1]
                 sorted_logits = relevant_logits[sorted_indices]
@@ -682,19 +640,13 @@ consists from several steps, illustrated on the diagram below:
                 relevant_logits[relevant_logits < v[-1]] = -float("Inf")
             probs = F.softmax(torch.from_numpy(relevant_logits) / temp, dim=-1)
             item_next = torch.multinomial(probs, num_samples=1)
-            if allow_early_stop and (
-                item_next == SEMANTIC_VOCAB_SIZE
-                or (min_eos_p is not None and probs[-1] >= min_eos_p)
-            ):
+            if allow_early_stop and (item_next == SEMANTIC_VOCAB_SIZE or (min_eos_p is not None and probs[-1] >= min_eos_p)):
                 # eos found, so break
                 pbar.update(100 - pbar_state)
                 break
             x = torch.cat((torch.from_numpy(x), item_next[None]), dim=1).numpy()
             tot_generated_duration_s += 1 / SEMANTIC_RATE_HZ
-            if (
-                max_gen_duration_s is not None
-                and tot_generated_duration_s > max_gen_duration_s
-            ):
+            if max_gen_duration_s is not None and tot_generated_duration_s > max_gen_duration_s:
                 pbar.update(100 - pbar_state)
                 break
             if n == n_tot_steps - 1:
@@ -784,24 +736,15 @@ consists from several steps, illustrated on the diagram below:
                     int(np.floor(len(x_coarse_history) / semantic_to_coarse_ratio)),
                 ]
             )
-            n_coarse_hist_provided = int(
-                round(n_semantic_hist_provided * semantic_to_coarse_ratio)
-            )
-            x_semantic_history = x_semantic_history[-n_semantic_hist_provided:].astype(
-                np.int32
-            )
+            n_coarse_hist_provided = int(round(n_semantic_hist_provided * semantic_to_coarse_ratio))
+            x_semantic_history = x_semantic_history[-n_semantic_hist_provided:].astype(np.int32)
             x_coarse_history = x_coarse_history[-n_coarse_hist_provided:].astype(np.int32)
             x_coarse_history = x_coarse_history[:-2]
         else:
             x_semantic_history = np.array([], dtype=np.int32)
             x_coarse_history = np.array([], dtype=np.int32)
         # start loop
-        n_steps = int(
-            round(
-                np.floor(len(x_semantic) * semantic_to_coarse_ratio / N_COARSE_CODEBOOKS)
-                * N_COARSE_CODEBOOKS
-            )
-        )
+        n_steps = int(round(np.floor(len(x_semantic) * semantic_to_coarse_ratio / N_COARSE_CODEBOOKS) * N_COARSE_CODEBOOKS))
         x_semantic = np.hstack([x_semantic_history, x_semantic]).astype(np.int32)
         x_coarse = x_coarse_history.astype(np.int32)
         base_semantic_idx = len(x_semantic_history)
@@ -839,12 +782,8 @@ consists from several steps, illustrated on the diagram below:
                     x_input = x_in
     
                 logits, kv_cache = ov_coarse_model(x_input, past_kv=kv_cache)
-                logit_start_idx = (
-                    SEMANTIC_VOCAB_SIZE + (1 - int(is_major_step)) * CODEBOOK_SIZE
-                )
-                logit_end_idx = (
-                    SEMANTIC_VOCAB_SIZE + (2 - int(is_major_step)) * CODEBOOK_SIZE
-                )
+                logit_start_idx = SEMANTIC_VOCAB_SIZE + (1 - int(is_major_step)) * CODEBOOK_SIZE
+                logit_end_idx = SEMANTIC_VOCAB_SIZE + (2 - int(is_major_step)) * CODEBOOK_SIZE
                 relevant_logits = logits[0, 0, logit_start_idx:logit_end_idx]
                 if top_p is not None:
                     sorted_indices = np.argsort(relevant_logits)[::-1]
@@ -863,9 +802,7 @@ consists from several steps, illustrated on the diagram below:
                 item_next = torch.multinomial(probs, num_samples=1)
                 item_next = item_next
                 item_next += logit_start_idx
-                x_coarse_in = torch.cat(
-                    (torch.from_numpy(x_coarse_in), item_next[None]), dim=1
-                ).numpy()
+                x_coarse_in = torch.cat((torch.from_numpy(x_coarse_in), item_next[None]), dim=1).numpy()
                 x_in = torch.cat((torch.from_numpy(x_in), item_next[None]), dim=1).numpy()
                 del logits, relevant_logits, probs, item_next
                 n_step += 1
@@ -873,9 +810,7 @@ consists from several steps, illustrated on the diagram below:
         del x_semantic_in
         gen_coarse_arr = x_coarse_in.squeeze()[len(x_coarse_history) :]
         del x_coarse_in
-        gen_coarse_audio_arr = (
-            gen_coarse_arr.reshape(-1, N_COARSE_CODEBOOKS).T - SEMANTIC_VOCAB_SIZE
-        )
+        gen_coarse_audio_arr = gen_coarse_arr.reshape(-1, N_COARSE_CODEBOOKS).T - SEMANTIC_VOCAB_SIZE
         for n in range(1, N_COARSE_CODEBOOKS):
             gen_coarse_audio_arr[n, :] -= n * CODEBOOK_SIZE
         return gen_coarse_audio_arr
@@ -908,8 +843,7 @@ consists from several steps, illustrated on the diagram below:
         in_arr = np.vstack(
             [
                 x_coarse_gen,
-                np.zeros((N_FINE_CODEBOOKS - n_coarse, x_coarse_gen.shape[1]))
-                + CODEBOOK_SIZE,
+                np.zeros((N_FINE_CODEBOOKS - n_coarse, x_coarse_gen.shape[1])) + CODEBOOK_SIZE,
             ]
         ).astype(
             np.int32
@@ -928,14 +862,10 @@ consists from several steps, illustrated on the diagram below:
             in_arr = np.hstack(
                 [
                     in_arr,
-                    np.zeros((N_FINE_CODEBOOKS, n_remove_from_end), dtype=np.int32)
-                    + CODEBOOK_SIZE,
+                    np.zeros((N_FINE_CODEBOOKS, n_remove_from_end), dtype=np.int32) + CODEBOOK_SIZE,
                 ]
             )
-        n_loops = (
-            np.max([0, int(np.ceil((x_coarse_gen.shape[1] - (1024 - n_history)) / 512))])
-            + 1
-        )
+        n_loops = np.max([0, int(np.ceil((x_coarse_gen.shape[1] - (1024 - n_history)) / 512))]) + 1
         in_arr = in_arr.T
         for n in tqdm.tqdm(range(n_loops), disable=silent):
             start_idx = np.min([n * 512, in_arr.shape[0] - 1024])
@@ -943,27 +873,18 @@ consists from several steps, illustrated on the diagram below:
             rel_start_fill_idx = start_fill_idx - start_idx
             in_buffer = in_arr[start_idx : start_idx + 1024, :][None]
             for nn in range(n_coarse, N_FINE_CODEBOOKS):
-                logits = ov_fine_model(
-                    np.array([nn]).astype(np.int64), in_buffer.astype(np.int64)
-                )
+                logits = ov_fine_model(np.array([nn]).astype(np.int64), in_buffer.astype(np.int64))
                 if temp is None:
                     relevant_logits = logits[0, rel_start_fill_idx:, :CODEBOOK_SIZE]
                     codebook_preds = torch.argmax(relevant_logits, -1)
                 else:
                     relevant_logits = logits[0, :, :CODEBOOK_SIZE] / temp
                     probs = F.softmax(torch.from_numpy(relevant_logits), dim=-1)
-                    codebook_preds = torch.hstack(
-                        [
-                            torch.multinomial(probs[nnn], num_samples=1)
-                            for nnn in range(rel_start_fill_idx, 1024)
-                        ]
-                    )
+                    codebook_preds = torch.hstack([torch.multinomial(probs[nnn], num_samples=1) for nnn in range(rel_start_fill_idx, 1024)])
                 in_buffer[0, rel_start_fill_idx:, nn] = codebook_preds.numpy()
                 del logits, codebook_preds
             for nn in range(n_coarse, N_FINE_CODEBOOKS):
-                in_arr[
-                    start_fill_idx : start_fill_idx + (1024 - rel_start_fill_idx), nn
-                ] = in_buffer[0, rel_start_fill_idx:, nn]
+                in_arr[start_fill_idx : start_fill_idx + (1024 - rel_start_fill_idx), nn] = in_buffer[0, rel_start_fill_idx:, nn]
             del in_buffer
         gen_fine_arr = in_arr.squeeze().T
         del in_arr
@@ -1016,9 +937,7 @@ select device from dropdown list for running inference using OpenVINO
 
     core = ov.Core()
     
-    ov_text_model = OVBarkTextEncoder(
-        core, device.value, text_encoder_path0, text_encoder_path1
-    )
+    ov_text_model = OVBarkTextEncoder(core, device.value, text_encoder_path0, text_encoder_path1)
     ov_coarse_model = OVBarkEncoder(core, device.value, coarse_encoder_path)
     ov_fine_model = OVBarkFineEncoder(core, device.value, fine_model_dir)
 
@@ -1198,9 +1117,7 @@ Interactive demo
         with gr.Row():
             with gr.Column():
                 input_text = gr.Textbox(label="Input Text", lines=2, value=default_text)
-                options = gr.Dropdown(
-                    AVAILABLE_PROMPTS, value="Speaker 1 (en)", label="Acoustic Prompt"
-                )
+                options = gr.Dropdown(AVAILABLE_PROMPTS, value="Speaker 1 (en)", label="Acoustic Prompt")
                 run_button = gr.Button()
             with gr.Column():
                 audio_out = gr.Audio(label="Generated Audio", type="numpy")
diff --git a/docs/notebooks/258-blip-diffusion-subject-generation-with-output.rst b/docs/notebooks/blip-diffusion-subject-generation-with-output.rst
similarity index 90%
rename from docs/notebooks/258-blip-diffusion-subject-generation-with-output.rst
rename to docs/notebooks/blip-diffusion-subject-generation-with-output.rst
index 5e7cddad9e0..8a3d4beb770 100644
--- a/docs/notebooks/258-blip-diffusion-subject-generation-with-output.rst
+++ b/docs/notebooks/blip-diffusion-subject-generation-with-output.rst
@@ -55,8 +55,8 @@ Prerequisites
 
     import platform
 
-    %pip install -q "openvino>=2023.1.0" Pillow gradio
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu torch transformers accelerate controlnet_aux "diffusers>=0.23.0" "peft==0.6.2"
+    %pip install -q "openvino>=2023.1.0" Pillow "gradio>=4.19"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "torch>=2.1.0" "transformers>=4.36" accelerate controlnet_aux "diffusers>=0.23.0" "peft==0.6.2"
 
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -76,7 +76,6 @@ Prerequisites
     import gc
     from typing import List, Optional, Union
     from functools import partial
-    from urllib.request import urlretrieve
 
     import diffusers
     import torch
@@ -109,13 +108,9 @@ Prerequisites
     DATA_DIR = Path("data")
     DOG_IMG_URL = "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg"
     DOG_IMG_PATH = DATA_DIR / "dog.jpg"
-    KETTLE_IMG_URL = (
-        "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg"
-    )
+    KETTLE_IMG_URL = "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg"
     KETTLE_IMG_PATH = DATA_DIR / "kettle.jpg"
-    FLOWER_IMG_URL = (
-        "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg"
-    )
+    FLOWER_IMG_URL = "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg"
     FLOWER_IMG_PATH = DATA_DIR / "flower.jpg"
     BAG_IMG_URL = "https://huggingface.co/lllyasviel/sd-controlnet-scribble/resolve/main/images/bag.png"
     BAG_IMG_PATH = DATA_DIR / "bag.jpg"
@@ -134,9 +129,7 @@ We use Hugging Face ``diffusers`` library to load the model using
 .. code:: ipython3
 
     pipe = diffusers.pipelines.BlipDiffusionPipeline.from_pretrained("ayushtues/blipdiffusion")
-    pipe_controlnet = diffusers.pipelines.BlipDiffusionControlNetPipeline.from_pretrained(
-        "ayushtues/blipdiffusion-controlnet"
-    )
+    pipe_controlnet = diffusers.pipelines.BlipDiffusionControlNetPipeline.from_pretrained("ayushtues/blipdiffusion-controlnet")
 
 
 .. parsed-literal::
@@ -163,11 +156,18 @@ We use Hugging Face ``diffusers`` library to load the model using
 
 .. code:: ipython3
 
+    import requests
+
     # Download images
-    urlretrieve(DOG_IMG_URL, DOG_IMG_PATH)
-    urlretrieve(KETTLE_IMG_URL, KETTLE_IMG_PATH)
-    urlretrieve(FLOWER_IMG_URL, FLOWER_IMG_PATH)
-    urlretrieve(BAG_IMG_URL, BAG_IMG_PATH);
+
+    IMAGE_URLS = [DOG_IMG_PATH, KETTLE_IMG_URL, FLOWER_IMG_URL, BAG_IMG_URL]
+    IMAGE_PATHS = [DOG_IMG_PATH, KETTLE_IMG_PATH, FLOWER_IMG_PATH, BAG_IMG_PATH]
+
+    for url, img_path in zip(IMAGE_URLS, IMAGE_PATHS):
+        r = requests.get(url)
+
+        with img_path.open("wb") as f:
+            f.write(r.content)
 
 Infer the original model
 ------------------------
@@ -226,7 +226,7 @@ is an image containing the subject with conditions from the prompt
 
 
 
-.. image:: 258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_12_0.png
+.. image:: blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_12_0.png
 
 
 Controlled subject-driven generation (Canny-edge)
@@ -290,7 +290,7 @@ description.
         "Conditioning image": cond_image,
         "Canny-edge mask": cldm_cond_image[0],
         "Style image": style_image,
-        "Output": output[0][0]
+        "Output": output[0][0],
     }
 
     plt.figure(figsize=(16, 4), layout="tight")
@@ -302,7 +302,7 @@ description.
 
 
 
-.. image:: 258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_16_0.png
+.. image:: blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_16_0.png
 
 
 Controlled subject-driven generation (Scribble)
@@ -363,7 +363,7 @@ edge map is the final output of HED and input of our diffusion model.
         "Conditioning image": bag_img,
         "Scribble mask": cldm_cond_image[0],
         "Style image": style_image,
-        "Output": output[0][0]
+        "Output": output[0][0],
     }
     plt.figure(figsize=(16, 4), layout="tight")
     for i, (title, img) in enumerate(title2img.items()):
@@ -374,7 +374,7 @@ edge map is the final output of HED and input of our diffusion model.
 
 
 
-.. image:: 258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_19_0.png
+.. image:: blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_19_0.png
 
 
 Convert the model to OpenVINO Intermediate Representation (IR)
@@ -384,7 +384,7 @@ Convert the model to OpenVINO Intermediate Representation (IR)
 
 BLIP-Diffusion pipeline has the following structure:
 
-.. image:: 258-blip-diffusion-subject-generation-with-output_files/1c472f1f-1fce-4a13-9d44-b10f6f760ddb.png
+.. image:: blip-diffusion-subject-generation-with-output_files/1c472f1f-1fce-4a13-9d44-b10f6f760ddb.png
 
 
 The output of the BLIP-2 multimodal encoder is connected to the input of
@@ -532,9 +532,7 @@ Original QFormer model takes raw text as input, so we redefine the
 
             # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
             # ourselves in which case we just need to make it broadcastable to all heads.
-            extended_attention_mask = self.get_extended_attention_mask(
-                attention_mask, input_shape, device
-            )
+            extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device)
 
             # If a 2D or 3D attention mask is provided for the cross-attention
             # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
@@ -542,7 +540,11 @@ Original QFormer model takes raw text as input, so we redefine the
                 if isinstance(encoder_hidden_states, list):
                     encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
                 else:
-                    encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+                    (
+                        encoder_batch_size,
+                        encoder_sequence_length,
+                        _,
+                    ) = encoder_hidden_states.size()
                 encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
                 encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
                 encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
@@ -736,7 +738,7 @@ facilitates the actual diffusion process.
             timestep_dtype=torch.int64,
             encoder_hidden_states=torch.float32,
             down_block_additional_residuals=torch.float32,
-            mid_block_additional_residual=torch.float32
+            mid_block_additional_residual=torch.float32,
         ):
             super().__init__()
             self.unet = unet
@@ -748,11 +750,11 @@ facilitates the actual diffusion process.
 
         def forward(
             self,
-            sample:torch.Tensor,
-            timestep:torch.Tensor,
-            encoder_hidden_states:torch.Tensor,
-            down_block_additional_residuals:Tuple[torch.Tensor],
-            mid_block_additional_residual:torch.Tensor
+            sample: torch.Tensor,
+            timestep: torch.Tensor,
+            encoder_hidden_states: torch.Tensor,
+            down_block_additional_residuals: Tuple[torch.Tensor],
+            mid_block_additional_residual: torch.Tensor,
         ):
             sample.to(self.sample_dtype)
             timestep.to(self.timestep_dtype)
@@ -764,9 +766,10 @@ facilitates the actual diffusion process.
                 timestep,
                 encoder_hidden_states,
                 down_block_additional_residuals=down_block_additional_residuals,
-                mid_block_additional_residual=mid_block_additional_residual
+                mid_block_additional_residual=mid_block_additional_residual,
             )
 
+
     def flatten_inputs(inputs):
         flat_inputs = []
         for input_data in inputs:
@@ -1024,9 +1027,7 @@ Inference
                 prompt_strength=prompt_strength,
                 prompt_reps=prompt_reps,
             )
-            qformer_input = self.qformer_tokenizer(
-                source_subject_category, return_tensors="pt", padding=True
-            )
+            qformer_input = self.qformer_tokenizer(source_subject_category, return_tensors="pt", padding=True)
             query_embeds = self.qformer(
                 image_input=reference_image,
                 text_input_ids=qformer_input.input_ids,
@@ -1079,9 +1080,7 @@ Inference
                 # expand the latents if we are doing classifier free guidance
                 do_classifier_free_guidance = guidance_scale > 1.0
 
-                latent_model_input = (
-                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                )
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                 if conditioning_image:
                     controlnet_output = self.controlnet(
                         [
@@ -1093,7 +1092,9 @@ Inference
                     )
                 noise_pred = (
                     self.unet(
-                        sample=latent_model_input, timestep=t, encoder_hidden_states=text_embeddings
+                        sample=latent_model_input,
+                        timestep=t,
+                        encoder_hidden_states=text_embeddings,
                     )
                     if not conditioning_image
                     else self.unet_controlnet(
@@ -1107,9 +1108,7 @@ Inference
                 # perform guidance
                 if do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (
-                        noise_pred_text - noise_pred_uncond
-                    )
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
                 latents = self.scheduler.step(
                     noise_pred,
@@ -1146,9 +1145,7 @@ Inference
             return text_embeddings
 
 
-    OvBlipDiffusionPipeline.prepare_control_image = (
-        diffusers.pipelines.BlipDiffusionControlNetPipeline.prepare_control_image
-    )
+    OvBlipDiffusionPipeline.prepare_control_image = diffusers.pipelines.BlipDiffusionControlNetPipeline.prepare_control_image
     OvBlipDiffusionPipeline._build_prompt = diffusers.pipelines.BlipDiffusionPipeline._build_prompt
     OvBlipDiffusionPipeline.prepare_latents = diffusers.pipelines.BlipDiffusionPipeline.prepare_latents
 
@@ -1170,7 +1167,7 @@ Zero-Shot subject-driven generation
         tgt_subject,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
-        neg_prompt=negative_prompt
+        neg_prompt=negative_prompt,
     )
 
 
@@ -1192,7 +1189,7 @@ Zero-Shot subject-driven generation
 
 
 
-.. image:: 258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_52_0.png
+.. image:: blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_52_0.png
 
 
 Controlled subject-driven generation (Canny-edge)
@@ -1241,7 +1238,7 @@ Controlled subject-driven generation (Canny-edge)
         "Conditioning image": cond_image,
         "Canny-edge mask": cldm_cond_image[0],
         "Style image": style_image,
-        "Output": output[0]
+        "Output": output[0],
     }
 
     plt.figure(figsize=(16, 4), layout="tight")
@@ -1253,7 +1250,7 @@ Controlled subject-driven generation (Canny-edge)
 
 
 
-.. image:: 258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_55_0.png
+.. image:: blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_55_0.png
 
 
 Controlled subject-driven generation (Scribble)
@@ -1299,7 +1296,7 @@ Controlled subject-driven generation (Scribble)
         "Conditioning image": bag_img,
         "Scribble mask": cldm_cond_image[0],
         "Style image": style_image,
-        "Output": output[0]
+        "Output": output[0],
     }
     plt.figure(figsize=(16, 4), layout="tight")
     for i, (title, img) in enumerate(title2img.items()):
@@ -1310,7 +1307,7 @@ Controlled subject-driven generation (Scribble)
 
 
 
-.. image:: 258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_58_0.png
+.. image:: blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_58_0.png
 
 
 Interactive inference
@@ -1417,9 +1414,21 @@ Interactive inference
                     inputs = [
                         gr.Textbox(label="Prompt"),
                         gr.Image(label="Reference image", type="pil"),
-                        gr.Textbox(label="Source subject category", info="String description of a subject that defines the style"),
-                        gr.Textbox(label="Target subject category", info="String description of a subject to generate"),
-                        gr.Slider(1.1, 10, value=7.5, label="Guidance scale", info="Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality"),
+                        gr.Textbox(
+                            label="Source subject category",
+                            info="String description of a subject that defines the style",
+                        ),
+                        gr.Textbox(
+                            label="Target subject category",
+                            info="String description of a subject to generate",
+                        ),
+                        gr.Slider(
+                            1.1,
+                            10,
+                            value=7.5,
+                            label="Guidance scale",
+                            info="Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality",
+                        ),
                         gr.Slider(1, 100, value=50, label="Number of inference steps"),
                         gr.Slider(0, 1_000_000, value=0, label="Random seed"),
                         gr.Textbox(label="Negative prompt"),
@@ -1449,10 +1458,22 @@ Interactive inference
                     inputs = [
                         gr.Textbox(label="Prompt"),
                         gr.Image(label="Reference image", type="pil"),
-                        gr.Textbox(label="Source subject category", info="String description of a subject that defines the style"),
-                        gr.Textbox(label="Target subject category", info="String description of a subject to generate"),
+                        gr.Textbox(
+                            label="Source subject category",
+                            info="String description of a subject that defines the style",
+                        ),
+                        gr.Textbox(
+                            label="Target subject category",
+                            info="String description of a subject to generate",
+                        ),
                         gr.Image(label="Conditioning image", type="pil"),
-                        gr.Slider(1.1, 10, value=7.5, label="Guidance scale", info="Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality"),
+                        gr.Slider(
+                            1.1,
+                            10,
+                            value=7.5,
+                            label="Guidance scale",
+                            info="Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality",
+                        ),
                         gr.Slider(1, 100, value=50, label="Number of inference steps"),
                         gr.Slider(0, 1_000_000, value=0, label="Random seed"),
                         gr.Textbox(label="Negative prompt"),
@@ -1483,10 +1504,22 @@ Interactive inference
                     inputs = [
                         gr.Textbox(label="Prompt"),
                         gr.Image(label="Reference image", type="pil"),
-                        gr.Textbox(label="Source subject category", info="String description of a subject that defines the style"),
-                        gr.Textbox(label="Target subject category", info="String description of a subject to generate"),
+                        gr.Textbox(
+                            label="Source subject category",
+                            info="String description of a subject that defines the style",
+                        ),
+                        gr.Textbox(
+                            label="Target subject category",
+                            info="String description of a subject to generate",
+                        ),
                         gr.Image(label="Conditioning image", type="pil"),
-                        gr.Slider(1.1, 10, value=7.5, label="Guidance scale", info="Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality"),
+                        gr.Slider(
+                            1.1,
+                            10,
+                            value=7.5,
+                            label="Guidance scale",
+                            info="Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality",
+                        ),
                         gr.Slider(1, 100, value=50, label="Number of inference steps"),
                         gr.Slider(0, 1_000_000, value=0, label="Random seed"),
                         gr.Textbox(label="Negative prompt"),
diff --git a/docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/1c472f1f-1fce-4a13-9d44-b10f6f760ddb.png b/docs/notebooks/blip-diffusion-subject-generation-with-output_files/1c472f1f-1fce-4a13-9d44-b10f6f760ddb.png
similarity index 100%
rename from docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/1c472f1f-1fce-4a13-9d44-b10f6f760ddb.png
rename to docs/notebooks/blip-diffusion-subject-generation-with-output_files/1c472f1f-1fce-4a13-9d44-b10f6f760ddb.png
diff --git a/docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_12_0.png b/docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_12_0.png
similarity index 100%
rename from docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_12_0.png
rename to docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_12_0.png
diff --git a/docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_16_0.png b/docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_16_0.png
similarity index 100%
rename from docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_16_0.png
rename to docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_16_0.png
diff --git a/docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_19_0.png b/docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_19_0.png
similarity index 100%
rename from docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_19_0.png
rename to docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_19_0.png
diff --git a/docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_52_0.png b/docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_52_0.png
similarity index 100%
rename from docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_52_0.png
rename to docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_52_0.png
diff --git a/docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_55_0.png b/docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_55_0.png
similarity index 100%
rename from docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_55_0.png
rename to docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_55_0.png
diff --git a/docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_58_0.png b/docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_58_0.png
similarity index 100%
rename from docs/notebooks/258-blip-diffusion-subject-generation-with-output_files/258-blip-diffusion-subject-generation-with-output_58_0.png
rename to docs/notebooks/blip-diffusion-subject-generation-with-output_files/blip-diffusion-subject-generation-with-output_58_0.png
diff --git a/docs/notebooks/233-blip-visual-language-processing-with-output.rst b/docs/notebooks/blip-visual-language-processing-with-output.rst
similarity index 84%
rename from docs/notebooks/233-blip-visual-language-processing-with-output.rst
rename to docs/notebooks/blip-visual-language-processing-with-output.rst
index 8fe90b73c05..3af5b98d5d0 100644
--- a/docs/notebooks/233-blip-visual-language-processing-with-output.rst
+++ b/docs/notebooks/blip-visual-language-processing-with-output.rst
@@ -14,9 +14,12 @@ image-text retrieval, image captioning, and visual question answering.
 pre-training framework for unified vision-language understanding and
 generation. BLIP achieves state-of-the-art results on a wide range of
 vision-language tasks. This tutorial demonstrates how to use BLIP for
-visual question answering and image captioning. additional part of
-tutorial demonstrates how to optimize model in low precision using
-`NNCF `__.
+visual question answering and image captioning. An additional part of
+tutorial demonstrates how to speed up the model by applying 8-bit
+post-training quantization and data free int8 weight compression from
+`NNCF `__ (Neural Network
+Compression Framework) to OpenVINO IR models and infer optimized BLIP
+model via OpenVINO™ Toolkit.
 
 The tutorial consists of the following parts:
 
@@ -49,8 +52,23 @@ Table of contents:
    -  `Image Captioning <#image-captioning>`__
    -  `Question Answering <#question-answering>`__
 
+-  `Optimize model using NNCF <#optimize-model-using-nncf>`__
+
+   -  `Prepare dataset <#prepare-dataset>`__
+   -  `Quantize vision model <#quantize-vision-model>`__
+   -  `Quantize text encoder <#quantize-text-encoder>`__
+   -  `Compress weights of text
+      decoder <#compress-weights-of-text-decoder>`__
+   -  `Run optimized OpenVINO model <#run-optimized-openvino-model>`__
+
+      -  `Image captioning <#image-captioning>`__
+      -  `Question answering <#question-answering>`__
+
+   -  `Compare file sizes <#compare-file-sizes>`__
+   -  `Compare inference time of the FP16 and optimized
+      models <#compare-inference-time-of-the-fp16-and-optimized-models>`__
+
 -  `Interactive demo <#interactive-demo>`__
--  `Next steps <#next-steps>`__
 
 Background
 ----------
@@ -252,26 +270,25 @@ text and vision modalities and postprocessing of generation results.
 
     import platform
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu torch torchvision "transformers>=4.26.0" gradio "openvino>=2023.3.0" "datasets>==2.14.6" "nncf>=2.8.1"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "torch>=2.1.0" torchvision "transformers>=4.26.0" "gradio>=4.19" "openvino>=2023.3.0" "datasets>==2.14.6" "nncf>=2.8.1" "tqdm"
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
 
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
 .. code:: ipython3
 
-    import sys
     import time
     from PIL import Image
     from transformers import BlipProcessor, BlipForQuestionAnswering
 
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
     # get model and processor
@@ -279,9 +296,9 @@ text and vision modalities and postprocessing of generation results.
     model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
 
     # setup test input: download and read image, prepare question
-    img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
+    img_url = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
     download_file(img_url, "demo.jpg")
-    raw_image = Image.open("demo.jpg").convert('RGB')
+    raw_image = Image.open("demo.jpg").convert("RGB")
     question = "how many dogs are in the picture?"
     # preprocess input data
     inputs = processor(raw_image, question, return_tensors="pt")
@@ -294,27 +311,6 @@ text and vision modalities and postprocessing of generation results.
     # postprocess result
     answer = processor.decode(out[0], skip_special_tokens=True)
 
-
-.. parsed-literal::
-
-    2024-02-27 18:27:06.101620: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-02-27 18:27:06.103343: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-02-27 18:27:06.140450: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-02-27 18:27:06.811047: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
-.. parsed-literal::
-
-    'demo.jpg' already exists.
-
-
-.. parsed-literal::
-
-    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/transformers/generation/utils.py:1178: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
-      warnings.warn(
-
-
 .. code:: ipython3
 
     print(f"Processing time: {end:.4f} s")
@@ -322,7 +318,7 @@ text and vision modalities and postprocessing of generation results.
 
 .. parsed-literal::
 
-    Processing time: 0.2996 s
+    Processing time: 0.3707 s
 
 
 .. code:: ipython3
@@ -333,7 +329,7 @@ text and vision modalities and postprocessing of generation results.
 
 
 
-.. image:: 233-blip-visual-language-processing-with-output_files/233-blip-visual-language-processing-with-output_7_0.png
+.. image:: blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_7_0.png
 
 
 Convert Models to OpenVINO IR
@@ -397,7 +393,13 @@ shape, containing RGB image pixel values normalized in the [0,1] range.
 
 .. parsed-literal::
 
-    Vision model will be loaded from blip_vision_model.xml
+    /home/ltalamanova/tmp_venv/lib/python3.11/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+      warnings.warn(
+
+
+.. parsed-literal::
+
+    Vision model successfuly converted and saved to blip_vision_model.xml
 
 
 Text Encoder
@@ -423,7 +425,12 @@ model and attention masks for them.
         # prepare example inputs
         image_embeds = vision_outputs[0]
         image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long)
-        input_dict = {"input_ids": inputs["input_ids"], "attention_mask": inputs["attention_mask"], "encoder_hidden_states": image_embeds, "encoder_attention_mask": image_attention_mask}
+        input_dict = {
+            "input_ids": inputs["input_ids"],
+            "attention_mask": inputs["attention_mask"],
+            "encoder_hidden_states": image_embeds,
+            "encoder_attention_mask": image_attention_mask,
+        }
         # export PyTorch model
         with torch.no_grad():
             ov_text_encoder = ov.convert_model(text_encoder, example_input=input_dict)
@@ -436,7 +443,7 @@ model and attention masks for them.
 
 .. parsed-literal::
 
-    Text encoder will be loaded from blip_text_encoder.xml
+    Text encoder successfuly converted and saved to blip_text_encoder.xml
 
 
 Text Decoder
@@ -500,7 +507,12 @@ shapes.
     encoder_hidden_states = torch.rand((1, 10, 768))  # encoder last hidden state from text_encoder
     encoder_attention_mask = torch.ones((1, 10), dtype=torch.long)  # attention mask for encoder hidden states
 
-    input_dict = {"input_ids": input_ids, "attention_mask": attention_mask, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask}
+    input_dict = {
+        "input_ids": input_ids,
+        "attention_mask": attention_mask,
+        "encoder_hidden_states": encoder_hidden_states,
+        "encoder_attention_mask": encoder_attention_mask,
+    }
     text_decoder_outs = text_decoder(**input_dict)
     # extend input dictionary with hidden states from previous step
     input_dict["past_key_values"] = text_decoder_outs["past_key_values"]
@@ -519,7 +531,15 @@ shapes.
 
 .. parsed-literal::
 
-    Text decoder will be loaded from blip_text_decoder_with_past.xml
+    /home/ltalamanova/tmp_venv/lib/python3.11/site-packages/transformers/models/blip/modeling_blip_text.py:635: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if causal_mask.shape[1] < attention_mask.shape[1]:
+    /home/ltalamanova/tmp_venv/lib/python3.11/site-packages/torch/jit/_trace.py:165: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:489.)
+      if a.grad is not None:
+
+
+.. parsed-literal::
+
+    Text decoder successfuly converted and saved to blip_text_decoder_with_past.xml
 
 
 Run OpenVINO Model
@@ -550,7 +570,7 @@ tokenized by ``BlipProcessor`` are provided to the text encoder and then
 multimodal question embedding is passed to the text decoder for
 performing generation of answers.
 
-|image1|
+|image11|
 
 The next step is implementing both pipelines using OpenVINO models.
 
@@ -575,19 +595,27 @@ select device from dropdown list for running inference using OpenVINO
 
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
 
     device
 
 
+.. parsed-literal::
+
+    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+    To disable this warning, you can either:
+    	- Avoid using `tokenizers` before the fork if possible
+    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+
+
 
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=3, options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='AUTO')
+    Dropdown(description='Device:', index=4, options=('CPU', 'GPU.0', 'GPU.1', 'GPU.2', 'AUTO'), value='AUTO')
 
 
 
@@ -634,7 +662,7 @@ Image Captioning
 
 
 
-.. image:: 233-blip-visual-language-processing-with-output_files/233-blip-visual-language-processing-with-output_25_0.png
+.. image:: blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_25_0.png
 
 
 Question Answering
@@ -652,7 +680,7 @@ Question Answering
 
 
 
-.. image:: 233-blip-visual-language-processing-with-output_files/233-blip-visual-language-processing-with-output_27_0.png
+.. image:: blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_27_0.png
 
 
 .. code:: ipython3
@@ -662,15 +690,12 @@ Question Answering
 
 .. parsed-literal::
 
-    Processing time: 0.1403
+    Processing time: 0.1186
 
 
 Optimize model using NNCF
 -------------------------
 
-Quantize
-~~~~~~~~
-
 
 
 `NNCF `__ enables
@@ -697,15 +722,22 @@ The optimization process contains the following steps:
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
 
     to_quantize
 
-.. code:: ipython3
 
-    from urllib.request import urlretrieve
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Quantization')
+
+
+
+.. code:: ipython3
 
     VISION_MODEL_OV_INT8 = Path(str(VISION_MODEL_OV).replace(".xml", "_int8.xml"))
     TEXT_ENCODER_OV_INT8 = Path(str(TEXT_ENCODER_OV).replace(".xml", "_int8.xml"))
@@ -713,15 +745,15 @@ The optimization process contains the following steps:
     int8_model = None
 
     # Fetch skip_kernel_extension module
-    urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/skip_kernel_extension.py',
-        filename='skip_kernel_extension.py'
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
+    open("skip_kernel_extension.py", "w").write(r.text)
 
     %load_ext skip_kernel_extension
 
 Prepare dataset
-^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~
 
 
 
@@ -772,8 +804,6 @@ understanding of vision, language and commonsense knowledge to answer.
         inputs_info = {}
         for idx, batch in enumerate(tqdm(dataloader, total=opt_init_steps, desc="Prepare calibration data")):
             preprocess_batch(batch, vision_model, inputs_info)
-            if idx >= opt_init_steps:
-                break
 
         calibration_subset = []
         for image_id in inputs_info:
@@ -795,9 +825,12 @@ understanding of vision, language and commonsense knowledge to answer.
         """
         Prepares a vision-text dataset for quantization.
         """
-        dataset = load_dataset("HuggingFaceM4/VQAv2", split="train", streaming=streaming)
-        train_dataset = dataset.shuffle(seed=42)
-        calibration_subset = prepare_input_data(train_dataset, vision_model, opt_init_steps)
+        split = f"train[:{opt_init_steps}]" if not streaming else "train"
+        dataset = load_dataset("HuggingFaceM4/VQAv2", split=split, streaming=streaming)
+        dataset = dataset.shuffle(seed=42)
+        if streaming:
+            dataset = dataset.take(opt_init_steps)
+        calibration_subset = prepare_input_data(dataset, vision_model, opt_init_steps)
         return calibration_subset
 
 Loading and processing the dataset in streaming mode may take a long
@@ -812,26 +845,8 @@ time and depends on your internet connection.
     comp_vision_model = core.compile_model(VISION_MODEL_OV, device.value)
     calibration_data = prepare_dataset(comp_vision_model)
 
-
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/huggingface_hub/repocard.py:105: UserWarning: Repo card metadata block was not found. Setting CardData to empty.
-      warnings.warn("Repo card metadata block was not found. Setting CardData to empty.")
-
-
-
-.. parsed-literal::
-
-    Prepare calibration data:   0%|          | 0/300 [00:00Processing time: {elapsed:.4f}
"
@@ -1311,7 +1374,7 @@ Interactive demo
         ],
         [gr.Text(label="Answer"), gr.HTML()],
         examples=[["demo.jpg", ""], ["demo.jpg", question]],
-        allow_flagging="never"
+        allow_flagging="never",
     )
     try:
         demo.launch(debug=False)
@@ -1320,13 +1383,3 @@ Interactive demo
     # if you are launching remotely, specify server_name and server_port
     # demo.launch(server_name='your server name', server_port='server port in int')
     # Read more in the docs: https://gradio.app/docs/
-
-Next steps
-----------
-
-
-
-Open the `233-blip-optimize <233-blip-optimize-with-output.html>`__ notebook to
-quantize vision and text encoder models with the Post-training
-Quantization API of NNCF and compress weights of the text decoder. Then
-compare the converted and optimized OpenVINO models.
diff --git a/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_25_0.png b/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_25_0.png
new file mode 100644
index 00000000000..c90aa04a52d
--- /dev/null
+++ b/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_25_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ec8d01e06da8fec020483260e6bbe7ac04612ca64dc4642197e8966d20af384f
+size 206675
diff --git a/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_27_0.png b/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_27_0.png
new file mode 100644
index 00000000000..7de75c31a7e
--- /dev/null
+++ b/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_27_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d6069f2803d8a4ea3d5855ed874b353af223ba5418714b3c5e637114ff206d5
+size 210267
diff --git a/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_47_0.png b/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_47_0.png
new file mode 100644
index 00000000000..1749467ee07
--- /dev/null
+++ b/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_47_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4efd1fbdfcf5ae4b424316254235eb40a09d5716b6cff4aadb5b4f4b9eefe34
+size 205951
diff --git a/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_49_0.png b/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_49_0.png
new file mode 100644
index 00000000000..7de75c31a7e
--- /dev/null
+++ b/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_49_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d6069f2803d8a4ea3d5855ed874b353af223ba5418714b3c5e637114ff206d5
+size 210267
diff --git a/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_7_0.png b/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_7_0.png
new file mode 100644
index 00000000000..7de75c31a7e
--- /dev/null
+++ b/docs/notebooks/blip-visual-language-processing-with-output_files/blip-visual-language-processing-with-output_7_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d6069f2803d8a4ea3d5855ed874b353af223ba5418714b3c5e637114ff206d5
+size 210267
diff --git a/docs/notebooks/232-clip-language-saliency-map-with-output.rst b/docs/notebooks/clip-language-saliency-map-with-output.rst
similarity index 88%
rename from docs/notebooks/232-clip-language-saliency-map-with-output.rst
rename to docs/notebooks/clip-language-saliency-map-with-output.rst
index 6065447e54a..9ac43fe4b42 100644
--- a/docs/notebooks/232-clip-language-saliency-map-with-output.rst
+++ b/docs/notebooks/clip-language-saliency-map-with-output.rst
@@ -111,18 +111,17 @@ Initial Implementation with Transformers and Pytorch
 
     # Install requirements
     %pip install -q "openvino>=2023.1.0"
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu transformers torch gradio
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu transformers "torch>=2.1" "gradio>=4.19"
 
 .. code:: ipython3
 
     from pathlib import Path
     from typing import Tuple, Union, Optional
-    from urllib.request import urlretrieve
+    import requests
     
     from matplotlib import colors
     import matplotlib.pyplot as plt
     import numpy as np
-    import requests
     import torch
     import tqdm
     from PIL import Image
@@ -164,37 +163,25 @@ formula above.
 
 .. code:: ipython3
 
-    def get_random_crop_params(
-        image_height: int, image_width: int, min_crop_size: int
-    ) -> Tuple[int, int, int, int]:
+    def get_random_crop_params(image_height: int, image_width: int, min_crop_size: int) -> Tuple[int, int, int, int]:
         crop_size = np.random.randint(min_crop_size, min(image_height, image_width))
         x = np.random.randint(image_width - crop_size + 1)
         y = np.random.randint(image_height - crop_size + 1)
         return x, y, crop_size
     
     
-    def get_cropped_image(
-        im_tensor: np.array, x: int, y: int, crop_size: int
-    ) -> np.array:
-        return im_tensor[
-            y : y + crop_size,
-            x : x + crop_size,
-            ...
-        ]
+    def get_cropped_image(im_tensor: np.array, x: int, y: int, crop_size: int) -> np.array:
+        return im_tensor[y : y + crop_size, x : x + crop_size, ...]
     
     
-    def update_saliency_map(
-        saliency_map: np.array, similarity: float, x: int, y: int, crop_size: int
-    ) -> None:
+    def update_saliency_map(saliency_map: np.array, similarity: float, x: int, y: int, crop_size: int) -> None:
         saliency_map[
             y : y + crop_size,
             x : x + crop_size,
         ] += similarity
     
     
-    def cosine_similarity(
-        one: Union[np.ndarray, torch.Tensor], other: Union[np.ndarray, torch.Tensor]
-    ) -> Union[np.ndarray, torch.Tensor]:
+    def cosine_similarity(one: Union[np.ndarray, torch.Tensor], other: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
         return one @ other.T / (np.linalg.norm(one) * np.linalg.norm(other))
 
 Parameters to be defined:
@@ -220,7 +207,11 @@ parameters at the end, when you get an optimized model.
     
     query = "Who developed the Theory of General Relativity?"
     image_path = Path("example.jpg")
-    urlretrieve("https://www.storypick.com/wp-content/uploads/2016/01/AE-2.jpg", image_path)
+    
+    r = requests.get("https://www.storypick.com/wp-content/uploads/2016/01/AE-2.jpg")
+    
+    with image_path.open("wb") as f:
+        f.write(r.content)
     image = Image.open(image_path)
     im_tensor = np.array(image)
     
@@ -258,7 +249,7 @@ items in the “How To Build a Saliency Map With CLIP?” list above.
     initial_similarity = cosine_similarity(results.text_embeds, results.image_embeds).item()  # 1. Computing query and image similarity
     saliency_map = np.zeros((y_dim, x_dim))
     
-    for _ in tqdm.notebook.tqdm(range(n_iters)):  # 6. Setting number of the procedure iterations 
+    for _ in tqdm.notebook.tqdm(range(n_iters)):  # 6. Setting number of the procedure iterations
         x, y, crop_size = get_random_crop_params(y_dim, x_dim, min_crop_size)
         im_crop = get_cropped_image(im_tensor, x, y, crop_size)  # 2. Getting a random crop of the image
     
@@ -266,7 +257,9 @@ items in the “How To Build a Saliency Map With CLIP?” list above.
         with torch.no_grad():
             results = model(**inputs)  # 3. Computing crop and query similarity
     
-        similarity = cosine_similarity(results.text_embeds, results.image_embeds).item() - initial_similarity  # 4. Subtracting query and image similarity from crop and query similarity
+        similarity = (
+            cosine_similarity(results.text_embeds, results.image_embeds).item() - initial_similarity
+        )  # 4. Subtracting query and image similarity from crop and query similarity
         update_saliency_map(saliency_map, similarity, x, y, crop_size)  # 5. Updating the region on the saliency map
 
 
@@ -281,15 +274,15 @@ To visualize the resulting saliency map, you can use ``matplotlib``:
 .. code:: ipython3
 
     plt.figure(dpi=150)
-    plt.imshow(saliency_map, norm=colors.TwoSlopeNorm(vcenter=0), cmap='jet')
+    plt.imshow(saliency_map, norm=colors.TwoSlopeNorm(vcenter=0), cmap="jet")
     plt.colorbar(location="bottom")
-    plt.title(f'Query: \"{query}\"')
+    plt.title(f'Query: "{query}"')
     plt.axis("off")
     plt.show()
 
 
 
-.. image:: 232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_15_0.png
+.. image:: clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_15_0.png
 
 
 The result map is not as smooth as in the example picture because of the
@@ -304,23 +297,22 @@ Let us overlay the saliency map on the image:
         fig = plt.figure(dpi=150)
         plt.imshow(image_tensor)
         plt.imshow(
-            saliency_map, 
-            norm=colors.TwoSlopeNorm(vcenter=0), 
-            cmap="jet", 
+            saliency_map,
+            norm=colors.TwoSlopeNorm(vcenter=0),
+            cmap="jet",
             alpha=0.5,  # make saliency map trasparent to see original picture
         )
         if query:
             plt.title(f'Query: "{query}"')
         plt.axis("off")
         return fig
-        
     
     
     plot_saliency_map(im_tensor, saliency_map, query);
 
 
 
-.. image:: 232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_17_0.png
+.. image:: clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_17_0.png
 
 
 Separate Text and Visual Processing
@@ -367,7 +359,7 @@ obtain embeddings for the cropped images.
 
 
 
-.. image:: 232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_19_1.png
+.. image:: clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_19_1.png
 
 
 The result might be slightly different because you use random crops to
@@ -406,17 +398,20 @@ details about that can be found in HuggingFace Transformers
     model.config.torchscript = True
     model.forward = model.get_text_features
     text_ov_model = ov.convert_model(
-        model, 
-        example_input={"input_ids": inputs.input_ids, "attention_mask": inputs.attention_mask}
+        model,
+        example_input={
+            "input_ids": inputs.input_ids,
+            "attention_mask": inputs.attention_mask,
+        },
     )
     
     # get image size after preprocessing from the processor
     crops_info = processor.image_processor.crop_size.values() if hasattr(processor, "image_processor") else processor.feature_extractor.crop_size.values()
     model.forward = model.get_image_features
     image_ov_model = ov.convert_model(
-        model, 
+        model,
         example_input={"pixel_values": inputs.pixel_values},
-        input=[1,3, *crops_info],
+        input=[1, 3, *crops_info],
     )
     
     ov_dir = Path("ir")
@@ -504,8 +499,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -537,9 +532,7 @@ the inference process is mostly similar.
 
 .. code:: ipython3
 
-    text_inputs = dict(
-        processor(text=[query], images=[im_tensor], return_tensors="np")
-    )
+    text_inputs = dict(processor(text=[query], images=[im_tensor], return_tensors="np"))
     image_inputs = text_inputs.pop("pixel_values")
     
     text_embeds = text_model(text_inputs)[0]
@@ -568,7 +561,7 @@ the inference process is mostly similar.
 
 
 
-.. image:: 232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_29_1.png
+.. image:: clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_29_1.png
 
 
 Accelerate Inference with ``AsyncInferQueue``
@@ -610,16 +603,14 @@ performance hint.
     image_model = core.read_model(image_model_path)
     
     image_model = core.compile_model(
-        model=image_model, 
-        device_name=device.value, 
-        config={"PERFORMANCE_HINT":"THROUGHPUT"},
+        model=image_model,
+        device_name=device.value,
+        config={"PERFORMANCE_HINT": "THROUGHPUT"},
     )
 
 .. code:: ipython3
 
-    text_inputs = dict(
-        processor(text=[query], images=[im_tensor], return_tensors="np")
-    )
+    text_inputs = dict(processor(text=[query], images=[im_tensor], return_tensors="np"))
     image_inputs = text_inputs.pop("pixel_values")
     
     text_embeds = text_model(text_inputs)[text_model.output()]
@@ -647,13 +638,11 @@ should pass a progress bar object and call ``update`` method after
         user_data: Dict[str, Any],  # data that you passed along with input pixel values
     ) -> None:
         pbar = user_data.pop("pbar")
-        
+    
         image_embeds = infer_request.get_output_tensor().data
-        similarity = (
-            cosine_similarity(user_data.pop("text_embeds"), image_embeds) - user_data.pop("initial_similarity")
-        )
+        similarity = cosine_similarity(user_data.pop("text_embeds"), image_embeds) - user_data.pop("initial_similarity")
         update_saliency_map(**user_data, similarity=similarity)
-        
+    
         pbar.update(1)  # update the progress bar
     
     
@@ -662,35 +651,63 @@ should pass a progress bar object and call ``update`` method after
 
 .. code:: ipython3
 
-    def infer(im_tensor, x_dim, y_dim, text_embeds, image_embeds, initial_similarity, saliency_map, query, n_iters, min_crop_size, _tqdm=tqdm.notebook.tqdm, include_query=True):
+    def infer(
+        im_tensor,
+        x_dim,
+        y_dim,
+        text_embeds,
+        image_embeds,
+        initial_similarity,
+        saliency_map,
+        query,
+        n_iters,
+        min_crop_size,
+        _tqdm=tqdm.notebook.tqdm,
+        include_query=True,
+    ):
         with _tqdm(total=n_iters) as pbar:
-            for _ in range(n_iters):    
+            for _ in range(n_iters):
                 x, y, crop_size = get_random_crop_params(y_dim, x_dim, min_crop_size)
                 im_crop = get_cropped_image(im_tensor, x, y, crop_size)
-        
+    
                 image_inputs = processor(images=[im_crop], return_tensors="np")
-                
+    
                 # push data to the queue
                 infer_queue.start_async(
                     # pass inference data as usual
                     image_inputs.pixel_values,
                     # the data that will be passed to the callback after the inference complete
                     {
-                        "text_embeds": text_embeds, 
+                        "text_embeds": text_embeds,
                         "saliency_map": saliency_map,
                         "initial_similarity": initial_similarity,
-                        "x": x, 
-                        "y": y, 
+                        "x": x,
+                        "y": y,
                         "crop_size": crop_size,
                         "pbar": pbar,
-                    }
+                    },
                 )
-            
+    
             # after you pushed all data to the queue you wait until all callbacks finished
             infer_queue.wait_all()
-        
+    
         return plot_saliency_map(im_tensor, saliency_map, query if include_query else None)
-    infer(im_tensor, x_dim, y_dim, text_embeds, image_embeds, initial_similarity, saliency_map, query, n_iters, min_crop_size, _tqdm=tqdm.notebook.tqdm, include_query=True);
+    
+    
+    infer(
+        im_tensor,
+        x_dim,
+        y_dim,
+        text_embeds,
+        image_embeds,
+        initial_similarity,
+        saliency_map,
+        query,
+        n_iters,
+        min_crop_size,
+        _tqdm=tqdm.notebook.tqdm,
+        include_query=True,
+    );
 
 
 
@@ -700,7 +717,7 @@ should pass a progress bar object and call ``update`` method after
 
 
 
-.. image:: 232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_35_1.png
+.. image:: clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_35_1.png
 
 
 Pack the Pipeline into a Function
@@ -715,13 +732,18 @@ Let us wrap all code in the function and add a user interface to it.
     import ipywidgets as widgets
     
     
-    def build_saliency_map(image: Image, query: str, n_iters: int = n_iters, min_crop_size=min_crop_size, _tqdm=tqdm.notebook.tqdm, include_query=True):
+    def build_saliency_map(
+        image: Image,
+        query: str,
+        n_iters: int = n_iters,
+        min_crop_size=min_crop_size,
+        _tqdm=tqdm.notebook.tqdm,
+        include_query=True,
+    ):
         x_dim, y_dim = image.size
         im_tensor = np.array(image)
     
-        text_inputs = dict(
-            processor(text=[query], images=[im_tensor], return_tensors="np")
-        )
+        text_inputs = dict(processor(text=[query], images=[im_tensor], return_tensors="np"))
         image_inputs = text_inputs.pop("pixel_values")
     
         text_embeds = text_model(text_inputs)[text_model.output()]
@@ -730,7 +752,20 @@ Let us wrap all code in the function and add a user interface to it.
         initial_similarity = cosine_similarity(text_embeds, image_embeds)
         saliency_map = np.zeros((y_dim, x_dim))
     
-        return infer(im_tensor, x_dim, y_dim, text_embeds, image_embeds, initial_similarity, saliency_map, query, n_iters, min_crop_size, _tqdm=_tqdm, include_query=include_query)
+        return infer(
+            im_tensor,
+            x_dim,
+            y_dim,
+            text_embeds,
+            image_embeds,
+            initial_similarity,
+            saliency_map,
+            query,
+            n_iters,
+            min_crop_size,
+            _tqdm=_tqdm,
+            include_query=include_query,
+        )
 
 The first version will enable passing a link to the image, as you have
 done so far in the notebook.
@@ -763,10 +798,10 @@ done so far in the notebook.
         except requests.RequestException as e:
             print(f"Cannot load image from link: {image_link}\nException: {e}")
             return
-        
+    
         image = Image.open(image_bytes)
         image = image.convert("RGB")  # remove transparency channel or convert grayscale 1 channel to 3 channels
-        
+    
         build_saliency_map(image, query, n_iters, min_crop_size)
 
 
@@ -784,11 +819,18 @@ The second version will enable loading the image from your computer.
     
     
     load_file_widget = widgets.FileUpload(
-        accept="image/*", multiple=False, description="Image file",
+        accept="image/*",
+        multiple=False,
+        description="Image file",
     )
     
     
-    @widgets.interact_manual(file=load_file_widget, query="", n_iters=n_iters_widget, min_crop_size=min_crop_size_widget)
+    @widgets.interact_manual(
+        file=load_file_widget,
+        query="",
+        n_iters=n_iters_widget,
+        min_crop_size=min_crop_size_widget,
+    )
     def build_saliency_map_from_file(
         file: Path,
         query: str = "",
@@ -801,9 +843,9 @@ The second version will enable loading the image from your computer.
         except Exception as e:
             print(f"Cannot load the image: {e}")
             return
-        
+    
         image = image.convert("RGB")
-        
+    
         build_saliency_map(image, query, n_iters, min_crop_size)
 
 
@@ -885,4 +927,4 @@ can explore the CLIP capabilities further. For example:
    `NNCF `__
    to get further acceleration. You can find example how to quantize
    CLIP model in `this
-   notebook <228-clip-zero-shot-image-classification-with-output.html>`__
+   notebook <../clip-zero-shot-image-classification>`__
diff --git a/docs/notebooks/232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_15_0.png b/docs/notebooks/clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_15_0.png
similarity index 100%
rename from docs/notebooks/232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_15_0.png
rename to docs/notebooks/clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_15_0.png
diff --git a/docs/notebooks/232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_17_0.png b/docs/notebooks/clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_17_0.png
similarity index 100%
rename from docs/notebooks/232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_17_0.png
rename to docs/notebooks/clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_17_0.png
diff --git a/docs/notebooks/232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_19_1.png b/docs/notebooks/clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_19_1.png
similarity index 100%
rename from docs/notebooks/232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_19_1.png
rename to docs/notebooks/clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_19_1.png
diff --git a/docs/notebooks/232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_29_1.png b/docs/notebooks/clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_29_1.png
similarity index 100%
rename from docs/notebooks/232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_29_1.png
rename to docs/notebooks/clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_29_1.png
diff --git a/docs/notebooks/232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_35_1.png b/docs/notebooks/clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_35_1.png
similarity index 100%
rename from docs/notebooks/232-clip-language-saliency-map-with-output_files/232-clip-language-saliency-map-with-output_35_1.png
rename to docs/notebooks/clip-language-saliency-map-with-output_files/clip-language-saliency-map-with-output_35_1.png
diff --git a/docs/notebooks/228-clip-zero-shot-classification-with-output.rst b/docs/notebooks/clip-zero-shot-classification-with-output.rst
similarity index 93%
rename from docs/notebooks/228-clip-zero-shot-classification-with-output.rst
rename to docs/notebooks/clip-zero-shot-classification-with-output.rst
index 9885913bfdf..495813ec267 100644
--- a/docs/notebooks/228-clip-zero-shot-classification-with-output.rst
+++ b/docs/notebooks/clip-zero-shot-classification-with-output.rst
@@ -101,7 +101,14 @@ tokenizer and preparing the images.
 
 .. code:: ipython3
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu gradio "openvino>=2023.1.0" "transformers[torch]>=4.30" "datasets" "nncf>=2.6.0"
+    import platform
+    
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "gradio>=4.19" "openvino>=2023.1.0" "transformers[torch]>=4.30" "datasets" "nncf>=2.6.0" "torch>=2.1" Pillow
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
 
 
 .. parsed-literal::
@@ -136,7 +143,7 @@ tokenizer and preparing the images.
     from PIL import Image
     
     
-    def visualize_result(image:Image, labels:List[str], probs:np.ndarray, top:int = 5):
+    def visualize_result(image: Image, labels: List[str], probs: np.ndarray, top: int = 5):
         """
         Utility function for visualization classification results
         params:
@@ -148,7 +155,7 @@ tokenizer and preparing the images.
           None
         """
         plt.figure(figsize=(64, 64))
-        top_labels = np.argsort(-probs)[:min(top, probs.shape[0])]
+        top_labels = np.argsort(-probs)[: min(top, probs.shape[0])]
         top_probs = probs[top_labels]
         plt.subplot(8, 8, 1)
         plt.imshow(image)
@@ -163,7 +170,6 @@ tokenizer and preparing the images.
         plt.yticks(y, [labels[index] for index in top_labels])
         plt.xlabel("probability")
 
-
 Run PyTorch model inference
 ---------------------------
 
@@ -181,31 +187,43 @@ similarity score for the final result.
 
 .. code:: ipython3
 
-    from urllib.request import urlretrieve
+    import requests
     from pathlib import Path
     
     
     sample_path = Path("data/coco.jpg")
     sample_path.parent.mkdir(parents=True, exist_ok=True)
-    urlretrieve(
-        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
-        sample_path,
-    )
+    r = requests.get("https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg")
+    
+    with sample_path.open("wb") as f:
+        f.write(r.content)
+    
     image = Image.open(sample_path)
     
-    input_labels = ['cat', 'dog', 'wolf', 'tiger', 'man', 'horse', 'frog', 'tree', 'house', 'computer']
+    input_labels = [
+        "cat",
+        "dog",
+        "wolf",
+        "tiger",
+        "man",
+        "horse",
+        "frog",
+        "tree",
+        "house",
+        "computer",
+    ]
     text_descriptions = [f"This is a photo of a {label}" for label in input_labels]
     
     inputs = processor(text=text_descriptions, images=[image], return_tensors="pt", padding=True)
     
     results = model(**inputs)
-    logits_per_image = results['logits_per_image']  # this is the image-text similarity score
+    logits_per_image = results["logits_per_image"]  # this is the image-text similarity score
     probs = logits_per_image.softmax(dim=1).detach().numpy()  # we can take the softmax to get the label probabilities
     visualize_result(image, input_labels, probs[0])
 
 
 
-.. image:: 228-clip-zero-shot-classification-with-output_files/228-clip-zero-shot-classification-with-output_6_0.png
+.. image:: clip-zero-shot-classification-with-output_files/clip-zero-shot-classification-with-output_6_0.png
 
 
 Convert model to OpenVINO Intermediate Representation (IR) format.
@@ -226,7 +244,7 @@ save it on disk for the next usage with ``ov.save_model``.
 
     import openvino as ov
     
-    fp16_model_path = Path('clip-vit-base-patch16.xml')
+    fp16_model_path = Path("clip-vit-base-patch16.xml")
     model.config.torchscript = True
     
     if not fp16_model_path.exists():
@@ -262,8 +280,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -291,7 +309,7 @@ select device from dropdown list for running inference using OpenVINO
 
 
 
-.. image:: 228-clip-zero-shot-classification-with-output_files/228-clip-zero-shot-classification-with-output_13_0.png
+.. image:: clip-zero-shot-classification-with-output_files/clip-zero-shot-classification-with-output_13_0.png
 
 
 Great! Looks like we got the same result.
@@ -299,7 +317,7 @@ Great! Looks like we got the same result.
 Quantize model to INT8 using NNCF
 ---------------------------------
 
-## Quantize model to INT8 using
+ ## Quantize model to INT8 using
 NNCF
 
 The goal of this part of tutorial is to demonstrate how to speed up the
@@ -328,7 +346,7 @@ inference faster. The optimization process contains the following steps:
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -346,10 +364,10 @@ inference faster. The optimization process contains the following steps:
 .. code:: ipython3
 
     # Fetch skip_kernel_extension module
-    urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/skip_kernel_extension.py',
-        filename='skip_kernel_extension.py'
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     %load_ext skip_kernel_extension
 
@@ -648,7 +666,7 @@ the same input data that we used before.
 
 
 
-.. image:: 228-clip-zero-shot-classification-with-output_files/228-clip-zero-shot-classification-with-output_26_0.png
+.. image:: clip-zero-shot-classification-with-output_files/clip-zero-shot-classification-with-output_26_0.png
 
 
 Nice! Results looks similar to fp16 model results before quantization.
@@ -681,7 +699,7 @@ Compare File Size
 Compare inference time of the FP16 IR and quantized models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-To measure the inference
+ To measure the inference
 performance of the ``FP16`` and ``INT8`` models, we use median inference
 time on calibration dataset. So we can approximately estimate the speed
 up of the dynamic quantized models.
@@ -724,7 +742,7 @@ up of the dynamic quantized models.
 Interactive demo
 ----------------
 
-## Interactive demo
+ ## Interactive demo
 
 Now, it is your turn! You can provide your own image and comma-separated
 list of labels for zero-shot classification.
@@ -737,11 +755,12 @@ example, ``cat,dog,bird``)
 
     import gradio as gr
     
-    model_path = Path('clip-vit-base-patch16-int8.xml')
+    model_path = Path("clip-vit-base-patch16-int8.xml")
     if not model_path.exists():
-        model_path = Path('clip-vit-base-patch16.xml')
+        model_path = Path("clip-vit-base-patch16.xml")
     compiled_model = core.compile_model(model_path, device.value)
     
+    
     def classify(image, text):
         """Classify image using classes listing.
         Args:
@@ -755,7 +774,7 @@ example, ``cat,dog,bird``)
         inputs = processor(text=text_descriptions, images=[image], return_tensors="np", padding=True)
         ov_logits_per_image = compiled_model(dict(inputs))[0]
         probs = softmax(ov_logits_per_image, axis=1)[0]
-        
+    
         return {label: float(prob) for label, prob in zip(labels, probs)}
     
     
diff --git a/docs/notebooks/228-clip-zero-shot-classification-with-output_files/228-clip-zero-shot-classification-with-output_13_0.png b/docs/notebooks/clip-zero-shot-classification-with-output_files/clip-zero-shot-classification-with-output_13_0.png
similarity index 100%
rename from docs/notebooks/228-clip-zero-shot-classification-with-output_files/228-clip-zero-shot-classification-with-output_13_0.png
rename to docs/notebooks/clip-zero-shot-classification-with-output_files/clip-zero-shot-classification-with-output_13_0.png
diff --git a/docs/notebooks/228-clip-zero-shot-classification-with-output_files/228-clip-zero-shot-classification-with-output_26_0.png b/docs/notebooks/clip-zero-shot-classification-with-output_files/clip-zero-shot-classification-with-output_26_0.png
similarity index 100%
rename from docs/notebooks/228-clip-zero-shot-classification-with-output_files/228-clip-zero-shot-classification-with-output_26_0.png
rename to docs/notebooks/clip-zero-shot-classification-with-output_files/clip-zero-shot-classification-with-output_26_0.png
diff --git a/docs/notebooks/228-clip-zero-shot-classification-with-output_files/228-clip-zero-shot-classification-with-output_6_0.png b/docs/notebooks/clip-zero-shot-classification-with-output_files/clip-zero-shot-classification-with-output_6_0.png
similarity index 100%
rename from docs/notebooks/228-clip-zero-shot-classification-with-output_files/228-clip-zero-shot-classification-with-output_6_0.png
rename to docs/notebooks/clip-zero-shot-classification-with-output_files/clip-zero-shot-classification-with-output_6_0.png
diff --git a/docs/notebooks/247-code-language-id-with-output.rst b/docs/notebooks/code-language-id-with-output.rst
similarity index 54%
rename from docs/notebooks/247-code-language-id-with-output.rst
rename to docs/notebooks/code-language-id-with-output.rst
index ffc39754ce4..4b10777c7a9 100644
--- a/docs/notebooks/247-code-language-id-with-output.rst
+++ b/docs/notebooks/code-language-id-with-output.rst
@@ -124,21 +124,13 @@ OpenVINO support - HuggingFace Evaluate to benchmark results
 
 .. code:: ipython3
 
-    %pip install -q "diffusers>=0.17.1" "openvino>=2023.1.0" "nncf>=2.5.0" "gradio" "onnx>=1.11.0" "transformers>=4.33.0" "evaluate" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "diffusers>=0.17.1" "openvino>=2023.1.0" "nncf>=2.5.0" "gradio>=4.19" "onnx>=1.11.0" "transformers>=4.33.0" "torch>=2.1" "evaluate" --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "git+https://github.com/huggingface/optimum-intel.git"
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 4.25.3 which is incompatible.
-    tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.25.3 which is incompatible.
-    tf2onnx 1.16.1 requires protobuf~=3.20, but you have protobuf 4.25.3 which is incompatible.
+    WARNING: typer 0.12.3 does not provide the extra 'all'
     
 
 .. parsed-literal::
@@ -146,11 +138,6 @@ OpenVINO support - HuggingFace Evaluate to benchmark results
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -173,26 +160,21 @@ equivalent to ``AutoModelForSequenceClassification`` from Transformers
     from datasets import load_dataset, Dataset
     import evaluate
     from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
-    from optimum.intel import OVModelForSequenceClassification  
-    from optimum.intel.openvino import OVConfig, OVQuantizer
+    from optimum.intel import OVModelForSequenceClassification
+    from optimum.intel.openvino import OVConfig, OVQuantizer, OVWeightQuantizationConfig
     from huggingface_hub.utils import RepositoryNotFoundError
 
 
 .. parsed-literal::
 
-    2024-03-12 23:58:37.556667: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 23:58:37.590917: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-17 23:25:25.155476: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 23:25:25.190609: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 23:58:38.099542: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
-.. parsed-literal::
-
-    OpenVINO Tokenizer version is not compatible with OpenVINO version. Installed OpenVINO version: 2024.0.0,OpenVINO Tokenizers requires . OpenVINO Tokenizers models will not be added during export.
+    2024-04-17 23:25:25.785655: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -202,7 +184,7 @@ equivalent to ``AutoModelForSequenceClassification`` from Transformers
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
 
 
@@ -238,8 +220,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -298,13 +280,7 @@ Download resources
 
 .. parsed-literal::
 
-    Using the export variant default. Available variants are:
-        - default: The default ONNX variant.
-
-
-.. parsed-literal::
-
-    Using framework PyTorch: 2.2.1+cpu
+    Using framework PyTorch: 2.2.2+cpu
 
 
 .. parsed-literal::
@@ -329,7 +305,7 @@ Download resources
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
@@ -340,7 +316,7 @@ Download resources
 
 .. parsed-literal::
 
-    Ressources cached locally at: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/247-code-language-id/model/CodeBERTa-language-id
+    Ressources cached locally at: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/code-language-id/model/CodeBERTa-language-id
 
 
 Create inference pipeline
@@ -352,12 +328,6 @@ Create inference pipeline
 
     code_classification_pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
 
-
-.. parsed-literal::
-
-    device must be of type  but got  instead
-
-
 Inference on new input
 ~~~~~~~~~~~~~~~~~~~~~~
 
@@ -422,9 +392,16 @@ Define constants and functions
     
     def map_labels(example: dict) -> dict:
         """Convert string labels to integers"""
-        label_mapping = {"go": 0, "java": 1, "javascript": 2, "php": 3, "python": 4, "ruby": 5}
+        label_mapping = {
+            "go": 0,
+            "java": 1,
+            "javascript": 2,
+            "php": 3,
+            "python": 4,
+            "ruby": 5,
+        }
         example["language"] = label_mapping[example["language"]]
-        return example 
+        return example
     
     
     def get_dataset_sample(dataset_split: str, num_samples: int) -> Dataset:
@@ -437,7 +414,7 @@ Define constants and functions
             subset = load_dataset("code_search_net", split=dataset_split, name=label, streaming=True)
             subset = subset.map(map_labels)
             examples.extend([example for example in subset.shuffle().take(example_per_label)])
-        
+    
         return Dataset.from_list(examples)
 
 Load resources
@@ -454,7 +431,8 @@ NOTE: the base model is loaded using
     base_model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
     
     quantizer = OVQuantizer.from_pretrained(base_model)
-    quantization_config = OVConfig()
+    quantization_config = OVWeightQuantizationConfig()
+    ov_config = OVConfig(quantization_config=quantization_config)
 
 
 .. parsed-literal::
@@ -491,7 +469,7 @@ NOTE: Uncomment the method below to download and use the full dataset
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1461: FutureWarning: The repository for code_search_net contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/code_search_net
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1461: FutureWarning: The repository for code_search_net contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/code_search_net
     You can avoid this message in future by passing the argument `trust_remote_code=True`.
     Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
       warnings.warn(
@@ -514,17 +492,12 @@ dataset to quantize and save the model
 .. code:: ipython3
 
     quantizer.quantize(
-        quantization_config=quantization_config,
+        ov_config=ov_config,
         calibration_dataset=calibration_sample,
         save_directory=QUANTIZED_MODEL_LOCAL_PATH,
     )
 
 
-.. parsed-literal::
-
-    The argument `quantization_config` is deprecated, and will be removed in optimum-intel v1.6.0, please use `ov_config` instead
-
-
 .. parsed-literal::
 
     The support of `torch.nn.Module` will be deprecated in a future release of optimum-intel, please use the corresponding `OVModelForXxx` class to load you model.To convert a PyTorch model to OpenVINO, you can set `export=True` when loading your model as `OVModelForXxx.from_pretrained(..., export=True)`
@@ -537,311 +510,39 @@ dataset to quantize and save the model
 
 .. parsed-literal::
 
-    INFO:nncf:Not adding activation input quantizer for operation: 15 RobertaForSequenceClassification/RobertaModel[roberta]/RobertaEmbeddings[embeddings]/NNCFEmbedding[token_type_embeddings]/embedding_0
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (41 / 41)            | 100% (41 / 41)                    |
+    +--------------+---------------------------+-----------------------------------+
+
 
 
 .. parsed-literal::
 
-    INFO:nncf:Not adding activation input quantizer for operation: 14 RobertaForSequenceClassification/RobertaModel[roberta]/RobertaEmbeddings[embeddings]/NNCFEmbedding[word_embeddings]/embedding_0
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
 
 
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-
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-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
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-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
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-
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-
-
-.. parsed-literal::
-
-    Using framework PyTorch: 2.2.1+cpu
+    Using framework PyTorch: 2.2.2+cpu
 
 
 .. parsed-literal::
@@ -866,12 +567,6 @@ dataset to quantize and save the model
     if `fn` already had 0-th `self` argument bound or never had it in the first place.
 
 
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
-      warnings.warn(
-
-
 .. parsed-literal::
 
     WARNING:nncf:You are setting `forward` on an NNCF-processed model object.
@@ -908,11 +603,6 @@ model is already in the OpenVINO format.
     Compiling the model to AUTO ...
 
 
-.. parsed-literal::
-
-    device must be of type  but got  instead
-
-
 Inference on new input using quantized model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -934,7 +624,7 @@ Inference on new input using quantized model
       df['speed'] = df.distance / df.time
     
     Predicted label: python
-    Predicted score: 0.83
+    Predicted score: 0.81
 
 
 Load evaluation set
@@ -951,15 +641,6 @@ NOTE: Uncomment the method below to download and use the full dataset
     
     # validation_sample = load_dataset(DATASET_NAME, split="validation")
 
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1461: FutureWarning: The repository for code_search_net contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/code_search_net
-    You can avoid this message in future by passing the argument `trust_remote_code=True`.
-    Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
-      warnings.warn(
-
-
 Evaluate model
 ~~~~~~~~~~~~~~
 
@@ -974,10 +655,10 @@ Evaluate model
             self.metric = metric
             self.metric_args = metric_args
             self.metric_kwargs = metric_kwargs
-        
+    
         def add(self, *args, **kwargs):
             return self.metric.add(*args, **kwargs)
-        
+    
         def add_batch(self, *args, **kwargs):
             return self.metric.add_batch(*args, **kwargs)
     
@@ -1002,9 +683,11 @@ displayed.
 
     code_classification_evaluator = evaluate.evaluator("text-classification")
     # instantiate an object that can contain multiple `evaluate` metrics
-    metrics = evaluate.combine([
-        ConfiguredMetric(evaluate.load('f1'), average='macro'),
-    ])
+    metrics = evaluate.combine(
+        [
+            ConfiguredMetric(evaluate.load("f1"), average="macro"),
+        ]
+    )
     
     base_results = code_classification_evaluator.compute(
         model_or_pipeline=code_classification_pipe,
@@ -1060,16 +743,16 @@ displayed.
         
           base
           1.0
-          2.230822
-          53.791827
-          0.018590
+          2.077464
+          57.762723
+          0.017312
         
         
           quantized
           1.0
-          2.871792
-          41.785751
-          0.023932
+          1.988597
+          60.344039
+          0.016572
         
       
     
@@ -1080,10 +763,9 @@ displayed.
 Additional resources
 --------------------
 
-- `Grammatical Error Correction
-with
-OpenVINO `__
-- `Quantize a Hugging Face Question-Answering Model with
+ - `Grammatical Error Correction
+with OpenVINO `__ -
+`Quantize a Hugging Face Question-Answering Model with
 OpenVINO `__\ \*\*
 
 Clean up
diff --git a/docs/notebooks/235-controlnet-stable-diffusion-with-output.rst b/docs/notebooks/controlnet-stable-diffusion-with-output.rst
similarity index 89%
rename from docs/notebooks/235-controlnet-stable-diffusion-with-output.rst
rename to docs/notebooks/controlnet-stable-diffusion-with-output.rst
index 598ef47282f..1e9957a95b8 100644
--- a/docs/notebooks/235-controlnet-stable-diffusion-with-output.rst
+++ b/docs/notebooks/controlnet-stable-diffusion-with-output.rst
@@ -66,7 +66,7 @@ For more details regarding Stable Diffusion work, refer to the `project
 website `__.
 There is a tutorial for Stable Diffusion Text-to-Image generation with
 OpenVINO, see the following
-`notebook <225-stable-diffusion-text-to-image-with-output.html>`__.
+`notebook `__.
 
 ControlNet
 ~~~~~~~~~~
@@ -186,7 +186,7 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "torch" "torchvision"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "torch>=2.1" "torchvision"
     %pip install -q "diffusers>=0.14.0" "transformers>=4.30.2" "controlnet-aux>=0.0.6" "gradio>=3.36" --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "openvino>=2023.1.0" "datasets>=2.14.6" "nncf>=2.7.0"
 
@@ -220,9 +220,7 @@ controlnet model and ``stable-diffusion-v1-5``:
     from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
     
     controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch.float32)
-    pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        "runwayml/stable-diffusion-v1-5", controlnet=controlnet
-    )
+    pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet)
 
 OpenPose
 ~~~~~~~~
@@ -267,10 +265,15 @@ Now, let us check its result on example image:
     pose = pose_estimator(img)
     
     
-    def visualize_pose_results(orig_img:Image.Image, skeleton_img:Image.Image, left_title:str = "Original image", right_title:str = "Pose"):
+    def visualize_pose_results(
+        orig_img: Image.Image,
+        skeleton_img: Image.Image,
+        left_title: str = "Original image",
+        right_title: str = "Pose",
+    ):
         """
         Helper function for pose estimationresults visualization
-        
+    
         Parameters:
            orig_img (Image.Image): original image
            skeleton_img (Image.Image): processed image with body keypoints
@@ -283,8 +286,14 @@ Now, let us check its result on example image:
         im_w, im_h = orig_img.size
         is_horizontal = im_h <= im_w
         figsize = (20, 10) if is_horizontal else (10, 20)
-        fig, axs = plt.subplots(2 if is_horizontal else 1, 1 if is_horizontal else 2, figsize=figsize, sharex='all', sharey='all')
-        fig.patch.set_facecolor('white')
+        fig, axs = plt.subplots(
+            2 if is_horizontal else 1,
+            1 if is_horizontal else 2,
+            figsize=figsize,
+            sharex="all",
+            sharey="all",
+        )
+        fig.patch.set_facecolor("white")
         list_axes = list(axs.flat)
         for a in list_axes:
             a.set_xticklabels([])
@@ -295,8 +304,8 @@ Now, let us check its result on example image:
         list_axes[0].imshow(np.array(orig_img))
         list_axes[1].imshow(np.array(skeleton_img))
         list_axes[0].set_title(left_title, fontsize=15)
-        list_axes[1].set_title(right_title, fontsize=15) 
-        fig.subplots_adjust(wspace=0.01 if is_horizontal else 0.00 , hspace=0.01 if is_horizontal else 0.1)
+        list_axes[1].set_title(right_title, fontsize=15)
+        fig.subplots_adjust(wspace=0.01 if is_horizontal else 0.00, hspace=0.01 if is_horizontal else 0.1)
         fig.tight_layout()
         return fig
     
@@ -305,7 +314,7 @@ Now, let us check its result on example image:
 
 
 
-.. image:: 235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_8_0.png
+.. image:: controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_8_0.png
 
 
 Convert models to OpenVINO Intermediate representation (IR) format
@@ -349,6 +358,7 @@ estimation part, which is located inside the wrapper
     
     OPENPOSE_OV_PATH = Path("openpose.xml")
     
+    
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -360,11 +370,15 @@ estimation part, which is located inside the wrapper
     
     if not OPENPOSE_OV_PATH.exists():
         with torch.no_grad():
-            ov_model = ov.convert_model(pose_estimator.body_estimation.model, example_input=torch.zeros([1, 3, 184, 136]), input=[[1,3,184,136]])
+            ov_model = ov.convert_model(
+                pose_estimator.body_estimation.model,
+                example_input=torch.zeros([1, 3, 184, 136]),
+                input=[[1, 3, 184, 136]],
+            )
             ov.save_model(ov_model, OPENPOSE_OV_PATH)
             del ov_model
             cleanup_torchscript_cache()
-        print('OpenPose successfully converted to IR')
+        print("OpenPose successfully converted to IR")
     else:
         print(f"OpenPose will be loaded from {OPENPOSE_OV_PATH}")
 
@@ -381,17 +395,19 @@ model with the OpenVINO model, using the following code:
 
     from collections import namedtuple
     
+    
     class OpenPoseOVModel:
-        """ Helper wrapper for OpenPose model inference"""
+        """Helper wrapper for OpenPose model inference"""
+    
         def __init__(self, core, model_path, device="AUTO"):
             self.core = core
-            self. model = core.read_model(model_path)
+            self.model = core.read_model(model_path)
             self.compiled_model = core.compile_model(self.model, device)
     
-        def __call__(self, input_tensor:torch.Tensor):
+        def __call__(self, input_tensor: torch.Tensor):
             """
             inference step
-            
+    
             Parameters:
               input_tensor (torch.Tensor): tensor with prerpcessed input image
             Returns:
@@ -404,10 +420,10 @@ model with the OpenVINO model, using the following code:
             results = self.compiled_model(input_tensor)
             return torch.from_numpy(results[self.compiled_model.output(0)]), torch.from_numpy(results[self.compiled_model.output(1)])
     
-        def reshape_model(self, height:int, width:int):
+        def reshape_model(self, height: int, width: int):
             """
             helper method for reshaping model to fit input data
-            
+    
             Parameters:
               height (int): input tensor height
               width (int): input tensor width
@@ -416,13 +432,12 @@ model with the OpenVINO model, using the following code:
             """
             self.model.reshape({0: [1, 3, height, width]})
             self.compiled_model = self.core.compile_model(self.model)
-            
+    
         def parameters(self):
             Device = namedtuple("Device", ["device"])
             return [Device(torch.device("cpu"))]
-        
     
-     
+    
     core = ov.Core()
 
 Select inference device
@@ -438,8 +453,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -466,7 +481,7 @@ select device from dropdown list for running inference using OpenVINO
 
 
 
-.. image:: 235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_17_0.png
+.. image:: controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_17_0.png
 
 
 Great! As we can see, it works perfectly.
@@ -497,17 +512,17 @@ blocks, which serves additional context for the UNet model.
     inputs = {
         "sample": torch.randn((2, 4, 64, 64)),
         "timestep": torch.tensor(1),
-        "encoder_hidden_states": torch.randn((2,77,768)),
-        "controlnet_cond": torch.randn((2,3,512,512))
+        "encoder_hidden_states": torch.randn((2, 77, 768)),
+        "controlnet_cond": torch.randn((2, 3, 512, 512)),
     }
     
-    input_info = [(name, ov.PartialShape(inp.shape)) for name, inp in inputs.items()] 
+    input_info = [(name, ov.PartialShape(inp.shape)) for name, inp in inputs.items()]
     
-    CONTROLNET_OV_PATH = Path('controlnet-pose.xml')
+    CONTROLNET_OV_PATH = Path("controlnet-pose.xml")
     controlnet.eval()
     with torch.no_grad():
         down_block_res_samples, mid_block_res_sample = controlnet(**inputs, return_dict=False)
-        
+    
     if not CONTROLNET_OV_PATH.exists():
         with torch.no_grad():
             controlnet.forward = partial(controlnet.forward, return_dict=False)
@@ -515,7 +530,7 @@ blocks, which serves additional context for the UNet model.
             ov.save_model(ov_model, CONTROLNET_OV_PATH)
             del ov_model
             cleanup_torchscript_cache()
-        print('ControlNet successfully converted to IR')
+        print("ControlNet successfully converted to IR")
     else:
         print(f"ControlNet will be loaded from {CONTROLNET_OV_PATH}")
     
@@ -549,24 +564,25 @@ ControlNet.
 
     from typing import Tuple
     
-    UNET_OV_PATH = Path('unet_controlnet.xml')
+    UNET_OV_PATH = Path("unet_controlnet.xml")
     
     dtype_mapping = {
         torch.float32: ov.Type.f32,
         torch.float64: ov.Type.f64,
         torch.int32: ov.Type.i32,
-        torch.int64: ov.Type.i64
+        torch.int64: ov.Type.i64,
     }
     
+    
     class UnetWrapper(torch.nn.Module):
         def __init__(
-            self, 
-            unet, 
-            sample_dtype=torch.float32, 
-            timestep_dtype=torch.int64, 
-            encoder_hidden_states=torch.float32, 
-            down_block_additional_residuals=torch.float32, 
-            mid_block_additional_residual=torch.float32
+            self,
+            unet,
+            sample_dtype=torch.float32,
+            timestep_dtype=torch.int64,
+            encoder_hidden_states=torch.float32,
+            down_block_additional_residuals=torch.float32,
+            mid_block_additional_residual=torch.float32,
         ):
             super().__init__()
             self.unet = unet
@@ -577,12 +593,12 @@ ControlNet.
             self.mid_block_additional_residual_dtype = mid_block_additional_residual
     
         def forward(
-            self, 
-            sample:torch.Tensor, 
-            timestep:torch.Tensor, 
-            encoder_hidden_states:torch.Tensor, 
-            down_block_additional_residuals:Tuple[torch.Tensor],  
-            mid_block_additional_residual:torch.Tensor
+            self,
+            sample: torch.Tensor,
+            timestep: torch.Tensor,
+            encoder_hidden_states: torch.Tensor,
+            down_block_additional_residuals: Tuple[torch.Tensor],
+            mid_block_additional_residual: torch.Tensor,
         ):
             sample.to(self.sample_dtype)
             timestep.to(self.timestep_dtype)
@@ -590,15 +606,14 @@ ControlNet.
             down_block_additional_residuals = [res.to(self.down_block_additional_residuals_dtype) for res in down_block_additional_residuals]
             mid_block_additional_residual.to(self.mid_block_additional_residual_dtype)
             return self.unet(
-                sample, 
-                timestep, 
-                encoder_hidden_states, 
-                down_block_additional_residuals=down_block_additional_residuals, 
-                mid_block_additional_residual=mid_block_additional_residual
+                sample,
+                timestep,
+                encoder_hidden_states,
+                down_block_additional_residuals=down_block_additional_residuals,
+                mid_block_additional_residual=mid_block_additional_residual,
             )
     
     
-    
     def flattenize_inputs(inputs):
         flatten_inputs = []
         for input_data in inputs:
@@ -610,6 +625,7 @@ ControlNet.
                 flatten_inputs.append(input_data)
         return flatten_inputs
     
+    
     if not UNET_OV_PATH.exists():
         inputs.pop("controlnet_cond", None)
         inputs["down_block_additional_residuals"] = down_block_res_samples
@@ -620,7 +636,7 @@ ControlNet.
     
         with torch.no_grad():
             ov_model = ov.convert_model(unet, example_input=inputs)
-            
+    
         flatten_inputs = flattenize_inputs(inputs.values())
         for input_data, input_tensor in zip(flatten_inputs, ov_model.inputs):
             input_tensor.get_node().set_partial_shape(ov.PartialShape(input_data.shape))
@@ -632,7 +648,7 @@ ControlNet.
         del unet
         del pipe.unet
         gc.collect()
-        print('Unet successfully converted to IR')
+        print("Unet successfully converted to IR")
     else:
         del pipe.unet
         print(f"Unet will be loaded from {UNET_OV_PATH}")
@@ -672,14 +688,14 @@ hidden states.
 
 .. code:: ipython3
 
-    TEXT_ENCODER_OV_PATH = Path('text_encoder.xml')
+    TEXT_ENCODER_OV_PATH = Path("text_encoder.xml")
     
     
-    def convert_encoder(text_encoder:torch.nn.Module, ir_path:Path):
+    def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
         """
-        Convert Text Encoder model to OpenVINO IR. 
+        Convert Text Encoder model to OpenVINO IR.
         Function accepts text encoder model, prepares example inputs for conversion, and convert it to OpenVINO Model
-        Parameters: 
+        Parameters:
             text_encoder (torch.nn.Module): text_encoder model
             ir_path (Path): File for storing model
         Returns:
@@ -695,13 +711,13 @@ hidden states.
                 ov_model = ov.convert_model(
                     text_encoder,  # model instance
                     example_input=input_ids,  # inputs for model tracing
-                    input=([1,77],)
+                    input=([1, 77],),
                 )
                 ov.save_model(ov_model, ir_path)
                 del ov_model
             cleanup_torchscript_cache()
-            print('Text Encoder successfully converted to IR')
-        
+            print("Text Encoder successfully converted to IR")
+    
     
     if not TEXT_ENCODER_OV_PATH.exists():
         convert_encoder(pipe.text_encoder, TEXT_ENCODER_OV_PATH)
@@ -742,24 +758,25 @@ process are converted back into images using the VAE decoder. During
 inference, we will see that we **only need the VAE decoder**. You can
 find instructions on how to convert the encoder part in a stable
 diffusion
-`notebook <225-stable-diffusion-text-to-image-with-output.html>`__.
+`notebook `__.
 
 .. code:: ipython3
 
-    VAE_DECODER_OV_PATH = Path('vae_decoder.xml')
+    VAE_DECODER_OV_PATH = Path("vae_decoder.xml")
     
     
     def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path):
         """
-        Convert VAE model to IR format. 
-        Function accepts pipeline, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for convert, 
-        Parameters: 
+        Convert VAE model to IR format.
+        Function accepts pipeline, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for convert,
+        Parameters:
             vae (torch.nn.Module): VAE model
             ir_path (Path): File for storing model
         Returns:
             None
         """
+    
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -774,11 +791,17 @@ diffusion
     
             vae_decoder.eval()
             with torch.no_grad():
-                ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[(1,4,64,64),])
+                ov_model = ov.convert_model(
+                    vae_decoder,
+                    example_input=latents,
+                    input=[
+                        (1, 4, 64, 64),
+                    ],
+                )
                 ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
-            print('VAE decoder successfully converted to IR')
+            print("VAE decoder successfully converted to IR")
     
     
     if not VAE_DECODER_OV_PATH.exists():
@@ -862,11 +885,11 @@ on OpenVINO.
     import cv2
     
     
-    def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
+    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
-        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio 
+        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-        
+    
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -886,7 +909,7 @@ on OpenVINO.
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-        
+    
         Parameters:
           image (Image.Image): input image
         Returns:
@@ -911,7 +934,7 @@ on OpenVINO.
     ):
         """
         Helper function for generation random values tensor with given shape and data type
-        
+    
         Parameters:
           shape (Union[Tuple, List]): shape for filling random values
           dtype (np.dtype, *optiona*, np.float32): data type for result
@@ -927,6 +950,7 @@ on OpenVINO.
         """
         OpenVINO inference pipeline for Stable Diffusion with ControlNet guidence
         """
+    
         def __init__(
             self,
             tokenizer: CLIPTokenizer,
@@ -936,7 +960,7 @@ on OpenVINO.
             text_encoder: ov.Model,
             unet: ov.Model,
             vae_decoder: ov.Model,
-            device:str = "AUTO"
+            device: str = "AUTO",
         ):
             super().__init__()
             self.tokenizer = tokenizer
@@ -945,10 +969,18 @@ on OpenVINO.
             self.load_models(core, device, controlnet, text_encoder, unet, vae_decoder)
             self.set_progress_bar_config(disable=True)
     
-        def load_models(self, core: ov.Core, device: str, controlnet:ov.Model, text_encoder: ov.Model, unet: ov.Model, vae_decoder: ov.Model):
+        def load_models(
+            self,
+            core: ov.Core,
+            device: str,
+            controlnet: ov.Model,
+            text_encoder: ov.Model,
+            unet: ov.Model,
+            vae_decoder: ov.Model,
+        ):
             """
             Function for loading models on device using OpenVINO
-            
+    
             Parameters:
               core (Core): OpenVINO runtime Core class instance
               device (str): inference device
@@ -1007,7 +1039,7 @@ on OpenVINO.
                     [PIL](https://pillow.readthedocs.io/en/stable/): `Image.Image` or `np.array`.
             Returns:
                 image ([List[Union[np.ndarray, Image.Image]]): generaited images
-                
+    
             """
     
             # 1. Define call parameters
@@ -1048,8 +1080,7 @@ on OpenVINO.
                     # Expand the latents if we are doing classifier free guidance.
                     # The latents are expanded 3 times because for pix2pix the guidance\
                     # is applied for both the text and the input image.
-                    latent_model_input = np.concatenate(
-                        [latents] * 2) if do_classifier_free_guidance else latents
+                    latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
     
                     result = self.controlnet([latent_model_input, t, text_embeddings, image])
@@ -1078,12 +1109,17 @@ on OpenVINO.
                 image = self.numpy_to_pil(image)
                 image = [img.resize((orig_width, orig_height), Image.Resampling.LANCZOS) for img in image]
             else:
-                image = [cv2.resize(img, (orig_width, orig_width))
-                         for img in image]
+                image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
     
             return image
     
-        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True, negative_prompt:Union[str, List[str]] = None):
+        def _encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+            negative_prompt: Union[str, List[str]] = None,
+        ):
             """
             Encodes the prompt into text encoder hidden states.
     
@@ -1107,16 +1143,13 @@ on OpenVINO.
             )
             text_input_ids = text_inputs.input_ids
     
-            text_embeddings = self.text_encoder(
-                text_input_ids)[self.text_encoder_out]
+            text_embeddings = self.text_encoder(text_input_ids)[self.text_encoder_out]
     
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(
-                    text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(
-                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
+                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
+                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
     
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
@@ -1150,11 +1183,19 @@ on OpenVINO.
     
             return text_embeddings
     
-        def prepare_latents(self, batch_size:int, num_channels_latents:int, height:int, width:int, dtype:np.dtype = np.float32, latents:np.ndarray = None):
+        def prepare_latents(
+            self,
+            batch_size: int,
+            num_channels_latents: int,
+            height: int,
+            width: int,
+            dtype: np.dtype = np.float32,
+            latents: np.ndarray = None,
+        ):
             """
-            Preparing noise to image generation. If initial latents are not provided, they will be generated randomly, 
+            Preparing noise to image generation. If initial latents are not provided, they will be generated randomly,
             then prepared latents scaled by the standard deviation required by the scheduler
-            
+    
             Parameters:
                batch_size (int): input batch size
                num_channels_latents (int): number of channels for noise generation
@@ -1165,7 +1206,12 @@ on OpenVINO.
             Returns:
                latents (np.ndarray): scaled initial noise for diffusion
             """
-            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            shape = (
+                batch_size,
+                num_channels_latents,
+                height // self.vae_scale_factor,
+                width // self.vae_scale_factor,
+            )
             if latents is None:
                 latents = randn_tensor(shape, dtype=dtype)
             else:
@@ -1175,10 +1221,10 @@ on OpenVINO.
             latents = latents * self.scheduler.init_noise_sigma
             return latents
     
-        def decode_latents(self, latents:np.array, pad:Tuple[int]):
+        def decode_latents(self, latents: np.array, pad: Tuple[int]):
             """
             Decode predicted image from latent space using VAE Decoder and unpad image result
-            
+    
             Parameters:
                latents (np.ndarray): image encoded in diffusion latent space
                pad (Tuple[int]): each side padding sizes obtained on preprocessing step
@@ -1201,14 +1247,14 @@ on OpenVINO.
     from transformers import CLIPTokenizer
     from diffusers import UniPCMultistepScheduler
     
-    tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
     scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
     
     
-    def visualize_results(orig_img:Image.Image, skeleton_img:Image.Image, result_img:Image.Image):
+    def visualize_results(orig_img: Image.Image, skeleton_img: Image.Image, result_img: Image.Image):
         """
         Helper function for results visualization
-        
+    
         Parameters:
            orig_img (Image.Image): original image
            skeleton_img (Image.Image): image with body pose keypoints
@@ -1222,8 +1268,14 @@ on OpenVINO.
         im_w, im_h = orig_img.size
         is_horizontal = im_h <= im_w
         figsize = (20, 20)
-        fig, axs = plt.subplots(3 if is_horizontal else 1, 1 if is_horizontal else 3, figsize=figsize, sharex='all', sharey='all')
-        fig.patch.set_facecolor('white')
+        fig, axs = plt.subplots(
+            3 if is_horizontal else 1,
+            1 if is_horizontal else 3,
+            figsize=figsize,
+            sharex="all",
+            sharey="all",
+        )
+        fig.patch.set_facecolor("white")
         list_axes = list(axs.flat)
         for a in list_axes:
             a.set_xticklabels([])
@@ -1235,11 +1287,11 @@ on OpenVINO.
         list_axes[1].imshow(np.array(skeleton_img))
         list_axes[2].imshow(np.array(result_img))
         list_axes[0].set_title(orig_title, fontsize=15)
-        list_axes[1].set_title(skeleton_title, fontsize=15) 
+        list_axes[1].set_title(skeleton_title, fontsize=15)
         list_axes[2].set_title("Result", fontsize=15)
-        fig.subplots_adjust(wspace=0.01 if is_horizontal else 0.00 , hspace=0.01 if is_horizontal else 0.1)
+        fig.subplots_adjust(wspace=0.01 if is_horizontal else 0.00, hspace=0.01 if is_horizontal else 0.1)
         fig.tight_layout()
-        fig.savefig("result.png", bbox_inches='tight')
+        fig.savefig("result.png", bbox_inches="tight")
         return fig
 
 Running Text-to-Image Generation with ControlNet Conditioning and OpenVINO
@@ -1272,8 +1324,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
     
@@ -1290,7 +1342,16 @@ select device from dropdown list for running inference using OpenVINO
 
 .. code:: ipython3
 
-    ov_pipe = OVContrlNetStableDiffusionPipeline(tokenizer, scheduler, core, CONTROLNET_OV_PATH, TEXT_ENCODER_OV_PATH, UNET_OV_PATH, VAE_DECODER_OV_PATH, device=device.value)
+    ov_pipe = OVContrlNetStableDiffusionPipeline(
+        tokenizer,
+        scheduler,
+        core,
+        CONTROLNET_OV_PATH,
+        TEXT_ENCODER_OV_PATH,
+        UNET_OV_PATH,
+        VAE_DECODER_OV_PATH,
+        device=device.value,
+    )
 
 .. code:: ipython3
 
@@ -1313,7 +1374,7 @@ select device from dropdown list for running inference using OpenVINO
 
 
 
-.. image:: 235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_34_1.png
+.. image:: controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_34_1.png
 
 
 
@@ -1348,10 +1409,7 @@ improve model inference speed.
 
 .. code:: ipython3
 
-    to_quantize = widgets.Checkbox(
-        value=True,
-        description='Quantization'
-    )
+    to_quantize = widgets.Checkbox(value=True, description="Quantization")
     
     to_quantize
 
@@ -1369,8 +1427,13 @@ Let’s load ``skip magic`` extension to skip quantization if
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     int8_pipe = None
     
@@ -1571,7 +1634,7 @@ pipelines.
 
 
 
-.. image:: 235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_50_0.png
+.. image:: controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_50_0.png
 
 
 Compare model file sizes
@@ -1691,7 +1754,7 @@ launch the interactive demo.
     
     use_quantized_model = widgets.Checkbox(
         value=True if quantized_model_present else False,
-        description='Use quantized model',
+        description="Use quantized model",
         disabled=not quantized_model_present,
     )
     
@@ -1700,11 +1763,16 @@ launch the interactive demo.
 .. code:: ipython3
 
     import gradio as gr
-    from urllib.request import urlretrieve
     
     pipeline = int8_pipe if use_quantized_model.value else ov_pipe
     
-    urlretrieve(example_url, "example.jpg")
+    r = requests.get(example_url)
+    
+    img_path = Path("example.jpg")
+    
+    with img_path.open("wb") as f:
+        f.write(r.content)
+    
     gr.close_all()
     with gr.Blocks() as demo:
         with gr.Row():
@@ -1713,10 +1781,8 @@ launch the interactive demo.
                 pose_btn = gr.Button("Extract pose")
                 examples = gr.Examples(["example.jpg"], inp_img)
             with gr.Column(visible=False) as step1:
-                out_pose = gr.Image(label="Estimated pose", type='pil')
-                inp_prompt = gr.Textbox(
-                    "Dancing Darth Vader, best quality, extremely detailed", label="Prompt"
-                )
+                out_pose = gr.Image(label="Estimated pose", type="pil")
+                inp_prompt = gr.Textbox("Dancing Darth Vader, best quality, extremely detailed", label="Prompt")
                 inp_neg_prompt = gr.Textbox(
                     "monochrome, lowres, bad anatomy, worst quality, low quality",
                     label="Negative prompt",
@@ -1730,15 +1796,30 @@ launch the interactive demo.
         def extract_pose(img):
             if img is None:
                 raise gr.Error("Please upload the image or use one from the examples list")
-            return {step1: gr.update(visible=True), step2: gr.update(visible=True), out_pose: pose_estimator(img)}
+            return {
+                step1: gr.update(visible=True),
+                step2: gr.update(visible=True),
+                out_pose: pose_estimator(img),
+            }
     
-        def generate(pose, prompt, negative_prompt, seed, num_steps, progress=gr.Progress(track_tqdm=True)):
+        def generate(
+            pose,
+            prompt,
+            negative_prompt,
+            seed,
+            num_steps,
+            progress=gr.Progress(track_tqdm=True),
+        ):
             np.random.seed(seed)
             result = pipeline(prompt, pose, num_steps, negative_prompt)[0]
             return result
     
         pose_btn.click(extract_pose, inp_img, [out_pose, step1, step2])
-        btn.click(generate, [out_pose, inp_prompt, inp_neg_prompt, inp_seed, inp_steps], out_result)
+        btn.click(
+            generate,
+            [out_pose, inp_prompt, inp_neg_prompt, inp_seed, inp_steps],
+            out_result,
+        )
     
     
     try:
diff --git a/docs/notebooks/235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_17_0.png b/docs/notebooks/controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_17_0.png
similarity index 100%
rename from docs/notebooks/235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_17_0.png
rename to docs/notebooks/controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_17_0.png
diff --git a/docs/notebooks/235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_34_1.jpg b/docs/notebooks/controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_34_1.jpg
similarity index 100%
rename from docs/notebooks/235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_34_1.jpg
rename to docs/notebooks/controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_34_1.jpg
diff --git a/docs/notebooks/235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_34_1.png b/docs/notebooks/controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_34_1.png
similarity index 100%
rename from docs/notebooks/235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_34_1.png
rename to docs/notebooks/controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_34_1.png
diff --git a/docs/notebooks/235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_50_0.png b/docs/notebooks/controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_50_0.png
similarity index 100%
rename from docs/notebooks/235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_50_0.png
rename to docs/notebooks/controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_50_0.png
diff --git a/docs/notebooks/235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_8_0.png b/docs/notebooks/controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_8_0.png
similarity index 100%
rename from docs/notebooks/235-controlnet-stable-diffusion-with-output_files/235-controlnet-stable-diffusion-with-output_8_0.png
rename to docs/notebooks/controlnet-stable-diffusion-with-output_files/controlnet-stable-diffusion-with-output_8_0.png
diff --git a/docs/notebooks/121-convert-to-openvino-with-output.rst b/docs/notebooks/convert-to-openvino-with-output.rst
similarity index 93%
rename from docs/notebooks/121-convert-to-openvino-with-output.rst
rename to docs/notebooks/convert-to-openvino-with-output.rst
index c3d08a4fdde..fcc99b2e27d 100644
--- a/docs/notebooks/121-convert-to-openvino-with-output.rst
+++ b/docs/notebooks/convert-to-openvino-with-output.rst
@@ -30,12 +30,12 @@ Table of contents:
     # Required imports. Please execute this cell first.
     %pip install --upgrade pip
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu \
-    "openvino-dev>=2024.0.0" "requests" "tqdm" "transformers[onnx]>=4.21.1" "torch" "torchvision" "tensorflow_hub" "tensorflow"
+    "openvino-dev>=2024.0.0" "requests" "tqdm" "transformers[onnx]>=4.31" "torch>=2.1" "torchvision" "tensorflow_hub" "tensorflow"
 
 
 .. parsed-literal::
 
-    Requirement already satisfied: pip in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (24.0)
+    Requirement already satisfied: pip in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (24.0)
 
 
 .. parsed-literal::
@@ -168,18 +168,12 @@ NLP model from Hugging Face and export it in ONNX format:
     ONNX_NLP_MODEL_PATH = MODEL_DIRECTORY_PATH / "distilbert.onnx"
     
     # download model
-    hf_model = AutoModelForSequenceClassification.from_pretrained(
-        "distilbert-base-uncased-finetuned-sst-2-english"
-    )
+    hf_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
     # initialize tokenizer
-    tokenizer = AutoTokenizer.from_pretrained(
-        "distilbert-base-uncased-finetuned-sst-2-english"
-    )
+    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
     
     # get model onnx config function for output feature format sequence-classification
-    model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(
-        hf_model, feature="sequence-classification"
-    )
+    model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(hf_model, feature="sequence-classification")
     # fill onnx config based on pytorch model config
     onnx_config = model_onnx_config(hf_model.config)
     
@@ -195,19 +189,19 @@ NLP model from Hugging Face and export it in ONNX format:
 
 .. parsed-literal::
 
-    2024-03-12 22:50:34.001865: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:50:34.035988: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-17 23:27:23.489620: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 23:27:23.524697: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 22:50:34.633732: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-17 23:27:24.035165: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:246: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:246: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       mask, torch.tensor(torch.finfo(scores.dtype).min)
 
 
@@ -430,9 +424,7 @@ Convert PyTorch model to ONNX format:
     else:
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore")
-            torch.onnx.export(
-                model=pytorch_model, args=torch.randn(1, 3, 224, 224), f=ONNX_CV_MODEL_PATH
-            )
+            torch.onnx.export(model=pytorch_model, args=torch.randn(1, 3, 224, 224), f=ONNX_CV_MODEL_PATH)
         print(f"ONNX model exported to {ONNX_CV_MODEL_PATH}")
 
 
@@ -510,9 +502,7 @@ documentation.
 
     import openvino as ov
     
-    ov_model = ov.convert_model(
-        ONNX_NLP_MODEL_PATH, input=[("input_ids", [1, 128]), ("attention_mask", [1, 128])]
-    )
+    ov_model = ov.convert_model(ONNX_NLP_MODEL_PATH, input=[("input_ids", [1, 128]), ("attention_mask", [1, 128])])
 
 .. code:: ipython3
 
@@ -549,9 +539,7 @@ conversion API parameter as ``-1`` or ``?`` when using ``ovc``:
 
     import openvino as ov
     
-    ov_model = ov.convert_model(
-        ONNX_NLP_MODEL_PATH, input=[("input_ids", [1, -1]), ("attention_mask", [1, -1])]
-    )
+    ov_model = ov.convert_model(ONNX_NLP_MODEL_PATH, input=[("input_ids", [1, -1]), ("attention_mask", [1, -1])])
 
 .. code:: ipython3
 
@@ -594,7 +582,11 @@ sequence length dimension:
     sequence_length_dim = ov.Dimension(10, 128)
     
     ov_model = ov.convert_model(
-        ONNX_NLP_MODEL_PATH, input=[("input_ids", [1, sequence_length_dim]), ("attention_mask", [1, sequence_length_dim])]
+        ONNX_NLP_MODEL_PATH,
+        input=[
+            ("input_ids", [1, sequence_length_dim]),
+            ("attention_mask", [1, sequence_length_dim]),
+        ],
     )
 
 .. code:: ipython3
@@ -637,7 +629,7 @@ disabled by setting ``compress_to_fp16`` flag to ``False``:
     import openvino as ov
     
     ov_model = ov.convert_model(ONNX_NLP_MODEL_PATH)
-    ov.save_model(ov_model, MODEL_DIRECTORY_PATH / 'distilbert.xml', compress_to_fp16=False)
+    ov.save_model(ov_model, MODEL_DIRECTORY_PATH / "distilbert.xml", compress_to_fp16=False)
 
 .. code:: ipython3
 
@@ -687,23 +679,27 @@ frameworks conversion guides.
 
 .. code:: ipython3
 
+    import os
+    
     import openvino as ov
     import tensorflow_hub as hub
     
+    os.environ["TFHUB_CACHE_DIR"] = str(Path("./tfhub_modules").resolve())
+    
     model = hub.load("https://www.kaggle.com/models/google/movenet/frameworks/TensorFlow2/variations/singlepose-lightning/versions/4")
-    movenet = model.signatures['serving_default']
+    movenet = model.signatures["serving_default"]
     
     ov_model = ov.convert_model(movenet)
 
 
 .. parsed-literal::
 
-    2024-03-12 22:50:53.831014: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-03-12 22:50:53.831046: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
-    2024-03-12 22:50:53.831051: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
-    2024-03-12 22:50:53.831226: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
-    2024-03-12 22:50:53.831242: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
-    2024-03-12 22:50:53.831247: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-04-17 23:27:44.237192: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-04-17 23:27:44.237227: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
+    2024-04-17 23:27:44.237231: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
+    2024-04-17 23:27:44.237454: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
+    2024-04-17 23:27:44.237472: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
+    2024-04-17 23:27:44.237475: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 Migration from Legacy conversion API
@@ -719,8 +715,8 @@ considered legacy API now. ``mo.convert_model()`` provides a wide range
 of preprocessing parameters. Most of these parameters have analogs in
 OVC or can be replaced with functionality from ``ov.PrePostProcessor``
 class. Refer to `Optimize Preprocessing
-notebook `__
-for more information about `Preprocessing
+notebook `__ for
+more information about `Preprocessing
 API `__.
 Here is the migration guide from legacy model preprocessing to
 Preprocessing API.
@@ -750,7 +746,7 @@ Resnet50 model that was exported to the ONNX format:
     ov_model = ov.convert_model(ONNX_CV_MODEL_PATH)
     
     prep = ov.preprocess.PrePostProcessor(ov_model)
-    prep.input('input.1').model().set_layout(ov.Layout("nchw"))
+    prep.input("input.1").model().set_layout(ov.Layout("nchw"))
     ov_model = prep.build()
 
 .. code:: ipython3
@@ -790,8 +786,8 @@ and the layout of an original model:
     ov_model = ov.convert_model(ONNX_CV_MODEL_PATH)
     
     prep = ov.preprocess.PrePostProcessor(ov_model)
-    prep.input('input.1').tensor().set_layout(ov.Layout("nhwc"))
-    prep.input('input.1').model().set_layout(ov.Layout("nchw"))
+    prep.input("input.1").tensor().set_layout(ov.Layout("nhwc"))
+    prep.input("input.1").model().set_layout(ov.Layout("nchw"))
     ov_model = prep.build()
 
 .. code:: ipython3
@@ -802,9 +798,7 @@ and the layout of an original model:
     ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, layout="nchw->nhwc")
     
     # alternatively use source_layout and target_layout parameters
-    ov_model = mo.convert_model(
-        ONNX_CV_MODEL_PATH, source_layout="nchw", target_layout="nhwc"
-    )
+    ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, source_layout="nchw", target_layout="nhwc")
 
 Specifying Mean and Scale Values
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -815,8 +809,8 @@ Using Preprocessing API ``mean`` and ``scale`` values can be set. Using
 these API, model embeds the corresponding preprocessing block for
 mean-value normalization of the input data and optimizes this block.
 Refer to `Optimize Preprocessing
-notebook `__
-for more examples.
+notebook `__ for
+more examples.
 
 .. code:: ipython3
 
@@ -863,8 +857,8 @@ the color channels before inference.
     ov_model = ov.convert_model(ONNX_CV_MODEL_PATH)
     
     prep = ov.preprocess.PrePostProcessor(ov_model)
-    prep.input('input.1').tensor().set_layout(ov.Layout("nchw"))
-    prep.input('input.1').preprocess().reverse_channels()
+    prep.input("input.1").tensor().set_layout(ov.Layout("nchw"))
+    prep.input("input.1").preprocess().reverse_channels()
     ov_model = prep.build()
 
 .. code:: ipython3
diff --git a/docs/notebooks/125-convnext-classification-with-output.rst b/docs/notebooks/convnext-classification-with-output.rst
similarity index 85%
rename from docs/notebooks/125-convnext-classification-with-output.rst
rename to docs/notebooks/convnext-classification-with-output.rst
index 6cd15182a70..b41fde8985c 100644
--- a/docs/notebooks/125-convnext-classification-with-output.rst
+++ b/docs/notebooks/convnext-classification-with-output.rst
@@ -48,11 +48,21 @@ Prerequisites
     %pip install -q  "openvino>=2023.1.0"
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -61,34 +71,34 @@ Prerequisites
 Get a test image
 ----------------
 
-First of all lets get a test
+ First of all lets get a test
 image from an open dataset.
 
 .. code:: ipython3
 
-    import urllib.request
+    import requests
     
     from torchvision.io import read_image
     import torchvision.transforms as transforms
     
     
-    img_path = 'cats_image.jpeg'
-    urllib.request.urlretrieve(
-        url='https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg',
-        filename=img_path
-    )
+    img_path = "cats_image.jpeg"
+    r = requests.get("https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg")
+    
+    with open(img_path, "wb") as f:
+        f.write(r.content)
     image = read_image(img_path)
     display(transforms.ToPILImage()(image))
 
 
 
-.. image:: 125-convnext-classification-with-output_files/125-convnext-classification-with-output_4_0.png
+.. image:: convnext-classification-with-output_files/convnext-classification-with-output_4_0.png
 
 
 Get a pretrained model
 ----------------------
 
-Torchvision provides a
+ Torchvision provides a
 mechanism of `listing and retrieving available
 models `__.
 
@@ -128,7 +138,7 @@ initialize pre-trained models
 Define a preprocessing and prepare an input data
 ------------------------------------------------
 
-You can use
+ You can use
 ``torchvision.transforms`` to make a preprocessing or
 use\ `preprocessing transforms from the model
 wight `__.
@@ -143,13 +153,6 @@ wight =3.4,<3.7"
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu requests pysbd transformers[torch] "openvino>=2023.1.0" seaborn ipywidgets
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu requests pysbd transformers "torch>=2.1" "openvino>=2023.1.0" seaborn ipywidgets
 
 Get Books
 ---------
@@ -197,7 +197,7 @@ which in a raw format looks like this:
 
 .. parsed-literal::
 
-    '\ufeffThe Project Gutenberg eBook of Anna Karenina\r\n    \r\nThis ebook is for the use of anyone anywhere in the United States and\r\nmost other parts of the world at no cost and with almost no restrictions\r\nwhatsoever. You may copy it, give it away or re-use it under the terms\r\nof the Project Gutenberg License included with this ebook or online\r\nat www.gutenberg.org. If you are not located in the United States,\r\nyou will have to check the laws of the country where you are located\r\nbefore using this eBook.\r\n\r\nTitle: Anna Karenina\r\n\r\n\r\nAuthor: graf Leo Tolstoy\r\n\r\nTranslator: Constance Garnett\r\n\r\nRelease date: July 1, 1998 [eBook #1399]\r\n                Most recently updated: April 9, 2023\r\n\r\nLanguage: English\r\n\r\n\r\n\r\n*\*\* START OF THE PROJECT GUTENBERG EBOOK ANNA KARENINA *\*\*\r\n[Illustration]\r\n\r\n\r\n\r\n\r\n ANNA KARENINA \r\n\r\n by Leo Tolstoy \r\n\r\n Translated by Constance Garnett \r\n\r\nContents\r\n\r\n\r\n PART ONE\r\n PART TWO\r\n PART THREE\r\n PART FOUR\r\n PART FIVE\r\n PART SIX\r\n PART SEVEN\r\n PART EIGHT\r\n\r\n\r\n\r\n\r\nPART ONE\r\n\r\nChapter 1\r\n\r\n\r\nHappy families are all alike; every unhappy family is unhappy in its\r\nown way.\r\n\r\nEverything was in confusion in the Oblonskys’ house. The wife had\r\ndiscovered that the husband was carrying on an intrigue with a French\r\ngirl, who had been a governess in their family, and she had announced\r\nto her husband that she could not go on living in the same house with\r\nhim. This position of affairs had now lasted three days, and not only\r\nthe husband and wife themselves, but all the me'
+    '\ufeffThe Project Gutenberg eBook of Anna Karenina\r\n    \r\nThis ebook is for the use of anyone anywhere in the United States and\r\nmost other parts of the world at no cost and with almost no restrictions\r\nwhatsoever. You may copy it, give it away or re-use it under the terms\r\nof the Project Gutenberg License included with this ebook or online\r\nat www.gutenberg.org. If you are not located in the United States,\r\nyou will have to check the laws of the country where you are located\r\nbefore using this eBook.\r\n\r\nTitle: Anna Karenina\r\n\r\n\r\nAuthor: graf Leo Tolstoy\r\n\r\nTranslator: Constance Garnett\r\n\r\nRelease date: July 1, 1998 [eBook #1399]\r\n                Most recently updated: April 9, 2023\r\n\r\nLanguage: English\r\n\r\n\r\n\r\n\*\*\* START OF THE PROJECT GUTENBERG EBOOK ANNA KARENINA \*\*\*\r\n[Illustration]\r\n\r\n\r\n\r\n\r\n ANNA KARENINA \r\n\r\n by Leo Tolstoy \r\n\r\n Translated by Constance Garnett \r\n\r\nContents\r\n\r\n\r\n PART ONE\r\n PART TWO\r\n PART THREE\r\n PART FOUR\r\n PART FIVE\r\n PART SIX\r\n PART SEVEN\r\n PART EIGHT\r\n\r\n\r\n\r\n\r\nPART ONE\r\n\r\nChapter 1\r\n\r\n\r\nHappy families are all alike; every unhappy family is unhappy in its\r\nown way.\r\n\r\nEverything was in confusion in the Oblonskys’ house. The wife had\r\ndiscovered that the husband was carrying on an intrigue with a French\r\ngirl, who had been a governess in their family, and she had announced\r\nto her husband that she could not go on living in the same house with\r\nhim. This position of affairs had now lasted three days, and not only\r\nthe husband and wife themselves, but all the me'
 
 
 
@@ -407,7 +407,7 @@ languages. It has the same architecture as the BERT model but has been
 trained on a different task: to produce identical embeddings for
 translation pairs.
 
-|image0|
+|image01|
 
 This makes LaBSE a great choice for our task and it can be reused for
 different language pairs still producing good results.
@@ -443,20 +443,10 @@ best fit.
         embedding_model: Union[BertModel, OVModel],
     ) -> np.ndarray:
         if isinstance(embedding_model, OVModel):
-            embeddings = [
-                embedding_model(tokenizer(sent, return_tensors="np").data)[
-                    "last_hidden_state"
-                ][0][0]
-                for sent in tqdm(sentences, disable=disable_tqdm)
-            ]
+            embeddings = [embedding_model(tokenizer(sent, return_tensors="np").data)["last_hidden_state"][0][0] for sent in tqdm(sentences, disable=disable_tqdm)]
             return np.vstack(embeddings)
         else:
-            embeddings = [
-                embedding_model(**tokenizer(sent, return_tensors="pt"))[
-                    "last_hidden_state"
-                ][0][0]
-                for sent in tqdm(sentences, disable=disable_tqdm)
-            ]
+            embeddings = [embedding_model(**tokenizer(sent, return_tensors="pt"))["last_hidden_state"][0][0] for sent in tqdm(sentences, disable=disable_tqdm)]
             return torch.vstack(embeddings)
 
 
@@ -499,8 +489,8 @@ For starting work, we should select device for inference first:
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
 
@@ -584,16 +574,12 @@ the converted model is the same as the original one.
         return x / np.var(x)
 
 
-    def calculate_alignment_matrix(
-        first: np.ndarray, second: np.ndarray, threshold: float = 1e-3
-    ) -> np.ndarray:
+    def calculate_alignment_matrix(first: np.ndarray, second: np.ndarray, threshold: float = 1e-3) -> np.ndarray:
         similarity = first @ second.T  # 1
         similarity_en_to_de = np.apply_along_axis(transform, -1, similarity)  # 2
         similarity_de_to_en = np.apply_along_axis(transform, -2, similarity)  # 2
 
-        both_one = (similarity_en_to_de > threshold) * (
-            similarity_de_to_en > threshold
-        )  # 3 and 4
+        both_one = (similarity_en_to_de > threshold) * (similarity_de_to_en > threshold)  # 3 and 4
         return both_one
 
 
@@ -608,9 +594,7 @@ the converted model is the same as the original one.
 
     graph, axis = plt.subplots(1, 2, figsize=(10, 5), sharey=True)
 
-    for matrix, ax, title in zip(
-        (alignment_matrix, alignment_matrix_pt), axis, ("OpenVINO", "PyTorch")
-    ):
+    for matrix, ax, title in zip((alignment_matrix, alignment_matrix_pt), axis, ("OpenVINO", "PyTorch")):
         plot = sns.heatmap(matrix, cbar=False, square=True, ax=ax)
         plot.set_title(f"Sentence Alignment Matrix {title}")
         plot.set_xlabel("German")
@@ -621,7 +605,7 @@ the converted model is the same as the original one.
 
 
 
-.. image:: 220-cross-lingual-books-alignment-with-output_files/220-cross-lingual-books-alignment-with-output_32_0.png
+.. image:: cross-lingual-books-alignment-with-output_files/cross-lingual-books-alignment-with-output_32_0.png
 
 
 After visualizing and comparing the alignment matrices, let’s transform
@@ -724,9 +708,7 @@ and JS.
     from io import StringIO
 
 
-    def create_interactive_table(
-        list1: List[str], list2: List[str], mapping: Dict[int, List[int]]
-    ) -> str:
+    def create_interactive_table(list1: List[str], list2: List[str], mapping: Dict[int, List[int]]) -> str:
         def inverse_mapping(mapping):
             inverse_map = {idx: [] for idx in range(len(list2))}
 
@@ -739,9 +721,7 @@ and JS.
         inversed_mapping = inverse_mapping(mapping)
 
         table_html = StringIO()
-        table_html.write(
-            ''
-        )
+        table_html.write('
Sentences ENSentences DE
')
         for i, (first, second) in enumerate(zip_longest(list1, list2)):
             table_html.write("")
             if i < len(list1):
@@ -940,9 +920,7 @@ advance and fill it in as the inference requests are executed.
         infer_queue = ov.AsyncInferQueue(embedding_model)
         infer_queue.set_callback(callback)
 
-        embedding_dim = (
-            embedding_model.output(0).get_partial_shape().get_dimension(2).get_length()
-        )
+        embedding_dim = embedding_model.output(0).get_partial_shape().get_dimension(2).get_length()
         embeddings = np.zeros((len(sentences), embedding_dim))
 
         with tqdm(total=len(sentences), disable=disable_tqdm) as pbar:
@@ -1006,9 +984,7 @@ Let’s compare the models and plot the results.
     for model, func, name in benchmarks_iterator:
         printable_name = name.replace("\n", " ")
         benchmarks_iterator.set_description(f"Run benchmark for {printable_name} model")
-        for run in tqdm(
-            range(10 + 1), leave=False, desc="Benchmark Runs: ", disable=disable_tqdm
-        ):
+        for run in tqdm(range(10 + 1), leave=False, desc="Benchmark Runs: ", disable=disable_tqdm):
             with disable_tqdm_context():
                 start = perf_counter()
                 func(benchmark_data, model)
@@ -1047,9 +1023,7 @@ Let’s compare the models and plot the results.
     cpu_name = core.get_property("CPU", "FULL_DEVICE_NAME")
 
     plot = sns.barplot(benchmark_dataframe, errorbar="sd")
-    plot.set(
-        ylabel="Sentences Per Second", title=f"Sentence Embeddings Benchmark\n{cpu_name}"
-    )
+    plot.set(ylabel="Sentences Per Second", title=f"Sentence Embeddings Benchmark\n{cpu_name}")
     perf_ratio = benchmark_dataframe.mean() / benchmark_dataframe.mean()[0]
     plot.spines["right"].set_visible(False)
     plot.spines["top"].set_visible(False)
@@ -1057,7 +1031,7 @@ Let’s compare the models and plot the results.
 
 
 
-.. image:: 220-cross-lingual-books-alignment-with-output_files/220-cross-lingual-books-alignment-with-output_49_0.png
+.. image:: cross-lingual-books-alignment-with-output_files/cross-lingual-books-alignment-with-output_49_0.png
 
 
 On an Intel Core i9-10980XE CPU, the OpenVINO model processed 45% more
diff --git a/docs/notebooks/220-cross-lingual-books-alignment-with-output_files/220-cross-lingual-books-alignment-with-output_32_0.png b/docs/notebooks/cross-lingual-books-alignment-with-output_files/cross-lingual-books-alignment-with-output_32_0.png
similarity index 100%
rename from docs/notebooks/220-cross-lingual-books-alignment-with-output_files/220-cross-lingual-books-alignment-with-output_32_0.png
rename to docs/notebooks/cross-lingual-books-alignment-with-output_files/cross-lingual-books-alignment-with-output_32_0.png
diff --git a/docs/notebooks/220-cross-lingual-books-alignment-with-output_files/220-cross-lingual-books-alignment-with-output_49_0.png b/docs/notebooks/cross-lingual-books-alignment-with-output_files/cross-lingual-books-alignment-with-output_49_0.png
similarity index 100%
rename from docs/notebooks/220-cross-lingual-books-alignment-with-output_files/220-cross-lingual-books-alignment-with-output_49_0.png
rename to docs/notebooks/cross-lingual-books-alignment-with-output_files/cross-lingual-books-alignment-with-output_49_0.png
diff --git a/docs/notebooks/110-ct-scan-live-inference-with-output.rst b/docs/notebooks/ct-scan-live-inference-with-output.rst
similarity index 87%
rename from docs/notebooks/110-ct-scan-live-inference-with-output.rst
rename to docs/notebooks/ct-scan-live-inference-with-output.rst
index f50dd0ab3ec..286355536e2 100644
--- a/docs/notebooks/110-ct-scan-live-inference-with-output.rst
+++ b/docs/notebooks/ct-scan-live-inference-with-output.rst
@@ -18,12 +18,12 @@ This notebook needs a quantized OpenVINO IR model and images from the
 `KiTS-19 `__ dataset, converted to
 2D images. (To learn how the model is quantized, see the `Convert and
 Quantize a UNet Model and Show Live
-Inference <110-ct-segmentation-quantize-nncf.ipynb>`__ tutorial.)
+Inference `__ tutorial.)
 
 This notebook provides a pre-trained model, trained for 20 epochs with
 the full KiTS-19 frames dataset, which has an F1 score on the validation
 set of 0.9. The training code is available in the `PyTorch MONAI
-Training <110-ct-segmentation-quantize-with-output.html>`__
+Training `__
 notebook.
 
 For demonstration purposes, this tutorial will download one converted CT
@@ -48,7 +48,7 @@ Table of contents:
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.3.0" "monai>=0.9.1" "nncf>=2.8.0"
+    %pip install -q "openvino>=2023.3.0" "monai>=0.9.1" "nncf>=2.8.0" "opencv-python" "tqdm"
 
 
 .. parsed-literal::
@@ -64,7 +64,6 @@ Imports
 .. code:: ipython3
 
     import os
-    import sys
     import zipfile
     from pathlib import Path
     
@@ -74,20 +73,24 @@ Imports
     
     from custom_segmentation import SegmentationModel
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py")
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 
 .. parsed-literal::
 
-    2024-03-12 22:35:25.130181: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:35:25.164310: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-17 23:29:02.693500: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 23:29:02.729260: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 22:35:25.734617: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-17 23:29:03.313117: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Settings
@@ -103,11 +106,11 @@ trained or optimized yourself, adjust the model paths.
 .. code:: ipython3
 
     # The directory that contains the IR model (xml and bin) files.
-    models_dir = Path('pretrained_model')
+    models_dir = Path("pretrained_model")
     
-    ir_model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/kidney-segmentation-kits19/FP16-INT8/'
-    ir_model_name_xml = 'quantized_unet_kits19.xml'
-    ir_model_name_bin = 'quantized_unet_kits19.bin'
+    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/kidney-segmentation-kits19/FP16-INT8/"
+    ir_model_name_xml = "quantized_unet_kits19.xml"
+    ir_model_name_bin = "quantized_unet_kits19.bin"
     
     download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory=models_dir)
     download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory=models_dir)
@@ -133,7 +136,7 @@ trained or optimized yourself, adjust the model paths.
 Benchmark Model Performance
 ---------------------------
 
-To measure the inference
+ To measure the inference
 performance of the IR model, use `Benchmark
 Tool `__
 - an inference performance measurement tool in OpenVINO. Benchmark tool
@@ -160,7 +163,7 @@ is a command-line application that can be run in the notebook with
     device = widgets.Dropdown(
         options=core.available_devices + device_list,
         value=device_list[0],
-        description='Device:',
+        description="Device:",
         disabled=False,
     )
     
@@ -198,7 +201,11 @@ is a command-line application that can be run in the notebook with
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.LATENCY.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 13.37 ms
+
+
+.. parsed-literal::
+
+    [ INFO ] Read model took 13.73 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     input.1 (node: input.1) : f32 / [...] / [1,1,512,512]
@@ -216,7 +223,7 @@ is a command-line application that can be run in the notebook with
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 259.78 ms
+    [ INFO ] Compile model took 303.13 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: pretrained_unet_kits19
@@ -251,21 +258,21 @@ is a command-line application that can be run in the notebook with
     [ INFO ] Fill input 'input.1' with random values 
     [Step 10/11] Measuring performance (Start inference synchronously, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 26.09 ms
+    [ INFO ] First inference took 25.71 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            1346 iterations
-    [ INFO ] Duration:         15003.91 ms
+    [ INFO ] Count:            1348 iterations
+    [ INFO ] Duration:         15011.04 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        10.91 ms
-    [ INFO ]    Average:       10.96 ms
-    [ INFO ]    Min:           10.64 ms
-    [ INFO ]    Max:           12.98 ms
-    [ INFO ] Throughput:   89.71 FPS
+    [ INFO ]    Median:        10.88 ms
+    [ INFO ]    Average:       10.94 ms
+    [ INFO ]    Min:           10.68 ms
+    [ INFO ]    Max:           13.65 ms
+    [ INFO ] Throughput:   89.80 FPS
 
 
 Download and Prepare Data
@@ -295,9 +302,7 @@ downloaded and extracted in the next cell.
     case_path = BASEDIR / f"case_{CASE:05d}"
     
     if not case_path.exists():
-        filename = download_file(
-            f"https://storage.openvinotoolkit.org/data/test_data/openvino_notebooks/kits19/case_{CASE:05d}.zip"
-        )
+        filename = download_file(f"https://storage.openvinotoolkit.org/data/test_data/openvino_notebooks/kits19/case_{CASE:05d}.zip")
         with zipfile.ZipFile(filename, "r") as zip_ref:
             zip_ref.extractall(path=BASEDIR)
         os.remove(filename)  # remove zipfile
@@ -331,7 +336,7 @@ card, model loading will be slow the first time you run this code. The
 model will be cached, so after the first time model loading will be
 faster. For more information on OpenVINO Runtime, including Model
 Caching, refer to the `OpenVINO API
-tutorial <002-openvino-api-with-output.html>`__.
+tutorial `__.
 
 We will use
 `AsyncInferQueue `__
@@ -366,9 +371,7 @@ to see the implementation.
 .. code:: ipython3
 
     core = ov.Core()
-    segmentation_model = SegmentationModel(
-        ie=core, model_path=Path(MODEL_PATH), sigmoid=True, rotate_and_flip=True
-    )
+    segmentation_model = SegmentationModel(ie=core, model_path=Path(MODEL_PATH), sigmoid=True, rotate_and_flip=True)
     image_paths = sorted(case_path.glob("imaging_frames/*jpg"))
     
     print(f"{case_path.name}, {len(image_paths)} images")
@@ -386,7 +389,7 @@ Prepare images
 
 Use the ``reader = LoadImage()`` function to read the images in the same
 way as in the
-`training <110-ct-segmentation-quantize-with-output.html>`__
+`training `__
 tutorial.
 
 .. code:: ipython3
@@ -441,10 +444,14 @@ The ``callback`` function will show the results of inference.
     
     from typing import Dict, Any
     
+    
     # Define a callback function that runs every time the asynchronous pipeline completes inference on a frame
-    def completion_callback(infer_request: ov.InferRequest, user_data: Dict[str, Any],) -> None:
-        preprocess_meta = user_data['preprocess_meta']
-        
+    def completion_callback(
+        infer_request: ov.InferRequest,
+        user_data: Dict[str, Any],
+    ) -> None:
+        preprocess_meta = user_data["preprocess_meta"]
+    
         raw_outputs = {idx: copy.deepcopy(res.data) for idx, (out, res) in enumerate(zip(infer_request.model_outputs, infer_request.output_tensors))}
         frame = segmentation_model.postprocess(raw_outputs, preprocess_meta)
     
@@ -479,7 +486,7 @@ Create asynchronous inference queue and perform it
     start_time = time.time()
     for i, input_frame in enumerate(framebuf):
         inputs, preprocessing_meta = segmentation_model.preprocess({segmentation_model.net.input(0): input_frame})
-        infer_queue.start_async(inputs, {'preprocess_meta': preprocessing_meta})
+        infer_queue.start_async(inputs, {"preprocess_meta": preprocessing_meta})
     
     # Wait until all inference requests in the AsyncInferQueue are completed
     infer_queue.wait_all()
@@ -488,21 +495,21 @@ Create asynchronous inference queue and perform it
     # Calculate total inference time and FPS
     total_time = stop_time - start_time
     fps = len(framebuf) / total_time
-    time_per_frame = 1 / fps 
+    time_per_frame = 1 / fps
     
     print(f"Loaded model to {device} in {load_end_time-load_start_time:.2f} seconds.")
     
-    print(f'Total time to infer all frames: {total_time:.3f}s')
-    print(f'Time per frame: {time_per_frame:.6f}s ({fps:.3f} FPS)')
+    print(f"Total time to infer all frames: {total_time:.3f}s")
+    print(f"Time per frame: {time_per_frame:.6f}s ({fps:.3f} FPS)")
 
 
 
-.. image:: 110-ct-scan-live-inference-with-output_files/110-ct-scan-live-inference-with-output_21_0.png
+.. image:: ct-scan-live-inference-with-output_files/ct-scan-live-inference-with-output_21_0.png
 
 
 .. parsed-literal::
 
     Loaded model to Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO') in 0.26 seconds.
-    Total time to infer all frames: 2.496s
-    Time per frame: 0.036711s (27.239 FPS)
+    Total time to infer all frames: 2.480s
+    Time per frame: 0.036477s (27.415 FPS)
 
diff --git a/docs/notebooks/110-ct-scan-live-inference-with-output_files/110-ct-scan-live-inference-with-output_21_0.png b/docs/notebooks/ct-scan-live-inference-with-output_files/ct-scan-live-inference-with-output_21_0.png
similarity index 100%
rename from docs/notebooks/110-ct-scan-live-inference-with-output_files/110-ct-scan-live-inference-with-output_21_0.png
rename to docs/notebooks/ct-scan-live-inference-with-output_files/ct-scan-live-inference-with-output_21_0.png
diff --git a/docs/notebooks/110-ct-segmentation-quantize-nncf-with-output.rst b/docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst
similarity index 91%
rename from docs/notebooks/110-ct-segmentation-quantize-nncf-with-output.rst
rename to docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst
index 30223aa7d9a..1da3c580e02 100644
--- a/docs/notebooks/110-ct-segmentation-quantize-nncf-with-output.rst
+++ b/docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst
@@ -30,7 +30,7 @@ All notebooks in this series:
 -  Convert and Quantize a Segmentation Model and Show Live Inference
    (this notebook)
 -  `Live Inference and Benchmark CT-scan
-   data <110-ct-scan-live-inference-with-output.html>`__
+   data `__
 
 Instructions
 ------------
@@ -87,7 +87,19 @@ Table of contents:
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.3.0" "monai>=0.9.1" "torchmetrics>=0.11.0" "nncf>=2.8.0" torch --extra-index-url https://download.pytorch.org/whl/cpu
+    import platform
+    
+    %pip install -q "openvino>=2023.3.0" "monai>=0.9.1" "torchmetrics>=0.11.0" "nncf>=2.8.0" "opencv-python" torch tqdm --extra-index-url https://download.pytorch.org/whl/cpu
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -105,7 +117,6 @@ Imports
     import logging
     import os
     import random
-    import sys
     import time
     import warnings
     import zipfile
@@ -130,20 +141,23 @@ Imports
     from custom_segmentation import SegmentationModel
     from async_pipeline import show_live_inference
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py")
     from notebook_utils import download_file
 
 
 .. parsed-literal::
 
-    2024-03-12 22:35:56.579274: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:35:56.613310: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-17 23:29:37.722637: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 23:29:37.758897: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 22:35:57.179215: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-17 23:29:38.341773: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -229,9 +243,7 @@ Download CT-scan Data
     CASE = 117
     if not (BASEDIR / f"case_{CASE:05d}").exists():
         BASEDIR.mkdir(exist_ok=True)
-        filename = download_file(
-            f"https://storage.openvinotoolkit.org/data/test_data/openvino_notebooks/kits19/case_{CASE:05d}.zip"
-        )
+        filename = download_file(f"https://storage.openvinotoolkit.org/data/test_data/openvino_notebooks/kits19/case_{CASE:05d}.zip")
         with zipfile.ZipFile(filename, "r") as zip_ref:
             zip_ref.extractall(path=BASEDIR)
         os.remove(filename)  # remove zipfile
@@ -280,7 +292,7 @@ method to display the images in the expected orientation:
             Source data should exist in basedir, in subdirectories case_00000 until case_00210,
             with each subdirectory containing directories imaging_frames, with jpg images, and
             segmentation_frames with segmentation masks as png files.
-            See https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/110-ct-segmentation-quantize/data-preparation-ct-scan.ipynb
+            See [data-preparation-ct-scan](./data-preparation-ct-scan.ipynb)
     
             :param basedir: Directory that contains the prepared CT scans
             """
@@ -288,10 +300,7 @@ method to display the images in the expected orientation:
     
             self.basedir = basedir
             self.dataset = masks
-            print(
-                f"Created dataset with {len(self.dataset)} items. "
-                f"Base directory for data: {basedir}"
-            )
+            print(f"Created dataset with {len(self.dataset)} items. " f"Base directory for data: {basedir}")
     
         def __getitem__(self, index):
             """
@@ -300,9 +309,7 @@ method to display the images in the expected orientation:
             :return: (image, segmentation_mask)
             """
             mask_path = self.dataset[index]
-            image_path = str(mask_path.with_suffix(".jpg")).replace(
-                "segmentation_frames", "imaging_frames"
-            )
+            image_path = str(mask_path.with_suffix(".jpg")).replace("segmentation_frames", "imaging_frames")
     
             # Load images with MONAI's LoadImage to match data loading in training notebook
             mask = LoadImage(image_only=True, dtype=np.uint8)(str(mask_path)).numpy()
@@ -347,7 +354,7 @@ kidney pixels to verify that the annotations look correct:
 
 
 
-.. image:: 110-ct-segmentation-quantize-nncf-with-output_files/110-ct-segmentation-quantize-nncf-with-output_14_1.png
+.. image:: ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_14_1.png
 
 
 Metric
@@ -412,7 +419,7 @@ this notebook.
 
 .. code:: ipython3
 
-    fp32_ir_path = MODEL_DIR / Path('unet_kits19_fp32.xml')
+    fp32_ir_path = MODEL_DIR / Path("unet_kits19_fp32.xml")
     
     fp32_ir_model = ov.convert_model(model, example_input=torch.ones(1, 1, 512, 512, dtype=torch.float32))
     ov.save_model(fp32_ir_model, str(fp32_ir_path))
@@ -430,12 +437,7 @@ this notebook.
 
 .. parsed-literal::
 
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if x_e.shape[-i - 1] != x_0.shape[-i - 1]:
 
 
@@ -475,7 +477,7 @@ steps:
         model,
         calibration_dataset,
         # Do not quantize LeakyReLU activations to allow the INT8 model to run on Intel GPU
-        ignored_scope=nncf.IgnoredScope(patterns=[".*LeakyReLU.*"])
+        ignored_scope=nncf.IgnoredScope(patterns=[".*LeakyReLU.*"]),
     )
 
 
@@ -534,22 +536,22 @@ Convert quantized model to OpenVINO IR model and save it.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:337: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:337: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       return self._level_low.item()
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:345: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:345: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       return self._level_high.item()
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if x_e.shape[-i - 1] != x_0.shape[-i - 1]:
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1093: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
     Tensor-likes are not close!
     
-    Mismatched elements: 248084 / 262144 (94.6%)
-    Greatest absolute difference: 3.0810165405273438 at index (0, 0, 135, 41) (up to 1e-05 allowed)
-    Greatest relative difference: 29099.340127879757 at index (0, 0, 139, 287) (up to 1e-05 allowed)
+    Mismatched elements: 248412 / 262144 (94.8%)
+    Greatest absolute difference: 3.334601879119873 at index (0, 0, 345, 29) (up to 1e-05 allowed)
+    Greatest relative difference: 21800.240266957342 at index (0, 0, 242, 213) (up to 1e-05 allowed)
       _check_trace(
 
 
@@ -601,7 +603,7 @@ Select Inference Device
     device = widgets.Dropdown(
         options=core.available_devices + device_list,
         value=device_list[0],
-        description='Device:',
+        description="Device:",
         disabled=False,
     )
     
@@ -623,8 +625,6 @@ Compare Metrics for the original model and the quantized model to be sure that t
 
 .. code:: ipython3
 
-    
-    
     int8_compiled_model = core.compile_model(int8_ir_model, device.value)
     int8_f1 = compute_f1(int8_compiled_model, dataset)
     
@@ -685,7 +685,7 @@ be run in the notebook with ``! benchmark_app`` or
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.LATENCY.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 8.66 ms
+    [ INFO ] Read model took 8.73 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     x (node: x) : f32 / [...] / [?,?,?,?]
@@ -703,7 +703,7 @@ be run in the notebook with ``! benchmark_app`` or
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 136.91 ms
+    [ INFO ] Compile model took 151.10 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -736,9 +736,9 @@ be run in the notebook with ``! benchmark_app`` or
     [Step 9/11] Creating infer requests and preparing input tensors
     [ ERROR ] Input x is dynamic. Provide data shapes!
     Traceback (most recent call last):
-      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 486, in main
+      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 486, in main
         data_queue = get_input_data(paths_to_input, app_inputs_info)
-      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/utils/inputs_filling.py", line 123, in get_input_data
+      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/utils/inputs_filling.py", line 123, in get_input_data
         raise Exception(f"Input {info.name} is dynamic. Provide data shapes!")
     Exception: Input x is dynamic. Provide data shapes!
 
@@ -770,7 +770,7 @@ be run in the notebook with ``! benchmark_app`` or
 
 .. parsed-literal::
 
-    [ INFO ] Read model took 13.46 ms
+    [ INFO ] Read model took 13.30 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     x (node: x) : f32 / [...] / [1,1,512,512]
@@ -788,7 +788,7 @@ be run in the notebook with ``! benchmark_app`` or
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 274.86 ms
+    [ INFO ] Compile model took 233.12 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model49
@@ -827,21 +827,21 @@ be run in the notebook with ``! benchmark_app`` or
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 30.24 ms
+    [ INFO ] First inference took 31.47 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            972 iterations
-    [ INFO ] Duration:         15004.50 ms
+    [ INFO ] Count:            965 iterations
+    [ INFO ] Duration:         15011.79 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        15.20 ms
-    [ INFO ]    Average:       15.24 ms
-    [ INFO ]    Min:           14.91 ms
-    [ INFO ]    Max:           16.93 ms
-    [ INFO ] Throughput:   64.78 FPS
+    [ INFO ]    Median:        15.31 ms
+    [ INFO ]    Average:       15.36 ms
+    [ INFO ]    Min:           15.02 ms
+    [ INFO ]    Max:           17.48 ms
+    [ INFO ] Throughput:   64.28 FPS
 
 
 Visually Compare Inference Results
@@ -926,11 +926,11 @@ seed is displayed to enable reproducing specific runs of this cell.
 
 .. parsed-literal::
 
-    Visualizing results with seed 1710279421
+    Visualizing results with seed 1713389447
 
 
 
-.. image:: 110-ct-segmentation-quantize-nncf-with-output_files/110-ct-segmentation-quantize-nncf-with-output_37_1.png
+.. image:: ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png
 
 
 Show Live Inference
@@ -942,7 +942,7 @@ To show live inference on the model in the notebook, we will use the
 asynchronous processing feature of OpenVINO.
 
 We use the ``show_live_inference`` function from `Notebook
-Utils `__ to show live inference. This
+Utils <../utils-with-output.html>`__ to show live inference. This
 function uses `Open Model
 Zoo `__\ ’s Async
 Pipeline and Model API to perform asynchronous inference. After
@@ -969,9 +969,7 @@ overlay of the segmentation mask on the original image/frame.
 
     CASE = 117
     
-    segmentation_model = SegmentationModel(
-        ie=core, model_path=int8_ir_path, sigmoid=True, rotate_and_flip=True
-    )
+    segmentation_model = SegmentationModel(ie=core, model_path=int8_ir_path, sigmoid=True, rotate_and_flip=True)
     case_path = BASEDIR / f"case_{CASE:05d}"
     image_paths = sorted(case_path.glob("imaging_frames/*jpg"))
     print(f"{case_path.name}, {len(image_paths)} images")
@@ -997,18 +995,22 @@ performs inference, and displays the results on the frames loaded in
 
     reader = LoadImage(image_only=True, dtype=np.uint8)
     show_live_inference(
-        ie=core, image_paths=image_paths, model=segmentation_model, device=device.value, reader=reader
+        ie=core,
+        image_paths=image_paths,
+        model=segmentation_model,
+        device=device.value,
+        reader=reader,
     )
 
 
 
-.. image:: 110-ct-segmentation-quantize-nncf-with-output_files/110-ct-segmentation-quantize-nncf-with-output_42_0.jpg
+.. image:: ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_42_0.jpg
 
 
 .. parsed-literal::
 
     Loaded model to AUTO in 0.22 seconds.
-    Total time for 68 frames: 2.67 seconds, fps:25.87
+    Total time for 68 frames: 2.70 seconds, fps:25.59
 
 
 References
@@ -1020,9 +1022,8 @@ References
 Repository `__ - `Neural
 Network Compression Framework for fast model
 inference `__ - `OpenVINO API
-Tutorial <002-openvino-api-with-output.html>`__ - `OpenVINO
-PyPI (pip install
-openvino-dev) `__
+Tutorial `__ - `OpenVINO PyPI (pip
+install openvino-dev) `__
 
 **Kits19 Data** - `Kits19 Challenge
 Homepage `__ - `Kits19 GitHub
diff --git a/docs/notebooks/110-ct-segmentation-quantize-nncf-with-output_files/110-ct-segmentation-quantize-nncf-with-output_14_1.png b/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_14_1.png
similarity index 100%
rename from docs/notebooks/110-ct-segmentation-quantize-nncf-with-output_files/110-ct-segmentation-quantize-nncf-with-output_14_1.png
rename to docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_14_1.png
diff --git a/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png b/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png
new file mode 100644
index 00000000000..156f435eba7
--- /dev/null
+++ b/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08f7f46aea3e4c64f7bcb4dfe777299e9cb77cf5ec36396e340c1be6e8206aef
+size 382874
diff --git a/docs/notebooks/110-ct-segmentation-quantize-nncf-with-output_files/110-ct-segmentation-quantize-nncf-with-output_42_0.jpg b/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_42_0.jpg
similarity index 100%
rename from docs/notebooks/110-ct-segmentation-quantize-nncf-with-output_files/110-ct-segmentation-quantize-nncf-with-output_42_0.jpg
rename to docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_42_0.jpg
diff --git a/docs/notebooks/259-decidiffusion-image-generation-with-output.rst b/docs/notebooks/decidiffusion-image-generation-with-output.rst
similarity index 87%
rename from docs/notebooks/259-decidiffusion-image-generation-with-output.rst
rename to docs/notebooks/decidiffusion-image-generation-with-output.rst
index 2b31ac01c58..07a0f1a2896 100644
--- a/docs/notebooks/259-decidiffusion-image-generation-with-output.rst
+++ b/docs/notebooks/decidiffusion-image-generation-with-output.rst
@@ -89,7 +89,7 @@ install required packages
 
 .. code:: ipython3
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  "diffusers" "transformers" "torch" "pillow" "openvino>=2023.1.0" "gradio" "datasets" "nncf>=2.7.0" "peft==0.6.2"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  "diffusers" "transformers" "torch>=2.1" "pillow" "openvino>=2023.1.0" "gradio>=4.19" "datasets>=2.14.6" "huggingface-hub>=0.19.4" "nncf>=2.7.0" "peft==0.6.2"
 
 Prepare DeciDiffusion models for OpenVINO format conversion
 -----------------------------------------------------------
@@ -172,19 +172,20 @@ to create diffusers pipeline for DeciDiffusion.
     from diffusers import StableDiffusionPipeline
     import warnings
     
-    warnings.filterwarnings('ignore')
+    warnings.filterwarnings("ignore")
     
     TEXT_ENCODER_OV_PATH = Path("model/text_encoder.xml")
-    UNET_OV_PATH = Path('model/unet_nas.xml')
+    UNET_OV_PATH = Path("model/unet_nas.xml")
     VAE_ENCODER_OV_PATH = Path("model/vae_encoder.xml")
-    VAE_DECODER_OV_PATH = Path('model/vae_decoder.xml')
+    VAE_DECODER_OV_PATH = Path("model/vae_decoder.xml")
     checkpoint = "Deci/DeciDiffusion-v1-0"
     scheduler_config_dir = Path("model/scheduler")
     tokenizer_dir = Path("model/tokenizer")
     
+    
     def load_orginal_pytorch_pipeline_componets():
         pipeline = StableDiffusionPipeline.from_pretrained(checkpoint, custom_pipeline=checkpoint, torch_dtype=torch.float32)
-        pipeline.unet = pipeline.unet.from_pretrained(checkpoint, subfolder='flexible_unet', torch_dtype=torch.float32)
+        pipeline.unet = pipeline.unet.from_pretrained(checkpoint, subfolder="flexible_unet", torch_dtype=torch.float32)
         text_encoder = pipeline.text_encoder
         text_encoder.eval()
         unet = pipeline.unet
@@ -193,9 +194,9 @@ to create diffusers pipeline for DeciDiffusion.
         vae.eval()
     
         del pipeline
-        gc.collect();
+        gc.collect()
         return text_encoder, unet, vae
-        
+    
     
     def cleanup_torchscript_cache():
         """
@@ -254,11 +255,11 @@ hidden states.
 
 .. code:: ipython3
 
-    def convert_encoder(text_encoder: torch.nn.Module, ir_path:Path):
+    def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
         """
-        Convert Text Encoder mode. 
-        Function accepts text encoder model, and prepares example inputs for conversion, 
-        Parameters: 
+        Convert Text Encoder mode.
+        Function accepts text encoder model, and prepares example inputs for conversion,
+        Parameters:
             text_encoder (torch.nn.Module): text_encoder model from Stable Diffusion pipeline
             ir_path (Path): File for storing model
         Returns:
@@ -271,13 +272,19 @@ hidden states.
         # disable gradients calculation for reducing memory consumption
         with torch.no_grad():
             # Export model to IR format
-            ov_model = ov.convert_model(text_encoder, example_input=input_ids, input=[(1,77),])
+            ov_model = ov.convert_model(
+                text_encoder,
+                example_input=input_ids,
+                input=[
+                    (1, 77),
+                ],
+            )
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        gc.collect();
-        print(f'Text Encoder successfully converted to IR and saved to {ir_path}')
-        
+        gc.collect()
+        print(f"Text Encoder successfully converted to IR and saved to {ir_path}")
+    
     
     if not TEXT_ENCODER_OV_PATH.exists():
         convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
@@ -312,17 +319,14 @@ Model predicts the ``sample`` state for the next step.
 
     import numpy as np
     
-    dtype_mapping = {
-        torch.float32: ov.Type.f32,
-        torch.float64: ov.Type.f64
-    }
+    dtype_mapping = {torch.float32: ov.Type.f32, torch.float64: ov.Type.f64}
     
     
-    def convert_unet(unet:torch.nn.Module, ir_path:Path):
+    def convert_unet(unet: torch.nn.Module, ir_path: Path):
         """
-        Convert U-net model to IR format. 
-        Function accepts unet model, prepares example inputs for conversion, 
-        Parameters: 
+        Convert U-net model to IR format.
+        Function accepts unet model, prepares example inputs for conversion,
+        Parameters:
             unet (StableDiffusionPipeline): unet from Stable Diffusion pipeline
             ir_path (Path): File for storing model
         Returns:
@@ -348,8 +352,8 @@ Model predicts the ``sample`` state for the next step.
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        gc.collect();
-        print(f'U-Net NAS successfully converted to IR and saved to {ir_path}')
+        gc.collect()
+        print(f"U-Net NAS successfully converted to IR and saved to {ir_path}")
     
     
     if not UNET_OV_PATH.exists():
@@ -391,15 +395,16 @@ of the pipeline, it will be better to convert them to separate models.
 
     def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path):
         """
-        Convert VAE model for encoding to IR format. 
-        Function accepts vae model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion, 
-        Parameters: 
-            vae (torch.nn.Module): VAE model from StableDiffusio pipeline 
+        Convert VAE model for encoding to IR format.
+        Function accepts vae model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion,
+        Parameters:
+            vae (torch.nn.Module): VAE model from StableDiffusio pipeline
             ir_path (Path): File for storing model
         Returns:
             None
         """
+    
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -407,16 +412,17 @@ of the pipeline, it will be better to convert them to separate models.
     
             def forward(self, image):
                 return self.vae.encode(x=image)["latent_dist"].sample()
+    
         vae_encoder = VAEEncoderWrapper(vae)
         vae_encoder.eval()
         image = torch.zeros((1, 3, 512, 512))
         with torch.no_grad():
-            ov_model = ov.convert_model(vae_encoder, example_input=image, input=[((1,3,512,512),)])
+            ov_model = ov.convert_model(vae_encoder, example_input=image, input=[((1, 3, 512, 512),)])
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        gc.collect();
-        print(f'VAE encoder successfully converted to IR and saved to {ir_path}')
+        gc.collect()
+        print(f"VAE encoder successfully converted to IR and saved to {ir_path}")
     
     
     if not VAE_ENCODER_OV_PATH.exists():
@@ -427,15 +433,16 @@ of the pipeline, it will be better to convert them to separate models.
     
     def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path):
         """
-        Convert VAE model for decoding to IR format. 
-        Function accepts vae model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion, 
-        Parameters: 
+        Convert VAE model for decoding to IR format.
+        Function accepts vae model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion,
+        Parameters:
             vae (torch.nn.Module): VAE model frm StableDiffusion pipeline
             ir_path (Path): File for storing model
         Returns:
             None
         """
+    
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -443,18 +450,18 @@ of the pipeline, it will be better to convert them to separate models.
     
             def forward(self, latents):
                 return self.vae.decode(latents)
-        
+    
         vae_decoder = VAEDecoderWrapper(vae)
         latents = torch.zeros((1, 4, 64, 64))
     
         vae_decoder.eval()
         with torch.no_grad():
-            ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1,4,64,64),)])
+            ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1, 4, 64, 64),)])
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        gc.collect();
-        print(f'VAE decoder successfully converted to IR and saved to {ir_path}')
+        gc.collect()
+        print(f"VAE decoder successfully converted to IR and saved to {ir_path}")
     
     
     if not VAE_DECODER_OV_PATH.exists():
@@ -582,11 +589,11 @@ between 0.4 and 0.6.
     from openvino.runtime import Model
     
     
-    def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
+    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
-        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio 
+        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-        
+    
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -606,7 +613,7 @@ between 0.4 and 0.6.
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-        
+    
         Parameters:
           image (PIL.Image.Image): input image
         Returns:
@@ -615,8 +622,7 @@ between 0.4 and 0.6.
         """
         src_width, src_height = image.size
         dst_width, dst_height = scale_fit_to_window(512, 512, src_width, src_height)
-        image = np.array(image.resize((dst_width, dst_height),
-                         resample=PIL.Image.Resampling.LANCZOS))[None, :]
+        image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
         pad_width = 512 - dst_width
         pad_height = 512 - dst_height
         pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
@@ -709,7 +715,7 @@ between 0.4 and 0.6.
                 gif (bool, *optional*, False):
                     Flag for storing all steps results or not.
             Returns:
-                Dictionary with keys: 
+                Dictionary with keys:
                     sample - the last generated image PIL.Image.Image or np.array
                     iterations - *optional* (if gif=True) images for all diffusion steps, List of PIL.Image.Image or np.array.
             """
@@ -719,8 +725,12 @@ between 0.4 and 0.6.
             img_buffer = []
             do_classifier_free_guidance = guidance_scale > 1.0
             # get prompt text embeddings
-            text_embeddings = self._encode_prompt(prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt)
-            
+            text_embeddings = self._encode_prompt(
+                prompt,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                negative_prompt=negative_prompt,
+            )
+    
             # set timesteps
             accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
             extra_set_kwargs = {}
@@ -756,7 +766,12 @@ between 0.4 and 0.6.
                     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     
                 # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs)["prev_sample"].numpy()
+                latents = self.scheduler.step(
+                    torch.from_numpy(noise_pred),
+                    t,
+                    torch.from_numpy(latents),
+                    **extra_step_kwargs,
+                )["prev_sample"].numpy()
                 if gif:
                     image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
                     image = self.postprocess_image(image, meta, output_type)
@@ -766,9 +781,15 @@ between 0.4 and 0.6.
             image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
     
             image = self.postprocess_image(image, meta, output_type)
-            return {"sample": image, 'iterations': img_buffer}
-        
-        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True, negative_prompt:Union[str, List[str]] = None):
+            return {"sample": image, "iterations": img_buffer}
+    
+        def _encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+            negative_prompt: Union[str, List[str]] = None,
+        ):
             """
             Encodes the prompt into text encoder hidden states.
     
@@ -792,16 +813,13 @@ between 0.4 and 0.6.
             )
             text_input_ids = text_inputs.input_ids
     
-            text_embeddings = self.text_encoder(
-                text_input_ids)[self._text_encoder_output]
+            text_embeddings = self.text_encoder(text_input_ids)[self._text_encoder_output]
     
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(
-                    text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(
-                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
+                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
+                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
     
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
@@ -835,11 +853,10 @@ between 0.4 and 0.6.
     
             return text_embeddings
     
-    
-        def prepare_latents(self, image:PIL.Image.Image = None, latent_timestep:torch.Tensor = None):
+        def prepare_latents(self, image: PIL.Image.Image = None, latent_timestep: torch.Tensor = None):
             """
             Function for getting initial latents for starting generation
-            
+    
             Parameters:
                 image (PIL.Image.Image, *optional*, None):
                     Input image for generation, if not provided randon noise will be used as starting point
@@ -861,11 +878,11 @@ between 0.4 and 0.6.
             latents = self.scheduler.add_noise(torch.from_numpy(latents), torch.from_numpy(noise), latent_timestep).numpy()
             return latents, meta
     
-        def postprocess_image(self, image:np.ndarray, meta:Dict, output_type:str = "pil"):
+        def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
             """
-            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required), 
+            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
             normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-            
+    
             Parameters:
                 image (np.ndarray):
                     Generated image
@@ -891,25 +908,23 @@ between 0.4 and 0.6.
                 image = self.numpy_to_pil(image)
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [img.resize((orig_width, orig_height),
-                                        PIL.Image.Resampling.LANCZOS) for img in image]
+                    image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
             else:
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [cv2.resize(img, (orig_width, orig_width))
-                             for img in image]
+                    image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
             return image
     
-        def get_timesteps(self, num_inference_steps:int, strength:float):
+        def get_timesteps(self, num_inference_steps: int, strength: float):
             """
             Helper function for getting scheduler timesteps for generation
             In case of image-to-image generation, it updates number of steps according to strength
-            
+    
             Parameters:
                num_inference_steps (int):
                   number of inference steps for generation
                strength (float):
-                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. 
+                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
                    Values that approach 1.0 enable lots of variations but will also produce images that are not semantically consistent with the input.
             """
             # get the original timestep using init_timestep
@@ -918,7 +933,7 @@ between 0.4 and 0.6.
             t_start = max(num_inference_steps - init_timestep, 0)
             timesteps = self.scheduler.timesteps[t_start:]
     
-            return timesteps, num_inference_steps - t_start 
+            return timesteps, num_inference_steps - t_start
 
 Configure Inference Pipeline
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -939,8 +954,8 @@ inference using OpenVINO.
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
     
@@ -970,7 +985,7 @@ Let us define them and put all components together
     from diffusers import DDIMScheduler
     
     if not tokenizer_dir.exists():
-        tokenizer = AutoTokenizer.from_pretrained(checkpoint, subfolder='tokenizer')
+        tokenizer = AutoTokenizer.from_pretrained(checkpoint, subfolder="tokenizer")
         tokenizer.save_pretrained(tokenizer_dir)
     else:
         tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir)
@@ -987,7 +1002,7 @@ Let us define them and put all components together
         unet=unet_model,
         vae_encoder=vae_encoder,
         vae_decoder=vae_decoder,
-        scheduler=scheduler
+        scheduler=scheduler,
     )
 
 Text-to-Image generation
@@ -999,16 +1014,16 @@ Now, let’s see model in action
 
 .. code:: ipython3
 
-    text_prompt = 'Highly detailed portrait of a small, adorable cat with round, expressive eyes and a friendly smile'
+    text_prompt = "Highly detailed portrait of a small, adorable cat with round, expressive eyes and a friendly smile"
     num_steps = 30
     seed = 4217
 
 .. code:: ipython3
 
-    print('Pipeline settings')
-    print(f'Input text: {text_prompt}')
-    print(f'Seed: {seed}')
-    print(f'Number of steps: {num_steps}')
+    print("Pipeline settings")
+    print(f"Input text: {text_prompt}")
+    print(f"Seed: {seed}")
+    print(f"Number of steps: {num_steps}")
 
 
 .. parsed-literal::
@@ -1032,10 +1047,10 @@ Now, let’s see model in action
 
 .. code:: ipython3
 
-    text = '\n\t'.join(text_prompt.split('.'))
+    text = "\n\t".join(text_prompt.split("."))
     print("Input text:")
     print("\t" + text)
-    display(result['sample'][0])
+    display(result["sample"][0])
 
 
 .. parsed-literal::
@@ -1045,7 +1060,7 @@ Now, let’s see model in action
 
 
 
-.. image:: 259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_26_1.png
+.. image:: decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_26_1.png
 
 
 Image-to-Image generation
@@ -1061,18 +1076,19 @@ diffusion models can be used to “enhance” an image.
 .. code:: ipython3
 
     from diffusers.utils import load_image
+    
     default_image_url = "https://user-images.githubusercontent.com/29454499/274843996-b0d97f9b-7bfb-4d33-a6d8-d1822eec41ce.jpg"
-    text_i2i_prompt = 'Highly detailed realistic portrait of a grumpy small, adorable cat with round, expressive eyes'
+    text_i2i_prompt = "Highly detailed realistic portrait of a grumpy small, adorable cat with round, expressive eyes"
     strength = 0.87
     guidance_scale = 7.5
     num_i2i_steps = 15
     seed_i2i = seed
     
     image = load_image(default_image_url)
-    print('Pipeline settings')
-    print(f'Input text: {text_i2i_prompt}')
-    print(f'Seed: {seed_i2i}')
-    print(f'Number of steps: {num_i2i_steps}')
+    print("Pipeline settings")
+    print(f"Input text: {text_i2i_prompt}")
+    print(f"Seed: {seed_i2i}")
+    print(f"Number of steps: {num_i2i_steps}")
     print(f"Strength: {strength}")
     print(f"Guidance scale: {guidance_scale}")
     display(image)
@@ -1089,12 +1105,19 @@ diffusion models can be used to “enhance” an image.
 
 
 
-.. image:: 259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_28_1.png
+.. image:: decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_28_1.png
 
 
 .. code:: ipython3
 
-    result = ov_pipe(text_i2i_prompt, image, guidance_scale=guidance_scale, strength=strength, num_inference_steps=num_i2i_steps, seed=seed_i2i)
+    result = ov_pipe(
+        text_i2i_prompt,
+        image,
+        guidance_scale=guidance_scale,
+        strength=strength,
+        num_inference_steps=num_i2i_steps,
+        seed=seed_i2i,
+    )
 
 
 
@@ -1105,10 +1128,10 @@ diffusion models can be used to “enhance” an image.
 
 .. code:: ipython3
 
-    text = '\n\t'.join(text_i2i_prompt.split('.'))
+    text = "\n\t".join(text_i2i_prompt.split("."))
     print("Input text:")
     print("\t" + text)
-    display(result['sample'][0])
+    display(result["sample"][0])
 
 
 .. parsed-literal::
@@ -1118,7 +1141,7 @@ diffusion models can be used to “enhance” an image.
 
 
 
-.. image:: 259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_30_1.png
+.. image:: decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_30_1.png
 
 
 Quantization
@@ -1153,7 +1176,7 @@ improve model inference speed.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -1173,8 +1196,13 @@ Let’s load ``skip magic`` extension to skip quantization if
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     int8_pipe = None
     
@@ -1367,7 +1395,7 @@ Text-to-Image generation
 
 
 
-.. image:: 259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_45_2.png
+.. image:: decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_45_2.png
 
 
 Image-to-Image generation
@@ -1394,7 +1422,7 @@ Image-to-Image generation
 
 
 
-.. image:: 259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_47_2.png
+.. image:: decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_47_2.png
 
 
 Compare inference time of the FP16 and INT8 pipelines
@@ -1489,7 +1517,7 @@ launch the interactive demo.
     
     use_quantized_model = widgets.Checkbox(
         value=True if quantized_model_present else False,
-        description='Use quantized model',
+        description="Use quantized model",
         disabled=not quantized_model_present,
     )
     
@@ -1513,13 +1541,44 @@ launch the interactive demo.
     img = load_image(sample_img_url).save("tower.jpg")
     pipeline = int8_pipe if use_quantized_model.value else ov_pipe
     
-    def generate_from_text(text, negative_prompt, seed, num_steps, guidance_scale, _=gr.Progress(track_tqdm=True)):
-        result = pipeline(text, negative_prompt=negative_prompt, num_inference_steps=num_steps, seed=seed, guidance_scale=guidance_scale)
+    
+    def generate_from_text(
+        text,
+        negative_prompt,
+        seed,
+        num_steps,
+        guidance_scale,
+        _=gr.Progress(track_tqdm=True),
+    ):
+        result = pipeline(
+            text,
+            negative_prompt=negative_prompt,
+            num_inference_steps=num_steps,
+            seed=seed,
+            guidance_scale=guidance_scale,
+        )
         return result["sample"][0]
     
     
-    def generate_from_image(img, text, negative_prompt, seed, num_steps, strength, guidance_scale, _=gr.Progress(track_tqdm=True)):
-        result = pipeline(text, img, negative_prompt=negative_prompt, num_inference_steps=num_steps, seed=seed, strength=strength, guidance_scale=guidance_scale)
+    def generate_from_image(
+        img,
+        text,
+        negative_prompt,
+        seed,
+        num_steps,
+        strength,
+        guidance_scale,
+        _=gr.Progress(track_tqdm=True),
+    ):
+        result = pipeline(
+            text,
+            img,
+            negative_prompt=negative_prompt,
+            num_inference_steps=num_steps,
+            seed=seed,
+            strength=strength,
+            guidance_scale=guidance_scale,
+        )
         return result["sample"][0]
     
     
@@ -1533,11 +1592,20 @@ launch the interactive demo.
                     steps_input = gr.Slider(1, 50, value=20, step=1, label="Steps")
                     guidance_scale = gr.Slider(label="Guidance Scale", minimum=0, maximum=50, value=0.7, step=0.1)
                 out = gr.Image(label="Result", type="pil")
-            sample_text = "futuristic synthwave city, retro sunset, crystals, spires, volumetric lighting, studio Ghibli style, rendered in unreal engine with clean details"
+            sample_text = (
+                "futuristic synthwave city, retro sunset, crystals, spires, volumetric lighting, studio Ghibli style, rendered in unreal engine with clean details"
+            )
             sample_text2 = "Highly detailed realistic portrait of a grumpy small, adorable cat with round, expressive eyes"
             btn = gr.Button()
-            btn.click(generate_from_text, [text_input, neg_text_input, seed_input, steps_input, guidance_scale], out)
-            gr.Examples([[sample_text, "", 42, 20, 0.7], [sample_text2, "", 4218, 20, 0.7]], [text_input, neg_text_input, seed_input, steps_input, guidance_scale])
+            btn.click(
+                generate_from_text,
+                [text_input, neg_text_input, seed_input, steps_input, guidance_scale],
+                out,
+            )
+            gr.Examples(
+                [[sample_text, "", 42, 20, 0.7], [sample_text2, "", 4218, 20, 0.7]],
+                [text_input, neg_text_input, seed_input, steps_input, guidance_scale],
+            )
         with gr.Tab("Image-to-Image generation"):
             with gr.Row():
                 with gr.Column():
@@ -1553,13 +1621,28 @@ launch the interactive demo.
             sample_i2i_text = "amazing watercolor painting"
             i2i_btn.click(
                 generate_from_image,
-                [i2i_input, i2i_text_input, i2i_neg_text_input, i2i_seed_input, i2i_steps_input, strength_input, i2i_guidance_scale],
+                [
+                    i2i_input,
+                    i2i_text_input,
+                    i2i_neg_text_input,
+                    i2i_seed_input,
+                    i2i_steps_input,
+                    strength_input,
+                    i2i_guidance_scale,
+                ],
                 i2i_out,
             )
             gr.Examples(
                 [["tower.jpg", sample_i2i_text, "", 6400023, 30, 0.6, 5]],
-                [i2i_input, i2i_text_input, i2i_neg_text_input, i2i_seed_input, i2i_steps_input, strength_input, i2i_guidance_scale],
-                
+                [
+                    i2i_input,
+                    i2i_text_input,
+                    i2i_neg_text_input,
+                    i2i_seed_input,
+                    i2i_steps_input,
+                    strength_input,
+                    i2i_guidance_scale,
+                ],
             )
     
     try:
diff --git a/docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_26_1.jpg b/docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_26_1.jpg
similarity index 100%
rename from docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_26_1.jpg
rename to docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_26_1.jpg
diff --git a/docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_26_1.png b/docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_26_1.png
similarity index 100%
rename from docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_26_1.png
rename to docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_26_1.png
diff --git a/docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_28_1.jpg b/docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_28_1.jpg
similarity index 100%
rename from docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_28_1.jpg
rename to docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_28_1.jpg
diff --git a/docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_28_1.png b/docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_28_1.png
similarity index 100%
rename from docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_28_1.png
rename to docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_28_1.png
diff --git a/docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_30_1.jpg b/docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_30_1.jpg
similarity index 100%
rename from docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_30_1.jpg
rename to docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_30_1.jpg
diff --git a/docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_30_1.png b/docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_30_1.png
similarity index 100%
rename from docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_30_1.png
rename to docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_30_1.png
diff --git a/docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_45_2.png b/docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_45_2.png
similarity index 100%
rename from docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_45_2.png
rename to docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_45_2.png
diff --git a/docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_47_2.png b/docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_47_2.png
similarity index 100%
rename from docs/notebooks/259-decidiffusion-image-generation-with-output_files/259-decidiffusion-image-generation-with-output_47_2.png
rename to docs/notebooks/decidiffusion-image-generation-with-output_files/decidiffusion-image-generation-with-output_47_2.png
diff --git a/docs/notebooks/238-deep-floyd-if-convert-with-output.rst b/docs/notebooks/deep-floyd-if-convert-with-output.rst
similarity index 86%
rename from docs/notebooks/238-deep-floyd-if-convert-with-output.rst
rename to docs/notebooks/deep-floyd-if-convert-with-output.rst
index 0dbdecfb961..499c29a6934 100644
--- a/docs/notebooks/238-deep-floyd-if-convert-with-output.rst
+++ b/docs/notebooks/deep-floyd-if-convert-with-output.rst
@@ -144,9 +144,9 @@ Install required packages.
     # Set up requirements
     
     %pip install -q --upgrade pip
-    %pip install -q transformers "diffusers>=0.16.1" accelerate safetensors sentencepiece huggingface_hub  --extra-index-url https://download.pytorch.org/whl/cpu
-    %pip install -q "openvino>=2023.3.0" opencv-python
-    %pip install -q gradio
+    %pip install -q "torch>2.1" transformers "diffusers>=0.16.1" accelerate safetensors sentencepiece huggingface_hub  --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2023.3.0" opencv-python tqdm
+    %pip install -q "gradio>=4.19"
 
 
 .. parsed-literal::
@@ -172,21 +172,28 @@ Install required packages.
     from diffusers import DiffusionPipeline
     import openvino as ov
     import torch
-    from utils import TextEncoder, UnetFirstStage, UnetSecondStage, convert_result_to_image, download_omz_model
+    from utils import (
+        TextEncoder,
+        UnetFirstStage,
+        UnetSecondStage,
+        convert_result_to_image,
+        download_omz_model,
+    )
 
 .. code:: ipython3
 
-    checkpoint_variant = 'fp16'
+    checkpoint_variant = "fp16"
     model_dtype = torch.float32
     ir_input_type = ov.Type.f32
     compress_to_fp16 = True
     
-    models_dir = Path('./models')
+    models_dir = Path("./models")
     models_dir.mkdir(exist_ok=True)
     
-    encoder_ir_path = models_dir / 'encoder_ir.xml'
-    first_stage_unet_ir_path = models_dir / 'unet_ir_I.xml'
-    second_stage_unet_ir_path = models_dir / 'unet_ir_II.xml'
+    encoder_ir_path = models_dir / "encoder_ir.xml"
+    first_stage_unet_ir_path = models_dir / "unet_ir_I.xml"
+    second_stage_unet_ir_path = models_dir / "unet_ir_II.xml"
+    
     
     def pt_to_pil(images):
         """
@@ -262,17 +269,13 @@ diffusion models. The code below demonstrates how to create a
 .. code:: ipython3
 
     # Downloading the model weights may take some time. The approximate total checkpoints size is 27GB.
-    stage_1 = DiffusionPipeline.from_pretrained(
-        "DeepFloyd/IF-I-M-v1.0",
-        variant=checkpoint_variant,
-        torch_dtype=model_dtype
-    )
+    stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-M-v1.0", variant=checkpoint_variant, torch_dtype=model_dtype)
     
     stage_2 = DiffusionPipeline.from_pretrained(
         "DeepFloyd/IF-II-M-v1.0",
         text_encoder=None,
         variant=checkpoint_variant,
-        torch_dtype=model_dtype
+        torch_dtype=model_dtype,
     )
 
 
@@ -505,8 +508,16 @@ resolution images.
     if not first_stage_unet_ir_path.exists():
         unet_1_ir = ov.convert_model(
             stage_1.unet,
-            example_input=(torch.ones(2, 3, 64, 64), torch.tensor(1).int(), torch.ones(2, 77, 4096)),
-            input=[((2, 3, 64, 64), ir_input_type), ((), ov.Type.i32), ((2, 77, 4096), ir_input_type)],
+            example_input=(
+                torch.ones(2, 3, 64, 64),
+                torch.tensor(1).int(),
+                torch.ones(2, 77, 4096),
+            ),
+            input=[
+                ((2, 3, 64, 64), ir_input_type),
+                ((), ov.Type.i32),
+                ((2, 77, 4096), ir_input_type),
+            ],
         )
     
         ov.save_model(unet_1_ir, first_stage_unet_ir_path, compress_to_fp16=compress_to_fp16)
@@ -603,8 +614,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -646,35 +657,44 @@ remains comparable with full FP16.
 .. code:: ipython3
 
     # Fetch `model_upcast_utils` which helps to restore accuracy when inferred on GPU
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/model_upcast_utils.py',
-        filename='model_upcast_utils.py'
+    import requests
+    
+    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/model_upcast_utils.py")
+    open("model_upcast_utils.py", "w").write(r.text)
+    
+    from model_upcast_utils import (
+        is_model_partially_upcasted,
+        partially_upcast_nodes_to_fp32,
     )
-    from model_upcast_utils import is_model_partially_upcasted, partially_upcast_nodes_to_fp32
     
     encoder_ov_model = core.read_model(encoder_ir_path)
-    if 'GPU' in core.available_devices and not is_model_partially_upcasted(encoder_ov_model):
-        example_input_prompt = 'ultra close color photo portrait of rainbow owl with deer horns in the woods'
+    if "GPU" in core.available_devices and not is_model_partially_upcasted(encoder_ov_model):
+        example_input_prompt = "ultra close color photo portrait of rainbow owl with deer horns in the woods"
         text_inputs = stage_1.tokenizer(example_input_prompt, max_length=77, padding="max_length", return_tensors="np")
-        upcasted_ov_model = partially_upcast_nodes_to_fp32(encoder_ov_model, text_inputs.input_ids, upcast_ratio=0.05,
-                                                           operation_types=["MatMul"], batch_size=10)
+        upcasted_ov_model = partially_upcast_nodes_to_fp32(
+            encoder_ov_model,
+            text_inputs.input_ids,
+            upcast_ratio=0.05,
+            operation_types=["MatMul"],
+            batch_size=10,
+        )
         del encoder_ov_model
         gc.collect()
     
         import os
+    
         os.remove(encoder_ir_path)
-        os.remove(str(encoder_ir_path).replace('.xml', '.bin'))
+        os.remove(str(encoder_ir_path).replace(".xml", ".bin"))
         ov.save_model(upcasted_ov_model, encoder_ir_path, compress_to_fp16=compress_to_fp16)
 
 .. code:: ipython3
 
-    prompt = 'ultra close color photo portrait of rainbow owl with deer horns in the woods'
-    negative_prompt = 'blurred unreal uncentered occluded'
+    prompt = "ultra close color photo portrait of rainbow owl with deer horns in the woods"
+    negative_prompt = "blurred unreal uncentered occluded"
     
     # Initialize TextEncoder wrapper class
     stage_1.text_encoder = TextEncoder(encoder_ir_path, dtype=model_dtype, device=device.value)
-    print('The model has been loaded')
+    print("The model has been loaded")
     
     # Generate text embeddings
     prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt, negative_prompt=negative_prompt)
@@ -722,20 +742,20 @@ First Stage diffusion block inference
     N_DIFFUSION_STEPS = 50
     
     # Initialize the First Stage UNet wrapper class
-    stage_1.unet = UnetFirstStage(
-        first_stage_unet_ir_path,
-        stage_1_config,
-        dtype=model_dtype,
-        device=device.value
-    )
-    print('The model has been loaded')
+    stage_1.unet = UnetFirstStage(first_stage_unet_ir_path, stage_1_config, dtype=model_dtype, device=device.value)
+    print("The model has been loaded")
     
     # Fix PRNG seed
     generator = torch.manual_seed(RANDOM_SEED)
     
     # Inference
-    image = stage_1(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds,
-                    generator=generator, output_type="pt", num_inference_steps=N_DIFFUSION_STEPS).images
+    image = stage_1(
+        prompt_embeds=prompt_embeds,
+        negative_prompt_embeds=negative_embeds,
+        generator=generator,
+        output_type="pt",
+        num_inference_steps=N_DIFFUSION_STEPS,
+    ).images
     
     # Delete the model to free up memory
     del stage_1.unet.unet_openvino
@@ -764,7 +784,7 @@ First Stage diffusion block inference
 
 
 
-.. image:: 238-deep-floyd-if-convert-with-output_files/238-deep-floyd-if-convert-with-output_29_3.png
+.. image:: deep-floyd-if-convert-with-output_files/deep-floyd-if-convert-with-output_29_3.png
 
 
 
@@ -776,17 +796,17 @@ Second Stage diffusion block inference
 .. code:: ipython3
 
     # Initialize the Second Stage UNet wrapper class
-    stage_2.unet = UnetSecondStage(
-        second_stage_unet_ir_path,
-        stage_2_config,
-        dtype=model_dtype,
-        device=device.value
-    )
-    print('The model has been loaded')
+    stage_2.unet = UnetSecondStage(second_stage_unet_ir_path, stage_2_config, dtype=model_dtype, device=device.value)
+    print("The model has been loaded")
     
     image = stage_2(
-        image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds,
-        generator=generator, output_type="pt", num_inference_steps=20).images
+        image=image,
+        prompt_embeds=prompt_embeds,
+        negative_prompt_embeds=negative_embeds,
+        generator=generator,
+        output_type="pt",
+        num_inference_steps=20,
+    ).images
     
     # Delete the model to free up memory
     del stage_2.unet.unet_openvino
@@ -816,7 +836,7 @@ Second Stage diffusion block inference
 
 
 
-.. image:: 238-deep-floyd-if-convert-with-output_files/238-deep-floyd-if-convert-with-output_31_3.png
+.. image:: deep-floyd-if-convert-with-output_files/deep-floyd-if-convert-with-output_31_3.png
 
 
 
@@ -835,7 +855,7 @@ Upscale the generated image using a Super Resolution network
 
 Though the third stage has not been officially released, we’ll employ
 the Super Resolution network from `Example
-#202 `__
+#202 `__
 to enhance our low-resolution result!
 
 Note, this step will be substituted with the Third IF stage upon its
@@ -869,10 +889,10 @@ Download the Super Resolution model weights
     import numpy as np
     
     # 1032: 4x superresolution, 1033: 3x superresolution
-    model_name = 'single-image-super-resolution-1032'
+    model_name = "single-image-super-resolution-1032"
     download_omz_model(model_name, models_dir)
     
-    sr_model_xml_path = models_dir / f'{model_name}.xml'
+    sr_model_xml_path = models_dir / f"{model_name}.xml"
 
 
 
@@ -900,10 +920,7 @@ Resolution model makes our target image size 1024x1024 pixel.
 .. code:: ipython3
 
     model = core.read_model(model=sr_model_xml_path)
-    model.reshape({
-        0: [1, 3, 256, 256],
-        1: [1, 3, 1024, 1024]
-    })
+    model.reshape({0: [1, 3, 256, 256], 1: [1, 3, 1024, 1024]})
     compiled_sr_model = core.compile_model(model=model, device_name=device.value)
 
 Prepare the input images and run the model
@@ -914,18 +931,14 @@ Prepare the input images and run the model
 .. code:: ipython3
 
     original_image = np.array(pil_image)
-    bicubic_image = cv2.resize(
-        src=original_image, dsize=(1024, 1024), interpolation=cv2.INTER_CUBIC
-    )
+    bicubic_image = cv2.resize(src=original_image, dsize=(1024, 1024), interpolation=cv2.INTER_CUBIC)
     
     # Reshape the images from (H,W,C) to (N,C,H,W) as expected by the model.
     input_image_original = np.expand_dims(original_image.transpose(2, 0, 1), axis=0)
     input_image_bicubic = np.expand_dims(bicubic_image.transpose(2, 0, 1), axis=0)
     
     # Model Inference
-    result = compiled_sr_model(
-        [input_image_original, input_image_bicubic]
-    )[compiled_sr_model.output(0)]
+    result = compiled_sr_model([input_image_original, input_image_bicubic])[compiled_sr_model.output(0)]
 
 Display the result
 ^^^^^^^^^^^^^^^^^^
@@ -940,7 +953,7 @@ Display the result
 
 
 
-.. image:: 238-deep-floyd-if-convert-with-output_files/238-deep-floyd-if-convert-with-output_41_0.png
+.. image:: deep-floyd-if-convert-with-output_files/deep-floyd-if-convert-with-output_41_0.png
 
 
 
@@ -956,52 +969,46 @@ package `__
 
     # Build up the pipeline
     stage_1.text_encoder = TextEncoder(encoder_ir_path, dtype=model_dtype, device=device.value)
-    print('The model has been loaded')
+    print("The model has been loaded")
     
-    stage_1.unet = UnetFirstStage(
-        first_stage_unet_ir_path,
-        stage_1_config,
-        dtype=model_dtype,
-        device=device.value
-    )
-    print('The Stage-1 UNet has been loaded')
+    stage_1.unet = UnetFirstStage(first_stage_unet_ir_path, stage_1_config, dtype=model_dtype, device=device.value)
+    print("The Stage-1 UNet has been loaded")
     
-    stage_2.unet = UnetSecondStage(
-        second_stage_unet_ir_path,
-        stage_2_config,
-        dtype=model_dtype,
-        device=device.value
-    )
-    print('The Stage-2 UNet has been loaded')
+    stage_2.unet = UnetSecondStage(second_stage_unet_ir_path, stage_2_config, dtype=model_dtype, device=device.value)
+    print("The Stage-2 UNet has been loaded")
     
     generator = torch.manual_seed(RANDOM_SEED)
     
+    
     # Combine the models' calls into a single `_generate` function
     def _generate(prompt, negative_prompt):
         # Text encoder inference
         prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt, negative_prompt=negative_prompt)
         # Stage-1 UNet Inference
         image = stage_1(
-            prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds,
-            generator=generator, output_type="pt", num_inference_steps=N_DIFFUSION_STEPS
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_embeds,
+            generator=generator,
+            output_type="pt",
+            num_inference_steps=N_DIFFUSION_STEPS,
         ).images
         # Stage-2 UNet Inference
         image = stage_2(
-            image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds,
-            generator=generator, output_type="pt", num_inference_steps=20
+            image=image,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_embeds,
+            generator=generator,
+            output_type="pt",
+            num_inference_steps=20,
         ).images
         # Infer Super Resolution model
         original_image = np.array(pt_to_pil(image)[0])
-        bicubic_image = cv2.resize(
-            src=original_image, dsize=(1024, 1024), interpolation=cv2.INTER_CUBIC
-        )
+        bicubic_image = cv2.resize(src=original_image, dsize=(1024, 1024), interpolation=cv2.INTER_CUBIC)
         # Reshape the images from (H,W,C) to (N,C,H,W) as expected by the model.
         input_image_original = np.expand_dims(original_image.transpose(2, 0, 1), axis=0)
         input_image_bicubic = np.expand_dims(bicubic_image.transpose(2, 0, 1), axis=0)
         # Model Inference
-        result = compiled_sr_model(
-            [input_image_original, input_image_bicubic]
-        )[compiled_sr_model.output(0)]
+        result = compiled_sr_model([input_image_original, input_image_bicubic])[compiled_sr_model.output(0)]
         return convert_result_to_image(result)
 
 
@@ -1022,17 +1029,15 @@ package `__
             gr.Textbox(label="Text Prompt"),
             gr.Textbox(label="Negative Text Prompt"),
         ],
-        outputs=[
-            "image"
-        ],
+        outputs=["image"],
         examples=[
             [
                 "ultra close color photo portrait of rainbow owl with deer horns in the woods",
-                "blurred unreal uncentered occluded"
+                "blurred unreal uncentered occluded",
             ],
             [
                 "A scaly mischievous dragon is driving the car in a street art style",
-                "blurred uncentered occluded"
+                "blurred uncentered occluded",
             ],
         ],
     )
@@ -1050,8 +1055,7 @@ Next steps
 
 
 
-Open the
-`238-deep-floyd-if-optimize <238-deep-floyd-if-optimize-with-output.html>`__
+Open the `deep-floyd-if-optimize `__
 notebook to quantize stage 1 and stage 2 U-Net models with the
 Post-training Quantization API of NNCF and compress weights of the text
 encoder. Then compare the converted and optimized OpenVINO models.
diff --git a/docs/notebooks/238-deep-floyd-if-convert-with-output_files/238-deep-floyd-if-convert-with-output_29_3.png b/docs/notebooks/deep-floyd-if-convert-with-output_files/deep-floyd-if-convert-with-output_29_3.png
similarity index 100%
rename from docs/notebooks/238-deep-floyd-if-convert-with-output_files/238-deep-floyd-if-convert-with-output_29_3.png
rename to docs/notebooks/deep-floyd-if-convert-with-output_files/deep-floyd-if-convert-with-output_29_3.png
diff --git a/docs/notebooks/238-deep-floyd-if-convert-with-output_files/238-deep-floyd-if-convert-with-output_31_3.png b/docs/notebooks/deep-floyd-if-convert-with-output_files/deep-floyd-if-convert-with-output_31_3.png
similarity index 100%
rename from docs/notebooks/238-deep-floyd-if-convert-with-output_files/238-deep-floyd-if-convert-with-output_31_3.png
rename to docs/notebooks/deep-floyd-if-convert-with-output_files/deep-floyd-if-convert-with-output_31_3.png
diff --git a/docs/notebooks/238-deep-floyd-if-convert-with-output_files/238-deep-floyd-if-convert-with-output_41_0.png b/docs/notebooks/deep-floyd-if-convert-with-output_files/deep-floyd-if-convert-with-output_41_0.png
similarity index 100%
rename from docs/notebooks/238-deep-floyd-if-convert-with-output_files/238-deep-floyd-if-convert-with-output_41_0.png
rename to docs/notebooks/deep-floyd-if-convert-with-output_files/deep-floyd-if-convert-with-output_41_0.png
diff --git a/docs/notebooks/238-deep-floyd-if-optimize-with-output.rst b/docs/notebooks/deep-floyd-if-optimize-with-output.rst
similarity index 84%
rename from docs/notebooks/238-deep-floyd-if-optimize-with-output.rst
rename to docs/notebooks/deep-floyd-if-optimize-with-output.rst
index eec8cffe1de..89d976895e2 100644
--- a/docs/notebooks/238-deep-floyd-if-optimize-with-output.rst
+++ b/docs/notebooks/deep-floyd-if-optimize-with-output.rst
@@ -7,17 +7,16 @@ applying 8-bit post-training quantization and weights compression from
 Compression Framework) and infer optimized model via OpenVINO™ Toolkit.
 
    **NOTE**: you should run
-   `238-deep-floyd-if-convert <238-deep-floyd-if-convert-with-output.html>`__
-   notebook first to generate OpenVINO IR model that is used for
-   optimization.
+   `deep-floyd-if-convert `__ notebook
+   first to generate OpenVINO IR model that is used for optimization.
 
 The optimization process contains the following steps: 1. Compress
 weights of the converted OpenVINO text encoder from
-`notebook <238-deep-floyd-if-convert-with-output.html>`__ with NNCF. 2. Quantize
-the converted stage_1 and stage_2 U-Nets from
-`notebook <238-deep-floyd-if-convert-with-output.html>`__ with NNCF. 2. Check the
-model result using the same input data from the
-`notebook <238-deep-floyd-if-convert-with-output.html>`__. 3. Compare model size of
+`notebook `__ with NNCF. 2. Quantize the
+converted stage_1 and stage_2 U-Nets from
+`notebook `__ with NNCF. 2. Check the model
+result using the same input data from the
+`notebook `__. 3. Compare model size of
 converted and optimized models. 4. Compare performance of converted and
 optimized models.
 
@@ -45,7 +44,7 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q datasets "nncf>=2.6.0"
+    %pip install -q datasets "nncf>=2.6.0" "torch>=2.1" tqdm
 
 .. code:: ipython3
 
@@ -60,7 +59,7 @@ Prerequisites
 
     from utils import TextEncoder, UnetFirstStage, UnetSecondStage
 
-    checkpoint_variant = 'fp16'
+    checkpoint_variant = "fp16"
     model_dtype = torch.float32
     RANDOM_SEED = 42
     N_DIFFUSION_STEPS = 50
@@ -76,13 +75,13 @@ Prerequisites
 
 .. code:: ipython3
 
-    MODEL_DIR = Path('./models')
+    MODEL_DIR = Path("./models")
     TEXT_ENCODER_IR_PATH = MODEL_DIR / "encoder_ir.xml"
     UNET_I_IR_PATH = MODEL_DIR / "unet_ir_I.xml"
     UNET_II_IR_PATH = MODEL_DIR / "unet_ir_II.xml"
 
     if not (TEXT_ENCODER_IR_PATH.exists() and UNET_I_IR_PATH.exists() and UNET_II_IR_PATH.exists()):
-        raise RuntimeError('This notebook should be run after 238-deep-floyd-if notebook')
+        raise RuntimeError("This notebook should be run after deep-floyd-if notebook")
 
 .. code:: ipython3
 
@@ -90,8 +89,8 @@ Prerequisites
 
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
 
@@ -167,16 +166,34 @@ contains over 2 billion objects.
 
     np.random.seed(RANDOM_SEED)
 
+
     def get_negative_prompt():
         negative_prompts = [
-            "amateur", "blurred", "deformed", "disfigured", "disgusting", "jpeg artifacts", "low contrast",
-            "low quality", "low saturation", "mangled", "morbid", "mutilated", "mutation",
-            "out of frame", "out of frame", "ugly", "uncentered", "underexposed", "unreal",
+            "amateur",
+            "blurred",
+            "deformed",
+            "disfigured",
+            "disgusting",
+            "jpeg artifacts",
+            "low contrast",
+            "low quality",
+            "low saturation",
+            "mangled",
+            "morbid",
+            "mutilated",
+            "mutation",
+            "out of frame",
+            "out of frame",
+            "ugly",
+            "uncentered",
+            "underexposed",
+            "unreal",
         ]
         num_elements = np.random.randint(2, 6)
         random_elements = np.random.choice(negative_prompts, num_elements)
         return [" ".join(random_elements)]
 
+
     def prepare_calibration_data(dataloader, stage_1):
         """
         This function prepares calibration data from a dataloader for a specified number of initialization steps.
@@ -210,11 +227,7 @@ contains over 2 billion objects.
     selection_prob = 0.5
     prompts_number = np.ceil(opt_init_steps // (min(N_DIFFUSION_STEPS, UNET_2_STEPS) * selection_prob))
 
-    stage_1 = DiffusionPipeline.from_pretrained(
-        "DeepFloyd/IF-I-M-v1.0",
-        variant=checkpoint_variant,
-        torch_dtype=model_dtype
-    )
+    stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-M-v1.0", variant=checkpoint_variant, torch_dtype=model_dtype)
     encoded_prompts = prepare_dataset(stage_1, int(prompts_number))
 
 
@@ -290,12 +303,7 @@ To collect intermediate model inputs for calibration we should customize
 
 .. code:: ipython3
 
-    stage_1.unet = UnetFirstStage(
-        UNET_I_IR_PATH,
-        stage_1.unet.config,
-        dtype=model_dtype,
-        device=device.value
-    )
+    stage_1.unet = UnetFirstStage(UNET_I_IR_PATH, stage_1.unet.config, dtype=model_dtype, device=device.value)
     stage_1.set_progress_bar_config(disable=True)
 
     stage_1_data_cache = []
@@ -305,8 +313,13 @@ To collect intermediate model inputs for calibration we should customize
     stage_2_inputs = []  # to speed up dataset preparation for stage 2 U-Net we can collect several images below
     for data in encoded_prompts:
         prompt_embeds, negative_embeds = data
-        image = stage_1(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds,
-                        generator=generator, output_type="pt", num_inference_steps=N_DIFFUSION_STEPS).images
+        image = stage_1(
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_embeds,
+            generator=generator,
+            output_type="pt",
+            num_inference_steps=N_DIFFUSION_STEPS,
+        ).images
         stage_2_inputs.append((image, prompt_embeds, negative_embeds))
 
         if len(stage_1_data_cache) >= opt_init_steps:
@@ -328,7 +341,7 @@ Quantize first stage U-Net
         model=ov_model,
         calibration_dataset=stage_1_calibration_dataset,
         model_type=nncf.ModelType.TRANSFORMER,
-        advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alpha=0.25)
+        advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alpha=0.25),
     )
 
     UNET_I_INT8_PATH = "_optimized.".join(UNET_I_IR_PATH.as_posix().split("."))
@@ -358,8 +371,13 @@ Quantize first stage U-Net
     start = len(stage_2_inputs)
     for i, data in tqdm(enumerate(encoded_prompts[start:])):
         prompt_embeds, negative_embeds = data
-        image = stage_1(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds,
-                        generator=generator, output_type="pt", num_inference_steps=N_DIFFUSION_STEPS).images
+        image = stage_1(
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_embeds,
+            generator=generator,
+            output_type="pt",
+            num_inference_steps=N_DIFFUSION_STEPS,
+        ).images
         stage_2_inputs.append((image, prompt_embeds, negative_embeds))
 
 
@@ -386,23 +404,24 @@ Quantize first stage U-Net
         "DeepFloyd/IF-II-M-v1.0",
         text_encoder=None,
         variant=checkpoint_variant,
-        torch_dtype=model_dtype
+        torch_dtype=model_dtype,
     )
     stage_2.set_progress_bar_config(disable=True)
 
-    stage_2.unet = UnetSecondStage(
-        UNET_II_IR_PATH,
-        stage_2.unet.config,
-        dtype=model_dtype,
-        device=device.value
-    )
+    stage_2.unet = UnetSecondStage(UNET_II_IR_PATH, stage_2.unet.config, dtype=model_dtype, device=device.value)
     stage_2_data_cache = []
     stage_2.unet.unet_openvino = CompiledModelDecorator(stage_2.unet.unet_openvino, prob=selection_prob, data_cache=stage_2_data_cache)
 
     for data in tqdm(stage_2_inputs):
         image, prompt_embeds, negative_embeds = data
-        image = stage_2(image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds,
-                        generator=generator, output_type="pt", num_inference_steps=UNET_2_STEPS).images
+        image = stage_2(
+            image=image,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_embeds,
+            generator=generator,
+            output_type="pt",
+            num_inference_steps=UNET_2_STEPS,
+        ).images
 
         if len(stage_2_data_cache) >= opt_init_steps:
             break
@@ -479,33 +498,24 @@ Run optimized OpenVINO model
 
 Let us check predictions with the optimized OpenVINO DeepFloyd IF model
 result using the same input data from the `1st
-notebook <238-deep-floyd-if-with-output.html>`__.
+notebook `__.
 
 .. code:: ipython3
 
-    prompt = 'ultra close color photo portrait of rainbow owl with deer horns in the woods'
-    negative_prompt = 'blurred unreal uncentered occluded'
+    prompt = "ultra close color photo portrait of rainbow owl with deer horns in the woods"
+    negative_prompt = "blurred unreal uncentered occluded"
 
 .. code:: ipython3
 
     %%time
 
-    stage_1 = DiffusionPipeline.from_pretrained(
-        "DeepFloyd/IF-I-M-v1.0",
-        variant=checkpoint_variant,
-        torch_dtype=model_dtype
-    )
+    stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-M-v1.0", variant=checkpoint_variant, torch_dtype=model_dtype)
 
     # Initialize the First Stage U-Net wrapper class
-    stage_1.unet = UnetFirstStage(
-        UNET_I_INT8_PATH,
-        stage_1.unet.config,
-        dtype=model_dtype,
-        device=device.value
-    )
+    stage_1.unet = UnetFirstStage(UNET_I_INT8_PATH, stage_1.unet.config, dtype=model_dtype, device=device.value)
 
     stage_1.text_encoder = TextEncoder(TEXT_ENCODER_INT8_IR_PATH, dtype=model_dtype, device=device.value)
-    print('The model has been loaded')
+    print("The model has been loaded")
 
     # Generate text embeddings
     prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt, negative_prompt=negative_prompt)
@@ -514,8 +524,13 @@ notebook <238-deep-floyd-if-with-output.html>`__.
     generator = torch.manual_seed(RANDOM_SEED)
 
     # Inference
-    image = stage_1(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds,
-                    generator=generator, output_type="pt", num_inference_steps=N_DIFFUSION_STEPS).images
+    image = stage_1(
+        prompt_embeds=prompt_embeds,
+        negative_prompt_embeds=negative_embeds,
+        generator=generator,
+        output_type="pt",
+        num_inference_steps=N_DIFFUSION_STEPS,
+    ).images
 
     # Show the image
     pt_to_pil(image)[0]
@@ -569,7 +584,7 @@ notebook <238-deep-floyd-if-with-output.html>`__.
 
 
 
-.. image:: 238-deep-floyd-if-optimize-with-output_files/238-deep-floyd-if-optimize-with-output_23_6.png
+.. image:: deep-floyd-if-optimize-with-output_files/deep-floyd-if-optimize-with-output_23_6.png
 
 
 
@@ -581,21 +596,21 @@ notebook <238-deep-floyd-if-with-output.html>`__.
         "DeepFloyd/IF-II-M-v1.0",
         text_encoder=None,
         variant=checkpoint_variant,
-        torch_dtype=model_dtype
+        torch_dtype=model_dtype,
     )
 
     # Initialize the Second Stage U-Net wrapper class
-    stage_2.unet = UnetSecondStage(
-        UNET_II_INT8_PATH,
-        stage_2.unet.config,
-        dtype=model_dtype,
-        device=device.value
-    )
-    print('The model has been loaded')
+    stage_2.unet = UnetSecondStage(UNET_II_INT8_PATH, stage_2.unet.config, dtype=model_dtype, device=device.value)
+    print("The model has been loaded")
 
     image = stage_2(
-        image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds,
-        generator=generator, output_type="pt", num_inference_steps=UNET_2_STEPS).images
+        image=image,
+        prompt_embeds=prompt_embeds,
+        negative_prompt_embeds=negative_embeds,
+        generator=generator,
+        output_type="pt",
+        num_inference_steps=UNET_2_STEPS,
+    ).images
 
     # Show the image
     pil_image = pt_to_pil(image)[0]
@@ -643,42 +658,34 @@ notebook <238-deep-floyd-if-with-output.html>`__.
 
 
 
-.. image:: 238-deep-floyd-if-optimize-with-output_files/238-deep-floyd-if-optimize-with-output_24_5.png
+.. image:: deep-floyd-if-optimize-with-output_files/deep-floyd-if-optimize-with-output_24_5.png
 
 
 
 .. code:: ipython3
 
-
     import cv2
     import numpy as np
     from utils import convert_result_to_image, download_omz_model
 
     # 1032: 4x superresolution, 1033: 3x superresolution
-    model_name = 'single-image-super-resolution-1032'
+    model_name = "single-image-super-resolution-1032"
     download_omz_model(model_name, MODEL_DIR)
 
-    sr_model_xml_path = MODEL_DIR / f'{model_name}.xml'
+    sr_model_xml_path = MODEL_DIR / f"{model_name}.xml"
     model = core.read_model(model=sr_model_xml_path)
-    model.reshape({
-        0: [1, 3, 256, 256],
-        1: [1, 3, 1024, 1024]
-    })
+    model.reshape({0: [1, 3, 256, 256], 1: [1, 3, 1024, 1024]})
     compiled_sr_model = core.compile_model(model=model, device_name=device.value)
 
     original_image = np.array(pil_image)
-    bicubic_image = cv2.resize(
-        src=original_image, dsize=(1024, 1024), interpolation=cv2.INTER_CUBIC
-    )
+    bicubic_image = cv2.resize(src=original_image, dsize=(1024, 1024), interpolation=cv2.INTER_CUBIC)
 
     # Reshape the images from (H,W,C) to (N,C,H,W) as expected by the model.
     input_image_original = np.expand_dims(original_image.transpose(2, 0, 1), axis=0)
     input_image_bicubic = np.expand_dims(bicubic_image.transpose(2, 0, 1), axis=0)
 
     # Model Inference
-    result = compiled_sr_model(
-        [input_image_original, input_image_bicubic]
-    )[compiled_sr_model.output(0)]
+    result = compiled_sr_model([input_image_original, input_image_bicubic])[compiled_sr_model.output(0)]
 
     img = convert_result_to_image(result)
     img
@@ -691,14 +698,15 @@ notebook <238-deep-floyd-if-with-output.html>`__.
 
 
 
-.. image:: 238-deep-floyd-if-optimize-with-output_files/238-deep-floyd-if-optimize-with-output_25_1.png
+.. image:: deep-floyd-if-optimize-with-output_files/deep-floyd-if-optimize-with-output_25_1.png
 
 
+..
 
-**NOTE**: Accuracy of quantized models can generally be improved by
-increasing calibration dataset size. For U-Net models, you can
-collect a more diverse dataset by using a smaller ``selection_prob``
-value, but this will increase the dataset collection time.
+   **NOTE**: Accuracy of quantized models can generally be improved by
+   increasing calibration dataset size. For U-Net models, you can
+   collect a more diverse dataset by using a smaller ``selection_prob``
+   value, but this will increase the dataset collection time.
 
 Compare file sizes
 ^^^^^^^^^^^^^^^^^^
@@ -759,8 +767,9 @@ Tool =2023.3.0" "datasets>=2.14.6" "nncf"
-    %pip install -q "typing-extensions>=4.9.0" eval-type-backport
+    %pip install -q "openvino>=2023.3.0" "datasets>=2.14.6" "nncf" "tqdm"
+    %pip install -q "typing-extensions>=4.9.0" eval-type-backport "gradio>=4.19"
     %pip install -q -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
     
-    if platform.python_version_tuple()[1] == "8": 
+    if platform.python_version_tuple()[1] in ["8", "9"]:
         %pip install -q "gradio-imageslider<=0.0.17" "typing-extensions>=4.9.0"
 
 
@@ -79,468 +79,478 @@ Prerequisites
 
 .. parsed-literal::
 
-    remote: Enumerating objects: 412, done.
-    remote: Counting objects:   0% (1/135)
-remote: Counting objects:   1% (2/135)
-remote: Counting objects:   2% (3/135)
-remote: Counting objects:   3% (5/135)
-remote: Counting objects:   4% (6/135)
-remote: Counting objects:   5% (7/135)
-remote: Counting objects:   6% (9/135)
-remote: Counting objects:   7% (10/135)
-remote: Counting objects:   8% (11/135)
-remote: Counting objects:   9% (13/135)
-remote: Counting objects:  10% (14/135)
-remote: Counting objects:  11% (15/135)
-remote: Counting objects:  12% (17/135)
-remote: Counting objects:  13% (18/135)
-remote: Counting objects:  14% (19/135)
-remote: Counting objects:  15% (21/135)
-remote: Counting objects:  16% (22/135)
-remote: Counting objects:  17% (23/135)
-remote: Counting objects:  18% (25/135)
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-remote: Counting objects:  59% (80/135)
-remote: Counting objects:  60% (81/135)
-remote: Counting objects:  61% (83/135)
-remote: Counting objects:  62% (84/135)
-remote: Counting objects:  63% (86/135)
-remote: Counting objects:  64% (87/135)
-remote: Counting objects:  65% (88/135)
-remote: Counting objects:  66% (90/135)
-remote: Counting objects:  67% (91/135)
-remote: Counting objects:  68% (92/135)
-remote: Counting objects:  69% (94/135)
-remote: Counting objects:  70% (95/135)
-remote: Counting objects:  71% (96/135)
-remote: Counting objects:  72% (98/135)
-remote: Counting objects:  73% (99/135)
-remote: Counting objects:  74% (100/135)
-remote: Counting objects:  75% (102/135)
-remote: Counting objects:  76% (103/135)
-remote: Counting objects:  77% (104/135)
-remote: Counting objects:  78% (106/135)
-remote: Counting objects:  79% (107/135)
-remote: Counting objects:  80% (108/135)
-remote: Counting objects:  81% (110/135)
-remote: Counting objects:  82% (111/135)
-remote: Counting objects:  83% (113/135)
-remote: Counting objects:  84% (114/135)
-remote: Counting objects:  85% (115/135)
-remote: Counting objects:  86% (117/135)
-remote: Counting objects:  87% (118/135)
-remote: Counting objects:  88% (119/135)
-remote: Counting objects:  89% (121/135)
-remote: Counting objects:  90% (122/135)
-remote: Counting objects:  91% (123/135)
-remote: Counting objects:  92% (125/135)
-remote: Counting objects:  93% (126/135)
-remote: Counting objects:  94% (127/135)
-remote: Counting objects:  95% (129/135)
-remote: Counting objects:  96% (130/135)
-remote: Counting objects:  97% (131/135)
-remote: Counting objects:  98% (133/135)
-remote: Counting objects:  99% (134/135)
-remote: Counting objects: 100% (135/135)
-remote: Counting objects: 100% (135/135), done.
-    remote: Compressing objects:   1% (1/96)
-remote: Compressing objects:   2% (2/96)
-remote: Compressing objects:   3% (3/96)
-remote: Compressing objects:   4% (4/96)
-remote: Compressing objects:   5% (5/96)
-remote: Compressing objects:   6% (6/96)
-remote: Compressing objects:   7% (7/96)
-remote: Compressing objects:   8% (8/96)
-remote: Compressing objects:   9% (9/96)
-remote: Compressing objects:  10% (10/96)
-remote: Compressing objects:  11% (11/96)
-remote: Compressing objects:  12% (12/96)
-remote: Compressing objects:  13% (13/96)
-remote: Compressing objects:  14% (14/96)
-remote: Compressing objects:  15% (15/96)
-remote: Compressing objects:  16% (16/96)
-remote: Compressing objects:  17% (17/96)
-remote: Compressing objects:  18% (18/96)
-remote: Compressing objects:  19% (19/96)
-remote: Compressing objects:  20% (20/96)
-remote: Compressing objects:  21% (21/96)
-remote: Compressing objects:  22% (22/96)
-remote: Compressing objects:  23% (23/96)
-remote: Compressing objects:  25% (24/96)
-remote: Compressing objects:  26% (25/96)
-remote: Compressing objects:  27% (26/96)
-remote: Compressing objects:  28% (27/96)
-remote: Compressing objects:  29% (28/96)
-remote: Compressing objects:  30% (29/96)
-remote: Compressing objects:  31% (30/96)
-remote: Compressing objects:  32% (31/96)
-remote: Compressing objects:  33% (32/96)
-remote: Compressing objects:  34% (33/96)
-remote: Compressing objects:  35% (34/96)
-remote: Compressing objects:  36% (35/96)
-remote: Compressing objects:  37% (36/96)
-remote: Compressing objects:  38% (37/96)
-remote: Compressing objects:  39% (38/96)
-remote: Compressing objects:  40% (39/96)
-remote: Compressing objects:  41% (40/96)
-remote: Compressing objects:  42% (41/96)
-remote: Compressing objects:  43% (42/96)
-remote: Compressing objects:  44% (43/96)
-remote: Compressing objects:  45% (44/96)
-remote: Compressing objects:  46% (45/96)
-remote: Compressing objects:  47% (46/96)
-remote: Compressing objects:  48% (47/96)
-remote: Compressing objects:  50% (48/96)
-remote: Compressing objects:  51% (49/96)
-remote: Compressing objects:  52% (50/96)
-remote: Compressing objects:  53% (51/96)
-remote: Compressing objects:  54% (52/96)
-remote: Compressing objects:  55% (53/96)
-remote: Compressing objects:  56% (54/96)
-remote: Compressing objects:  57% (55/96)
-remote: Compressing objects:  58% (56/96)
-remote: Compressing objects:  59% (57/96)
-remote: Compressing objects:  60% (58/96)
-remote: Compressing objects:  61% (59/96)
-remote: Compressing objects:  62% (60/96)
-remote: Compressing objects:  63% (61/96)
-remote: Compressing objects:  64% (62/96)
-remote: Compressing objects:  65% (63/96)
-remote: Compressing objects:  66% (64/96)
-remote: Compressing objects:  67% (65/96)
-remote: Compressing objects:  68% (66/96)
-remote: Compressing objects:  69% (67/96)
-remote: Compressing objects:  70% (68/96)
-remote: Compressing objects:  71% (69/96)
-remote: Compressing objects:  72% (70/96)
-remote: Compressing objects:  73% (71/96)
-remote: Compressing objects:  75% (72/96)
-remote: Compressing objects:  76% (73/96)
-remote: Compressing objects:  77% (74/96)
-remote: Compressing objects:  78% (75/96)
-remote: Compressing objects:  79% (76/96)
-remote: Compressing objects:  80% (77/96)
-remote: Compressing objects:  81% (78/96)
-remote: Compressing objects:  82% (79/96)
-remote: Compressing objects:  83% (80/96)
-remote: Compressing objects:  84% (81/96)
-remote: Compressing objects:  85% (82/96)
-remote: Compressing objects:  86% (83/96)
-remote: Compressing objects:  87% (84/96)
-remote: Compressing objects:  88% (85/96)
-remote: Compressing objects:  89% (86/96)
-remote: Compressing objects:  90% (87/96)
-remote: Compressing objects:  91% (88/96)
-remote: Compressing objects:  92% (89/96)
-remote: Compressing objects:  93% (90/96)
-remote: Compressing objects:  94% (91/96)
-remote: Compressing objects:  95% (92/96)
-remote: Compressing objects:  96% (93/96)
-remote: Compressing objects:  97% (94/96)
-remote: Compressing objects:  98% (95/96)
-remote: Compressing objects: 100% (96/96)
-remote: Compressing objects: 100% (96/96), done.
+    remote: Enumerating objects: 421, done.
+    remote: Counting objects:   0% (1/144)
+remote: Counting objects:   1% (2/144)
+remote: Counting objects:   2% (3/144)
+remote: Counting objects:   3% (5/144)
+remote: Counting objects:   4% (6/144)
+remote: Counting objects:   5% (8/144)
+remote: Counting objects:   6% (9/144)
+remote: Counting objects:   7% (11/144)
+remote: Counting objects:   8% (12/144)
+remote: Counting objects:   9% (13/144)
+remote: Counting objects:  10% (15/144)
+remote: Counting objects:  11% (16/144)
+remote: Counting objects:  12% (18/144)
+remote: Counting objects:  13% (19/144)
+remote: Counting objects:  14% (21/144)
+remote: Counting objects:  15% (22/144)
+remote: Counting objects:  16% (24/144)
+remote: Counting objects:  17% (25/144)
+remote: Counting objects:  18% (26/144)
+remote: Counting objects:  19% (28/144)
+remote: Counting objects:  20% (29/144)
+remote: Counting objects:  21% (31/144)
+remote: Counting objects:  22% (32/144)
+remote: Counting objects:  23% (34/144)
+remote: Counting objects:  24% (35/144)
+remote: Counting objects:  25% (36/144)
+remote: Counting objects:  26% (38/144)
+remote: Counting objects:  27% (39/144)
+remote: Counting objects:  28% (41/144)
+remote: Counting objects:  29% (42/144)
+remote: Counting objects:  30% (44/144)
+remote: Counting objects:  31% (45/144)
+remote: Counting objects:  32% (47/144)
+remote: Counting objects:  33% (48/144)
+remote: Counting objects:  34% (49/144)
+remote: Counting objects:  35% (51/144)
+remote: Counting objects:  36% (52/144)
+remote: Counting objects:  37% (54/144)
+remote: Counting objects:  38% (55/144)
+remote: Counting objects:  39% (57/144)
+remote: Counting objects:  40% (58/144)
+remote: Counting objects:  41% (60/144)
+remote: Counting objects:  42% (61/144)
+remote: Counting objects:  43% (62/144)
+remote: Counting objects:  44% (64/144)
+remote: Counting objects:  45% (65/144)
+remote: Counting objects:  46% (67/144)
+remote: Counting objects:  47% (68/144)
+remote: Counting objects:  48% (70/144)
+remote: Counting objects:  49% (71/144)
+remote: Counting objects:  50% (72/144)
+remote: Counting objects:  51% (74/144)
+remote: Counting objects:  52% (75/144)
+remote: Counting objects:  53% (77/144)
+remote: Counting objects:  54% (78/144)
+remote: Counting objects:  55% (80/144)
+remote: Counting objects:  56% (81/144)
+remote: Counting objects:  57% (83/144)
+remote: Counting objects:  58% (84/144)
+remote: Counting objects:  59% (85/144)
+remote: Counting objects:  60% (87/144)
+remote: Counting objects:  61% (88/144)
+remote: Counting objects:  62% (90/144)
+remote: Counting objects:  63% (91/144)
+remote: Counting objects:  64% (93/144)
+remote: Counting objects:  65% (94/144)
+remote: Counting objects:  66% (96/144)
+remote: Counting objects:  67% (97/144)
+remote: Counting objects:  68% (98/144)
+remote: Counting objects:  69% (100/144)
+remote: Counting objects:  70% (101/144)
+remote: Counting objects:  71% (103/144)
+remote: Counting objects:  72% (104/144)
+remote: Counting objects:  73% (106/144)
+remote: Counting objects:  74% (107/144)
+remote: Counting objects:  75% (108/144)
+remote: Counting objects:  76% (110/144)
+remote: Counting objects:  77% (111/144)
+remote: Counting objects:  78% (113/144)
+remote: Counting objects:  79% (114/144)
+remote: Counting objects:  80% (116/144)
+remote: Counting objects:  81% (117/144)
+remote: Counting objects:  82% (119/144)
+remote: Counting objects:  83% (120/144)
+remote: Counting objects:  84% (121/144)
+remote: Counting objects:  85% (123/144)
+remote: Counting objects:  86% (124/144)
+remote: Counting objects:  87% (126/144)
+remote: Counting objects:  88% (127/144)
+remote: Counting objects:  89% (129/144)
+remote: Counting objects:  90% (130/144)
+remote: Counting objects:  91% (132/144)
+remote: Counting objects:  92% (133/144)
+remote: Counting objects:  93% (134/144)
+remote: Counting objects:  94% (136/144)
+remote: Counting objects:  95% (137/144)
+remote: Counting objects:  96% (139/144)
+remote: Counting objects:  97% (140/144)
+remote: Counting objects:  98% (142/144)
+remote: Counting objects:  99% (143/144)
+remote: Counting objects: 100% (144/144)
+remote: Counting objects: 100% (144/144), done.
+    remote: Compressing objects:   0% (1/105)
+remote: Compressing objects:   1% (2/105)
+remote: Compressing objects:   2% (3/105)
+remote: Compressing objects:   3% (4/105)
+remote: Compressing objects:   4% (5/105)
+remote: Compressing objects:   5% (6/105)
+remote: Compressing objects:   6% (7/105)
+remote: Compressing objects:   7% (8/105)
+remote: Compressing objects:   8% (9/105)
+remote: Compressing objects:   9% (10/105)
+remote: Compressing objects:  10% (11/105)
+remote: Compressing objects:  11% (12/105)
+remote: Compressing objects:  12% (13/105)
+remote: Compressing objects:  13% (14/105)
+remote: Compressing objects:  14% (15/105)
+remote: Compressing objects:  15% (16/105)
+remote: Compressing objects:  16% (17/105)
+remote: Compressing objects:  17% (18/105)
+remote: Compressing objects:  18% (19/105)
+remote: Compressing objects:  19% (20/105)
+remote: Compressing objects:  20% (21/105)
+remote: Compressing objects:  21% (23/105)
+remote: Compressing objects:  22% (24/105)
+remote: Compressing objects:  23% (25/105)
+remote: Compressing objects:  24% (26/105)
+remote: Compressing objects:  25% (27/105)
+remote: Compressing objects:  26% (28/105)
+remote: Compressing objects:  27% (29/105)
+remote: Compressing objects:  28% (30/105)
+remote: Compressing objects:  29% (31/105)
+remote: Compressing objects:  30% (32/105)
+remote: Compressing objects:  31% (33/105)
+remote: Compressing objects:  32% (34/105)
+remote: Compressing objects:  33% (35/105)
+remote: Compressing objects:  34% (36/105)
+remote: Compressing objects:  35% (37/105)
+remote: Compressing objects:  36% (38/105)
+remote: Compressing objects:  37% (39/105)
+remote: Compressing objects:  38% (40/105)
+remote: Compressing objects:  39% (41/105)
+remote: Compressing objects:  40% (42/105)
+remote: Compressing objects:  41% (44/105)
+remote: Compressing objects:  42% (45/105)
+remote: Compressing objects:  43% (46/105)
+remote: Compressing objects:  44% (47/105)
+remote: Compressing objects:  45% (48/105)
+remote: Compressing objects:  46% (49/105)
+remote: Compressing objects:  47% (50/105)
+remote: Compressing objects:  48% (51/105)
+remote: Compressing objects:  49% (52/105)
+remote: Compressing objects:  50% (53/105)
+remote: Compressing objects:  51% (54/105)
+remote: Compressing objects:  52% (55/105)
+remote: Compressing objects:  53% (56/105)
+remote: Compressing objects:  54% (57/105)
+remote: Compressing objects:  55% (58/105)
+remote: Compressing objects:  56% (59/105)
+remote: Compressing objects:  57% (60/105)
+remote: Compressing objects:  58% (61/105)
+remote: Compressing objects:  59% (62/105)
+remote: Compressing objects:  60% (63/105)
+remote: Compressing objects:  61% (65/105)
+remote: Compressing objects:  62% (66/105)
+remote: Compressing objects:  63% (67/105)
+remote: Compressing objects:  64% (68/105)
+remote: Compressing objects:  65% (69/105)
+remote: Compressing objects:  66% (70/105)
+remote: Compressing objects:  67% (71/105)
+remote: Compressing objects:  68% (72/105)
+remote: Compressing objects:  69% (73/105)
+remote: Compressing objects:  70% (74/105)
+remote: Compressing objects:  71% (75/105)
+remote: Compressing objects:  72% (76/105)
+remote: Compressing objects:  73% (77/105)
+remote: Compressing objects:  74% (78/105)
+remote: Compressing objects:  75% (79/105)
+remote: Compressing objects:  76% (80/105)
+remote: Compressing objects:  77% (81/105)
+remote: Compressing objects:  78% (82/105)
+remote: Compressing objects:  79% (83/105)
+remote: Compressing objects:  80% (84/105)
+remote: Compressing objects:  81% (86/105)
+remote: Compressing objects:  82% (87/105)
+remote: Compressing objects:  83% (88/105)
+remote: Compressing objects:  84% (89/105)
+remote: Compressing objects:  85% (90/105)
+remote: Compressing objects:  86% (91/105)
+remote: Compressing objects:  87% (92/105)
+remote: Compressing objects:  88% (93/105)
+remote: Compressing objects:  89% (94/105)
+remote: Compressing objects:  90% (95/105)
+remote: Compressing objects:  91% (96/105)
+remote: Compressing objects:  92% (97/105)
+remote: Compressing objects:  93% (98/105)
+remote: Compressing objects:  94% (99/105)
+remote: Compressing objects:  95% (100/105)
+remote: Compressing objects:  96% (101/105)
+remote: Compressing objects:  97% (102/105)
+remote: Compressing objects:  98% (103/105)
+remote: Compressing objects:  99% (104/105)
+remote: Compressing objects: 100% (105/105)
+remote: Compressing objects: 100% (105/105), done.
 
 
 .. parsed-literal::
 
-    Receiving objects:   0% (1/412)
+    Receiving objects:   0% (1/421)
 
 .. parsed-literal::
 
-    Receiving objects:   1% (5/412), 11.29 MiB | 22.58 MiB/s
+    Receiving objects:   1% (5/421), 10.76 MiB | 21.55 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   1% (8/412), 23.78 MiB | 23.77 MiB/s
+    Receiving objects:   1% (8/421), 19.25 MiB | 19.12 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   2% (9/412), 23.78 MiB | 23.77 MiB/s
+    Receiving objects:   2% (9/421), 19.25 MiB | 19.12 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   3% (13/412), 23.78 MiB | 23.77 MiB/s
+    Receiving objects:   3% (13/421), 31.03 MiB | 20.47 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   4% (17/412), 36.27 MiB | 24.15 MiB/s
+    Receiving objects:   4% (17/421), 31.03 MiB | 20.47 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   4% (19/412), 49.09 MiB | 24.48 MiB/s
+    Receiving objects:   4% (19/421), 43.39 MiB | 21.41 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   5% (21/412), 49.09 MiB | 24.48 MiB/s
+    Receiving objects:   5% (22/421), 56.34 MiB | 22.23 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   6% (25/412), 61.89 MiB | 24.70 MiB/s
+    Receiving objects:   5% (25/421), 56.34 MiB | 22.23 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   7% (29/412), 61.89 MiB | 24.70 MiB/s
+    Receiving objects:   6% (26/421), 56.34 MiB | 22.23 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   8% (33/412), 61.89 MiB | 24.70 MiB/s
+    Receiving objects:   7% (30/421), 69.25 MiB | 22.74 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   8% (33/412), 74.70 MiB | 24.81 MiB/s
+    Receiving objects:   8% (34/421), 69.25 MiB | 22.74 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   9% (38/412), 87.74 MiB | 24.97 MiB/s
+    Receiving objects:   8% (37/421), 82.28 MiB | 22.91 MiB/s
+Receiving objects:   9% (38/421), 82.28 MiB | 22.91 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:   9% (40/412), 100.92 MiB | 25.09 MiB/s
+    Receiving objects:  10% (43/421), 110.97 MiB | 24.17 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  10% (42/412), 114.27 MiB | 25.27 MiB/s
-Receiving objects:  11% (46/412), 114.27 MiB | 25.27 MiB/s
+    Receiving objects:  11% (47/421), 110.97 MiB | 24.17 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  11% (49/412), 127.68 MiB | 25.72 MiB/s
-Receiving objects:  12% (50/412), 127.68 MiB | 25.72 MiB/s
+    Receiving objects:  11% (49/421), 124.93 MiB | 24.86 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  13% (54/412), 127.68 MiB | 25.72 MiB/s
+    Receiving objects:  12% (51/421), 124.93 MiB | 24.86 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  14% (58/412), 127.68 MiB | 25.72 MiB/s
+    Receiving objects:  13% (55/421), 124.93 MiB | 24.86 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  14% (61/412), 155.21 MiB | 26.27 MiB/s
+    Receiving objects:  14% (59/421), 139.53 MiB | 26.18 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  15% (62/412), 155.21 MiB | 26.27 MiB/s
+    Receiving objects:  14% (60/421), 152.32 MiB | 26.35 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  16% (66/412), 169.30 MiB | 26.55 MiB/s
+    Receiving objects:  15% (64/421), 167.60 MiB | 26.99 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  16% (67/412), 183.34 MiB | 26.82 MiB/s
+    Receiving objects:  15% (66/421), 167.60 MiB | 26.99 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  17% (71/412), 183.34 MiB | 26.82 MiB/s
-Receiving objects:  18% (75/412), 183.34 MiB | 26.82 MiB/s
+    Receiving objects:  16% (68/421), 183.43 MiB | 27.60 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  19% (79/412), 197.58 MiB | 27.15 MiB/s
+    Receiving objects:  17% (72/421), 183.43 MiB | 27.60 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  20% (83/412), 197.58 MiB | 27.15 MiB/s
-Receiving objects:  21% (87/412), 197.58 MiB | 27.15 MiB/s
+    Receiving objects:  18% (76/421), 183.43 MiB | 27.60 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  21% (88/412), 211.98 MiB | 27.45 MiB/s
+    Receiving objects:  19% (80/421), 200.00 MiB | 28.39 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  22% (91/412), 211.98 MiB | 27.45 MiB/s
-Receiving objects:  23% (95/412), 211.98 MiB | 27.45 MiB/s
-Receiving objects:  24% (99/412), 211.98 MiB | 27.45 MiB/s
-Receiving objects:  25% (103/412), 211.98 MiB | 27.45 MiB/s
+    Receiving objects:  20% (85/421), 200.00 MiB | 28.39 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  26% (108/412), 211.98 MiB | 27.45 MiB/s
-Receiving objects:  27% (112/412), 211.98 MiB | 27.45 MiB/s
-Receiving objects:  28% (116/412), 211.98 MiB | 27.45 MiB/s
+    Receiving objects:  20% (88/421), 216.59 MiB | 29.40 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  29% (120/412), 226.54 MiB | 27.82 MiB/s
+    Receiving objects:  21% (89/421), 216.59 MiB | 29.40 MiB/s
+Receiving objects:  22% (93/421), 216.59 MiB | 29.40 MiB/s
+Receiving objects:  23% (97/421), 216.59 MiB | 29.40 MiB/s
+Receiving objects:  24% (102/421), 216.59 MiB | 29.40 MiB/s
+Receiving objects:  25% (106/421), 216.59 MiB | 29.40 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  30% (124/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  31% (128/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  32% (132/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  33% (136/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  34% (141/412), 226.54 MiB | 27.82 MiB/s
+    Receiving objects:  26% (110/421), 216.59 MiB | 29.40 MiB/s
+Receiving objects:  27% (114/421), 216.59 MiB | 29.40 MiB/s
+Receiving objects:  28% (118/421), 216.59 MiB | 29.40 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  35% (145/412), 226.54 MiB | 27.82 MiB/s
+    Receiving objects:  29% (123/421), 216.59 MiB | 29.40 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  36% (149/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  37% (153/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  38% (157/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  39% (161/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  40% (165/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  41% (169/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  42% (174/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  43% (178/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  44% (182/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  45% (186/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  46% (190/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  47% (194/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  48% (198/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  49% (202/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  50% (206/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  51% (211/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  52% (215/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  53% (219/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  54% (223/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  55% (227/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  56% (231/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  57% (235/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  58% (239/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  59% (244/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  60% (248/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  61% (252/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  62% (256/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  63% (260/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  64% (264/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  65% (268/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  66% (272/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  67% (277/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  68% (281/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  69% (285/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  70% (289/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  71% (293/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  72% (297/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  73% (301/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  74% (305/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  75% (309/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  76% (314/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  77% (318/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  78% (322/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  79% (326/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  80% (330/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  81% (334/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  82% (338/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  83% (342/412), 226.54 MiB | 27.82 MiB/s
-remote: Total 412 (delta 95), reused 39 (delta 39), pack-reused 277
-    Receiving objects:  84% (347/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  85% (351/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  86% (355/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  87% (359/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  88% (363/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  89% (367/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  90% (371/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  91% (375/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  92% (380/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  93% (384/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  94% (388/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  95% (392/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  96% (396/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  97% (400/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  98% (404/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects:  99% (408/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects: 100% (412/412), 226.54 MiB | 27.82 MiB/s
-Receiving objects: 100% (412/412), 237.89 MiB | 26.66 MiB/s, done.
-    Resolving deltas:   0% (0/138)
-Resolving deltas:   1% (2/138)
-Resolving deltas:   2% (3/138)
-Resolving deltas:   3% (5/138)
-Resolving deltas:   4% (6/138)
-Resolving deltas:   5% (8/138)
-Resolving deltas:   7% (10/138)
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-Resolving deltas:  13% (19/138)
-Resolving deltas:  17% (24/138)
-Resolving deltas:  22% (31/138)
-Resolving deltas:  26% (36/138)
-Resolving deltas:  28% (39/138)
-Resolving deltas:  29% (41/138)
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-Resolving deltas:  52% (73/138)
-Resolving deltas:  62% (86/138)
-Resolving deltas:  67% (93/138)
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-Resolving deltas:  72% (100/138)
-Resolving deltas:  73% (101/138)
-Resolving deltas:  75% (104/138)
-Resolving deltas:  78% (108/138)
-Resolving deltas:  79% (110/138)
-Resolving deltas:  84% (116/138)
-Resolving deltas:  85% (118/138)
-Resolving deltas:  86% (119/138)
-Resolving deltas: 100% (138/138)
-Resolving deltas: 100% (138/138), done.
+    Receiving objects:  30% (127/421), 216.59 MiB | 29.40 MiB/s
+Receiving objects:  31% (131/421), 216.59 MiB | 29.40 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  32% (135/421), 216.59 MiB | 29.40 MiB/s
+Receiving objects:  33% (139/421), 216.59 MiB | 29.40 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  34% (144/421), 233.69 MiB | 29.70 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  35% (148/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  36% (152/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  37% (156/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  38% (160/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  39% (165/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  40% (169/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  41% (173/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  42% (177/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  43% (182/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  44% (186/421), 233.69 MiB | 29.70 MiB/s
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+Receiving objects:  51% (215/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  52% (219/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  53% (224/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  54% (228/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  55% (232/421), 233.69 MiB | 29.70 MiB/s
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+Receiving objects:  57% (240/421), 233.69 MiB | 29.70 MiB/s
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+Receiving objects:  59% (249/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  60% (253/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  61% (257/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  62% (262/421), 233.69 MiB | 29.70 MiB/s
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+Receiving objects:  64% (270/421), 233.69 MiB | 29.70 MiB/s
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+Receiving objects:  66% (278/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  67% (283/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  68% (287/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  69% (291/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  70% (295/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  71% (299/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  72% (304/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  73% (308/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  74% (312/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  75% (316/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  76% (320/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  77% (325/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  78% (329/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  79% (333/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  80% (337/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  81% (342/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  82% (346/421), 233.69 MiB | 29.70 MiB/s
+remote: Total 421 (delta 101), reused 40 (delta 39), pack-reused 277
+    Receiving objects:  83% (350/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  84% (354/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  85% (358/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  86% (363/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  87% (367/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  88% (371/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  89% (375/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  90% (379/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  91% (384/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  92% (388/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  93% (392/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  94% (396/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  95% (400/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  96% (405/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  97% (409/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  98% (413/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects:  99% (417/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects: 100% (421/421), 233.69 MiB | 29.70 MiB/s
+Receiving objects: 100% (421/421), 237.89 MiB | 27.05 MiB/s, done.
+    Resolving deltas:   0% (0/144)
+Resolving deltas:   1% (2/144)
+Resolving deltas:   2% (3/144)
+Resolving deltas:   4% (6/144)
+Resolving deltas:   5% (8/144)
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+Resolving deltas:   7% (11/144)
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+Resolving deltas:   9% (14/144)
+Resolving deltas:  13% (19/144)
+Resolving deltas:  16% (24/144)
+Resolving deltas:  21% (31/144)
+Resolving deltas:  25% (36/144)
+Resolving deltas:  27% (39/144)
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+Resolving deltas:  40% (58/144)
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+Resolving deltas:  46% (67/144)
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+Resolving deltas:  72% (104/144)
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+Resolving deltas:  78% (113/144)
+Resolving deltas:  81% (118/144)
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+Resolving deltas:  84% (121/144)
+Resolving deltas: 100% (144/144)
+Resolving deltas: 100% (144/144), done.
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/280-depth-anything/Depth-Anything
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
+    Note: you may need to restart the kernel to use updated packages.
+
 
 .. parsed-literal::
 
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    optimum-intel 1.16.0.dev0 requires transformers<4.39.0,>=4.36.0, but you have transformers 4.34.1 which is incompatible.
-    pyannote-audio 2.0.1 requires torchaudio<1.0,>=0.10, but you have torchaudio 2.2.1+cpu which is incompatible.
-    tokenizers 0.14.1 requires huggingface_hub<0.18,>=0.16.4, but you have huggingface-hub 0.21.4 which is incompatible.
+    WARNING: typer 0.12.3 does not provide the extra 'all'
     
 
 .. parsed-literal::
@@ -550,7 +560,7 @@ Resolving deltas: 100% (138/138), done.
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    WARNING: typer 0.12.3 does not provide the extra 'all'
     
 
 .. parsed-literal::
@@ -558,21 +568,6 @@ Resolving deltas: 100% (138/138), done.
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -593,7 +588,7 @@ attention optimizations first.
     
     if not orig_attention_path.exists():
         attention_file_path.rename(orig_attention_path)
-        
+    
         with orig_attention_path.open("r") as f:
             data = f.read()
             data = data.replace("XFORMERS_AVAILABLE = True", "XFORMERS_AVAILABLE = False")
@@ -614,9 +609,9 @@ DepthAnything family.
 
     from depth_anything.dpt import DepthAnything
     
-    encoder = 'vits'  # can also be 'vitb' or 'vitl'
-    model_id = 'depth_anything_{:}14'.format(encoder)
-    depth_anything = DepthAnything.from_pretrained(f'LiheYoung/{model_id}')
+    encoder = "vits"  # can also be 'vitb' or 'vitl'
+    model_id = "depth_anything_{:}14".format(encoder)
+    depth_anything = DepthAnything.from_pretrained(f"LiheYoung/{model_id}")
 
 
 .. parsed-literal::
@@ -636,17 +631,21 @@ Prepare input data
 
 .. code:: ipython3
 
-    import urllib.request
     from PIL import Image
     
+    import requests
     
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
     
-    download_file("https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/3f779fc1-c1b2-4dec-915a-64dae510a2bb", "furseal.png")
+    download_file(
+        "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/3f779fc1-c1b2-4dec-915a-64dae510a2bb",
+        "furseal.png",
+    )
     
     Image.open("furseal.png").resize((600, 400))
 
@@ -659,7 +658,7 @@ Prepare input data
 
 
 
-.. image:: 280-depth-anything-with-output_files/280-depth-anything-with-output_9_1.png
+.. image:: depth-anything-with-output_files/depth-anything-with-output_9_1.png
 
 
 
@@ -676,23 +675,25 @@ range.
     import cv2
     import torch
     
-    transform = Compose([
-        Resize(
-            width=518,
-            height=518,
-            resize_target=False,
-            ensure_multiple_of=14,
-            resize_method='lower_bound',
-            image_interpolation_method=cv2.INTER_CUBIC,
-        ),
-        NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        PrepareForNet(),
-    ])
+    transform = Compose(
+        [
+            Resize(
+                width=518,
+                height=518,
+                resize_target=False,
+                ensure_multiple_of=14,
+                resize_method="lower_bound",
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+            PrepareForNet(),
+        ]
+    )
     
     
-    image = cv2.cvtColor(cv2.imread('furseal.png'), cv2.COLOR_BGR2RGB) / 255.0
+    image = cv2.cvtColor(cv2.imread("furseal.png"), cv2.COLOR_BGR2RGB) / 255.0
     h, w = image.shape[:-1]
-    image = transform({'image': image})['image']
+    image = transform({"image": image})["image"]
     image = torch.from_numpy(image).unsqueeze(0)
 
 Run model inference
@@ -717,9 +718,9 @@ image size and prepare it for visualization.
     import torch.nn.functional as F
     import numpy as np
     
-    depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0]
+    depth = F.interpolate(depth[None], (h, w), mode="bilinear", align_corners=False)[0, 0]
     depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
-            
+    
     depth = depth.cpu().detach().numpy().astype(np.uint8)
     depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
 
@@ -731,7 +732,7 @@ image size and prepare it for visualization.
 
 
 
-.. image:: 280-depth-anything-with-output_files/280-depth-anything-with-output_16_0.png
+.. image:: depth-anything-with-output_files/depth-anything-with-output_16_0.png
 
 
 Convert Model to OpenVINO IR format
@@ -751,7 +752,7 @@ loading on device using ``core.complie_model``.
 
     import openvino as ov
     
-    OV_DEPTH_ANYTHING_PATH = Path(f'{model_id}.xml') 
+    OV_DEPTH_ANYTHING_PATH = Path(f"{model_id}.xml")
     
     if not OV_DEPTH_ANYTHING_PATH.exists():
         ov_model = ov.convert_model(depth_anything, example_input=image, input=[1, 3, 518, 518])
@@ -760,17 +761,17 @@ loading on device using ``core.complie_model``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/280-depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:73: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:73: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/280-depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:74: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:74: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/280-depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/vision_transformer.py:183: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/vision_transformer.py:183: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if npatch == N and w == h:
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/280-depth-anything/Depth-Anything/depth_anything/dpt.py:133: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/depth_anything/dpt.py:133: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
 
 
@@ -836,6 +837,7 @@ Run inference on image
         depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
         return depth_color
     
+    
     depth_color = get_depth_map(res)
 
 .. code:: ipython3
@@ -844,7 +846,7 @@ Run inference on image
 
 
 
-.. image:: 280-depth-anything-with-output_files/280-depth-anything-with-output_25_0.png
+.. image:: depth-anything-with-output_files/depth-anything-with-output_25_0.png
 
 
 Run inference on video
@@ -854,7 +856,10 @@ Run inference on video
 
 .. code:: ipython3
 
-    download_file("https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/Coco%20Walking%20in%20Berkeley.mp4", "./Coco Walking in Berkeley.mp4")
+    download_file(
+        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/Coco%20Walking%20in%20Berkeley.mp4",
+        "./Coco Walking in Berkeley.mp4",
+    )
     
     VIDEO_FILE = "./Coco Walking in Berkeley.mp4"
     # Number of seconds of input video to process. Set `NUM_SECONDS` to 0 to process
@@ -900,10 +905,7 @@ Run inference on video
     target_frame_width = int(input_video_frame_width * SCALE_OUTPUT)
     
     cap.release()
-    print(
-        f"The input video has a frame width of {input_video_frame_width}, "
-        f"frame height of {input_video_frame_height} and runs at {input_fps:.2f} fps"
-    )
+    print(f"The input video has a frame width of {input_video_frame_width}, " f"frame height of {input_video_frame_height} and runs at {input_fps:.2f} fps")
     print(
         "The output video will be scaled with a factor "
         f"{SCALE_OUTPUT}, have width {target_frame_width}, "
@@ -960,6 +962,7 @@ Run inference on video
         display,
     )
     
+    
     def process_video(compiled_model, video_file, result_video_path):
         # Initialize variables.
         input_video_frame_nr = 0
@@ -994,7 +997,7 @@ Run inference on video
     
                 h, w = image.shape[:-1]
                 input_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
-                input_image = transform({'image': input_image})['image']
+                input_image = transform({"image": input_image})["image"]
                 # Reshape the image to network input shape NCHW.
                 input_image = np.expand_dims(input_image, 0)
     
@@ -1060,7 +1063,7 @@ Run inference on video
 
 .. parsed-literal::
 
-    Processed 60 frames in 13.18 seconds. Total FPS (including video processing): 4.55.Inference FPS: 10.71 
+    Processed 60 frames in 13.27 seconds. Total FPS (including video processing): 4.52.Inference FPS: 10.65 
     Video saved to 'output/Coco Walking in Berkeley_depth_anything.mp4'.
 
 
@@ -1073,10 +1076,7 @@ Run inference on video
             raise ValueError("OpenCV was unable to write the video file. Showing one video frame.")
         else:
             print(f"Showing video saved at\n{result_video_path.resolve()}")
-            print(
-                "If you cannot see the video in your browser, please click on the "
-                "following link to download the video "
-            )
+            print("If you cannot see the video in your browser, please click on the " "following link to download the video ")
             video_link = FileLink(result_video_path)
             video_link.html_link_str = "%s"
             display(HTML(video_link._repr_html_()))
@@ -1090,7 +1090,7 @@ Run inference on video
 .. parsed-literal::
 
     Showing video saved at
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/280-depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
     If you cannot see the video in your browser, please click on the following link to download the video 
 
 
@@ -1134,7 +1134,7 @@ improve model inference speed.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -1151,10 +1151,13 @@ improve model inference speed.
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../../utils")
+    # Fetch `skip_kernel_extension` module
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
-    OV_DEPTH_ANYTHING_INT8_PATH = Path(f'{model_id}_int8.xml')
+    OV_DEPTH_ANYTHING_INT8_PATH = Path(f"{model_id}_int8.xml")
     
     %load_ext skip_kernel_extension
 
@@ -1224,14 +1227,14 @@ quantization code below may take some time.
 
 .. parsed-literal::
 
-    2024-03-13 00:23:02.898107: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:23:02.930029: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-17 23:32:53.792726: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 23:32:53.826550: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 00:23:03.531201: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-17 23:32:54.388126: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -1309,7 +1312,7 @@ quantization code below may take some time.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
       return Tensor(self.data * unwrap_tensor_data(other))
 
 
@@ -1391,7 +1394,7 @@ data.
 
 
 
-.. image:: 280-depth-anything-with-output_files/280-depth-anything-with-output_44_0.png
+.. image:: depth-anything-with-output_files/depth-anything-with-output_44_0.png
 
 
 .. code:: ipython3
@@ -1405,10 +1408,10 @@ data.
 
 .. parsed-literal::
 
-    Processed 60 frames in 12.72 seconds. Total FPS (including video processing): 4.72.Inference FPS: 12.84 
+    Processed 60 frames in 12.69 seconds. Total FPS (including video processing): 4.73.Inference FPS: 12.74 
     Video saved to 'output/Coco Walking in Berkeley_depth_anything_int8.mp4'.
     Showing video saved at
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/280-depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
     If you cannot see the video in your browser, please click on the following link to download the video 
 
 
@@ -1468,11 +1471,13 @@ Tool =2024.0.0" --extra-index-url https://download.pytorch.org/whl/cpu "pytorch-lightning" "timm>=0.6.12"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "openvino>=2024.0.0" torch torchvision  "pytorch-lightning" "timm>=0.6.12" tqdm
+
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-
-
-.. parsed-literal::
-
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    googleapis-common-protos 1.63.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
-    onnx 1.15.0 requires protobuf>=3.20.2, but you have protobuf 3.20.1 which is incompatible.
-    paddlepaddle 2.6.0 requires protobuf>=3.20.2; platform_system != "Windows", but you have protobuf 3.20.1 which is incompatible.
-    tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.
-    tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -147,6 +138,7 @@ Imports
 
 .. code:: ipython3
 
+    import sys
     import matplotlib.pyplot as plt
     import matplotlib.image as mpimg
     import numpy as np
@@ -157,10 +149,17 @@ Imports
     from shutil import rmtree
     from typing import Optional, Tuple
 
-    sys.path.append('../utils')
+    # Fetch `notebook_utils` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
-    sys.path.append('vi_depth_utils')
+    sys.path.append("vi_depth_utils")
     import data_loader
     import modules.midas.transforms as transforms
     import modules.midas.utils as utils
@@ -217,16 +216,18 @@ depth prediction model as you will see.
 .. code:: ipython3
 
     # Base directory in which models would be stored as a pathlib.Path variable
-    MODEL_DIR = Path('model')
+    MODEL_DIR = Path("model")
 
     # Mapping between depth predictors and the corresponding scale map learners
-    PREDICTOR_MODEL_MAP = {'dpt_beit_large_512': 'DPT_BEiT_L_512',
-                           'dpt_swin2_large_384': 'DPT_SwinV2_L_384',
-                           'dpt_large': 'DPT_Large',
-                           'dpt_hybrid': 'DPT_Hybrid',
-                           'dpt_swin2_tiny_256': 'DPT_SwinV2_T_256',
-                           'dpt_levit_224': 'DPT_LeViT_224',
-                           'midas_small': 'MiDaS_small'}
+    PREDICTOR_MODEL_MAP = {
+        "dpt_beit_large_512": "DPT_BEiT_L_512",
+        "dpt_swin2_large_384": "DPT_SwinV2_L_384",
+        "dpt_large": "DPT_Large",
+        "dpt_hybrid": "DPT_Hybrid",
+        "dpt_swin2_tiny_256": "DPT_SwinV2_T_256",
+        "dpt_levit_224": "DPT_LeViT_224",
+        "midas_small": "MiDaS_small",
+    }
 
 .. code:: ipython3
 
@@ -235,7 +236,11 @@ depth prediction model as you will see.
 
     # Here we will be downloading the SML model corresponding to the MiDaS-small depth predictor for
     # the checkpoint captured after training on 1500 points of the density level. Suppress errors if the file already exists
-    download_file('https://github.com/isl-org/VI-Depth/releases/download/v1/sml_model.dpredictor.midas_small.nsamples.1500.ckpt', directory=MODEL_DIR, silent=True)
+    download_file(
+        "https://github.com/isl-org/VI-Depth/releases/download/v1/sml_model.dpredictor.midas_small.nsamples.1500.ckpt",
+        directory=MODEL_DIR,
+        silent=True,
+    )
 
     # Take a note of the samples. It would be of major use later on
     NSAMPLES = 1500
@@ -256,7 +261,7 @@ depth prediction model as you will see.
 
     # A utility function for utilising the mapping between depth predictors and
     # scale map learners so as to download the former
-    def get_model_for_predictor(depth_predictor: str, remote_repo: str = 'intel-isl/MiDaS') -> str:
+    def get_model_for_predictor(depth_predictor: str, remote_repo: str = "intel-isl/MiDaS") -> str:
         """
         Download a model from the pre-validated 'isl-org/MiDaS:2.1' set of releases on the GitHub repo
         while simultaneously trusting the repo permanently
@@ -269,13 +274,18 @@ depth prediction model as you will see.
         # Workaround for avoiding rate limit errors
         torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
 
-        return torch.hub.load(remote_repo, PREDICTOR_MODEL_MAP[depth_predictor], skip_validation=True, trust_repo=True)
+        return torch.hub.load(
+            remote_repo,
+            PREDICTOR_MODEL_MAP[depth_predictor],
+            skip_validation=True,
+            trust_repo=True,
+        )
 
 .. code:: ipython3
 
     # Execute the above function so as to download the MiDaS-small model
     # and get the output of the model callable in return
-    depth_model = get_model_for_predictor('midas_small')
+    depth_model = get_model_for_predictor("midas_small")
 
 
 .. parsed-literal::
@@ -290,7 +300,7 @@ depth prediction model as you will see.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/hub.py:294: UserWarning: You are about to download and run code from an untrusted repository. In a future release, this won't be allowed. To add the repository to your trusted list, change the command to {calling_fn}(..., trust_repo=False) and a command prompt will appear asking for an explicit confirmation of trust, or load(..., trust_repo=True), which will assume that the prompt is to be answered with 'yes'. You can also use load(..., trust_repo='check') which will only prompt for confirmation if the repo is not already trusted. This will eventually be the default behaviour
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/hub.py:294: UserWarning: You are about to download and run code from an untrusted repository. In a future release, this won't be allowed. To add the repository to your trusted list, change the command to {calling_fn}(..., trust_repo=False) and a command prompt will appear asking for an explicit confirmation of trust, or load(..., trust_repo=True), which will assume that the prompt is to be answered with 'yes'. You can also use load(..., trust_repo='check') which will only prompt for confirmation if the repo is not already trusted. This will eventually be the default behaviour
       warnings.warn(
     Downloading: "https://github.com/rwightman/gen-efficientnet-pytorch/zipball/master" to model/master.zip
 
@@ -318,87 +328,93 @@ depth prediction model as you will see.
 .. parsed-literal::
 
 
-  3%|▎         | 2.75M/81.8M [00:00<00:05, 16.4MB/s]
+  3%|▎         | 2.72M/81.8M [00:00<00:05, 16.1MB/s]
 
 .. parsed-literal::
 
 
-  9%|▉         | 7.59M/81.8M [00:00<00:02, 32.1MB/s]
+ 15%|█▌        | 12.4M/81.8M [00:00<00:01, 54.8MB/s]
 
 .. parsed-literal::
 
 
- 21%|██        | 16.8M/81.8M [00:00<00:01, 57.5MB/s]
+ 22%|██▏       | 17.6M/81.8M [00:00<00:01, 51.8MB/s]
 
 .. parsed-literal::
 
 
- 27%|██▋       | 22.3M/81.8M [00:00<00:01, 52.9MB/s]
+ 28%|██▊       | 22.6M/81.8M [00:00<00:01, 48.7MB/s]
 
 .. parsed-literal::
 
 
- 33%|███▎      | 27.4M/81.8M [00:00<00:01, 50.4MB/s]
+ 33%|███▎      | 27.2M/81.8M [00:00<00:01, 47.1MB/s]
 
 .. parsed-literal::
 
 
- 39%|███▉      | 32.3M/81.8M [00:00<00:01, 42.1MB/s]
+ 39%|███▉      | 31.8M/81.8M [00:00<00:01, 46.3MB/s]
 
 .. parsed-literal::
 
 
- 46%|████▋     | 38.0M/81.8M [00:00<00:00, 46.9MB/s]
+ 44%|████▍     | 36.2M/81.8M [00:00<00:01, 45.3MB/s]
 
 .. parsed-literal::
 
 
- 52%|█████▏    | 42.7M/81.8M [00:01<00:00, 46.3MB/s]
+ 50%|████▉     | 40.5M/81.8M [00:00<00:00, 44.9MB/s]
 
 .. parsed-literal::
 
 
- 58%|█████▊    | 47.3M/81.8M [00:01<00:00, 45.9MB/s]
+ 55%|█████▍    | 44.8M/81.8M [00:01<00:00, 45.0MB/s]
 
 .. parsed-literal::
 
 
- 63%|██████▎   | 51.8M/81.8M [00:01<00:00, 45.4MB/s]
+ 60%|██████    | 49.1M/81.8M [00:01<00:00, 44.8MB/s]
 
 .. parsed-literal::
 
 
- 69%|██████▊   | 56.2M/81.8M [00:01<00:00, 45.0MB/s]
+ 65%|██████▌   | 53.5M/81.8M [00:01<00:00, 45.1MB/s]
 
 .. parsed-literal::
 
 
- 74%|███████▍  | 60.5M/81.8M [00:01<00:00, 41.8MB/s]
+ 71%|███████   | 57.9M/81.8M [00:01<00:00, 45.3MB/s]
 
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- 79%|███████▉  | 64.6M/81.8M [00:01<00:00, 41.9MB/s]
+ 76%|███████▌  | 62.2M/81.8M [00:01<00:00, 45.0MB/s]
 
 .. parsed-literal::
 
 
- 84%|████████▍ | 68.9M/81.8M [00:01<00:00, 42.8MB/s]
+ 81%|████████▏ | 66.5M/81.8M [00:01<00:00, 44.8MB/s]
 
 .. parsed-literal::
 
 
- 89%|████████▉ | 73.0M/81.8M [00:01<00:00, 42.6MB/s]
+ 87%|████████▋ | 70.8M/81.8M [00:01<00:00, 44.7MB/s]
 
 .. parsed-literal::
 
 
- 94%|█████████▍| 77.1M/81.8M [00:01<00:00, 35.0MB/s]
+ 92%|█████████▏| 75.1M/81.8M [00:01<00:00, 44.7MB/s]
 
 .. parsed-literal::
 
 
-   100%|██████████| 81.8M/81.8M [00:01<00:00, 43.0MB/s]
+ 97%|█████████▋| 79.3M/81.8M [00:01<00:00, 33.3MB/s]
+
+.. parsed-literal::
+
+
+    100%|██████████| 81.8M/81.8M [00:02<00:00, 41.9MB/s]
+
 
 
 Cleaning up the model directory
@@ -415,12 +431,15 @@ process.
 .. code:: ipython3
 
     # Remove unnecessary directories and files and suppress errors(if any)
-    rmtree(path=str(MODEL_DIR / 'intel-isl_MiDaS_master'), ignore_errors=True)
-    rmtree(path=str(MODEL_DIR / 'rwightman_gen-efficientnet-pytorch_master'), ignore_errors=True)
-    rmtree(path=str(MODEL_DIR / 'checkpoints'), ignore_errors=True)
+    rmtree(path=str(MODEL_DIR / "intel-isl_MiDaS_master"), ignore_errors=True)
+    rmtree(
+        path=str(MODEL_DIR / "rwightman_gen-efficientnet-pytorch_master"),
+        ignore_errors=True,
+    )
+    rmtree(path=str(MODEL_DIR / "checkpoints"), ignore_errors=True)
 
     # Check for the existence of the trusted list file and then remove
-    list_file = Path(MODEL_DIR / 'trusted_list')
+    list_file = Path(MODEL_DIR / "trusted_list")
     if list_file.is_file():
         list_file.unlink()
 
@@ -453,13 +472,12 @@ model are always in direct correspondence with each other.
         :returns: The transformed models as the resut of torchvision.transforms.Compose operations
         """
         model_transforms = transforms.get_transforms(depth_predictor, "void", str(nsamples))
-        return model_transforms['depth_model'], model_transforms['sml_model']
+        return model_transforms["depth_model"], model_transforms["sml_model"]
 
 .. code:: ipython3
 
     # Obtain transforms of both the models here
-    depth_model_transform, scale_map_learner_transform = get_model_transforms(depth_predictor='midas_small',
-                                                                              nsamples=NSAMPLES)
+    depth_model_transform, scale_map_learner_transform = get_model_transforms(depth_predictor="midas_small", nsamples=NSAMPLES)
 
 Dummy input creation
 ^^^^^^^^^^^^^^^^^^^^
@@ -490,23 +508,33 @@ dataset
 .. code:: ipython3
 
     # Base directory in which data would be stored as a pathlib.Path variable
-    DATA_DIR = Path('data')
+    DATA_DIR = Path("data")
 
     # Create the data directory tree adjacent to the notebook and suppress errors if the directory already exists
     # Create a directory each for the images and their corresponding depth maps
     DATA_DIR.mkdir(exist_ok=True)
-    Path(DATA_DIR / 'image').mkdir(exist_ok=True)
-    Path(DATA_DIR / 'sparse_depth').mkdir(exist_ok=True)
+    Path(DATA_DIR / "image").mkdir(exist_ok=True)
+    Path(DATA_DIR / "sparse_depth").mkdir(exist_ok=True)
 
     # Download the dummy image and its depth scale (take a note of the image hashes for possible later use)
     # On the fly download is being done to avoid unnecessary memory/data load during testing and
     # creation of PRs
-    download_file('https://user-images.githubusercontent.com/22426058/254174385-161b9f0e-5991-4308-ba89-d81bc02bcb7c.png', filename='dummy_img.png', directory=Path(DATA_DIR / 'image'), silent=True)
-    download_file('https://user-images.githubusercontent.com/22426058/254174398-8c71c59f-0adf-43c6-ad13-c04431e02349.png', filename='dummy_depth.png', directory=Path(DATA_DIR / 'sparse_depth'), silent=True)
+    download_file(
+        "https://user-images.githubusercontent.com/22426058/254174385-161b9f0e-5991-4308-ba89-d81bc02bcb7c.png",
+        filename="dummy_img.png",
+        directory=Path(DATA_DIR / "image"),
+        silent=True,
+    )
+    download_file(
+        "https://user-images.githubusercontent.com/22426058/254174398-8c71c59f-0adf-43c6-ad13-c04431e02349.png",
+        filename="dummy_depth.png",
+        directory=Path(DATA_DIR / "sparse_depth"),
+        silent=True,
+    )
 
     # Load the dummy image and its depth scale
-    dummy_input = data_loader.load_input_image('data/image/dummy_img.png')
-    dummy_depth = data_loader.load_sparse_depth('data/sparse_depth/dummy_depth.png')
+    dummy_input = data_loader.load_input_image("data/image/dummy_img.png")
+    dummy_depth = data_loader.load_sparse_depth("data/sparse_depth/dummy_depth.png")
 
 
 
@@ -523,7 +551,11 @@ dataset
 
 .. code:: ipython3
 
-    def transform_image_for_depth(input_image: np.ndarray, depth_model_transform: np.ndarray, device: torch.device = 'cpu') -> torch.Tensor:
+    def transform_image_for_depth(
+        input_image: np.ndarray,
+        depth_model_transform: np.ndarray,
+        device: torch.device = "cpu",
+    ) -> torch.Tensor:
         """
         Transform the input_image for processing by a PyTorch depth estimation model
 
@@ -534,9 +566,9 @@ dataset
         """
         input_height, input_width = np.shape(input_image)[:2]
 
-        sample = {'image' : input_image}
+        sample = {"image": input_image}
         sample = depth_model_transform(sample)
-        im = sample['image'].to(device)
+        im = sample["image"].to(device)
         return im.unsqueeze(0)
 
 .. code:: ipython3
@@ -574,10 +606,10 @@ we shall follow the following steps:
     _ = depth_model(transformed_dummy_image)
 
     # convert model to OpenVINO IR
-    ov_model = ov.convert_model(depth_model, example_input=(transformed_dummy_image, ))
+    ov_model = ov.convert_model(depth_model, example_input=(transformed_dummy_image,))
 
     # save model for next usage
-    ov.save_model(ov_model, 'depth_model.xml')
+    ov.save_model(ov_model, "depth_model.xml")
 
 Select inference device
 '''''''''''''''''''''''
@@ -594,8 +626,8 @@ select device from dropdown list for running inference using OpenVINO
 
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
 
@@ -624,9 +656,12 @@ Now we can go ahead and compile our depth model.
 
 .. code:: ipython3
 
-    def run_depth_model(input_image_h: int, input_image_w: int,
-                        transformed_image: torch.Tensor, compiled_depth_model: type_compiled_model) -> np.ndarray:
-
+    def run_depth_model(
+        input_image_h: int,
+        input_image_w: int,
+        transformed_image: torch.Tensor,
+        compiled_depth_model: type_compiled_model,
+    ) -> np.ndarray:
         """
         Run the compiled_depth_model on the transformed_image of dimensions
         input_image_w x input_image_h
@@ -649,7 +684,7 @@ Now we can go ahead and compile our depth model.
                 torch.nn.functional.interpolate(
                     depth_pred.unsqueeze(1),
                     size=(input_image_h, input_image_w),
-                    mode='bicubic',
+                    mode="bicubic",
                     align_corners=False,
                 )
                 .squeeze()
@@ -664,8 +699,12 @@ Now we can go ahead and compile our depth model.
     # Run the compiled depth model using the dummy input
     # It will be used to compute the metrics associated with the ScaleMapLearner model
     # and hence obtain a compiled version of the same later
-    depth_pred_dummy = run_depth_model(input_image_h=IMAGE_H, input_image_w=IMAGE_W,
-                                       transformed_image=transformed_dummy_image, compiled_depth_model=compiled_depth_model)
+    depth_pred_dummy = run_depth_model(
+        input_image_h=IMAGE_H,
+        input_image_w=IMAGE_W,
+        transformed_image=transformed_dummy_image,
+        compiled_depth_model=compiled_depth_model,
+    )
 
 Computation of scale and shift parameters
 '''''''''''''''''''''''''''''''''''''''''
@@ -679,11 +718,15 @@ purpose has already been created.
 
 .. code:: ipython3
 
-    def compute_global_scale_and_shift(input_sparse_depth: np.ndarray, validity_map: Optional[np.ndarray],
-                                       depth_pred: np.ndarray,
-                                       min_pred: float = 0.1, max_pred: float = 8.0,
-                                       min_depth: float = 0.2, max_depth: float = 5.0) -> Tuple[np.ndarray, np.ndarray]:
-
+    def compute_global_scale_and_shift(
+        input_sparse_depth: np.ndarray,
+        validity_map: Optional[np.ndarray],
+        depth_pred: np.ndarray,
+        min_pred: float = 0.1,
+        max_pred: float = 8.0,
+        min_depth: float = 0.2,
+        max_depth: float = 5.0,
+    ) -> Tuple[np.ndarray, np.ndarray]:
         """
         Compute the global scale and shift alignment required for SML model to work on
         with the input_sparse_depth map being provided and the depth estimation output depth_pred
@@ -711,27 +754,20 @@ purpose has already been created.
         input_sparse_depth = 1.0 / input_sparse_depth
 
         # global scale and shift alignment
-        GlobalAlignment = LeastSquaresEstimator(
-            estimate=depth_pred,
-            target=input_sparse_depth,
-            valid=input_sparse_depth_valid
-        )
+        GlobalAlignment = LeastSquaresEstimator(estimate=depth_pred, target=input_sparse_depth, valid=input_sparse_depth_valid)
         GlobalAlignment.compute_scale_and_shift()
         GlobalAlignment.apply_scale_and_shift()
         GlobalAlignment.clamp_min_max(clamp_min=min_pred, clamp_max=max_pred)
         int_depth = GlobalAlignment.output.astype(np.float32)
 
         # interpolation of scale map
-        assert (np.sum(input_sparse_depth_valid) >= 3), 'not enough valid sparse points'
+        assert np.sum(input_sparse_depth_valid) >= 3, "not enough valid sparse points"
         ScaleMapInterpolator = Interpolator2D(
             pred_inv=int_depth,
             sparse_depth_inv=input_sparse_depth,
             valid=input_sparse_depth_valid,
         )
-        ScaleMapInterpolator.generate_interpolated_scale_map(
-            interpolate_method='linear',
-            fill_corners=False
-        )
+        ScaleMapInterpolator.generate_interpolated_scale_map(interpolate_method="linear", fill_corners=False)
 
         int_scales = ScaleMapInterpolator.interpolated_scale_map.astype(np.float32)
         int_scales = utils.normalize_unit_range(int_scales)
@@ -746,9 +782,13 @@ purpose has already been created.
 
 .. code:: ipython3
 
-    def transform_image_for_depth_scale(input_image: np.ndarray, scale_map_learner_transform: type_transform_compose,
-                                        int_depth: np.ndarray, int_scales: np.ndarray,
-                                        device: torch.device = 'cpu') -> Tuple[torch.Tensor, torch.Tensor]:
+    def transform_image_for_depth_scale(
+        input_image: np.ndarray,
+        scale_map_learner_transform: type_transform_compose,
+        int_depth: np.ndarray,
+        int_scales: np.ndarray,
+        device: torch.device = "cpu",
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         """
         Transform the input_image for processing by a PyTorch SML model
 
@@ -760,11 +800,16 @@ purpose has already been created.
         :returns: The transformed tensor inputs suitable to be used with an SML model
         """
 
-        sample = {'image' : input_image, 'int_depth' : int_depth, 'int_scales' : int_scales, 'int_depth_no_tf' : int_depth}
+        sample = {
+            "image": input_image,
+            "int_depth": int_depth,
+            "int_scales": int_scales,
+            "int_depth_no_tf": int_depth,
+        }
         sample = scale_map_learner_transform(sample)
-        x = torch.cat([sample['int_depth'], sample['int_scales']], 0)
+        x = torch.cat([sample["int_depth"], sample["int_scales"]], 0)
         x = x.to(device)
-        d = sample['int_depth_no_tf'].to(device)
+        d = sample["int_depth_no_tf"].to(device)
 
         return x.unsqueeze(0), d.unsqueeze(0)
 
@@ -773,9 +818,12 @@ purpose has already been created.
     # Transform the dummy input image for the ScaleMapLearner model
     # Note that this will lead to a tuple as an output. Both the elements
     # which is fed to ScaleMapLearner during the conversion process to onxx
-    transformed_dummy_image_scale = transform_image_for_depth_scale(input_image=dummy_input,
-                                                                    scale_map_learner_transform=scale_map_learner_transform,
-                                                                    int_depth=d_depth, int_scales=d_scales)
+    transformed_dummy_image_scale = transform_image_for_depth_scale(
+        input_image=dummy_input,
+        scale_map_learner_transform=scale_map_learner_transform,
+        int_depth=d_depth,
+        int_scales=d_scales,
+    )
 
 Conversion of Scale Map Learner model to OpenVINO IR format
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -814,8 +862,11 @@ common format of all checkpoint files from the model releases.
 
     # Run with the same min_pred and max_pred arguments which were used to compute
     # global scale and shift alignment
-    scale_map_learner = MidasNet_small_videpth(path=str(MODEL_DIR / 'sml_model.dpredictor.midas_small.nsamples.1500.ckpt'),
-                                               min_pred=0.1, max_pred=8.0)
+    scale_map_learner = MidasNet_small_videpth(
+        path=str(MODEL_DIR / "sml_model.dpredictor.midas_small.nsamples.1500.ckpt"),
+        min_pred=0.1,
+        max_pred=8.0,
+    )
 
 
 .. parsed-literal::
@@ -828,27 +879,18 @@ common format of all checkpoint files from the model releases.
     Downloading: "https://github.com/rwightman/gen-efficientnet-pytorch/zipball/master" to model/master.zip
 
 
-.. parsed-literal::
-
-    2024-03-12 23:58:07.773970: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 23:58:07.806115: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-
-
-.. parsed-literal::
-
-    2024-03-12 23:58:08.415108: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
 .. code:: ipython3
 
     # As usual, since the MidasNet_small_videpthc class internally downloads a repo again from torch hub
     # we shall clean the same since the model callable is now available to us
     # Remove unnecessary directories and files and suppress errors(if any)
-    rmtree(path=str(MODEL_DIR / 'rwightman_gen-efficientnet-pytorch_master'), ignore_errors=True)
+    rmtree(
+        path=str(MODEL_DIR / "rwightman_gen-efficientnet-pytorch_master"),
+        ignore_errors=True,
+    )
 
     # Check for the existence of the trusted list file and then remove
-    list_file = Path(MODEL_DIR / 'trusted_list')
+    list_file = Path(MODEL_DIR / "trusted_list")
     if list_file.is_file():
         list_file.unlink()
 
@@ -869,12 +911,6 @@ common format of all checkpoint files from the model releases.
     # Save model on disk for next usage
     ov.save_model(scale_map_learner, "scale_map_learner.xml")
 
-
-.. parsed-literal::
-
-    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
-
-
 Select inference device
 '''''''''''''''''''''''
 
@@ -910,10 +946,12 @@ Now we can go ahead and compile our SML model.
 
 .. code:: ipython3
 
-    def run_depth_scale_model(input_image_h: int, input_image_w: int,
-                              transformed_image_for_depth_scale: Tuple[torch.Tensor, torch.Tensor],
-                              compiled_scale_map_learner: type_compiled_model) -> np.ndarray:
-
+    def run_depth_scale_model(
+        input_image_h: int,
+        input_image_w: int,
+        transformed_image_for_depth_scale: Tuple[torch.Tensor, torch.Tensor],
+        compiled_scale_map_learner: type_compiled_model,
+    ) -> np.ndarray:
         """
         Run the compiled_scale_map_learner on the transformed image of dimensions
         input_image_w x input_image_h suitable to be used on such a model
@@ -937,7 +975,7 @@ Now we can go ahead and compile our SML model.
                 torch.nn.functional.interpolate(
                     sml_pred,
                     size=(input_image_h, input_image_w),
-                    mode='bicubic',
+                    mode="bicubic",
                     align_corners=False,
                 )
                 .squeeze()
@@ -950,9 +988,12 @@ Now we can go ahead and compile our SML model.
 .. code:: ipython3
 
     # Run the compiled SML model using the set of dummy inputs
-    sml_pred_dummy = run_depth_scale_model(input_image_h=IMAGE_H, input_image_w=IMAGE_W,
-                                           transformed_image_for_depth_scale=transformed_dummy_image_scale,
-                                           compiled_scale_map_learner=compiled_scale_map_learner)
+    sml_pred_dummy = run_depth_scale_model(
+        input_image_h=IMAGE_H,
+        input_image_w=IMAGE_W,
+        transformed_image_for_depth_scale=transformed_dummy_image_scale,
+        compiled_scale_map_learner=compiled_scale_map_learner,
+    )
 
 Storing and visualizing dummy results obtained
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -962,7 +1003,7 @@ Storing and visualizing dummy results obtained
 .. code:: ipython3
 
     # Base directory in which outputs would be stored as a pathlib.Path variable
-    OUTPUT_DIR = Path('output')
+    OUTPUT_DIR = Path("output")
 
     # Create the output directory adjacent to the notebook and suppress errors if the directory already exists
     OUTPUT_DIR.mkdir(exist_ok=True)
@@ -970,16 +1011,16 @@ Storing and visualizing dummy results obtained
     # Utility functions are directly available in modules.midas.utils
     # Provide path names without any extension and let the write_depth
     # function provide them for you. Take note of the arguments.
-    utils.write_depth(path=str(OUTPUT_DIR / 'dummy_input'), depth=d_depth, bits=2)
-    utils.write_depth(path=str(OUTPUT_DIR / 'dummy_input_sml'), depth=sml_pred_dummy, bits=2)
+    utils.write_depth(path=str(OUTPUT_DIR / "dummy_input"), depth=d_depth, bits=2)
+    utils.write_depth(path=str(OUTPUT_DIR / "dummy_input_sml"), depth=sml_pred_dummy, bits=2)
 
 .. code:: ipython3
 
     plt.figure()
 
-    img_dummy_in = mpimg.imread('data/image/dummy_img.png')
-    img_dummy_out = mpimg.imread(OUTPUT_DIR / 'dummy_input.png')
-    img_dummy_sml_out = mpimg.imread(OUTPUT_DIR / 'dummy_input_sml.png')
+    img_dummy_in = mpimg.imread("data/image/dummy_img.png")
+    img_dummy_out = mpimg.imread(OUTPUT_DIR / "dummy_input.png")
+    img_dummy_sml_out = mpimg.imread(OUTPUT_DIR / "dummy_input_sml.png")
 
     f, axes = plt.subplots(1, 3)
     plt.subplots_adjust(right=2.0)
@@ -988,9 +1029,9 @@ Storing and visualizing dummy results obtained
     axes[1].imshow(img_dummy_out)
     axes[2].imshow(img_dummy_sml_out)
 
-    axes[0].set_title('dummy input')
-    axes[1].set_title('depth prediction on dummy input')
-    axes[2].set_title('SML on depth estimate')
+    axes[0].set_title("dummy input")
+    axes[1].set_title("depth prediction on dummy input")
+    axes[2].set_title("SML on depth estimate")
 
 
 
@@ -1008,7 +1049,7 @@ Storing and visualizing dummy results obtained
 
 
 
-.. image:: 246-depth-estimation-videpth-with-output_files/246-depth-estimation-videpth-with-output_48_2.png
+.. image:: depth-estimation-videpth-with-output_files/depth-estimation-videpth-with-output_48_2.png
 
 
 Running inference on a test image
@@ -1024,7 +1065,7 @@ plotting of the depth map.
 
 If you haven’t noticed already the data directory of this tutorial has
 been arranged as follows. This allows us to comply to these
-`rules `__.
+`rules `__.
 
 .. code:: bash
 
@@ -1062,19 +1103,33 @@ present*\ `here `__ is
 Facebook AI Research’s library that provides state-of-the-art detection
@@ -93,20 +93,20 @@ reading model config.
 .. code:: ipython3
 
     import detectron2.model_zoo as detectron_zoo
-
-
-    def get_model_and_config(model_name:str):
+    
+    
+    def get_model_and_config(model_name: str):
         """
         Helper function for downloading PyTorch model and its configuration from Detectron2 Model Zoo
-
+    
         Parameters:
           model_name (str): model_id from Detectron2 Model Zoo
         Returns:
           model (torch.nn.Module): Pretrained model instance
           cfg (Config): Configuration for model
         """
-        cfg = detectron_zoo.get_config(model_name + '.yaml', trained=True)
-        model = detectron_zoo.get(model_name + '.yaml', trained=True)
+        cfg = detectron_zoo.get_config(model_name + ".yaml", trained=True)
+        model = detectron_zoo.get(model_name + ".yaml", trained=True)
         return model, cfg
 
 Detectron2 library is based on PyTorch. Starting from 2023.0 release
@@ -131,12 +131,13 @@ simplify model’s structure making it more export-friendly.
     import openvino as ov
     import warnings
     from typing import List, Dict
-
-    def convert_detectron2_model(model:torch.nn.Module, sample_input:List[Dict[str, torch.Tensor]]):
+    
+    
+    def convert_detectron2_model(model: torch.nn.Module, sample_input: List[Dict[str, torch.Tensor]]):
         """
         Function for converting Detectron2 models, creates TracingAdapter for making model tracing-friendly,
         prepares inputs and converts model to OpenVINO Model
-
+    
         Parameters:
           model (torch.nn.Module): Model object for conversion
           sample_input (List[Dict[str, torch.Tensor]]): sample input for tracing
@@ -144,17 +145,19 @@ simplify model’s structure making it more export-friendly.
           ov_model (ov.Model): OpenVINO Model
         """
         # prepare input for tracing adapter
-        tracing_input = [{'image': sample_input[0]["image"]}]
-
+        tracing_input = [{"image": sample_input[0]["image"]}]
+    
         # override model forward and disable postprocessing if required
         if isinstance(model, GeneralizedRCNN):
+    
             def inference(model, inputs):
                 # use do_postprocess=False so it returns ROI mask
                 inst = model.inference(inputs, do_postprocess=False)[0]
                 return [{"instances": inst}]
+    
         else:
             inference = None  # assume that we just call the model directly
-
+    
         # create traceable model
         traceable_model = TracingAdapter(model, tracing_input, inference)
         warnings.filterwarnings("ignore")
@@ -176,29 +179,29 @@ steps based on model specific transformations defined in model config.
     import requests
     from pathlib import Path
     from PIL import Image
-
+    
     MODEL_DIR = Path("model")
     DATA_DIR = Path("data")
-
+    
     MODEL_DIR.mkdir(exist_ok=True)
     DATA_DIR.mkdir(exist_ok=True)
-
+    
     input_image_url = "https://farm9.staticflickr.com/8040/8017130856_1b46b5f5fc_z.jpg"
-
+    
     image_file = DATA_DIR / "example_image.jpg"
-
+    
     if not image_file.exists():
         image = Image.open(requests.get(input_image_url, stream=True).raw)
         image.save(image_file)
     else:
         image = Image.open(image_file)
-
+    
     image
 
 
 
 
-.. image:: 123-detectron2-to-openvino-with-output_files/123-detectron2-to-openvino-with-output_8_0.png
+.. image:: detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_8_0.png
 
 
 
@@ -207,7 +210,8 @@ steps based on model specific transformations defined in model config.
     import detectron2.data.transforms as T
     from detectron2.data import detection_utils
     import torch
-
+    
+    
     def get_sample_inputs(image_path, cfg):
         # get a sample data
         original_image = detection_utils.read_image(image_path, format=cfg.INPUT.FORMAT)
@@ -216,9 +220,9 @@ steps based on model specific transformations defined in model config.
         height, width = original_image.shape[:2]
         image = aug.get_transform(original_image).apply_image(original_image)
         image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
-
+    
         inputs = {"image": image, "height": height, "width": width}
-
+    
         # Sample ready
         sample_inputs = [inputs]
         return sample_inputs
@@ -240,7 +244,7 @@ Download faster_rcnn_R_50_FPN_1x from Detectron Model Zoo.
 
 .. code:: ipython3
 
-    model_name = 'COCO-Detection/faster_rcnn_R_50_FPN_1x'
+    model_name = "COCO-Detection/faster_rcnn_R_50_FPN_1x"
     model, cfg = get_model_and_config(model_name)
     sample_input = get_sample_inputs(image_file, cfg)
 
@@ -256,10 +260,10 @@ directory.
 
 .. code:: ipython3
 
-    model_xml_path = MODEL_DIR / (model_name.split("/")[-1] + '.xml')
+    model_xml_path = MODEL_DIR / (model_name.split("/")[-1] + ".xml")
     if not model_xml_path.exists():
         ov_model = convert_detectron2_model(model, sample_input)
-        ov.save_model(ov_model, MODEL_DIR / (model_name.split("/")[-1] + '.xml'))
+        ov.save_model(ov_model, MODEL_DIR / (model_name.split("/")[-1] + ".xml"))
     else:
         ov_model = model_xml_path
 
@@ -273,16 +277,16 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-
+    
     core = ov.Core()
-
+    
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
-
+    
     device
 
 
@@ -334,11 +338,12 @@ provide helpers for wrapping output in original Detectron2 format.
     from detectron2.utils.visualizer import ColorMode, Visualizer
     from detectron2.data import MetadataCatalog
     import numpy as np
-
-    def postprocess_detection_result(outputs:Dict, orig_height:int, orig_width:int, conf_threshold:float = 0.0):
+    
+    
+    def postprocess_detection_result(outputs: Dict, orig_height: int, orig_width: int, conf_threshold: float = 0.0):
         """
         Helper function for postprocessing prediction results
-
+    
         Parameters:
           outputs (Dict): OpenVINO model output dictionary
           orig_height (int): original image height before preprocessing
@@ -352,7 +357,10 @@ provide helpers for wrapping output in original Detectron2 format.
         has_mask = len(outputs) >= 5
         masks = None if not has_mask else outputs[2]
         scores = outputs[2 if not has_mask else 3]
-        model_input_size = (int(outputs[3 if not has_mask else 4][0]), int(outputs[3 if not has_mask else 4][1]))
+        model_input_size = (
+            int(outputs[3 if not has_mask else 4][0]),
+            int(outputs[3 if not has_mask else 4][1]),
+        )
         filtered_detections = scores >= conf_threshold
         boxes = Boxes(boxes[filtered_detections])
         scores = scores[filtered_detections]
@@ -363,11 +371,12 @@ provide helpers for wrapping output in original Detectron2 format.
             out_dict["pred_masks"] = torch.from_numpy(masks)
         instances = Instances(model_input_size, **out_dict)
         return detector_postprocess(instances, orig_height, orig_width)
-
-    def draw_instance_prediction(img:np.ndarray, results:Instances, cfg:"Config"):
+    
+    
+    def draw_instance_prediction(img: np.ndarray, results: Instances, cfg: "Config"):
         """
         Helper function for visualization prediction results
-
+    
         Parameters:
           img (np.ndarray): original image for drawing predictions
           results (instances): model predictions
@@ -380,7 +389,6 @@ provide helpers for wrapping output in original Detectron2 format.
         img_with_res = visualizer.draw_instance_predictions(results)
         return img_with_res
 
-
 .. code:: ipython3
 
     results = postprocess_detection_result(results, sample_input[0]["height"], sample_input[0]["width"], conf_threshold=0.05)
@@ -390,7 +398,7 @@ provide helpers for wrapping output in original Detectron2 format.
 
 
 
-.. image:: 123-detectron2-to-openvino-with-output_files/123-detectron2-to-openvino-with-output_22_0.png
+.. image:: detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.png
 
 
 
@@ -422,11 +430,11 @@ Convert Instance Segmentation Model to OpenVINO Intermediate Representation
 
 .. code:: ipython3
 
-    model_xml_path = MODEL_DIR / (model_name.split("/")[-1] + '.xml')
-
+    model_xml_path = MODEL_DIR / (model_name.split("/")[-1] + ".xml")
+    
     if not model_xml_path.exists():
         ov_model = convert_detectron2_model(model, sample_input)
-        ov.save_model(ov_model, MODEL_DIR / (model_name.split("/")[-1] + '.xml'))
+        ov.save_model(ov_model, MODEL_DIR / (model_name.split("/")[-1] + ".xml"))
     else:
         ov_model = model_xml_path
 
@@ -473,6 +481,6 @@ postprocessing function handle this difference.
 
 
 
-.. image:: 123-detectron2-to-openvino-with-output_files/123-detectron2-to-openvino-with-output_32_0.png
+.. image:: detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.png
 
 
diff --git a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.jpg b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.jpg
new file mode 100644
index 00000000000..c3108b5b58f
--- /dev/null
+++ b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32e589c25e3dbe3506f7daff8bf41d86686035b32d3bb85fb342937707eb7c2e
+size 58113
diff --git a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.png b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.png
new file mode 100644
index 00000000000..46ab495c7f9
--- /dev/null
+++ b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1163cb4251bc3314b3a014f0a883967d75e45aef3d7cd1df2701db45c459ef61
+size 509139
diff --git a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.jpg b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.jpg
new file mode 100644
index 00000000000..2b49c278587
--- /dev/null
+++ b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5cfdeeeb8c2dcb115730a7e3de247f28573c7d2c18d1ed88821842333e72edd7
+size 54890
diff --git a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.png b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.png
new file mode 100644
index 00000000000..8ca760b79eb
--- /dev/null
+++ b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9e1a0544ca93d604126537c34e33e4be04525b1223b3cd712e812c3047838ae
+size 458489
diff --git a/docs/notebooks/123-detectron2-to-openvino-with-output_files/123-detectron2-to-openvino-with-output_8_0.jpg b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_8_0.jpg
similarity index 100%
rename from docs/notebooks/123-detectron2-to-openvino-with-output_files/123-detectron2-to-openvino-with-output_8_0.jpg
rename to docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_8_0.jpg
diff --git a/docs/notebooks/123-detectron2-to-openvino-with-output_files/123-detectron2-to-openvino-with-output_8_0.png b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_8_0.png
similarity index 100%
rename from docs/notebooks/123-detectron2-to-openvino-with-output_files/123-detectron2-to-openvino-with-output_8_0.png
rename to docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_8_0.png
diff --git a/docs/notebooks/267-distil-whisper-asr-with-output.rst b/docs/notebooks/distil-whisper-asr-with-output.rst
similarity index 99%
rename from docs/notebooks/267-distil-whisper-asr-with-output.rst
rename to docs/notebooks/distil-whisper-asr-with-output.rst
index 597b9b3ced0..2f89cacd5ad 100644
--- a/docs/notebooks/267-distil-whisper-asr-with-output.rst
+++ b/docs/notebooks/distil-whisper-asr-with-output.rst
@@ -74,7 +74,7 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q "transformers>=4.35" onnx "git+https://github.com/huggingface/optimum-intel.git" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "transformers>=4.35" "torch>=2.1" onnx "git+https://github.com/huggingface/optimum-intel.git" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "openvino>=2023.2.0" datasets  "gradio>=4.0" "librosa" "soundfile"
     %pip install -q "nncf>=2.6.0" "jiwer"
 
@@ -110,7 +110,7 @@ using tokenizer.
         "Distil-Whisper": [
             "distil-whisper/distil-large-v2",
             "distil-whisper/distil-medium.en",
-            "distil-whisper/distil-small.en"
+            "distil-whisper/distil-small.en",
         ],
         "Whisper": [
             "openai/whisper-large-v3",
@@ -124,7 +124,7 @@ using tokenizer.
             "openai/whisper-small.en",
             "openai/whisper-base.en",
             "openai/whisper-tiny.en",
-        ]
+        ],
     }
     
     model_type = widgets.Dropdown(
@@ -176,6 +176,7 @@ by Hugging Face datasets implementation.
 
     from datasets import load_dataset
     
+    
     def extract_input_features(sample):
         input_features = processor(
             sample["audio"]["array"],
@@ -184,9 +185,8 @@ by Hugging Face datasets implementation.
         ).input_features
         return input_features
     
-    dataset = load_dataset(
-        "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation"
-    )
+    
+    dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
     sample = dataset[0]
     input_features = extract_input_features(sample)
 
@@ -271,19 +271,21 @@ OpenVINO model. It means that we can reuse initialized early processor.
     from pathlib import Path
     from optimum.intel.openvino import OVModelForSpeechSeq2Seq
     
-    model_path = Path(model_id.value.replace('/', '_'))
+    model_path = Path(model_id.value.replace("/", "_"))
     ov_config = {"CACHE_DIR": ""}
     
     if not model_path.exists():
         ov_model = OVModelForSpeechSeq2Seq.from_pretrained(
-            model_id.value, ov_config=ov_config, export=True, compile=False, load_in_8bit=False
+            model_id.value,
+            ov_config=ov_config,
+            export=True,
+            compile=False,
+            load_in_8bit=False,
         )
         ov_model.half()
         ov_model.save_pretrained(model_path)
     else:
-        ov_model = OVModelForSpeechSeq2Seq.from_pretrained(
-            model_path, ov_config=ov_config, compile=False
-        )
+        ov_model = OVModelForSpeechSeq2Seq.from_pretrained(model_path, ov_config=ov_config, compile=False)
 
 
 .. parsed-literal::
@@ -433,8 +435,8 @@ Compare performance PyTorch vs OpenVINO
     Performance distil-large-v2 openvino speedup: 1.684
 
 
-load_in_8bit### Compare with OpenAI Whisper `back to top
-⬆️ <#table-of-contents>`__
+load_in_8bit### Compare with OpenAI Whisper 
+
 
 Usage OpenVINO model with HuggingFace pipelines
 -----------------------------------------------
@@ -496,9 +498,7 @@ seconds is optimal. To activate batching, pass the argument batch_size.
         seconds = milliseconds // 1_000
         milliseconds -= seconds * 1_000
     
-        return (
-            f"{hours}:" if hours > 0 else "00:"
-        ) + f"{minutes:02d}:{seconds:02d},{milliseconds:03d}"
+        return (f"{hours}:" if hours > 0 else "00:") + f"{minutes:02d}:{seconds:02d},{milliseconds:03d}"
     
     
     def prepare_srt(transcription):
@@ -530,9 +530,7 @@ popular subtitles format.
 
     srt_lines = prepare_srt(result)
     
-    display(
-        ipd.Audio(sample_long["audio"]["array"], rate=sample_long["audio"]["sampling_rate"])
-    )
+    display(ipd.Audio(sample_long["audio"]["array"], rate=sample_long["audio"]["sampling_rate"]))
     print("".join(srt_lines))
 
 
@@ -634,7 +632,7 @@ quantization.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -651,13 +649,13 @@ quantization.
 
 .. code:: ipython3
 
-    # Fetch notebook_utils module
-    import urllib.request
+    # Fetch `skip_kernel_extension` module
+    import requests
     
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/skip_kernel_extension.py',
-        filename='skip_kernel_extension.py'
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     %load_ext skip_kernel_extension
 
@@ -684,22 +682,22 @@ improves quantization quality.
     
     def collect_calibration_dataset(ov_model: OVModelForSpeechSeq2Seq, calibration_dataset_size: int):
         # Overwrite model request properties, saving the original ones for restoring later
-        original_encoder_request = ov_model.encoder.request
-        original_decoder_with_past_request = ov_model.decoder_with_past.request
         encoder_calibration_data = []
         decoder_calibration_data = []
-        ov_model.encoder.request = InferRequestWrapper(original_encoder_request, encoder_calibration_data)
-        ov_model.decoder_with_past.request = InferRequestWrapper(original_decoder_with_past_request,
-                                                                 decoder_calibration_data)
+        ov_model.encoder.request = InferRequestWrapper(ov_model.encoder.request, encoder_calibration_data, apply_caching=True)
+        ov_model.decoder_with_past.request = InferRequestWrapper(ov_model.decoder_with_past.request,
+                                                                 decoder_calibration_data,
+                                                                 apply_caching=True)
     
-        calibration_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
-        for sample in tqdm(islice(calibration_dataset, calibration_dataset_size), desc="Collecting calibration data",
-                           total=calibration_dataset_size):
-            input_features = extract_input_features(sample)
-            ov_model.generate(input_features)
-    
-        ov_model.encoder.request = original_encoder_request
-        ov_model.decoder_with_past.request = original_decoder_with_past_request
+        try:
+            calibration_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
+            for sample in tqdm(islice(calibration_dataset, calibration_dataset_size), desc="Collecting calibration data",
+                               total=calibration_dataset_size):
+                input_features = extract_input_features(sample)
+                ov_model.generate(input_features)
+        finally:
+            ov_model.encoder.request = ov_model.encoder.request.request
+            ov_model.decoder_with_past.request = ov_model.decoder_with_past.request.request
     
         return encoder_calibration_data, decoder_calibration_data
 
@@ -913,9 +911,9 @@ decoder-with-past model forwards, and for the whole model inference too.
     
         def wrapper(*args, **kwargs):
             if not MEASURE_TIME:
-                return original_fn(*args, **kwargs)
+                return original_fn(\*args, \*\*kwargs)
             start_time = time.perf_counter()
-            result = original_fn(*args, **kwargs)
+            result = original_fn(\*args, \*\*kwargs)
             end_time = time.perf_counter()
             time_list.append(end_time - start_time)
             return result
@@ -1007,12 +1005,12 @@ recognition. Multilingual support will be provided later.
 
     from transformers.pipelines.audio_utils import ffmpeg_read
     import gradio as gr
-    import urllib.request
     
-    urllib.request.urlretrieve(
-        url="https://huggingface.co/spaces/distil-whisper/whisper-vs-distil-whisper/resolve/main/assets/example_1.wav",
-        filename="example_1.wav",
-    )
+    r = requests.get("https://huggingface.co/spaces/distil-whisper/whisper-vs-distil-whisper/resolve/main/assets/example_1.wav")
+    
+    with open("example_1.wav", "wb") as f:
+        f.write(r.content)
+    
     
     BATCH_SIZE = 16
     MAX_AUDIO_MINS = 30  # maximum audio input in minutes
@@ -1049,9 +1047,7 @@ recognition. Multilingual support will be provided later.
         pipe_forward = ov_quantized_pipe_forward if quantized else ov_pipe_forward
     
         if inputs is None:
-            raise gr.Error(
-                "No audio file submitted! Please record or upload an audio file before submitting your request."
-            )
+            raise gr.Error("No audio file submitted! Please record or upload an audio file before submitting your request.")
     
         with open(inputs, "rb") as f:
             inputs = f.read()
@@ -1102,20 +1098,15 @@ recognition. Multilingual support will be provided later.
             if to_quantize.value:
                 button_q = gr.Button("Transcribe quantized")
         with gr.Row():
-            infer_time = gr.components.Textbox(
-                label="OpenVINO Distil-Whisper Transcription Time (s)"
-            )
+            infer_time = gr.components.Textbox(label="OpenVINO Distil-Whisper Transcription Time (s)")
             if to_quantize.value:
-                infer_time_q = gr.components.Textbox(
-                    label="OpenVINO Quantized Distil-Whisper Transcription Time (s)"
-                )
+                infer_time_q = gr.components.Textbox(label="OpenVINO Quantized Distil-Whisper Transcription Time (s)")
         with gr.Row():
-            transcription = gr.components.Textbox(
-                label="OpenVINO Distil-Whisper Transcription", show_copy_button=True
-            )
+            transcription = gr.components.Textbox(label="OpenVINO Distil-Whisper Transcription", show_copy_button=True)
             if to_quantize.value:
                 transcription_q = gr.components.Textbox(
-                    label="OpenVINO Quantized Distil-Whisper Transcription", show_copy_button=True
+                    label="OpenVINO Quantized Distil-Whisper Transcription",
+                    show_copy_button=True,
                 )
         button.click(
             fn=transcribe,
diff --git a/docs/notebooks/229-distilbert-sequence-classification-with-output.rst b/docs/notebooks/distilbert-sequence-classification-with-output.rst
similarity index 71%
rename from docs/notebooks/229-distilbert-sequence-classification-with-output.rst
rename to docs/notebooks/distilbert-sequence-classification-with-output.rst
index 260ec7d7e4b..bfdee960d39 100644
--- a/docs/notebooks/229-distilbert-sequence-classification-with-output.rst
+++ b/docs/notebooks/distilbert-sequence-classification-with-output.rst
@@ -30,45 +30,50 @@ Imports
 
 .. code:: ipython3
 
-    %pip install "openvino>=2023.1.0" transformers --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install "openvino>=2023.1.0" transformers "torch>=2.1" tqdm --extra-index-url https://download.pytorch.org/whl/cpu
 
 
 .. parsed-literal::
 
     Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cpu
-    Requirement already satisfied: openvino>=2023.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.0.0)
-    Requirement already satisfied: transformers in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.38.2)
-    Requirement already satisfied: numpy>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (1.23.5)
-    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (2023.2.1)
-    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (24.0)
+    Requirement already satisfied: openvino>=2023.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.0.0)
+    Requirement already satisfied: transformers in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.39.3)
+    Requirement already satisfied: torch>=2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2.2.2+cpu)
+    Requirement already satisfied: tqdm in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.66.2)
+    Requirement already satisfied: numpy>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (1.23.5)
+    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (2024.1.0)
+    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (24.0)
 
 
 .. parsed-literal::
 
-    Requirement already satisfied: filelock in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (3.13.1)
-    Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.21.4)
-    Requirement already satisfied: pyyaml>=5.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (6.0.1)
-    Requirement already satisfied: regex!=2019.12.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (2023.12.25)
-    Requirement already satisfied: requests in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (2.31.0)
-    Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.15.2)
-    Requirement already satisfied: safetensors>=0.4.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.4.2)
-    Requirement already satisfied: tqdm>=4.27 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (4.66.2)
+    Requirement already satisfied: filelock in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (3.13.4)
+    Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.22.2)
+    Requirement already satisfied: pyyaml>=5.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (6.0.1)
+    Requirement already satisfied: regex!=2019.12.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (2024.4.16)
+    Requirement already satisfied: requests in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (2.31.0)
+    Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.15.2)
+    Requirement already satisfied: safetensors>=0.4.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.4.3)
+    Requirement already satisfied: typing-extensions>=4.8.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (4.11.0)
+    Requirement already satisfied: sympy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (1.12)
+    Requirement already satisfied: networkx in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1)
+    Requirement already satisfied: jinja2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1.3)
+    Requirement already satisfied: fsspec in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (2024.2.0)
 
 
 .. parsed-literal::
 
-    Requirement already satisfied: fsspec>=2023.5.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2024.2.0)
-    Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.10.0)
-    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (3.3.2)
-    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (3.6)
-    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (2.2.1)
-    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (2024.2.2)
+    Requirement already satisfied: MarkupSafe>=2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jinja2->torch>=2.1) (2.1.5)
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
+    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (3.3.2)
+    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (3.7)
+    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (2.2.1)
+    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (2024.2.2)
+    Requirement already satisfied: mpmath>=0.19 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from sympy->torch>=2.1) (1.3.0)
+
 
 .. parsed-literal::
 
@@ -96,9 +101,7 @@ model from Hugging Face.
 .. code:: ipython3
 
     checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
-    model = AutoModelForSequenceClassification.from_pretrained(
-        pretrained_model_name_or_path=checkpoint
-    )
+    model = AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path=checkpoint)
 
 Initializing the Tokenizer
 --------------------------
@@ -118,9 +121,7 @@ understand the context of a sentence. Here, we will use
 
 .. code:: ipython3
 
-    tokenizer = AutoTokenizer.from_pretrained(
-        pretrained_model_name_or_path=checkpoint
-    )
+    tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=checkpoint)
 
 Convert Model to OpenVINO Intermediate Representation format
 ------------------------------------------------------------
@@ -142,7 +143,10 @@ optimal execution on end-point target devices.
     ir_xml_path = Path(MODEL_DIR) / ir_xml_name
     
     MAX_SEQ_LENGTH = 128
-    input_info = [(ov.PartialShape([1, -1]), ov.Type.i64), (ov.PartialShape([1, -1]), ov.Type.i64)]
+    input_info = [
+        (ov.PartialShape([1, -1]), ov.Type.i64),
+        (ov.PartialShape([1, -1]), ov.Type.i64),
+    ]
     default_input = torch.ones(1, MAX_SEQ_LENGTH, dtype=torch.int64)
     inputs = {
         "input_ids": default_input,
@@ -155,9 +159,9 @@ optimal execution on end-point target devices.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:246: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:246: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       mask, torch.tensor(torch.finfo(scores.dtype).min)
 
 
@@ -188,8 +192,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -281,17 +285,19 @@ Read from a text file
 .. code:: ipython3
 
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
     
     # Download the text from the openvino_notebooks storage
     vocab_file_path = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/text/food_reviews.txt",
-        directory="data"
+        directory="data",
     )
 
 
@@ -304,7 +310,7 @@ Read from a text file
 .. code:: ipython3
 
     start_time = time.perf_counter()
-    with vocab_file_path.open(mode='r') as f:
+    with vocab_file_path.open(mode="r") as f:
         input_text = f.readlines()
         for lines in input_text:
             print("User Input: ", lines)
diff --git a/docs/notebooks/240-dolly-2-instruction-following-with-output.rst b/docs/notebooks/dolly-2-instruction-following-with-output.rst
similarity index 88%
rename from docs/notebooks/240-dolly-2-instruction-following-with-output.rst
rename to docs/notebooks/dolly-2-instruction-following-with-output.rst
index 47685d62f5e..170ac68c2db 100644
--- a/docs/notebooks/240-dolly-2-instruction-following-with-output.rst
+++ b/docs/notebooks/dolly-2-instruction-following-with-output.rst
@@ -121,8 +121,8 @@ documentation `__.
 
 .. code:: ipython3
 
-    %pip install -q "diffusers>=0.16.1" "transformers>=4.33.0" "openvino>=2023.2.0" "nncf>=2.6.0" onnx gradio --extra-index-url https://download.pytorch.org/whl/cpu
-    %pip install -q --upgrade "git+https://github.com/huggingface/optimum-intel.git" 
+    %pip install -q "diffusers>=0.16.1" "transformers>=4.33.0" "torch>=2.1" "openvino>=2023.2.0" "nncf>=2.6.0" onnx "gradio>=4.19" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q --upgrade "git+https://github.com/huggingface/optimum-intel.git"
 
 Select inference device
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -140,8 +140,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
     
@@ -201,12 +201,18 @@ compatible with Optimum models.
     
     current_device = device.value
     
-    ov_config = {'PERFORMANCE_HINT': 'LATENCY', 'NUM_STREAMS': '1', "CACHE_DIR": ""}
+    ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
     
     if model_path.exists():
         ov_model = OVModelForCausalLM.from_pretrained(model_path, device=current_device, ov_config=ov_config)
     else:
-        ov_model = OVModelForCausalLM.from_pretrained(model_id, device=current_device, export=True, ov_config=ov_config, load_in_8bit=False)
+        ov_model = OVModelForCausalLM.from_pretrained(
+            model_id,
+            device=current_device,
+            export=True,
+            ov_config=ov_config,
+            load_in_8bit=False,
+        )
         ov_model.half()
         ov_model.save_pretrained(model_path)
 
@@ -247,7 +253,7 @@ accuracy drop.
 
     to_compress = widgets.Checkbox(
         value=True,
-        description='INT8 Compression',
+        description="INT8 Compression",
         disabled=False,
     )
     print("Click on checkbox for enabling / disabling weights compression")
@@ -270,25 +276,28 @@ accuracy drop.
 .. code:: ipython3
 
     import gc
-    from optimum.intel import OVQuantizer
+    from optimum.intel import OVQuantizer, OVConfig, OVWeightQuantizationConfig
+    
+    compressed_model_path = Path(f"{model_path}_compressed")
     
-    compressed_model_path = Path(f'{model_path}_compressed')
     
     def calculate_compression_rate(model_path_ov, model_path_ov_compressed):
-        model_size_original = model_path_ov.with_suffix(".bin").stat().st_size / 2 ** 20
-        model_size_compressed = model_path_ov_compressed.with_suffix(".bin").stat().st_size / 2 ** 20
+        model_size_original = model_path_ov.with_suffix(".bin").stat().st_size / 2**20
+        model_size_compressed = model_path_ov_compressed.with_suffix(".bin").stat().st_size / 2**20
         print(f"* Original IR model size: {model_size_original:.2f} MB")
         print(f"* Compressed IR model size: {model_size_compressed:.2f} MB")
         print(f"* Model compression rate: {model_size_original / model_size_compressed:.3f}")
     
+    
     if to_compress.value:
         if not compressed_model_path.exists():
             quantizer = OVQuantizer.from_pretrained(ov_model)
-            quantizer.quantize(save_directory=compressed_model_path, weights_only=True)
+            ov_config = OVConfig(quantization_config=OVWeightQuantizationConfig(bits=8))
+            quantizer.quantize(save_directory=compressed_model_path, ov_config=ov_config)
             del quantizer
             gc.collect()
-        
-        calculate_compression_rate(model_path / 'openvino_model.xml', compressed_model_path / 'openvino_model.xml')
+    
+        calculate_compression_rate(model_path / "openvino_model.xml", compressed_model_path / "openvino_model.xml")
         ov_model = OVModelForCausalLM.from_pretrained(compressed_model_path, device=current_device, ov_config=ov_config)
 
 
@@ -438,9 +447,7 @@ into model with providing additional context.
     INSTRUCTION_KEY = "### Instruction:"
     RESPONSE_KEY = "### Response:"
     END_KEY = "### End"
-    INTRO_BLURB = (
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request."
-    )
+    INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
     
     # This is the prompt that is used for generating responses using an already trained model.  It ends with the response
     # key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
@@ -489,7 +496,11 @@ the code below find its id for using it as generation stop-criteria.
             raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
         return token_ids[0]
     
-    tokenizer_response_key = next((token for token in tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None)
+    
+    tokenizer_response_key = next(
+        (token for token in tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)),
+        None,
+    )
     
     end_key_token_id = None
     if tokenizer_response_key:
@@ -510,10 +521,17 @@ parameter and returns model response.
 
 .. code:: ipython3
 
-    def run_generation(user_text:str, top_p:float, temperature:float, top_k:int, max_new_tokens:int, perf_text:str):
+    def run_generation(
+        user_text: str,
+        top_p: float,
+        temperature: float,
+        top_k: int,
+        max_new_tokens: int,
+        perf_text: str,
+    ):
         """
         Text generation function
-        
+    
         Parameters:
           user_text (str): User-provided instruction for a generation.
           top_p (float):  Nucleus sampling. If set to < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for a generation.
@@ -525,10 +543,10 @@ parameter and returns model response.
           model_output (str) - model-generated text
           perf_text (str) - updated perf text filed content
         """
-        
+    
         # Prepare input prompt according to model expected template
         prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=user_text)
-        
+    
         # Tokenize the user text.
         model_inputs = tokenizer(prompt_text, return_tensors="pt")
     
@@ -543,7 +561,7 @@ parameter and returns model response.
             top_p=top_p,
             temperature=float(temperature),
             top_k=top_k,
-            eos_token_id=end_key_token_id
+            eos_token_id=end_key_token_id,
         )
         t = Thread(target=ov_model.generate, kwargs=generate_kwargs)
         t.start()
@@ -572,17 +590,23 @@ elements.
 
 .. code:: ipython3
 
-    def estimate_latency(current_time:float, current_perf_text:str, new_gen_text:str, per_token_time:List[float], num_tokens:int):
+    def estimate_latency(
+        current_time: float,
+        current_perf_text: str,
+        new_gen_text: str,
+        per_token_time: List[float],
+        num_tokens: int,
+    ):
         """
         Helper function for performance estimation
-        
+    
         Parameters:
           current_time (float): This step time in seconds.
           current_perf_text (str): Current content of performance UI field.
           new_gen_text (str): New generated text.
           per_token_time (List[float]): history of performance from previous steps.
           num_tokens (int): Total number of generated tokens.
-          
+    
         Returns:
           update for performance text field
           update for a total number of tokens
@@ -592,28 +616,32 @@ elements.
         per_token_time.append(num_current_toks / current_time)
         if len(per_token_time) > 10 and len(per_token_time) % 4 == 0:
             current_bucket = per_token_time[:-10]
-            return f"Average generation speed: {np.mean(current_bucket):.2f} tokens/s. Total generated tokens: {num_tokens}", num_tokens
+            return (
+                f"Average generation speed: {np.mean(current_bucket):.2f} tokens/s. Total generated tokens: {num_tokens}",
+                num_tokens,
+            )
         return current_perf_text, num_tokens
     
-    def reset_textbox(instruction:str, response:str, perf:str):
+    
+    def reset_textbox(instruction: str, response: str, perf: str):
         """
         Helper function for resetting content of all text fields
-        
+    
         Parameters:
           instruction (str): Content of user instruction field.
           response (str): Content of model response field.
           perf (str): Content of performance info filed
-        
+    
         Returns:
           empty string for each placeholder
         """
         return "", "", ""
     
     
-    def select_device(device_str:str, current_text:str = "", progress:gr.Progress = gr.Progress()):
+    def select_device(device_str: str, current_text: str = "", progress: gr.Progress = gr.Progress()):
         """
         Helper function for uploading model on the device.
-        
+    
         Parameters:
           device_str (str): Device name.
           current_text (str): Current content of user instruction field (used only for backup purposes, temporally replacing it on the progress bar during model loading).
@@ -624,7 +652,7 @@ elements.
         if device_str != ov_model._device:
             ov_model.request = None
             ov_model._device = device_str
-            
+    
             for i in progress.tqdm(range(1), desc=f"Model loading on {device_str}"):
                 ov_model.compile()
         return current_text
@@ -679,7 +707,7 @@ generation parameters:
             with gr.Column(scale=4):
                 user_text = gr.Textbox(
                     placeholder="Write an email about an alpaca that likes flan",
-                    label="User instruction"
+                    label="User instruction",
                 )
                 model_output = gr.Textbox(label="Model response", interactive=False)
                 performance = gr.Textbox(label="Performance", lines=1, interactive=False)
@@ -690,22 +718,54 @@ generation parameters:
             with gr.Column(scale=1):
                 device = gr.Dropdown(choices=available_devices, value=current_device, label="Device")
                 max_new_tokens = gr.Slider(
-                    minimum=1, maximum=1000, value=256, step=1, interactive=True, label="Max New Tokens",
+                    minimum=1,
+                    maximum=1000,
+                    value=256,
+                    step=1,
+                    interactive=True,
+                    label="Max New Tokens",
                 )
                 top_p = gr.Slider(
-                    minimum=0.05, maximum=1.0, value=0.92, step=0.05, interactive=True, label="Top-p (nucleus sampling)",
+                    minimum=0.05,
+                    maximum=1.0,
+                    value=0.92,
+                    step=0.05,
+                    interactive=True,
+                    label="Top-p (nucleus sampling)",
                 )
                 top_k = gr.Slider(
-                    minimum=0, maximum=50, value=0, step=1, interactive=True, label="Top-k",
+                    minimum=0,
+                    maximum=50,
+                    value=0,
+                    step=1,
+                    interactive=True,
+                    label="Top-k",
                 )
                 temperature = gr.Slider(
-                    minimum=0.1, maximum=5.0, value=0.8, step=0.1, interactive=True, label="Temperature",
+                    minimum=0.1,
+                    maximum=5.0,
+                    value=0.8,
+                    step=0.1,
+                    interactive=True,
+                    label="Temperature",
                 )
     
-        user_text.submit(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens, performance], [model_output, performance])
+        user_text.submit(
+            run_generation,
+            [user_text, top_p, temperature, top_k, max_new_tokens, performance],
+            [model_output, performance],
+        )
         button_submit.click(select_device, [device, user_text], [user_text])
-        button_submit.click(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens, performance], [model_output, performance])
-        button_clear.click(reset_textbox, [user_text, model_output, performance], [user_text, model_output, performance])
+        button_submit.click(
+            run_generation,
+            [user_text, top_p, temperature, top_k, max_new_tokens, performance],
+            [model_output, performance],
+        )
+        button_clear.click(
+            reset_textbox,
+            [user_text, model_output, performance],
+            [user_text, model_output, performance],
+        )
         device.change(select_device, [device, user_text], [user_text])
     
     if __name__ == "__main__":
diff --git a/docs/notebooks/274-efficient-sam-with-output.rst b/docs/notebooks/efficient-sam-with-output.rst
similarity index 66%
rename from docs/notebooks/274-efficient-sam-with-output.rst
rename to docs/notebooks/efficient-sam-with-output.rst
index 534fc837601..4f7c2cde7e1 100644
--- a/docs/notebooks/274-efficient-sam-with-output.rst
+++ b/docs/notebooks/efficient-sam-with-output.rst
@@ -78,14 +78,9 @@ Prerequisites
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
     
-    %pip install -q "openvino>=2023.3.0" "nncf>=2.7.0" opencv-python "gradio>=4.13" torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2023.3.0" "nncf>=2.7.0" opencv-python "gradio>=4.13" torch torchvision tqdm  --extra-index-url https://download.pytorch.org/whl/cpu
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -93,7 +88,7 @@ Prerequisites
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    WARNING: typer 0.12.3 does not provide the extra 'all'
     
 
 .. parsed-literal::
@@ -120,441 +115,393 @@ Prerequisites
 .. parsed-literal::
 
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-
-.. parsed-literal::
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-remote: Counting objects:  99% (139/140)
-remote: Counting objects: 100% (140/140)
-remote: Counting objects: 100% (140/140), done.
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-
-.. parsed-literal::
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-remote: Compressing objects:  98% (84/85)
-remote: Compressing objects: 100% (85/85)
-remote: Compressing objects: 100% (85/85), done.
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     Receiving objects:   0% (1/424)
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 .. parsed-literal::
 
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-
-.. parsed-literal::
-
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 .. parsed-literal::
 
-    Receiving objects:   5% (22/424)
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+    Receiving objects:   5% (24/424), 7.11 MiB | 7.01 MiB/s
 
 .. parsed-literal::
 
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 .. parsed-literal::
 
-    Receiving objects:   6% (26/424), 32.47 MiB | 15.70 MiB/s
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 .. parsed-literal::
 
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 .. parsed-literal::
 
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 .. parsed-literal::
 
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 .. parsed-literal::
 
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-Receiving objects:  45% (191/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  46% (196/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  47% (200/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  48% (204/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  49% (208/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  50% (212/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  51% (217/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  52% (221/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  53% (225/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  54% (229/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  55% (234/424), 171.10 MiB | 25.39 MiB/s
-Receiving objects:  56% (238/424), 171.10 MiB | 25.39 MiB/s
+    Receiving objects:  13% (56/424), 93.08 MiB | 11.64 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  56% (240/424), 185.16 MiB | 25.90 MiB/s
+    Receiving objects:  13% (56/424), 105.71 MiB | 11.89 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  56% (241/424), 199.39 MiB | 26.22 MiB/s
-Receiving objects:  57% (242/424), 199.39 MiB | 26.22 MiB/s
-Receiving objects:  58% (246/424), 199.39 MiB | 26.22 MiB/s
-Receiving objects:  59% (251/424), 199.39 MiB | 26.22 MiB/s
-Receiving objects:  60% (255/424), 199.39 MiB | 26.22 MiB/s
-Receiving objects:  61% (259/424), 199.39 MiB | 26.22 MiB/s
+    Receiving objects:  14% (60/424), 105.71 MiB | 11.89 MiB/s
+Receiving objects:  15% (64/424), 105.71 MiB | 11.89 MiB/s
+Receiving objects:  16% (68/424), 105.71 MiB | 11.89 MiB/s
+Receiving objects:  17% (73/424), 105.71 MiB | 11.89 MiB/s
+Receiving objects:  18% (77/424), 105.71 MiB | 11.89 MiB/s
+Receiving objects:  19% (81/424), 105.71 MiB | 11.89 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  62% (263/424), 199.39 MiB | 26.22 MiB/s
+    Receiving objects:  20% (85/424), 105.71 MiB | 11.89 MiB/s
+Receiving objects:  21% (90/424), 105.71 MiB | 11.89 MiB/s
+Receiving objects:  22% (94/424), 105.71 MiB | 11.89 MiB/s
+Receiving objects:  23% (98/424), 105.71 MiB | 11.89 MiB/s
+Receiving objects:  24% (102/424), 105.71 MiB | 11.89 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  63% (268/424), 212.89 MiB | 26.59 MiB/s
+    Receiving objects:  25% (106/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  26% (111/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  27% (115/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  28% (119/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  29% (123/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  30% (128/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  31% (132/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  32% (136/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  33% (140/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  34% (145/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  35% (149/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  36% (153/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  37% (157/424), 112.21 MiB | 11.99 MiB/s
+Receiving objects:  38% (162/424), 112.21 MiB | 11.99 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  64% (272/424), 212.89 MiB | 26.59 MiB/s
-Receiving objects:  65% (276/424), 212.89 MiB | 26.59 MiB/s
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-Receiving objects:  86% (365/424), 212.89 MiB | 26.59 MiB/s
-Receiving objects:  87% (369/424), 212.89 MiB | 26.59 MiB/s
+    Receiving objects:  38% (164/424), 118.79 MiB | 12.04 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  87% (370/424), 242.73 MiB | 27.66 MiB/s
+    Receiving objects:  38% (164/424), 132.56 MiB | 12.57 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  87% (371/424), 271.36 MiB | 28.10 MiB/s
+    Receiving objects:  38% (164/424), 147.18 MiB | 13.34 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  87% (371/424), 293.98 MiB | 27.09 MiB/s
+    Receiving objects:  38% (164/424), 159.14 MiB | 13.27 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  88% (374/424), 293.98 MiB | 27.09 MiB/s
-Receiving objects:  89% (378/424), 293.98 MiB | 27.09 MiB/s
-Receiving objects:  90% (382/424), 293.98 MiB | 27.09 MiB/s
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-Receiving objects:  94% (399/424), 293.98 MiB | 27.09 MiB/s
-Receiving objects:  95% (403/424), 293.98 MiB | 27.09 MiB/s
+    Receiving objects:  38% (164/424), 172.36 MiB | 13.30 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  95% (407/424), 319.41 MiB | 26.57 MiB/s
+    Receiving objects:  38% (164/424), 186.46 MiB | 13.47 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  96% (408/424), 332.44 MiB | 26.47 MiB/s
-Receiving objects:  97% (412/424), 332.44 MiB | 26.47 MiB/s
-Receiving objects:  98% (416/424), 332.44 MiB | 26.47 MiB/s
-Receiving objects:  99% (420/424), 332.44 MiB | 26.47 MiB/s
+    Receiving objects:  38% (164/424), 201.36 MiB | 13.62 MiB/s
 
 .. parsed-literal::
 
-    remote: Total 424 (delta 84), reused 99 (delta 55), pack-reused 284
-    Receiving objects: 100% (424/424), 332.44 MiB | 26.47 MiB/s
-Receiving objects: 100% (424/424), 334.57 MiB | 24.35 MiB/s, done.
-    Resolving deltas:   0% (0/226)
-Resolving deltas:   4% (11/226)
-Resolving deltas:   7% (17/226)
-Resolving deltas:   9% (22/226)
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-Resolving deltas:  20% (47/226)
-Resolving deltas:  26% (59/226)
+    Receiving objects:  39% (166/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  40% (170/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  41% (174/424), 208.90 MiB | 13.65 MiB/s
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+Receiving objects:  44% (187/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  45% (191/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  46% (196/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  47% (200/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  48% (204/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  49% (208/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  50% (212/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  51% (217/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  52% (221/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  53% (225/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  54% (229/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  55% (234/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  56% (238/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  57% (242/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  58% (246/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  59% (251/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  60% (255/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  61% (259/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  62% (263/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  63% (268/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  64% (272/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  65% (276/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  66% (280/424), 208.90 MiB | 13.65 MiB/s
+Receiving objects:  67% (285/424), 208.90 MiB | 13.65 MiB/s
 
 .. parsed-literal::
 
-    Resolving deltas:  28% (65/226)
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-Resolving deltas:  58% (133/226)
-Resolving deltas:  59% (135/226)
-Resolving deltas:  60% (136/226)
+    Receiving objects:  67% (288/424), 216.43 MiB | 13.99 MiB/s
 
 .. parsed-literal::
 
-    Resolving deltas:  61% (138/226)
-Resolving deltas:  69% (158/226)
-Resolving deltas:  83% (188/226)
-Resolving deltas:  92% (208/226)
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-Resolving deltas:  96% (217/226)
-Resolving deltas:  97% (220/226)
-Resolving deltas:  98% (222/226)
+    Receiving objects:  67% (288/424), 232.14 MiB | 14.78 MiB/s
 
 .. parsed-literal::
 
-    Resolving deltas:  99% (224/226)
+    Receiving objects:  68% (289/424), 240.34 MiB | 15.09 MiB/s
+Receiving objects:  69% (293/424), 240.34 MiB | 15.09 MiB/s
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+Receiving objects:  71% (302/424), 240.34 MiB | 15.09 MiB/s
+Receiving objects:  72% (306/424), 240.34 MiB | 15.09 MiB/s
+Receiving objects:  73% (310/424), 240.34 MiB | 15.09 MiB/s
 
 .. parsed-literal::
 
-    Resolving deltas: 100% (226/226)
-Resolving deltas: 100% (226/226), done.
+    Receiving objects:  73% (310/424), 248.86 MiB | 15.43 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  74% (314/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  75% (318/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  76% (323/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  77% (327/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  78% (331/424), 257.49 MiB | 15.77 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  79% (335/424), 257.49 MiB | 15.77 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  80% (340/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  81% (344/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  82% (348/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  83% (352/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  84% (357/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  85% (361/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  86% (365/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  87% (369/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  88% (374/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  89% (378/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  90% (382/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  91% (386/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  92% (391/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  93% (395/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  94% (399/424), 257.49 MiB | 15.77 MiB/s
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+Receiving objects:  97% (412/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects:  98% (416/424), 257.49 MiB | 15.77 MiB/s
+remote: Total 424 (delta 76), reused 52 (delta 52), pack-reused 339
+    Receiving objects:  99% (420/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects: 100% (424/424), 257.49 MiB | 15.77 MiB/s
+Receiving objects: 100% (424/424), 262.14 MiB | 12.58 MiB/s, done.
+    Resolving deltas:   0% (0/246)
+Resolving deltas:   4% (10/246)
+Resolving deltas:   6% (17/246)
+Resolving deltas:  14% (36/246)
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+Resolving deltas:  36% (89/246)
+Resolving deltas:  37% (92/246)
+Resolving deltas:  41% (101/246)
+
+.. parsed-literal::
+
+    Resolving deltas:  42% (105/246)
+Resolving deltas:  44% (110/246)
+Resolving deltas:  46% (114/246)
+
+.. parsed-literal::
+
+    Resolving deltas:  48% (120/246)
+Resolving deltas:  49% (122/246)
+Resolving deltas:  52% (128/246)
+Resolving deltas:  54% (134/246)
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+Resolving deltas:  96% (237/246)
+Resolving deltas:  97% (240/246)
+Resolving deltas:  98% (243/246)
+
+.. parsed-literal::
+
+    Resolving deltas:  99% (245/246)
+
+.. parsed-literal::
+
+    Resolving deltas: 100% (246/246)
+Resolving deltas: 100% (246/246), done.
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/274-efficient-sam/EfficientSAM
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM
 
 
 Load PyTorch model
@@ -578,13 +525,19 @@ one of them as example.
 
 .. code:: ipython3
 
-    from efficient_sam.build_efficient_sam import build_efficient_sam_vitt, build_efficient_sam_vits
+    from efficient_sam.build_efficient_sam import (
+        build_efficient_sam_vitt,
+        build_efficient_sam_vits,
+    )
     import zipfile
     
-    MODELS_LIST = {"efficient-sam-vitt": build_efficient_sam_vitt, "efficient-sam-vits": build_efficient_sam_vits}
+    MODELS_LIST = {
+        "efficient-sam-vitt": build_efficient_sam_vitt,
+        "efficient-sam-vits": build_efficient_sam_vits,
+    }
     
     # Since EfficientSAM-S checkpoint file is >100MB, we store the zip file.
-    with zipfile.ZipFile("weights/efficient_sam_vits.pt.zip", 'r') as zip_ref:
+    with zipfile.ZipFile("weights/efficient_sam_vits.pt.zip", "r") as zip_ref:
         zip_ref.extractall("weights")
 
 Select one from supported models:
@@ -624,7 +577,7 @@ build PyTorch model
 Run PyTorch model inference
 ---------------------------
 
-Now, when we selected and
+ Now, when we selected and
 loaded PyTorch model, we can check its result
 
 Prepare input data
@@ -653,7 +606,7 @@ bounding box, 3 - right-bottom point of bounding box.
 
 
 
-.. image:: 274-efficient-sam-with-output_files/274-efficient-sam-with-output_11_0.png
+.. image:: efficient-sam-with-output_files/efficient-sam-with-output_11_0.png
 
 
 
@@ -690,9 +643,7 @@ points. We also provided some helper function for results visualization.
     def postprocess_results(predicted_iou, predicted_logits):
         sorted_ids = np.argsort(-predicted_iou, axis=-1)
         predicted_iou = np.take_along_axis(predicted_iou, sorted_ids, axis=2)
-        predicted_logits = np.take_along_axis(
-            predicted_logits, sorted_ids[..., None, None], axis=2
-        )
+        predicted_logits = np.take_along_axis(predicted_logits, sorted_ids[..., None, None], axis=2)
     
         return predicted_logits[0, 0, 0, :, :] >= 0
     
@@ -723,9 +674,7 @@ points. We also provided some helper function for results visualization.
     def show_box(box, ax):
         x0, y0 = box[0], box[1]
         w, h = box[2] - box[0], box[3] - box[1]
-        ax.add_patch(
-            plt.Rectangle((x0, y0), w, h, edgecolor="yellow", facecolor=(0, 0, 0, 0), lw=5)
-        )
+        ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor="yellow", facecolor=(0, 0, 0, 0), lw=5))
     
     
     def show_anns(mask, ax):
@@ -768,11 +717,11 @@ The complete model inference example demonstrated below
 
 
 
-.. image:: 274-efficient-sam-with-output_files/274-efficient-sam-with-output_16_0.png
+.. image:: efficient-sam-with-output_files/efficient-sam-with-output_16_0.png
 
 
 
-.. image:: 274-efficient-sam-with-output_files/274-efficient-sam-with-output_16_1.png
+.. image:: efficient-sam-with-output_files/efficient-sam-with-output_16_1.png
 
 
 Convert model to OpenVINO IR format
@@ -796,7 +745,7 @@ disk using ``openvino.save_model``.
     
     core = ov.Core()
     
-    ov_model_path = Path(f'{model_id.value}.xml')
+    ov_model_path = Path(f"{model_id.value}.xml")
     
     if not ov_model_path.exists():
         ov_model = ov.convert_model(pt_model, example_input=example_input)
@@ -807,27 +756,27 @@ disk using ``openvino.save_model``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/274-efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:220: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:220: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if (
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/274-efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:241: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:241: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert (
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/274-efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:163: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:163: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       size = int(math.sqrt(xy_num))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/274-efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:164: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:164: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert size * size == xy_num
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/274-efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:166: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:166: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if size != h or size != w:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/274-efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:251: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:251: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert x.shape[2] == num_patches
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/274-efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if num_pts > self.decoder_max_num_input_points:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/274-efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:92: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:92: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       elif num_pts < self.decoder_max_num_input_points:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/274-efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:126: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:126: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if output_w > 0 and output_h > 0:
 
 
@@ -879,7 +828,6 @@ Now, we can take a look on OpenVINO model prediction
 
 .. code:: ipython3
 
-    
     example_input = prepare_input(image, input_points, input_labels, torch_tensor=False)
     result = compiled_model(example_input)
     
@@ -900,11 +848,11 @@ Now, we can take a look on OpenVINO model prediction
 
 
 
-.. image:: 274-efficient-sam-with-output_files/274-efficient-sam-with-output_24_0.png
+.. image:: efficient-sam-with-output_files/efficient-sam-with-output_24_0.png
 
 
 
-.. image:: 274-efficient-sam-with-output_files/274-efficient-sam-with-output_24_1.png
+.. image:: efficient-sam-with-output_files/efficient-sam-with-output_24_1.png
 
 
 Quantization
@@ -937,7 +885,7 @@ quantization.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -954,12 +902,13 @@ quantization.
 
 .. code:: ipython3
 
-    import urllib.request
+    # Fetch `skip_kernel_extension` module
+    import requests
     
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/skip_kernel_extension.py',
-        filename='skip_kernel_extension.py'
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     %load_ext skip_kernel_extension
 
@@ -981,11 +930,12 @@ creates DataLoader for preparing inputs for EfficientSAM model.
     
     from zipfile import ZipFile
     
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    r = requests.get(
+        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py',
     )
     
+    open('notebook_utils.py', 'w').write(r.text)
+    
     from notebook_utils import download_file
     
     DATA_URL = "https://ultralytics.com/assets/coco128.zip"
@@ -1090,14 +1040,14 @@ architecture type, we should specify ``transformer`` in ``model_type``.
 
 .. parsed-literal::
 
-    2024-03-13 00:10:17.192537: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:10:17.225119: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-17 23:43:02.085337: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 23:43:02.119379: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 00:10:17.833750: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-17 23:43:02.732513: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -1175,7 +1125,7 @@ architecture type, we should specify ``transformer`` in ``model_type``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
       return Tensor(self.data * unwrap_tensor_data(other))
 
 
@@ -1235,11 +1185,11 @@ Verify quantized model inference
 
 
 
-.. image:: 274-efficient-sam-with-output_files/274-efficient-sam-with-output_35_0.png
+.. image:: efficient-sam-with-output_files/efficient-sam-with-output_35_0.png
 
 
 
-.. image:: 274-efficient-sam-with-output_files/274-efficient-sam-with-output_35_1.png
+.. image:: efficient-sam-with-output_files/efficient-sam-with-output_35_1.png
 
 
 Save quantize model on disk
@@ -1316,7 +1266,7 @@ models, we use ``bencmark_app``.
 
 .. parsed-literal::
 
-    [ INFO ] Read model took 42.81 ms
+    [ INFO ] Read model took 42.86 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     batched_images (node: batched_images) : f32 / [...] / [?,?,?,?]
@@ -1340,7 +1290,7 @@ models, we use ``bencmark_app``.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 1395.23 ms
+    [ INFO ] Compile model took 1414.09 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -1348,6 +1298,10 @@ models, we use ``bencmark_app``.
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
     [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
     [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
+
+
+.. parsed-literal::
+
     [ INFO ]   CPU:
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
@@ -1377,31 +1331,27 @@ models, we use ``bencmark_app``.
     [ INFO ] Fill input 'batched_images' with random values 
     [ INFO ] Fill input 'batched_points' with random values 
     [ INFO ] Fill input 'batched_point_labels' with random values 
-
-
-.. parsed-literal::
-
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in full mode (inputs filling are included in measurement loop).
 
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 637.21 ms
+    [ INFO ] First inference took 644.24 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            55 iterations
-    [ INFO ] Duration:         17119.37 ms
+    [ INFO ] Count:            49 iterations
+    [ INFO ] Duration:         15719.86 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        1831.80 ms
-    [ INFO ]    Average:       1819.34 ms
-    [ INFO ]    Min:           600.66 ms
-    [ INFO ]    Max:           1947.48 ms
-    [ INFO ] Throughput:   3.21 FPS
+    [ INFO ]    Median:        1890.94 ms
+    [ INFO ]    Average:       1870.83 ms
+    [ INFO ]    Min:           622.00 ms
+    [ INFO ]    Max:           1963.97 ms
+    [ INFO ] Throughput:   3.12 FPS
 
 
 .. code:: ipython3
@@ -1431,11 +1381,15 @@ models, we use ``bencmark_app``.
 
 .. parsed-literal::
 
-    [ INFO ] Read model took 65.78 ms
+    [ INFO ] Read model took 66.17 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     batched_images (node: batched_images) : f32 / [...] / [?,?,?,?]
     [ INFO ]     batched_points (node: batched_points) : i64 / [...] / [?,?,?,?]
+
+
+.. parsed-literal::
+
     [ INFO ]     batched_point_labels (node: batched_point_labels) : i64 / [...] / [?,?,?]
     [ INFO ] Model outputs:
     [ INFO ]     ***NO_NAME*** (node: aten::reshape/Reshape_3) : f32 / [...] / [?,?,?,?,?]
@@ -1455,7 +1409,7 @@ models, we use ``bencmark_app``.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 1890.02 ms
+    [ INFO ] Compile model took 1891.51 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -1463,10 +1417,6 @@ models, we use ``bencmark_app``.
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
     [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
     [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
-
-
-.. parsed-literal::
-
     [ INFO ]   CPU:
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
@@ -1502,7 +1452,7 @@ models, we use ``bencmark_app``.
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 582.30 ms
+    [ INFO ] First inference took 588.40 ms
 
 
 .. parsed-literal::
@@ -1510,13 +1460,13 @@ models, we use ``bencmark_app``.
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
     [ INFO ] Count:            55 iterations
-    [ INFO ] Duration:         16025.25 ms
+    [ INFO ] Duration:         16253.02 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        1729.05 ms
-    [ INFO ]    Average:       1708.52 ms
-    [ INFO ]    Min:           526.74 ms
-    [ INFO ]    Max:           1796.14 ms
-    [ INFO ] Throughput:   3.43 FPS
+    [ INFO ]    Median:        1752.95 ms
+    [ INFO ]    Average:       1732.80 ms
+    [ INFO ]    Min:           518.84 ms
+    [ INFO ]    Max:           1804.26 ms
+    [ INFO ] Throughput:   3.38 FPS
 
 
 Interactive segmentation demo
@@ -1543,7 +1493,10 @@ Interactive segmentation demo
     examples_dir.mkdir(exist_ok=True)
     
     for img_id, image_url in enumerate(example_images):
-        urllib.request.urlretrieve(image_url, examples_dir / f"example_{img_id}.jpg")
+        r = requests.get(image_url)
+        img_path = examples_dir / f"example_{img_id}.jpg"
+        with img_path.open("wb") as f:
+            f.write(r.content)
     
     
     def sigmoid(x):
@@ -1566,7 +1519,12 @@ Interactive segmentation demo
                 continue
             annotation["id"] = i
             annotation["segmentation"] = mask
-            annotation["bbox"] = [np.min(tmp[0]), np.min(tmp[1]), np.max(tmp[1]), np.max(tmp[0])]
+            annotation["bbox"] = [
+                np.min(tmp[0]),
+                np.min(tmp[1]),
+                np.max(tmp[1]),
+                np.max(tmp[0]),
+            ]
             annotation["score"] = scores[i]
             annotation["area"] = annotation["segmentation"].sum()
             annotations.append(annotation)
@@ -1577,10 +1535,7 @@ Interactive segmentation demo
         h = masks[0]["segmentation"].shape[0]
         w = masks[0]["segmentation"].shape[1]
         if h != target_height or w != target_width:
-            points = [
-                [int(point[0] * w / target_width), int(point[1] * h / target_height)]
-                for point in points
-            ]
+            points = [[int(point[0] * w / target_width), int(point[1] * h / target_height)] for point in points]
         onemask = np.zeros((h, w))
         for i, annotation in enumerate(masks):
             if isinstance(annotation, dict):
@@ -1625,9 +1580,7 @@ Interactive segmentation demo
     
         mask = np.zeros((height, weight, 4))
     
-        h_indices, w_indices = np.meshgrid(
-            np.arange(height), np.arange(weight), indexing="ij"
-        )
+        h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing="ij")
         indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
     
         mask[h_indices, w_indices, :] = mask_image[indices]
@@ -1690,9 +1643,7 @@ Interactive segmentation demo
                         (original_w, original_h),
                         interpolation=cv2.INTER_NEAREST,
                     )
-                contours, _ = cv2.findContours(
-                    annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
-                )
+                contours, _ = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
                 for contour in contours:
                     contour_all.append(contour)
             cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2 // scale)
@@ -1710,7 +1661,6 @@ Interactive segmentation demo
         return image
     
     
-    
     # Description
     title = "
Efficient Segment Anything with OpenVINO and EfficientSAM 
"
     
@@ -1779,7 +1729,6 @@ Interactive segmentation demo
         all_masks = sigmoid(predicted_logits[0, 0, :, :, :]) >= 0.5
         predicted_iou = predicted_iou[0, 0, ...]
     
-    
         max_predicted_iou = -1
         selected_mask_using_predicted_iou = None
         selected_predicted_iou = None
@@ -1788,8 +1737,8 @@ Interactive segmentation demo
             curr_predicted_iou = predicted_iou[m]
             if curr_predicted_iou > max_predicted_iou or selected_mask_using_predicted_iou is None:
                 max_predicted_iou = curr_predicted_iou
-                selected_mask_using_predicted_iou = all_masks[m:m + 1]
-                selected_predicted_iou = predicted_iou[m:m + 1]
+                selected_mask_using_predicted_iou = all_masks[m : m + 1]
+                selected_predicted_iou = predicted_iou[m : m + 1]
     
         results = format_results(selected_mask_using_predicted_iou, selected_predicted_iou, predicted_logits, 0)
     
@@ -1877,14 +1826,28 @@ Interactive segmentation demo
             image = copy.deepcopy(cond_image)
         x, y = evt.index[0], evt.index[1]
         label = "Add Mask"
-        point_radius, point_color = 15, (255, 255, 0) if label == "Add Mask" else (255, 0, 255)
+        point_radius, point_color = 15, (
+            (255, 255, 0)
+            if label == "Add Mask"
+            else (
+                255,
+                0,
+                255,
+            )
+        )
         global_points.append([x, y])
         global_point_label.append(1 if label == "Add Mask" else 0)
     
         if image is not None:
             draw = ImageDraw.Draw(image)
     
-            draw.ellipse([(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], fill=point_color)
+            draw.ellipse(
+                [
+                    (x - point_radius, y - point_radius),
+                    (x + point_radius, y + point_radius),
+                ],
+                fill=point_color,
+            )
     
         return image, global_points, global_point_label
     
@@ -1896,13 +1859,27 @@ Interactive segmentation demo
             return image, global_points, global_point_label
         x, y = evt.index[0], evt.index[1]
         label = "Add Mask"
-        point_radius, point_color = 15, (255, 255, 0) if label == "Add Mask" else (255, 0, 255)
+        point_radius, point_color = 15, (
+            (255, 255, 0)
+            if label == "Add Mask"
+            else (
+                255,
+                0,
+                255,
+            )
+        )
         global_points.append([x, y])
         global_point_label.append(1 if label == "Add Mask" else 0)
     
         if image is not None:
             draw = ImageDraw.Draw(image)
-            draw.ellipse([(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], fill=point_color)
+            draw.ellipse(
+                [
+                    (x - point_radius, y - point_radius),
+                    (x + point_radius, y + point_radius),
+                ],
+                fill=point_color,
+            )
     
         if len(global_points) == 2:
             x1, y1 = global_points[0]
@@ -1959,11 +1936,8 @@ Interactive segmentation demo
             # ###
             with gr.Row():
                 with gr.Column():
-    
                     with gr.Column():
-                        segment_btn_p = gr.Button(
-                            "Segment with Point Prompt", variant="primary"
-                        )
+                        segment_btn_p = gr.Button("Segment with Point Prompt", variant="primary")
                         clear_btn_p = gr.Button("Clear", variant="secondary")
     
                     gr.Markdown("Try some of the examples below ⬇️")
@@ -1989,18 +1963,14 @@ Interactive segmentation demo
             # Submit & Clear
             with gr.Row():
                 with gr.Column():
-    
                     with gr.Column():
-                        segment_btn_b = gr.Button(
-                            "Segment with Box Prompt", variant="primary"
-                        )
+                        segment_btn_b = gr.Button("Segment with Box Prompt", variant="primary")
                         clear_btn_b = gr.Button("Clear", variant="secondary")
     
                     gr.Markdown("Try some of the examples below ⬇️")
                     gr.Examples(
                         examples=examples,
                         inputs=[cond_img_b],
-    
                         examples_per_page=4,
                     )
     
@@ -2008,16 +1978,28 @@ Interactive segmentation demo
                     # Description
                     gr.Markdown(description_p)
     
-        cond_img_p.select(get_points_with_draw, inputs=[segm_img_p, cond_img_p, global_points, global_point_label], outputs=[segm_img_p, global_points, global_point_label])
+        cond_img_p.select(
+            get_points_with_draw,
+            inputs=[segm_img_p, cond_img_p, global_points, global_point_label],
+            outputs=[segm_img_p, global_points, global_point_label],
+        )
     
-        cond_img_b.select(get_points_with_draw_, [segm_img_b, cond_img_b, global_points, global_point_label], [segm_img_b, global_points, global_point_label])
+        cond_img_b.select(
+            get_points_with_draw_,
+            [segm_img_b, cond_img_b, global_points, global_point_label],
+            [segm_img_b, global_points, global_point_label],
+        )
     
         segment_btn_p.click(
-            segment_with_points, inputs=[cond_img_p, global_points, global_point_label], outputs=[segm_img_p, global_points, global_point_label]
+            segment_with_points,
+            inputs=[cond_img_p, global_points, global_point_label],
+            outputs=[segm_img_p, global_points, global_point_label],
         )
     
         segment_btn_b.click(
-            segment_with_boxs, inputs=[cond_img_b, segm_img_b, global_points, global_point_label], outputs=[segm_img_b, global_points, global_point_label]
+            segment_with_boxs,
+            inputs=[cond_img_b, segm_img_b, global_points, global_point_label],
+            outputs=[segm_img_b, global_points, global_point_label],
         )
     
         clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p, global_points, global_point_label])
diff --git a/docs/notebooks/274-efficient-sam-with-output_files/274-efficient-sam-with-output_11_0.jpg b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_11_0.jpg
similarity index 100%
rename from docs/notebooks/274-efficient-sam-with-output_files/274-efficient-sam-with-output_11_0.jpg
rename to docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_11_0.jpg
diff --git a/docs/notebooks/274-efficient-sam-with-output_files/274-efficient-sam-with-output_11_0.png b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_11_0.png
similarity index 100%
rename from docs/notebooks/274-efficient-sam-with-output_files/274-efficient-sam-with-output_11_0.png
rename to docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_11_0.png
diff --git a/docs/notebooks/274-efficient-sam-with-output_files/274-efficient-sam-with-output_16_0.png b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_16_0.png
similarity index 100%
rename from docs/notebooks/274-efficient-sam-with-output_files/274-efficient-sam-with-output_16_0.png
rename to docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_16_0.png
diff --git a/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_16_1.png b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_16_1.png
new file mode 100644
index 00000000000..cedf338d0b6
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+++ b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_16_1.png
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+size 1260793
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similarity index 100%
rename from docs/notebooks/274-efficient-sam-with-output_files/274-efficient-sam-with-output_24_0.png
rename to docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_24_0.png
diff --git a/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_24_1.png b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_24_1.png
new file mode 100644
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similarity index 100%
rename from docs/notebooks/274-efficient-sam-with-output_files/274-efficient-sam-with-output_35_0.png
rename to docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_35_0.png
diff --git a/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_35_1.png b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_35_1.png
new file mode 100644
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+size 1263513
diff --git a/docs/notebooks/234-encodec-audio-compression-with-output.rst b/docs/notebooks/encodec-audio-compression-with-output.rst
similarity index 99%
rename from docs/notebooks/234-encodec-audio-compression-with-output.rst
rename to docs/notebooks/encodec-audio-compression-with-output.rst
index 66da395ef24..0446a02865b 100644
--- a/docs/notebooks/234-encodec-audio-compression-with-output.rst
+++ b/docs/notebooks/encodec-audio-compression-with-output.rst
@@ -56,20 +56,12 @@ Install required dependencies:
 
 .. code:: ipython3
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "openvino>=2023.3.0" "torch>=2.1" "torchaudio>=2.1" encodec gradio "librosa>=0.8.1"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "openvino>=2023.3.0" "torch>=2.1" "torchaudio>=2.1" "encodec>=0.1.1" "gradio>=4.19" "librosa>=0.8.1" "matplotlib<=3.7" tqdm
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    pyannote-audio 2.0.1 requires torchaudio<1.0,>=0.10, but you have torchaudio 2.2.1+cpu which is incompatible.
-    pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 4.25.3 which is incompatible.
-    torchvision 0.14.1+cpu requires torch==1.13.1, but you have torch 2.2.1+cpu which is incompatible.
+    WARNING: typer 0.12.3 does not provide the extra 'all'
     
 
 .. parsed-literal::
@@ -142,7 +134,7 @@ bandwidth.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
       warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
 
 
@@ -155,19 +147,24 @@ Let us explore model capabilities on example audio:
 
 .. code:: ipython3
 
-    import sys
     import librosa
     import matplotlib.pyplot as plt
     import librosa.display
     import IPython.display as ipd
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
     
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
     
     test_data_url = "https://github.com/facebookresearch/encodec/raw/main/test_24k.wav"
     
-    sample_file = 'test_24k.wav'
+    sample_file = "test_24k.wav"
     download_file(test_data_url, sample_file)
     audio, sr = librosa.load(sample_file)
     plt.figure(figsize=(14, 5))
@@ -196,7 +193,7 @@ Let us explore model capabilities on example audio:
 
 
 
-.. image:: 234-encodec-audio-compression-with-output_files/234-encodec-audio-compression-with-output_6_2.png
+.. image:: encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_6_2.png
 
 
 Preprocessing
@@ -297,7 +294,7 @@ similar as possible to the original.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
       warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
 
 
@@ -333,7 +330,7 @@ audio.
 
 
 
-.. image:: 234-encodec-audio-compression-with-output_files/234-encodec-audio-compression-with-output_19_1.png
+.. image:: encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_19_1.png
 
 
 Nice! Audio sounds close to original.
@@ -363,7 +360,7 @@ with ``ov.save_model``.
             codes, scale = self.model._encode_frame(x)
             if not self.model.normalize:
                 return codes
-            return codes, scale        
+            return codes, scale
 
 .. code:: ipython3
 
@@ -397,13 +394,13 @@ with ``ov.save_model``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:60: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:60: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:87: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:87: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       max_pad = max(padding_left, padding_right)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:89: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:89: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if length <= max_pad:
 
 
@@ -411,7 +408,11 @@ with ``ov.save_model``.
 
     OV_DECODER_PATH = Path("encodec_decoder.xml")
     if not OV_DECODER_PATH.exists():
-        decoder_ov = ov.convert_model(decoder, example_input=torch.zeros([1, 8, 1500], dtype=torch.long), input=[[1, 8, 1500]])
+        decoder_ov = ov.convert_model(
+            decoder,
+            example_input=torch.zeros([1, 8, 1500], dtype=torch.long),
+            input=[[1, 8, 1500]],
+        )
         ov.save_model(decoder_ov, OV_DECODER_PATH)
     else:
         decoder_ov = core.read_model(OV_DECODER_PATH)
@@ -419,11 +420,11 @@ with ``ov.save_model``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/quantization/core_vq.py:358: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/quantization/core_vq.py:358: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       quantized_out = torch.tensor(0.0, device=q_indices.device)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/quantization/core_vq.py:359: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/quantization/core_vq.py:359: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
       for i, indices in enumerate(q_indices):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:103: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:103: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert (padding_left + padding_right) <= x.shape[-1]
 
 
@@ -453,8 +454,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -506,7 +507,7 @@ select device from dropdown list for running inference using OpenVINO
     model._encode_frame = encode_frame
     model._decode_frame = decode_frame
     
-    MODELS[model_id] = lambda : model
+    MODELS[model_id] = lambda: model
 
 Run EnCodec with OpenVINO
 -------------------------
@@ -563,7 +564,7 @@ like with the original PyTorch models.
 
 
 
-.. image:: 234-encodec-audio-compression-with-output_files/234-encodec-audio-compression-with-output_38_1.png
+.. image:: encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_38_1.png
 
 
 .. code:: ipython3
@@ -572,12 +573,13 @@ like with the original PyTorch models.
     from typing import Tuple
     import numpy as np
     
+    
     def preprocess(input, sample_rate, model_sr, model_channels):
         input = torch.tensor(input, dtype=torch.float32)
         input = input / 2**15  # adjust to int16 scale
         input = input.unsqueeze(0)
         input = convert_audio(input, sample_rate, model_sr, model_channels)
-        
+    
         return input
     
     
@@ -588,25 +590,21 @@ like with the original PyTorch models.
         output = output.astype(np.int16)
     
         return output
-        
+    
     
     def _compress(input: Tuple[int, np.ndarray]):
         sample_rate, waveform = input
         waveform = preprocess(waveform, sample_rate, model_sr, model_channels)
-        
+    
         b = compress(model, waveform, use_lm=False)
         out, out_sr = decompress(b)
-        
+    
         out = postprocess(out)
         return out_sr, out
-        
-    demo = gr.Interface(
-        _compress,
-        'audio',
-        'audio',
-        examples=['test_24k.wav']
-    )
-        
+    
+    
+    demo = gr.Interface(_compress, "audio", "audio", examples=["test_24k.wav"])
+    
     try:
         demo.launch(debug=False)
     except Exception:
diff --git a/docs/notebooks/encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_19_1.png b/docs/notebooks/encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_19_1.png
new file mode 100644
index 00000000000..0aeedba5d00
--- /dev/null
+++ b/docs/notebooks/encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_19_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:160e17b680bd3d5e8ae8d05736f6c8794af22597097cc8481d0986915fe9d696
+size 44175
diff --git a/docs/notebooks/encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_38_1.png b/docs/notebooks/encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_38_1.png
new file mode 100644
index 00000000000..cb5978d13a0
--- /dev/null
+++ b/docs/notebooks/encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_38_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c02accf9f1cf8a5dff3d4c78aa47ae41802250a855310353044c2f58a5689c10
+size 44203
diff --git a/docs/notebooks/encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_6_2.png b/docs/notebooks/encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_6_2.png
new file mode 100644
index 00000000000..a8af4e5b615
--- /dev/null
+++ b/docs/notebooks/encodec-audio-compression-with-output_files/encodec-audio-compression-with-output_6_2.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2df9c2103837505ffcf5543e55a8d1589385ddb5e73b917d5efe9a6ebfd0368c
+size 44933
diff --git a/docs/notebooks/261-fast-segment-anything-with-output.rst b/docs/notebooks/fast-segment-anything-with-output.rst
similarity index 77%
rename from docs/notebooks/261-fast-segment-anything-with-output.rst
rename to docs/notebooks/fast-segment-anything-with-output.rst
index 50091029c90..1880f1a574a 100644
--- a/docs/notebooks/261-fast-segment-anything-with-output.rst
+++ b/docs/notebooks/fast-segment-anything-with-output.rst
@@ -66,15 +66,15 @@ Install requirements
 
 .. code:: ipython3
 
-    %pip install -q "ultralytics==8.0.200" onnx --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "ultralytics==8.1.42" onnx tqdm --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "openvino-dev>=2024.0.0"
-    %pip install -q "nncf>=2.6.0"
+    %pip install -q "nncf>=2.9.0"
     %pip install -q "gradio>=4.13"
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -82,11 +82,6 @@ Install requirements
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -94,17 +89,7 @@ Install requirements
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    WARNING: typer 0.12.3 does not provide the extra 'all'
 
 
 .. parsed-literal::
@@ -127,18 +112,23 @@ Imports
     from PIL import Image, ImageDraw
     from ultralytics import FastSAM
 
-    import urllib.request
     # Fetch skip_kernel_extension module
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/skip_kernel_extension.py',
-        filename='skip_kernel_extension.py'
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
+    open("skip_kernel_extension.py", "w").write(r.text)
     # Fetch `notebook_utils` module
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
+
     %load_ext skip_kernel_extension
 
 FastSAM in Ultralytics
@@ -167,7 +157,7 @@ model and generate a segmentation map.
 
 .. parsed-literal::
 
-    Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/FastSAM-x.pt to 'FastSAM-x.pt'...
+    Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/FastSAM-x.pt to 'FastSAM-x.pt'...
 
 
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@@ -178,167 +168,82 @@ model and generate a segmentation map.
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@@ -358,12 +263,12 @@ model and generate a segmentation map.
 
 .. parsed-literal::
 
-    image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/261-fast-segment-anything/coco_bike.jpg: 768x1024 37 objects, 617.2ms
+    image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/fast-segment-anything/coco_bike.jpg: 768x1024 37 objects, 624.0ms
 
 
 .. parsed-literal::
 
-    Speed: 3.0ms preprocess, 617.2ms inference, 27.3ms postprocess per image at shape (1, 3, 768, 1024)
+    Speed: 3.1ms preprocess, 624.0ms inference, 27.7ms postprocess per image at shape (1, 3, 768, 1024)
 
 
 The model returns segmentation maps for all the objects on the image.
@@ -376,7 +281,7 @@ Observe the results below.
 
 
 
-.. image:: 261-fast-segment-anything-with-output_files/261-fast-segment-anything-with-output_9_0.png
+.. image:: fast-segment-anything-with-output_files/fast-segment-anything-with-output_9_0.png
 
 
 
@@ -396,13 +301,12 @@ tracing. The FastSAM model itself is based on YOLOv8 model.
     # instance segmentation model
     ov_model_path = Path(f"{model_name}_openvino_model/{model_name}.xml")
     if not ov_model_path.exists():
-        ov_model = model.export(format="openvino", dynamic=True, half=False)
-
+        ov_model = model.export(format="openvino", dynamic=False, half=False)
 
 
 .. parsed-literal::
 
-    Ultralytics YOLOv8.0.200 🚀 Python-3.8.10 torch-2.2.1+cpu CPU (Intel Core(TM) i9-10920X 3.50GHz)
+    Ultralytics YOLOv8.1.42 🚀 Python-3.8.10 torch-2.2.2+cpu CPU (Intel Core(TM) i9-10920X 3.50GHz)
 
 
 .. parsed-literal::
@@ -411,17 +315,6 @@ tracing. The FastSAM model itself is based on YOLOv8 model.
     PyTorch: starting from 'FastSAM-x.pt' with input shape (1, 3, 1024, 1024) BCHW and output shape(s) ((1, 37, 21504), (1, 32, 256, 256)) (138.2 MB)
 
 
-.. parsed-literal::
-
-
-    ONNX: starting export with onnx 1.15.0 opset 17...
-
-
-.. parsed-literal::
-
-    ONNX: export success ✅ 3.6s, saved as 'FastSAM-x.onnx' (275.5 MB)
-
-
 .. parsed-literal::
 
 
@@ -430,14 +323,14 @@ tracing. The FastSAM model itself is based on YOLOv8 model.
 
 .. parsed-literal::
 
-    OpenVINO: export success ✅ 1.0s, saved as 'FastSAM-x_openvino_model/' (275.9 MB)
+    OpenVINO: export success ✅ 6.1s, saved as 'FastSAM-x_openvino_model/' (276.1 MB)
 
 
 .. parsed-literal::
 
 
-    Export complete (7.6s)
-    Results saved to /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/261-fast-segment-anything
+    Export complete (9.0s)
+    Results saved to /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/fast-segment-anything
     Predict:         yolo predict task=segment model=FastSAM-x_openvino_model imgsz=1024
     Validate:        yolo val task=segment model=FastSAM-x_openvino_model imgsz=1024 data=ultralytics/datasets/sa.yaml
     Visualize:       https://netron.app
@@ -510,11 +403,12 @@ object, so we need to redefine the magic ``__call__`` method.
 .. code:: ipython3
 
     class OVWrapper:
-        def __init__(self, ov_model, device="CPU", stride=32) -> None:
-            self.model = core.compile_model(ov_model, device_name=device)
+        def __init__(self, ov_model, device="CPU", stride=32, ov_config=None) -> None:
+            ov_config = ov_config or {}
+            self.model = core.compile_model(ov_model, device, ov_config)
 
             self.stride = stride
-            self.pt = True
+            self.pt = False
             self.fp16 = False
             self.names = {0: "object"}
 
@@ -527,10 +421,19 @@ pipeline.
 
 .. code:: ipython3
 
-    wrapped_model = OVWrapper(ov_model_path, device=device.value, stride=model.predictor.model.stride)
+    ov_config = {}
+    if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
+        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
+
+    wrapped_model = OVWrapper(
+        ov_model_path,
+        device=device.value,
+        stride=model.predictor.model.stride,
+        ov_config=ov_config,
+    )
     model.predictor.model = wrapped_model
 
-    ov_results = model(image_uri, device=device.value, retina_masks=True, imgsz=640, conf=0.6, iou=0.9)
+    ov_results = model(image_uri, device=device.value, retina_masks=True, imgsz=1024, conf=0.6, iou=0.9)
 
 
 
@@ -540,12 +443,12 @@ pipeline.
 
 .. parsed-literal::
 
-    image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/261-fast-segment-anything/coco_bike.jpg: 480x640 33 objects, 299.4ms
+    image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/fast-segment-anything/coco_bike.jpg: 1024x1024 42 objects, 504.2ms
 
 
 .. parsed-literal::
 
-    Speed: 2.4ms preprocess, 299.4ms inference, 25.3ms postprocess per image at shape (1, 3, 480, 640)
+    Speed: 6.5ms preprocess, 504.2ms inference, 31.9ms postprocess per image at shape (1, 3, 1024, 1024)
 
 
 One can observe the converted model outputs in the next cell, they is
@@ -558,7 +461,7 @@ the same as of the original model.
 
 
 
-.. image:: 261-fast-segment-anything-with-output_files/261-fast-segment-anything-with-output_21_0.png
+.. image:: fast-segment-anything-with-output_files/fast-segment-anything-with-output_21_0.png
 
 
 
@@ -583,7 +486,7 @@ The optimization process contains the following steps:
 
     do_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
 
@@ -614,7 +517,7 @@ The quantization algorithm is based on `The YOLOv8 quantization
 example `__
 in the NNCF repo, refer there for more details. Moreover, you can check
 out other quantization tutorials in the `OV notebooks
-repo <230-yolov8-optimizati-with-output.html>`__.
+repo <../yolov8-optimization/>`__.
 
    **Note**: Model post-training quantization is time-consuming process.
    Be patient, it can take several minutes depending on your hardware.
@@ -652,7 +555,7 @@ repo <230-yolov8-optimizati-with-output.html>`__.
             self.compiled_model = core.compile_model(self.model, device_name="CPU")
 
             self.stride = stride
-            self.pt = True
+            self.pt = False
             self.fp16 = False
             self.names = {0: "object"}
 
@@ -700,7 +603,7 @@ repo <230-yolov8-optimizati-with-output.html>`__.
             coco_dataset = COCOLoader(OUT_DIR / 'coco128/images/train2017')
             with calibration_data_collection():
                 for image in tqdm(coco_dataset[:calibration_dataset_size], desc="Collecting calibration data"):
-                    model(image, retina_masks=True, imgsz=640, conf=0.6, iou=0.9, verbose=False)
+                    model(image, retina_masks=True, imgsz=1024, conf=0.6, iou=0.9, verbose=False)
             calibration_cache_path.parent.mkdir(parents=True, exist_ok=True)
             with open(calibration_cache_path, "wb") as f:
                 pickle.dump(calibration_data, f)
@@ -724,18 +627,9 @@ repo <230-yolov8-optimizati-with-output.html>`__.
             ignored_scope=nncf.IgnoredScope(
                 types=["Multiply", "Subtract", "Sigmoid"],  # ignore operations
                 names=[
-                    "/model.22/dfl/conv/Conv",  # in the post-processing subgraph
-                    "/model.22/Add",
-                    "/model.22/Add_1",
-                    "/model.22/Add_2",
-                    "/model.22/Add_3",
-                    "/model.22/Add_4",
-                    "/model.22/Add_5",
-                    "/model.22/Add_6",
-                    "/model.22/Add_7",
-                    "/model.22/Add_8",
-                    "/model.22/Add_9",
-                    "/model.22/Add_10",
+                    "__module.model.22.dfl.conv/aten::_convolution/Convolution",  # in the post-processing subgraph
+                    "__module.model.22/aten::add/Add",
+                    "__module.model.22/aten::add/Add_1"
                 ],
             )
         )
@@ -772,42 +666,49 @@ repo <230-yolov8-optimizati-with-output.html>`__.
 
 .. parsed-literal::
 
-    INFO:nncf:12 ignored nodes were found by name in the NNCFGraph
+    INFO:nncf:3 ignored nodes were found by name in the NNCFGraph
 
 
 .. parsed-literal::
 
-    INFO:nncf:13 ignored nodes were found by types in the NNCFGraph
+    INFO:nncf:8 ignored nodes were found by types in the NNCFGraph
 
 
 .. parsed-literal::
 
-    INFO:nncf:Not adding activation input quantizer for operation: 204 /model.22/Sigmoid
+    INFO:nncf:Not adding activation input quantizer for operation: 275 __module.model.22/aten::sigmoid/Sigmoid
 
 
 .. parsed-literal::
 
-    INFO:nncf:Not adding activation input quantizer for operation: 246 /model.22/dfl/conv/Conv
+    INFO:nncf:Not adding activation input quantizer for operation: 325 __module.model.22.dfl.conv/aten::_convolution/Convolution
 
 
 .. parsed-literal::
 
-    INFO:nncf:Not adding activation input quantizer for operation: 275 /model.22/Sub
+    INFO:nncf:Not adding activation input quantizer for operation: 351 __module.model.22/aten::sub/Subtract
 
 
 .. parsed-literal::
 
-    INFO:nncf:Not adding activation input quantizer for operation: 276 /model.22/Add_10
+    INFO:nncf:Not adding activation input quantizer for operation: 352 __module.model.22/aten::add/Add
 
 
 .. parsed-literal::
 
-    INFO:nncf:Not adding activation input quantizer for operation: 299 /model.22/Sub_1
+    INFO:nncf:Not adding activation input quantizer for operation: 365 __module.model.22/aten::add/Add_1
+    378 __module.model.22/aten::div/Divide
+
 
 
 .. parsed-literal::
 
-    INFO:nncf:Not adding activation input quantizer for operation: 341 /model.22/Mul_5
+    INFO:nncf:Not adding activation input quantizer for operation: 366 __module.model.22/aten::sub/Subtract_1
+
+
+.. parsed-literal::
+
+    INFO:nncf:Not adding activation input quantizer for operation: 388 __module.model.22/aten::mul/Multiply
 
 
 
@@ -833,7 +734,7 @@ repo <230-yolov8-optimizati-with-output.html>`__.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
       return Tensor(self.data * unwrap_tensor_data(other))
 
 
@@ -880,7 +781,7 @@ calibration dataset to measure the performance.
 
     start_time = datetime.datetime.now()
     for image in tqdm(coco_dataset, desc="Measuring inference time"):
-        model(image, retina_masks=True, imgsz=640, conf=0.6, iou=0.9, verbose=False)
+        model(image, retina_masks=True, imgsz=1024, conf=0.6, iou=0.9, verbose=False)
     duration_base = (datetime.datetime.now() - start_time).seconds
     print("Segmented in", duration_base, "seconds.")
     print("Resulting in", round(calibration_dataset_size / duration_base, 2), "fps")
@@ -894,8 +795,8 @@ calibration dataset to measure the performance.
 
 .. parsed-literal::
 
-    Segmented in 21 seconds.
-    Resulting in 6.1 fps
+    Segmented in 68 seconds.
+    Resulting in 1.88 fps
 
 
 .. code:: ipython3
@@ -907,7 +808,7 @@ calibration dataset to measure the performance.
 
     start_time = datetime.datetime.now()
     for image in tqdm(coco_dataset, desc="Measuring inference time"):
-        model(image, retina_masks=True, imgsz=640, conf=0.6, iou=0.9, verbose=False)
+        model(image, retina_masks=True, imgsz=1024, conf=0.6, iou=0.9, verbose=False)
     duration_quantized = (datetime.datetime.now() - start_time).seconds
     print("Segmented in", duration_quantized, "seconds")
     print("Resulting in", round(calibration_dataset_size / duration_quantized, 2), "fps")
@@ -922,9 +823,9 @@ calibration dataset to measure the performance.
 
 .. parsed-literal::
 
-    Segmented in 11 seconds
-    Resulting in 11.64 fps
-    That is 1.91 times faster!
+    Segmented in 23 seconds
+    Resulting in 5.57 fps
+    That is 2.96 times faster!
 
 
 Try out the converted pipeline
@@ -945,6 +846,7 @@ bounding boxes on input image.
     import numpy as np
     import matplotlib.pyplot as plt
 
+
     def fast_process(
         annotations,
         image,
@@ -955,7 +857,6 @@ bounding boxes on input image.
         use_retina=True,
         with_contours=True,
     ):
-
         original_h = image.height
         original_w = image.width
 
@@ -1049,8 +950,11 @@ bounding boxes on input image.
 
     import gradio as gr
 
-    examples = [[image_uri], ["https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/empty_road_mapillary.jpg"],
-                ["https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/wall.jpg"]]
+    examples = [
+        [image_uri],
+        ["https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/empty_road_mapillary.jpg"],
+        ["https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/wall.jpg"],
+    ]
 
     object_points = []
     background_points = []
@@ -1063,17 +967,17 @@ based on user input.
 .. code:: ipython3
 
     def segment(
-            image,
-            model_type,
-            input_size=1024,
-            iou_threshold=0.75,
-            conf_threshold=0.4,
-            better_quality=True,
-            with_contours=True,
-            use_retina=True,
-            mask_random_color=True,
+        image,
+        model_type,
+        input_size=1024,
+        iou_threshold=0.75,
+        conf_threshold=0.4,
+        better_quality=True,
+        with_contours=True,
+        use_retina=True,
+        mask_random_color=True,
     ):
-        if do_quantize.value and model_type == 'Quantized model':
+        if do_quantize.value and model_type == "Quantized model":
             model.predictor.model = quantized_wrapped_model
         else:
             model.predictor.model = wrapped_model
@@ -1085,12 +989,13 @@ based on user input.
         new_h = int(h * scale)
         image = image.resize((new_w, new_h))
 
-        results = model(image,
-                        device=device.value,
-                        retina_masks=use_retina,
-                        iou=iou_threshold,
-                        conf=conf_threshold,
-                        imgsz=input_size,)
+        results = model(
+            image,
+            retina_masks=use_retina,
+            iou=iou_threshold,
+            conf=conf_threshold,
+            imgsz=input_size,
+        )
 
         masks = results[0].masks.data
         # Calculate annotations
@@ -1143,7 +1048,7 @@ based on user input.
             mask_random_color=mask_random_color,
             bbox=None,
             use_retina=use_retina,
-            with_contours=with_contours
+            with_contours=with_contours,
         )
 
 .. code:: ipython3
@@ -1177,10 +1082,11 @@ based on user input.
         # Draw a point
         ImageDraw.Draw(img).ellipse(
             [(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)],
-            fill=color
+            fill=color,
         )
         return img
 
+
     def clear_points() -> (Image.Image, None):
         """Gradio clear points callback."""
         global object_points, background_points, bbox_points
@@ -1190,6 +1096,7 @@ based on user input.
         bbox_points = []
         return last_image, None
 
+
     def save_last_picked_image(img: Image.Image) -> None:
         """Gradio callback saves the last used image."""
         global last_image
@@ -1200,6 +1107,7 @@ based on user input.
         # Removes the segmentation map output
         return None
 
+
     with gr.Blocks(title="Fast SAM") as demo:
         with gr.Row(variant="panel"):
             original_img = gr.Image(label="Input", value=examples[0][0], type="pil")
@@ -1207,23 +1115,27 @@ based on user input.
         with gr.Row():
             point_type = gr.Radio(
                 ["Object point", "Background point", "Bounding Box"],
-                value="Object point", label="Pixel selector type"
+                value="Object point",
+                label="Pixel selector type",
             )
             model_type = gr.Radio(
                 ["FP32 model", "Quantized model"] if do_quantize.value else ["FP32 model"],
-                value="FP32 model", label="Select model variant"
+                value="FP32 model",
+                label="Select model variant",
             )
         with gr.Row(variant="panel"):
             segment_button = gr.Button("Segment", variant="primary")
             clear_button = gr.Button("Clear points", variant="secondary")
-        gr.Examples(examples, inputs=original_img,
-                    fn=save_last_picked_image, run_on_click=True, outputs=segmented_img
+        gr.Examples(
+            examples,
+            inputs=original_img,
+            fn=save_last_picked_image,
+            run_on_click=True,
+            outputs=segmented_img,
         )
 
         # Callbacks
-        original_img.select(select_point,
-                            inputs=[original_img, point_type],
-                            outputs=original_img)
+        original_img.select(select_point, inputs=[original_img, point_type], outputs=original_img)
         original_img.upload(save_last_picked_image, inputs=original_img, outputs=segmented_img)
         clear_button.click(clear_points, outputs=[original_img, segmented_img])
         segment_button.click(segment, inputs=[original_img, model_type], outputs=segmented_img)
diff --git a/docs/notebooks/fast-segment-anything-with-output_files/fast-segment-anything-with-output_21_0.jpg b/docs/notebooks/fast-segment-anything-with-output_files/fast-segment-anything-with-output_21_0.jpg
new file mode 100644
index 00000000000..0a0ff4547b6
--- /dev/null
+++ b/docs/notebooks/fast-segment-anything-with-output_files/fast-segment-anything-with-output_21_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f7a417ababdcb2d4255987f0526f7e1e4eb4426cb2cb1a486fac37b420be4a6
+size 119342
diff --git a/docs/notebooks/fast-segment-anything-with-output_files/fast-segment-anything-with-output_21_0.png b/docs/notebooks/fast-segment-anything-with-output_files/fast-segment-anything-with-output_21_0.png
new file mode 100644
index 00000000000..827cd40e73b
--- /dev/null
+++ b/docs/notebooks/fast-segment-anything-with-output_files/fast-segment-anything-with-output_21_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c93d6c226025f3080cb2f0f5e7dbc85b8a9605398121d6f42117e0f1eb117e6
+size 799175
diff --git a/docs/notebooks/261-fast-segment-anything-with-output_files/261-fast-segment-anything-with-output_9_0.jpg b/docs/notebooks/fast-segment-anything-with-output_files/fast-segment-anything-with-output_9_0.jpg
similarity index 100%
rename from docs/notebooks/261-fast-segment-anything-with-output_files/261-fast-segment-anything-with-output_9_0.jpg
rename to docs/notebooks/fast-segment-anything-with-output_files/fast-segment-anything-with-output_9_0.jpg
diff --git a/docs/notebooks/261-fast-segment-anything-with-output_files/261-fast-segment-anything-with-output_9_0.png b/docs/notebooks/fast-segment-anything-with-output_files/fast-segment-anything-with-output_9_0.png
similarity index 100%
rename from docs/notebooks/261-fast-segment-anything-with-output_files/261-fast-segment-anything-with-output_9_0.png
rename to docs/notebooks/fast-segment-anything-with-output_files/fast-segment-anything-with-output_9_0.png
diff --git a/docs/notebooks/252-fastcomposer-image-generation-with-output.rst b/docs/notebooks/fastcomposer-image-generation-with-output.rst
similarity index 89%
rename from docs/notebooks/252-fastcomposer-image-generation-with-output.rst
rename to docs/notebooks/fastcomposer-image-generation-with-output.rst
index ad096c2ccb8..67294a27a84 100644
--- a/docs/notebooks/252-fastcomposer-image-generation-with-output.rst
+++ b/docs/notebooks/fastcomposer-image-generation-with-output.rst
@@ -52,13 +52,13 @@ Table of contents:
 Install Prerequisites
 ---------------------
 
-Install required packages.
+ Install required packages.
 
 .. code:: ipython3
 
     %pip install -q --upgrade pip
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu torch torchvision
-    %pip install -q transformers  huggingface-hub accelerate "diffusers>=0.16.1" gradio
+    %pip install -q transformers  huggingface-hub accelerate "diffusers>=0.16.1" "gradio>=4.19"
     %pip install -q "openvino>=2023.1.0"
 
 
@@ -96,7 +96,7 @@ Download pretrained model.
     from huggingface_hub import hf_hub_download
     
     
-    model_path = hf_hub_download(repo_id='mit-han-lab/fastcomposer', filename='pytorch_model.bin')
+    model_path = hf_hub_download(repo_id="mit-han-lab/fastcomposer", filename="pytorch_model.bin")
 
 Convert models to OpenVINO Intermediate representation (IR) format
 ------------------------------------------------------------------
@@ -116,7 +116,7 @@ Define a configuration and make instance of ``FastComposerModel``.
     @dataclass()
     class Config:
         finetuned_model_path = str(model_path)
-        image_encoder_name_or_path = 'openai/clip-vit-large-patch14'
+        image_encoder_name_or_path = "openai/clip-vit-large-patch14"
         localization_layers = 5
         mask_loss = False
         mask_loss_prob = 0.5
@@ -124,7 +124,7 @@ Define a configuration and make instance of ``FastComposerModel``.
         object_localization = False
         object_localization_weight = 0.01
         object_resolution = 256
-        pretrained_model_name_or_path = 'runwayml/stable-diffusion-v1-5'
+        pretrained_model_name_or_path = "runwayml/stable-diffusion-v1-5"
         revision = None
     
     
@@ -193,7 +193,7 @@ function.
             with torch.no_grad():
                 converted_model = ov.convert_model(model, example_input=example_input)
             ov.save_model(converted_model, xml_path)
-            
+    
             # cleanup memory
             torch._C._jit_clear_class_registry()
             torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
@@ -210,7 +210,7 @@ padded to the maximum length accepted by the model.
 
 .. code:: ipython3
 
-    text_encoder_ir_xml_path = Path('models/text_encoder_ir.xml')
+    text_encoder_ir_xml_path = Path("models/text_encoder_ir.xml")
     example_input = torch.zeros((1, 77), dtype=torch.int64)
     
     model.text_encoder.eval()
@@ -268,7 +268,7 @@ tensor of size [3, height, width].
         )
     )
     
-    object_transforms_ir_xml_path = Path('models/object_transforms_ir.xml')
+    object_transforms_ir_xml_path = Path("models/object_transforms_ir.xml")
     example_input = torch.zeros([3, 1500, 1453], dtype=torch.uint8)
     
     object_transforms.eval()
@@ -280,9 +280,9 @@ tensor of size [3, height, width].
 
 .. parsed-literal::
 
-    /home/ea/work/openvino_notebooks/notebooks/252-fastcomposer-image-generation/fastcomposer/fastcomposer/transforms.py:35: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /home/ea/work/openvino_notebooks/notebooks/fastcomposer-image-generation/fastcomposer/fastcomposer/transforms.py:35: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if h == w:
-    /home/ea/work/openvino_notebooks/notebooks/252-fastcomposer-image-generation/fastcomposer/fastcomposer/transforms.py:37: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /home/ea/work/openvino_notebooks/notebooks/fastcomposer-image-generation/fastcomposer/fastcomposer/transforms.py:37: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       elif h > w:
 
 
@@ -297,7 +297,7 @@ input and transforms it into a high-dimensional vector or embeddings.
 
 .. code:: ipython3
 
-    image_encoder_ir_xml_path = Path('models/image_encoder_ir.xml')
+    image_encoder_ir_xml_path = Path("models/image_encoder_ir.xml")
     example_input = torch.zeros((1, 2, 3, 256, 256), dtype=torch.float32)
     
     model.image_encoder.eval()
@@ -309,7 +309,7 @@ input and transforms it into a high-dimensional vector or embeddings.
 
 .. parsed-literal::
 
-    /home/ea/work/openvino_notebooks/notebooks/252-fastcomposer-image-generation/model.py:108: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /home/ea/work/openvino_notebooks/notebooks/fastcomposer-image-generation/model.py:108: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if h != self.image_size or w != self.image_size:
 
 
@@ -326,13 +326,13 @@ MLP.
 
 .. code:: ipython3
 
-    postfuse_module_ir_xml_path = Path('models/postfuse_module_ir.xml')
+    postfuse_module_ir_xml_path = Path("models/postfuse_module_ir.xml")
     
     example_input = [
         torch.zeros((1, 77, 768), dtype=torch.float32),
         torch.zeros((1, 2, 1, 768), dtype=torch.float32),
         torch.zeros((1, 77), dtype=torch.bool),
-        torch.zeros((1,), dtype=torch.int64)
+        torch.zeros((1,), dtype=torch.int64),
     ]
     
     model.postfuse_module.eval()
@@ -351,12 +351,12 @@ text encoder hidden state.
 
 .. code:: ipython3
 
-    unet_ir_xml_path = Path('models/unet_ir.xml')
+    unet_ir_xml_path = Path("models/unet_ir.xml")
     
     example_input = [
         torch.zeros((8, 4, 64, 64), dtype=torch.float32),
         torch.zeros((), dtype=torch.int64),
-        torch.zeros((8, 77, 768), dtype=torch.float32)
+        torch.zeros((8, 77, 768), dtype=torch.float32),
     ]
     model.unet.eval()
     convert(model.unet, unet_ir_xml_path, example_input)
@@ -421,7 +421,6 @@ convert outputs from numpy to torch types.
         library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
         """
     
-    
         @torch.no_grad()
         def _tokenize_and_mask_noun_phrases_ends(self, caption):
             input_ids = self.special_tokenizer.encode(caption)
@@ -441,23 +440,25 @@ convert outputs from numpy to torch types.
             if len(clean_input_ids) > max_len:
                 clean_input_ids = clean_input_ids[:max_len]
             else:
-                clean_input_ids = clean_input_ids + [self.tokenizer.pad_token_id] * (
-                    max_len - len(clean_input_ids)
-                )
+                clean_input_ids = clean_input_ids + [self.tokenizer.pad_token_id] * (max_len - len(clean_input_ids))
     
             if len(noun_phrase_end_mask) > max_len:
                 noun_phrase_end_mask = noun_phrase_end_mask[:max_len]
             else:
-                noun_phrase_end_mask = noun_phrase_end_mask + [False] * (
-                    max_len - len(noun_phrase_end_mask)
-                )
+                noun_phrase_end_mask = noun_phrase_end_mask + [False] * (max_len - len(noun_phrase_end_mask))
     
             clean_input_ids = torch.tensor(clean_input_ids, dtype=torch.long)
             noun_phrase_end_mask = torch.tensor(noun_phrase_end_mask, dtype=torch.bool)
             return clean_input_ids.unsqueeze(0), noun_phrase_end_mask.unsqueeze(0)
     
         @torch.no_grad()
-        def _encode_augmented_prompt(self, prompt: str, reference_images: List[Image.Image], device: torch.device, weight_dtype: torch.dtype):
+        def _encode_augmented_prompt(
+            self,
+            prompt: str,
+            reference_images: List[Image.Image],
+            device: torch.device,
+            weight_dtype: torch.dtype,
+        ):
             # TODO: check this
             # encode reference images
             object_pixel_values = []
@@ -478,12 +479,7 @@ convert outputs from numpy to torch types.
             num_objects = image_token_mask.sum(dim=1)
     
             text_embeds = torch.from_numpy(self.text_encoder(input_ids)[0])
-            augmented_prompt_embeds = self.postfuse_module([
-                text_embeds,
-                object_embeds,
-                image_token_mask,
-                num_objects
-            ])[0]
+            augmented_prompt_embeds = self.postfuse_module([text_embeds, object_embeds, image_token_mask, num_objects])[0]
             return torch.from_numpy(augmented_prompt_embeds)
     
         def _encode_prompt(
@@ -492,7 +488,7 @@ convert outputs from numpy to torch types.
             device,
             num_images_per_prompt,
             do_classifier_free_guidance,
-            negative_prompt=None
+            negative_prompt=None,
         ):
             r"""
             Encodes the prompt into text encoder hidden states.
@@ -530,12 +526,8 @@ convert outputs from numpy to torch types.
             text_input_ids = text_inputs.input_ids
             untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
     
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
                 print(
                     "The following part of your input was truncated because CLIP can only handle sequences up to"
                     f" {self.tokenizer.model_max_length} tokens: {removed_text}"
@@ -555,10 +547,7 @@ convert outputs from numpy to torch types.
                 if negative_prompt is None:
                     uncond_tokens = [""] * batch_size
                 elif type(prompt) is not type(negative_prompt):
-                    raise TypeError(
-                        f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                        f" {type(prompt)}."
-                    )
+                    raise TypeError(f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}.")
                 elif isinstance(negative_prompt, str):
                     uncond_tokens = [negative_prompt]
                 elif batch_size != len(negative_prompt):
@@ -602,7 +591,6 @@ convert outputs from numpy to torch types.
     
             return prompt_embeds
     
-    
         @torch.no_grad()
         def __call__(
             self,
@@ -625,7 +613,7 @@ convert outputs from numpy to torch types.
             cross_attention_kwargs: Optional[Dict[str, Any]] = None,
             alpha_: float = 0.7,
             reference_subject_images: List[Image.Image] = None,
-            augmented_prompt_embeds: Optional[torch.FloatTensor] = None
+            augmented_prompt_embeds: Optional[torch.FloatTensor] = None,
         ):
             r"""
             Function invoked when calling the pipeline for generation.
@@ -719,8 +707,9 @@ convert outputs from numpy to torch types.
                 negative_prompt_embeds,
             )
     
-            assert (prompt is not None and reference_subject_images is not None) or (prompt_embeds is not None and augmented_prompt_embeds is not None),  \
-                "Prompt and reference subject images or prompt_embeds and augmented_prompt_embeds must be provided."
+            assert (prompt is not None and reference_subject_images is not None) or (
+                prompt_embeds is not None and augmented_prompt_embeds is not None
+            ), "Prompt and reference subject images or prompt_embeds and augmented_prompt_embeds must be provided."
     
             # 2. Define call parameters
             if prompt is not None and isinstance(prompt, str):
@@ -779,56 +768,44 @@ convert outputs from numpy to torch types.
             (
                 null_prompt_embeds,
                 text_prompt_embeds,
-                augmented_prompt_embeds
+                augmented_prompt_embeds,
             ) = prompt_embeds.chunk(3)
     
             # 7. Denoising loop
             num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
             with self.progress_bar(total=num_inference_steps) as progress_bar:
                 for i, t in enumerate(timesteps):
-                    latent_model_input = (
-                        torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                    )
+                    latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
     
                     if i <= start_subject_conditioning_step:
-                        current_prompt_embeds = torch.cat(
-                            [null_prompt_embeds, text_prompt_embeds], dim=0
-                        )
+                        current_prompt_embeds = torch.cat([null_prompt_embeds, text_prompt_embeds], dim=0)
                     else:
-                        current_prompt_embeds = torch.cat(
-                            [null_prompt_embeds, augmented_prompt_embeds], dim=0
-                        )
+                        current_prompt_embeds = torch.cat([null_prompt_embeds, augmented_prompt_embeds], dim=0)
     
                     # predict the noise residual
-                    noise_pred = self.unet([
-                        latent_model_input,
-                        t,
-                        current_prompt_embeds,
-                        # cross_attention_kwargs
-                    ],
+                    noise_pred = self.unet(
+                        [
+                            latent_model_input,
+                            t,
+                            current_prompt_embeds,
+                            # cross_attention_kwargs
+                        ],
                     )[0]
                     noise_pred = torch.from_numpy(noise_pred)
     
-    
                     # perform guidance
                     if do_classifier_free_guidance:
                         noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                        noise_pred = noise_pred_uncond + guidance_scale * (
-                            noise_pred_text - noise_pred_uncond
-                        )
+                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                     else:
                         assert 0, "Not Implemented"
     
                     # compute the previous noisy sample x_t -> x_t-1
-                    latents = self.scheduler.step(
-                        noise_pred, t, latents, **extra_step_kwargs
-                    ).prev_sample
+                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
     
                     # call the callback, if provided
-                    if i == len(timesteps) - 1 or (
-                        (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
-                    ):
+                    if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                         progress_bar.update()
                         if callback is not None and i % callback_steps == 0:
                             callback(i, t, latents)
@@ -841,9 +818,7 @@ convert outputs from numpy to torch types.
                 image = self.decode_latents(latents)
     
                 # 9. Run safety checker
-                image, has_nsfw_concept = self.run_safety_checker(
-                    image, device, prompt_embeds.dtype
-                )
+                image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
     
                 # 10. Convert to PIL
                 image = self.numpy_to_pil(image)
@@ -852,9 +827,7 @@ convert outputs from numpy to torch types.
                 image = self.decode_latents(latents)
     
                 # 9. Run safety checker
-                image, has_nsfw_concept = self.run_safety_checker(
-                    image, device, prompt_embeds.dtype
-                )
+                image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
     
             # Offload last model to CPU
             if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
@@ -863,9 +836,7 @@ convert outputs from numpy to torch types.
             if not return_dict:
                 return (image, has_nsfw_concept)
     
-            return StableDiffusionPipelineOutput(
-                images=image, nsfw_content_detected=has_nsfw_concept
-            )
+            return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
 
 And replace all model in the pipeline by converted models.
 
@@ -876,13 +847,13 @@ And replace all model in the pipeline by converted models.
     
     
     def create_pipeline(
-            args,
-            *,
-            text_encoder,
-            image_encoder,
-            unet,
-            object_transforms,
-            postfuse_module,
+        args,
+        *,
+        text_encoder,
+        image_encoder,
+        unet,
+        object_transforms,
+        postfuse_module,
     ):
         weight_dtype = torch.float32
     
@@ -894,9 +865,7 @@ And replace all model in the pipeline by converted models.
         tokenizer.add_tokens(["img"], special_tokens=True)
         image_token_id = tokenizer.convert_tokens_to_ids("img")
     
-        pipe = StableDiffusionFastCompposerPipeline.from_pretrained(
-            args.pretrained_model_name_or_path, torch_dtype=weight_dtype
-        )
+        pipe = StableDiffusionFastCompposerPipeline.from_pretrained(args.pretrained_model_name_or_path, torch_dtype=weight_dtype)
     
         pipe.object_transforms = object_transforms
         pipe.unet = unet
@@ -937,10 +906,7 @@ And replace all model in the pipeline by converted models.
             if len(image) == 0:
                 return [], "You need to upload at least one image."
     
-            num_subject_in_text = (
-                np.array(self.model.special_tokenizer.encode(prompt))
-                == self.model.image_token_id
-            ).sum()
+            num_subject_in_text = (np.array(self.model.special_tokenizer.encode(prompt)) == self.model.image_token_id).sum()
             if num_subject_in_text != len(image):
                 return (
                     [],
@@ -968,7 +934,6 @@ And replace all model in the pipeline by converted models.
                 "run successfully",
             )
 
-
 .. code:: ipython3
 
     import ipywidgets as widgets
@@ -976,8 +941,8 @@ And replace all model in the pipeline by converted models.
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -1037,10 +1002,10 @@ that correlates with input images.
 
 .. code:: ipython3
 
-    image1 = Image.open('fastcomposer/data/newton_einstein/einstein/0.png')
-    image2 = Image.open('fastcomposer/data/newton_einstein/newton/0.png')
-    prompt = 'A man img and a man img sitting in a park'
-    negative_prompt = '((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)), [out of frame], extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))). out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck)))'
+    image1 = Image.open("fastcomposer/data/newton_einstein/einstein/0.png")
+    image2 = Image.open("fastcomposer/data/newton_einstein/newton/0.png")
+    prompt = "A man img and a man img sitting in a park"
+    negative_prompt = "((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)), [out of frame], extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))). out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck)))"
     alpha_ = 0.7
     num_images = 1  # each extra image requires ~11GB of free memory
     num_steps = 50
@@ -1057,7 +1022,7 @@ that correlates with input images.
         guidance_scale,
         alpha_,
         num_steps,
-        num_images
+        num_images,
     )
 
 
@@ -1087,7 +1052,7 @@ to display them.
 
 
 
-.. image:: 252-fastcomposer-image-generation-with-output_files/252-fastcomposer-image-generation-with-output_32_0.png
+.. image:: fastcomposer-image-generation-with-output_files/fastcomposer-image-generation-with-output_32_0.png
 
 
 Run Gradio
@@ -1139,7 +1104,7 @@ Also, it is possible to run with Gradio
                     negative_prompt = gr.Text(
                         value="((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)), [out of frame], extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))). out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck)))",
                         label="Negative Prompt",
-                        info='Features that you want to avoid.',
+                        info="Features that you want to avoid.",
                     )
                     alpha_ = gr.Slider(
                         label="alpha",
@@ -1196,9 +1161,7 @@ Also, it is possible to run with Gradio
                 num_steps,
                 num_images,
             ]
-            run_button.click(
-                fn=wrapped_model.inference, inputs=inputs, outputs=[result, error_message]
-            )
+            run_button.click(fn=wrapped_model.inference, inputs=inputs, outputs=[result, error_message])
         return demo
     
     
diff --git a/docs/notebooks/252-fastcomposer-image-generation-with-output_files/252-fastcomposer-image-generation-with-output_32_0.jpg b/docs/notebooks/fastcomposer-image-generation-with-output_files/fastcomposer-image-generation-with-output_32_0.jpg
similarity index 100%
rename from docs/notebooks/252-fastcomposer-image-generation-with-output_files/252-fastcomposer-image-generation-with-output_32_0.jpg
rename to docs/notebooks/fastcomposer-image-generation-with-output_files/fastcomposer-image-generation-with-output_32_0.jpg
diff --git a/docs/notebooks/252-fastcomposer-image-generation-with-output_files/252-fastcomposer-image-generation-with-output_32_0.png b/docs/notebooks/fastcomposer-image-generation-with-output_files/fastcomposer-image-generation-with-output_32_0.png
similarity index 100%
rename from docs/notebooks/252-fastcomposer-image-generation-with-output_files/252-fastcomposer-image-generation-with-output_32_0.png
rename to docs/notebooks/fastcomposer-image-generation-with-output_files/fastcomposer-image-generation-with-output_32_0.png
diff --git a/docs/notebooks/269-film-slowmo-with-output.rst b/docs/notebooks/film-slowmo-with-output.rst
similarity index 99%
rename from docs/notebooks/269-film-slowmo-with-output.rst
rename to docs/notebooks/film-slowmo-with-output.rst
index 98dd21aef94..5a3c12de371 100644
--- a/docs/notebooks/269-film-slowmo-with-output.rst
+++ b/docs/notebooks/film-slowmo-with-output.rst
@@ -68,16 +68,18 @@ Prerequisites
 
 .. code:: ipython3
 
-    import sys
     import platform
+    import os
     
-    if sys.version_info.minor <= 8:
-        %pip install -q "tensorflow<=2.12.0" "typing-extensions>=4.9"
-    else:
-        %pip install -q "tensorflow"
-        
+    %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin' and python_version <= '3.8'"
     
-    %pip install -q tensorflow_hub numpy "opencv-python" tqdm gradio Pillow "openvino>=2023.2.0"
+    
+    %pip install -q tensorflow_hub tf_keras numpy "opencv-python" tqdm "gradio>=4.19" Pillow "openvino>=2023.2.0"
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -87,20 +89,27 @@ Prerequisites
 
 .. parsed-literal::
 
-    
-    [notice] A new release of pip is available: 23.3.2 -> 24.0
-    [notice] To update, run: pip install --upgrade pip
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
 
 
 .. code:: ipython3
 
     from pathlib import Path
-    from urllib.request import urlretrieve
+    import requests
     from typing import Optional, Generator
     from datetime import datetime
     import gc
     
+    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+    os.environ["TF_USE_LEGACY_KERAS"] = "1"
+    os.environ["TFHUB_CACHE_DIR"] = str(Path("./tfhub_modules").resolve())
     
     import tensorflow_hub as hub
     import tensorflow as tf
@@ -113,24 +122,13 @@ Prerequisites
     import PIL
     import IPython
 
-
-.. parsed-literal::
-
-    2024-02-13 21:01:58.412173: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-02-13 21:01:58.414314: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-02-13 21:01:58.453968: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-02-13 21:01:58.455540: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-02-13 21:01:59.181583: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
 .. code:: ipython3
 
     MODEL_PATH = Path("models/model.xml")
     DATA_PATH = Path("data")
     IMAGES = {
         "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/c3ddf65f-95ec-44ca-9ed4-3ef2d8f4b47e": Path("data/one.jpg"),
-        "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/6d21f1ce-69eb-41b5-aedd-6e3c29013b30": Path("data/two.jpg")
+        "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/6d21f1ce-69eb-41b5-aedd-6e3c29013b30": Path("data/two.jpg"),
     }
     OUTPUT_VIDEO_PATH = DATA_PATH / "output.webm"
     OV_OUTPUT_VIDEO_PATH = DATA_PATH / "ov_output.webm"
@@ -154,23 +152,27 @@ inputs.
         result = result[np.newaxis, ...]  # add batch dim
         return result
     
+    
     def prepare_input(img_url: str):
         if not IMAGES[img_url].exists():
-            urlretrieve(img_url, IMAGES[img_url])
+            r = requests.get(img_url)
+            with IMAGES[img_url].open("wb") as f:
+                f.write(r.content)
         filename = str(IMAGES[img_url])
         img = cv2.imread(filename)
         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         img = np.array(img)
         img = preprocess_np_frame(img)
-        
+    
         return img
     
+    
     input_images = [prepare_input(url) for url in IMAGES]
     
     input = {
         "x0": input_images[0],
         "x1": input_images[1],
-        "time": np.array([[0.5]], dtype=np.float32)
+        "time": np.array([[0.5]], dtype=np.float32),
     }
 
 .. code:: ipython3
@@ -185,7 +187,7 @@ inputs.
 
 
 
-.. image:: 269-film-slowmo-with-output_files/269-film-slowmo-with-output_7_0.png
+.. image:: film-slowmo-with-output_files/film-slowmo-with-output_7_0.png
 
 
 Load the model
@@ -215,13 +217,6 @@ Hub `__.
     film_layer = hub.KerasLayer(model_url)(inputs)
     film_model = tf.keras.Model(inputs=inputs, outputs=film_layer)
 
-
-.. parsed-literal::
-
-    2024-02-13 21:02:06.658414: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
-    Skipping registering GPU devices...
-
-
 Infer the model
 ---------------
 
@@ -242,7 +237,7 @@ Single middle frame interpolation
 
     def draw(img1, mid_img, img2):
         title2img = {"First frame": img1, "Interpolated frame": mid_img, "Last frame": img2}
-        plt.figure(figsize=(16,8), layout="tight")
+        plt.figure(figsize=(16, 8), layout="tight")
         for i, (title, img) in enumerate(title2img.items()):
             ax = plt.subplot(1, 3, i + 1)
             ax.set_title(title)
@@ -255,7 +250,7 @@ Single middle frame interpolation
 
 
 
-.. image:: 269-film-slowmo-with-output_files/269-film-slowmo-with-output_14_0.png
+.. image:: film-slowmo-with-output_files/film-slowmo-with-output_14_0.png
 
 
 Recursive frame generation
@@ -303,9 +298,7 @@ generating :math:`2^t-1` images.
                 yield from self._recursive_generator(frame1, mid_frame, num_recursions - 1, bar)
                 yield from self._recursive_generator(mid_frame, frame2, num_recursions - 1, bar)
     
-        def interpolate_recursively(
-            self, frame1: np.ndarray, frame2: np.ndarray, times_to_interpolate: int
-        ) -> Generator[np.ndarray, None, None]:
+        def interpolate_recursively(self, frame1: np.ndarray, frame2: np.ndarray, times_to_interpolate: int) -> Generator[np.ndarray, None, None]:
             """Generates interpolated frames by repeatedly interpolating the midpoint.
     
             Args:
@@ -326,7 +319,7 @@ generating :math:`2^t-1` images.
 .. code:: ipython3
 
     def save_as_video(frames: Generator[np.ndarray, None, None], width: int, height: int, filename: Path):
-        out = cv2.VideoWriter(str(filename), cv2.VideoWriter_fourcc(*'VP90'), 30, (width, height))
+        out = cv2.VideoWriter(str(filename), cv2.VideoWriter_fourcc(*"VP90"), 30, (width, height))
         for frame in frames:
             img = frame[0]
             img = np.clip(img, 0, 1)
@@ -339,7 +332,7 @@ generating :math:`2^t-1` images.
 .. code:: ipython3
 
     height, width = input_images[0][0].shape[:2]
-    interpolator = Interpolator(film_model) 
+    interpolator = Interpolator(film_model)
     frames = interpolator.interpolate_recursively(input_images[0], input_images[1], 5)
     save_as_video(frames, width, height, OUTPUT_VIDEO_PATH)
 
@@ -365,7 +358,7 @@ generating :math:`2^t-1` images.
 .. raw:: html
 
     
 
@@ -395,7 +388,7 @@ object to disk using the ``openvino.save_model()`` function.
 
 .. parsed-literal::
 
-    8771
+    1356
 
 
 
@@ -418,8 +411,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     device
@@ -459,7 +452,7 @@ Model returned intermediate image. Let’s see what it is.
 
 
 
-.. image:: 269-film-slowmo-with-output_files/269-film-slowmo-with-output_29_0.png
+.. image:: film-slowmo-with-output_files/film-slowmo-with-output_29_0.png
 
 
 Recursive frame generation
@@ -499,7 +492,7 @@ initial, middle and final images.
 .. raw:: html
 
     
 
@@ -520,17 +513,24 @@ Interactive inference
         save_as_video(frames, width, height, filename)
         return filename
     
+    
     demo = gr.Interface(
         generate,
         [
             gr.Image(label="First image"),
             gr.Image(label="Last image"),
-            gr.Slider(1, 8, step=1, label="Times to interpolate", info="""Controls the number of times the frame interpolator is invoked.
-            The output will be the interpolation video with (2^value + 1) frames, fps of 30.""")
+            gr.Slider(
+                1,
+                8,
+                step=1,
+                label="Times to interpolate",
+                info="""Controls the number of times the frame interpolator is invoked.
+            The output will be the interpolation video with (2^value + 1) frames, fps of 30.""",
+            ),
         ],
         gr.Video(),
         examples=[[*IMAGES.values(), 5]],
-        allow_flagging="never"
+        allow_flagging="never",
     )
     try:
         demo.queue().launch(debug=False)
diff --git a/docs/notebooks/film-slowmo-with-output_files/film-slowmo-with-output_14_0.png b/docs/notebooks/film-slowmo-with-output_files/film-slowmo-with-output_14_0.png
new file mode 100644
index 00000000000..4ccfd95299d
--- /dev/null
+++ b/docs/notebooks/film-slowmo-with-output_files/film-slowmo-with-output_14_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4cfb9fec7a17248d0934e5f9b83b9b86eaa175535901064744bfdf5dfba2626d
+size 466103
diff --git a/docs/notebooks/film-slowmo-with-output_files/film-slowmo-with-output_29_0.png b/docs/notebooks/film-slowmo-with-output_files/film-slowmo-with-output_29_0.png
new file mode 100644
index 00000000000..234a5ae52cf
--- /dev/null
+++ b/docs/notebooks/film-slowmo-with-output_files/film-slowmo-with-output_29_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8ccc0d255c0d77a422d4a84d5864b5cff4a415b97a78555f254e74dae9bf54e
+size 465989
diff --git a/docs/notebooks/film-slowmo-with-output_files/film-slowmo-with-output_7_0.png b/docs/notebooks/film-slowmo-with-output_files/film-slowmo-with-output_7_0.png
new file mode 100644
index 00000000000..5930e6fd40b
--- /dev/null
+++ b/docs/notebooks/film-slowmo-with-output_files/film-slowmo-with-output_7_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab82d808cf872d2714801a251bb8b3173c12ca1cec12474a07e04aeecaa7f177
+size 484833
diff --git a/docs/notebooks/108-gpu-device-with-output.rst b/docs/notebooks/gpu-device-with-output.rst
similarity index 90%
rename from docs/notebooks/108-gpu-device-with-output.rst
rename to docs/notebooks/gpu-device-with-output.rst
index d4eeafce1fc..7970745c386 100644
--- a/docs/notebooks/108-gpu-device-with-output.rst
+++ b/docs/notebooks/gpu-device-with-output.rst
@@ -108,24 +108,13 @@ Install required packages
 
 .. code:: ipython3
 
-    %pip install -q "openvino-dev>=2024.0.0"
-    %pip install -q tensorflow
-    
-    # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
-    )
-
-
-
-
-.. parsed-literal::
-
-    ('notebook_utils.py', )
-
-
+    %pip install -q "openvino-dev>=2024.0.0" "opencv-python" "tqdm"
+    %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin' and python_version <= '3.8'"
 
 Checking GPUs with Query Device
 -------------------------------
@@ -218,11 +207,15 @@ for that property.
     indent = len(max(supported_properties, key=len))
     
     for property_key in supported_properties:
-        if property_key not in ('SUPPORTED_METRICS', 'SUPPORTED_CONFIG_KEYS', 'SUPPORTED_PROPERTIES'):
+        if property_key not in (
+            "SUPPORTED_METRICS",
+            "SUPPORTED_CONFIG_KEYS",
+            "SUPPORTED_PROPERTIES",
+        ):
             try:
                 property_val = core.get_property(device, property_key)
             except TypeError:
-                property_val = 'UNSUPPORTED TYPE'
+                property_val = "UNSUPPORTED TYPE"
             print(f"{property_key:<{indent}}: {property_val}")
 
 
@@ -325,13 +318,18 @@ package is already downloaded.
 
 .. code:: ipython3
 
-    import sys
     import tarfile
     from pathlib import Path
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
     
-    import notebook_utils as utils
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
+    from notebook_utils import download_file
     
     # A directory where the model will be downloaded.
     base_model_dir = Path("./model").expanduser()
@@ -343,7 +341,7 @@ package is already downloaded.
     downloaded_model_path = base_model_dir / archive_name
     if not downloaded_model_path.exists():
         model_url = f"http://download.tensorflow.org/models/object_detection/{archive_name}"
-        utils.download_file(model_url, downloaded_model_path.name, downloaded_model_path.parent)
+        download_file(model_url, downloaded_model_path.name, downloaded_model_path.parent)
     
     # Unpack the model
     tf_model_path = base_model_dir / archive_name.with_suffix("").stem / "frozen_inference_graph.pb"
@@ -386,22 +384,24 @@ directory. For more details about model conversion, see this
 
     from openvino.tools.mo.front import tf as ov_tf_front
     
-    precision = 'FP16'
+    precision = "FP16"
     
     # The output path for the conversion.
-    model_path = base_model_dir / 'ir_model' / f'{model_name}_{precision.lower()}.xml'
+    model_path = base_model_dir / "ir_model" / f"{model_name}_{precision.lower()}.xml"
     
     trans_config_path = Path(ov_tf_front.__file__).parent / "ssd_v2_support.json"
     pipeline_config = base_model_dir / archive_name.with_suffix("").stem / "pipeline.config"
     
     model = None
     if not model_path.exists():
-        model = ov.tools.mo.convert_model(input_model=tf_model_path,
-                                          input_shape=[1, 300, 300, 3],
-                                          layout='NHWC',
-                                          transformations_config=trans_config_path,
-                                          tensorflow_object_detection_api_pipeline_config=pipeline_config,
-                                          reverse_input_channels=True)
+        model = ov.tools.mo.convert_model(
+            input_model=tf_model_path,
+            input_shape=[1, 300, 300, 3],
+            layout="NHWC",
+            transformations_config=trans_config_path,
+            tensorflow_object_detection_api_pipeline_config=pipeline_config,
+            reverse_input_channels=True,
+        )
         ov.save_model(model, model_path, compress_to_fp16=(precision == "FP16"))
         print("IR model saved to {}".format(model_path))
     else:
@@ -441,7 +441,7 @@ inference (which is often the GPU). To learn more about AUTO plugin,
 visit the `Automatic Device
 Selection `__
 page as well as the `AUTO device
-tutorial `__.
+tutorial `__.
 
 Reduce Compile Time through Model Caching
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -470,7 +470,7 @@ following:
     core = ov.Core()
     
     # Set cache folder
-    core.set_property({'CACHE_DIR': cache_folder})
+    core.set_property({"CACHE_DIR": cache_folder})
     
     # Compile the model as before
     model = core.read_model(model=model_path)
@@ -490,7 +490,7 @@ compile times with caching enabled and disabled as follows:
 
     start = time.time()
     core = ov.Core()
-    core.set_property({'CACHE_DIR': 'cache'})
+    core.set_property({"CACHE_DIR": "cache"})
     model = core.read_model(model=model_path)
     compiled_model = core.compile_model(model, device)
     print(f"Cache enabled  - compile time: {time.time() - start}s")
@@ -588,7 +588,7 @@ manually specify devices to use. Below is an example showing how to use
    `Asynchronous
    Inferencing `__
    in OpenVINO as well as the `Asynchronous Inference
-   notebook `__.
+   notebook `__.
 
 Performance Comparison with benchmark_app
 -----------------------------------------
@@ -1158,7 +1158,7 @@ Compile the Model
     
     # Get the input size
     num, height, width, channels = input_layer.shape
-    print('Model input shape:', num, height, width, channels)
+    print("Model input shape:", num, height, width, channels)
 
 
 .. parsed-literal::
@@ -1179,24 +1179,24 @@ Load and Preprocess Video Frames
     framebuf = []
     
     # Go through every frame of video and resize it
-    print('Loading video...')
+    print("Loading video...")
     while video.isOpened():
         ret, frame = video.read()
         if not ret:
-            print('Video loaded!')
+            print("Video loaded!")
             video.release()
             break
-        
+    
         # Preprocess frames - convert them to shape expected by model
         input_frame = cv2.resize(src=frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
         input_frame = np.expand_dims(input_frame, axis=0)
     
         # Append frame to framebuffer
         framebuf.append(input_frame)
-        
     
-    print('Frame shape: ', framebuf[0].shape)
-    print('Number of frames: ', len(framebuf))
+    
+    print("Frame shape: ", framebuf[0].shape)
+    print("Number of frames: ", len(framebuf))
     
     # Show original video file
     # If the video does not display correctly inside the notebook, please open it with your favorite media player
@@ -1220,18 +1220,98 @@ Define Model Output Classes
 
     # Define the model's labelmap (this model uses COCO classes)
     classes = [
-        "background", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train",
-        "truck", "boat", "traffic light", "fire hydrant", "street sign", "stop sign",
-        "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant",
-        "bear", "zebra", "giraffe", "hat", "backpack", "umbrella", "shoe", "eye glasses",
-        "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
-        "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
-        "plate", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
-        "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair",
-        "couch", "potted plant", "bed", "mirror", "dining table", "window", "desk", "toilet",
-        "door", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
-        "toaster", "sink", "refrigerator", "blender", "book", "clock", "vase", "scissors",
-        "teddy bear", "hair drier", "toothbrush", "hair brush"
+        "background",
+        "person",
+        "bicycle",
+        "car",
+        "motorcycle",
+        "airplane",
+        "bus",
+        "train",
+        "truck",
+        "boat",
+        "traffic light",
+        "fire hydrant",
+        "street sign",
+        "stop sign",
+        "parking meter",
+        "bench",
+        "bird",
+        "cat",
+        "dog",
+        "horse",
+        "sheep",
+        "cow",
+        "elephant",
+        "bear",
+        "zebra",
+        "giraffe",
+        "hat",
+        "backpack",
+        "umbrella",
+        "shoe",
+        "eye glasses",
+        "handbag",
+        "tie",
+        "suitcase",
+        "frisbee",
+        "skis",
+        "snowboard",
+        "sports ball",
+        "kite",
+        "baseball bat",
+        "baseball glove",
+        "skateboard",
+        "surfboard",
+        "tennis racket",
+        "bottle",
+        "plate",
+        "wine glass",
+        "cup",
+        "fork",
+        "knife",
+        "spoon",
+        "bowl",
+        "banana",
+        "apple",
+        "sandwich",
+        "orange",
+        "broccoli",
+        "carrot",
+        "hot dog",
+        "pizza",
+        "donut",
+        "cake",
+        "chair",
+        "couch",
+        "potted plant",
+        "bed",
+        "mirror",
+        "dining table",
+        "window",
+        "desk",
+        "toilet",
+        "door",
+        "tv",
+        "laptop",
+        "mouse",
+        "remote",
+        "keyboard",
+        "cell phone",
+        "microwave",
+        "oven",
+        "toaster",
+        "sink",
+        "refrigerator",
+        "blender",
+        "book",
+        "clock",
+        "vase",
+        "scissors",
+        "teddy bear",
+        "hair drier",
+        "toothbrush",
+        "hair brush",
     ]
 
 Set up Asynchronous Pipeline
@@ -1254,7 +1334,7 @@ Callback Definition
     
         predictions = next(iter(infer_request.results.values()))
         results[frame_id] = predictions[:10]  # Grab first 10 predictions for this frame
-        
+    
         total_time = stop_time - start_time
         frame_fps[frame_id] = frame_number / total_time
 
@@ -1290,9 +1370,9 @@ Perform Inference
     # Calculate total inference time and FPS
     total_time = stop_time - start_time
     fps = len(framebuf) / total_time
-    time_per_frame = 1 / fps 
-    print(f'Total time to infer all frames: {total_time:.3f}s')
-    print(f'Time per frame: {time_per_frame:.6f}s ({fps:.3f} FPS)')
+    time_per_frame = 1 / fps
+    print(f"Total time to infer all frames: {total_time:.3f}s")
+    print(f"Time per frame: {time_per_frame:.6f}s ({fps:.3f} FPS)")
 
 
 .. parsed-literal::
@@ -1309,7 +1389,7 @@ Process Results
 .. code:: ipython3
 
     # Set minimum detection threshold
-    min_thresh = .6
+    min_thresh = 0.6
     
     # Load video
     video = cv2.VideoCapture(video_file)
@@ -1321,23 +1401,50 @@ Process Results
     fourcc = int(video.get(cv2.CAP_PROP_FOURCC))
     
     # Create folder and VideoWriter to save output video
-    Path('./output').mkdir(exist_ok=True)
-    output = cv2.VideoWriter('output/output.mp4', fourcc, fps, (frame_width, frame_height))
+    Path("./output").mkdir(exist_ok=True)
+    output = cv2.VideoWriter("output/output.mp4", fourcc, fps, (frame_width, frame_height))
     
     # Draw detection results on every frame of video and save as a new video file
     while video.isOpened():
         current_frame = int(video.get(cv2.CAP_PROP_POS_FRAMES))
         ret, frame = video.read()
         if not ret:
-            print('Video loaded!')
+            print("Video loaded!")
             output.release()
             video.release()
             break
-            
+    
         # Draw info at the top left such as current fps, the devices and the performance hint being used
-        cv2.putText(frame, f"fps {str(round(frame_fps[current_frame], 2))}", (5, 20), cv2.FONT_ITALIC, 0.6, (0, 0, 0), 1, cv2.LINE_AA)
-        cv2.putText(frame, f"device {device_name}", (5, 40), cv2.FONT_ITALIC, 0.6, (0, 0, 0), 1, cv2.LINE_AA) 
-        cv2.putText(frame, f"hint {compiled_model.get_property('PERFORMANCE_HINT')}", (5, 60), cv2.FONT_ITALIC, 0.6, (0, 0, 0), 1, cv2.LINE_AA)
+        cv2.putText(
+            frame,
+            f"fps {str(round(frame_fps[current_frame], 2))}",
+            (5, 20),
+            cv2.FONT_ITALIC,
+            0.6,
+            (0, 0, 0),
+            1,
+            cv2.LINE_AA,
+        )
+        cv2.putText(
+            frame,
+            f"device {device_name}",
+            (5, 40),
+            cv2.FONT_ITALIC,
+            0.6,
+            (0, 0, 0),
+            1,
+            cv2.LINE_AA,
+        )
+        cv2.putText(
+            frame,
+            f"hint {compiled_model.get_property('PERFORMANCE_HINT')}",
+            (5, 60),
+            cv2.FONT_ITALIC,
+            0.6,
+            (0, 0, 0),
+            1,
+            cv2.LINE_AA,
+        )
     
         # prediction contains [image_id, label, conf, x_min, y_min, x_max, y_max] according to model
         for prediction in np.squeeze(results[current_frame]):
@@ -1347,10 +1454,19 @@ Process Results
                 x_max = int(prediction[5] * frame_width)
                 y_max = int(prediction[6] * frame_height)
                 label = classes[int(prediction[1])]
-                
+    
                 # Draw a bounding box with its label above it
                 cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0, 255, 0), 1, cv2.LINE_AA)
-                cv2.putText(frame, label, (x_min, y_min - 10), cv2.FONT_ITALIC, 1, (255, 0, 0), 1, cv2.LINE_AA)
+                cv2.putText(
+                    frame,
+                    label,
+                    (x_min, y_min - 10),
+                    cv2.FONT_ITALIC,
+                    1,
+                    (255, 0, 0),
+                    1,
+                    cv2.LINE_AA,
+                )
     
         output.write(frame)
     
diff --git a/docs/notebooks/214-grammar-correction-with-output.rst b/docs/notebooks/grammar-correction-with-output.rst
similarity index 74%
rename from docs/notebooks/214-grammar-correction-with-output.rst
rename to docs/notebooks/grammar-correction-with-output.rst
index 5fff47d819d..b4b1a0c93d7 100644
--- a/docs/notebooks/214-grammar-correction-with-output.rst
+++ b/docs/notebooks/grammar-correction-with-output.rst
@@ -132,8 +132,8 @@ documentation `__.
 
 .. code:: ipython3
 
-    %pip install -q "git+https://github.com/huggingface/optimum-intel.git" "openvino>=2023.1.0" onnx gradio "transformers>=4.33.0" --extra-index-url https://download.pytorch.org/whl/cpu
-    %pip install -q "nncf>=2.7.0" datasets jiwer
+    %pip install -q "torch>=2.1.0" "git+https://github.com/huggingface/optimum-intel.git" "openvino>=2024.0.0" onnx tqdm "gradio>=4.19" "transformers>=4.33.0" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "nncf>=2.9.0" datasets jiwer
 
 
 .. parsed-literal::
@@ -182,10 +182,15 @@ Tokenizer class and pipelines API are compatible with Optimum models.
 
 .. parsed-literal::
 
-    2023-09-27 14:53:36.462575: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2023-09-27 14:53:36.496914: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-03-25 11:56:04.043628: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-03-25 11:56:04.045940: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
+    2024-03-25 11:56:04.079112: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+    2024-03-25 11:56:04.079147: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
+    2024-03-25 11:56:04.079167: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+    2024-03-25 11:56:04.085243: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
+    2024-03-25 11:56:04.085971: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2023-09-27 14:53:37.063292: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-03-25 11:56:05.314633: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -193,13 +198,6 @@ Tokenizer class and pipelines API are compatible with Optimum models.
     INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
 
 
-.. parsed-literal::
-
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda-11.7'
-    /home/nsavel/venvs/ov_notebooks_tmp/lib/python3.8/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
-      warnings.warn(
-
-
 Select inference device
 ~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -211,16 +209,16 @@ select device from dropdown list for running inference using OpenVINO
 
     import ipywidgets as widgets
     import openvino as ov
-
+    
     core = ov.Core()
-
+    
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
-
+    
     device
 
 
@@ -228,7 +226,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+    Dropdown(description='Device:', index=3, options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='AUTO')
 
 
 
@@ -242,7 +240,7 @@ Grammar Checker
     grammar_checker_model_id = "textattack/roberta-base-CoLA"
     grammar_checker_dir = Path("roberta-base-cola")
     grammar_checker_tokenizer = AutoTokenizer.from_pretrained(grammar_checker_model_id)
-
+    
     if grammar_checker_dir.exists():
         grammar_checker_model = OVModelForSequenceClassification.from_pretrained(grammar_checker_dir, device=device.value)
     else:
@@ -252,25 +250,18 @@ Grammar Checker
 
 .. parsed-literal::
 
-    Framework not specified. Using pt to export to ONNX.
-    Some weights of the model checkpoint at textattack/roberta-base-CoLA were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']
+    Framework not specified. Using pt to export the model.
+    Some weights of the model checkpoint at textattack/roberta-base-CoLA were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
     - This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
     - This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
-    Using framework PyTorch: 1.13.1+cpu
+    Using the export variant default. Available variants are:
+        - default: The default ONNX variant.
+    Using framework PyTorch: 2.2.1+cpu
     Overriding 1 configuration item(s)
     	- use_cache -> False
-
-
-.. parsed-literal::
-
-    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
-
-
-.. parsed-literal::
-
-    [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    Compiling the model to CPU ...
-    Set CACHE_DIR to /tmp/tmpcqv99eqb/model_cache
+    /home/ea/miniconda3/lib/python3.11/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+      warnings.warn(
+    Compiling the model to AUTO ...
 
 
 Let us check model work, using inference pipeline for
@@ -281,7 +272,11 @@ Hugging Face inference pipelines in this
 .. code:: ipython3
 
     input_text = "They are moved by salar energy"
-    grammar_checker_pipe = pipeline("text-classification", model=grammar_checker_model, tokenizer=grammar_checker_tokenizer)
+    grammar_checker_pipe = pipeline(
+        "text-classification",
+        model=grammar_checker_model,
+        tokenizer=grammar_checker_tokenizer,
+    )
     result = grammar_checker_pipe(input_text)[0]
     print(f"input text: {input_text}")
     print(f'predicted label: {"contains_errors" if result["label"] == "LABEL_1" else "no errors"}')
@@ -313,7 +308,7 @@ to run it.
     grammar_corrector_model_id = "pszemraj/flan-t5-large-grammar-synthesis"
     grammar_corrector_dir = Path("flan-t5-large-grammar-synthesis")
     grammar_corrector_tokenizer = AutoTokenizer.from_pretrained(grammar_corrector_model_id)
-
+    
     if grammar_corrector_dir.exists():
         grammar_corrector_model = OVModelForSeq2SeqLM.from_pretrained(grammar_corrector_dir, device=device.value)
     else:
@@ -323,28 +318,42 @@ to run it.
 
 .. parsed-literal::
 
-    Framework not specified. Using pt to export to ONNX.
-    Using framework PyTorch: 1.13.1+cpu
+    Framework not specified. Using pt to export the model.
+    Using the export variant default. Available variants are:
+        - default: The default ONNX variant.
+    Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
+    Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
+    Using framework PyTorch: 2.2.1+cpu
     Overriding 1 configuration item(s)
     	- use_cache -> False
-    Using framework PyTorch: 1.13.1+cpu
+    /home/ea/miniconda3/lib/python3.11/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+      warnings.warn(
+    Using framework PyTorch: 2.2.1+cpu
     Overriding 1 configuration item(s)
     	- use_cache -> True
-    /home/nsavel/venvs/ov_notebooks_tmp/lib/python3.8/site-packages/transformers/modeling_utils.py:875: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /home/ea/miniconda3/lib/python3.11/site-packages/transformers/modeling_utils.py:943: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if causal_mask.shape[1] < attention_mask.shape[1]:
-    Using framework PyTorch: 1.13.1+cpu
+    Using framework PyTorch: 2.2.1+cpu
     Overriding 1 configuration item(s)
     	- use_cache -> True
-    /home/nsavel/venvs/ov_notebooks_tmp/lib/python3.8/site-packages/transformers/models/t5/modeling_t5.py:509: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /home/ea/miniconda3/lib/python3.11/site-packages/transformers/models/t5/modeling_t5.py:509: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       elif past_key_value.shape[2] != key_value_states.shape[1]:
+    Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
+    Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
     Compiling the encoder to AUTO ...
     Compiling the decoder to AUTO ...
     Compiling the decoder to AUTO ...
+    Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
+    Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
 
 
 .. code:: ipython3
 
-    grammar_corrector_pipe = pipeline("text2text-generation", model=grammar_corrector_model, tokenizer=grammar_corrector_tokenizer)
+    grammar_corrector_pipe = pipeline(
+        "text2text-generation",
+        model=grammar_corrector_model,
+        tokenizer=grammar_corrector_tokenizer,
+    )
 
 .. code:: ipython3
 
@@ -353,14 +362,6 @@ to run it.
     print(f'generated text: {result["generated_text"]}')
 
 
-.. parsed-literal::
-
-    /home/nsavel/venvs/ov_notebooks_tmp/lib/python3.8/site-packages/optimum/intel/openvino/modeling_seq2seq.py:339: FutureWarning: `shared_memory` is deprecated and will be removed in 2024.0. Value of `shared_memory` is going to override `share_inputs` value. Please use only `share_inputs` explicitly.
-      last_hidden_state = torch.from_numpy(self.request(inputs, shared_memory=True)["last_hidden_state"]).to(
-    /home/nsavel/venvs/ov_notebooks_tmp/lib/python3.8/site-packages/optimum/intel/openvino/modeling_seq2seq.py:416: FutureWarning: `shared_memory` is deprecated and will be removed in 2024.0. Value of `shared_memory` is going to override `share_inputs` value. Please use only `share_inputs` explicitly.
-      self.request.start_async(inputs, shared_memory=True)
-
-
 .. parsed-literal::
 
     input text:     They are moved by salar energy
@@ -389,82 +390,83 @@ several steps:
     import re
     import transformers
     from tqdm.notebook import tqdm
-
-
+    
+    
     def split_text(text: str) -> list:
         """
         Split a string of text into a list of sentence batches.
-
+    
         Parameters:
         text (str): The text to be split into sentence batches.
-
+    
         Returns:
         list: A list of sentence batches. Each sentence batch is a list of sentences.
         """
         # Split the text into sentences using regex
         sentences = re.split(r"(?<=[^A-Z].[.?]) +(?=[A-Z])", text)
-
+    
         # Initialize a list to store the sentence batches
         sentence_batches = []
-
+    
         # Initialize a temporary list to store the current batch of sentences
         temp_batch = []
-
+    
         # Iterate through the sentences
         for sentence in sentences:
             # Add the sentence to the temporary batch
             temp_batch.append(sentence)
-
+    
             # If the length of the temporary batch is between 2 and 3 sentences, or if it is the last batch, add it to the list of sentence batches
             if len(temp_batch) >= 2 and len(temp_batch) <= 3 or sentence == sentences[-1]:
                 sentence_batches.append(temp_batch)
                 temp_batch = []
-
+    
         return sentence_batches
-
-
-    def correct_text(text: str, checker: transformers.pipelines.Pipeline, corrector: transformers.pipelines.Pipeline, separator: str = " ") -> str:
+    
+    
+    def correct_text(
+        text: str,
+        checker: transformers.pipelines.Pipeline,
+        corrector: transformers.pipelines.Pipeline,
+        separator: str = " ",
+    ) -> str:
         """
         Correct the grammar in a string of text using a text-classification and text-generation pipeline.
-
+    
         Parameters:
         text (str): The inpur text to be corrected.
         checker (transformers.pipelines.Pipeline): The text-classification pipeline to use for checking the grammar quality of the text.
         corrector (transformers.pipelines.Pipeline): The text-generation pipeline to use for correcting the text.
         separator (str, optional): The separator to use when joining the corrected text into a single string. Default is a space character.
-
+    
         Returns:
         str: The corrected text.
         """
         # Split the text into sentence batches
         sentence_batches = split_text(text)
-
+    
         # Initialize a list to store the corrected text
         corrected_text = []
-
+    
         # Iterate through the sentence batches
-        for batch in tqdm(
-            sentence_batches, total=len(sentence_batches), desc="correcting text.."
-        ):
+        for batch in tqdm(sentence_batches, total=len(sentence_batches), desc="correcting text.."):
             # Join the sentences in the batch into a single string
             raw_text = " ".join(batch)
-
+    
             # Check the grammar quality of the text using the text-classification pipeline
             results = checker(raw_text)
-
+    
             # Only correct the text if the results of the text-classification are not LABEL_1 or are LABEL_1 with a score below 0.9
-            if results[0]["label"] != "LABEL_1" or (
-                results[0]["label"] == "LABEL_1" and results[0]["score"] < 0.9
-            ):
+            if results[0]["label"] != "LABEL_1" or (results[0]["label"] == "LABEL_1" and results[0]["score"] < 0.9):
                 # Correct the text using the text-generation pipeline
                 corrected_batch = corrector(raw_text)
                 corrected_text.append(corrected_batch[0]["generated_text"])
             else:
                 corrected_text.append(raw_text)
-
+    
         # Join the corrected text into a single string
         corrected_text = separator.join(corrected_text)
-
+    
         return corrected_text
 
 Let us see it in action.
@@ -477,18 +479,10 @@ Let us see it in action.
         " this point, He introduces herself as his native English speaker and goes on to say that if"
         " you contine to work on social scnce"
     )
-
+    
     corrected_text = correct_text(default_text, grammar_checker_pipe, grammar_corrector_pipe)
 
 
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
 
 .. parsed-literal::
 
@@ -498,13 +492,13 @@ Let us see it in action.
 .. code:: ipython3
 
     print(f"input text:     {default_text}\n")
-    print(f'generated text: {corrected_text}')
+    print(f"generated text: {corrected_text}")
 
 
 .. parsed-literal::
 
     input text:     Most of the course is about semantic or  content of language but there are also interesting topics to be learned from the servicefeatures except statistics in characters in documents.At this point, He introduces herself as his native English speaker and goes on to say that if you contine to work on social scnce
-
+    
     generated text: Most of the course is about the semantic content of language but there are also interesting topics to be learned from the service features except statistics in characters in documents. At this point, she introduces herself as a native English speaker and goes on to say that if you continue to work on social science, you will continue to be successful.
 
 
@@ -540,10 +534,10 @@ improve model inference speed.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
-
+    
     to_quantize
 
 
@@ -566,14 +560,45 @@ some time to complete.
 
 .. code:: ipython3
 
-    from utils import get_quantized_pipeline
-
+    from utils import get_quantized_pipeline, CALIBRATION_DATASET_SIZE
+    
     grammar_corrector_pipe_fp32 = grammar_corrector_pipe
     grammar_corrector_pipe_int8 = None
     if to_quantize.value:
         quantized_model_path = Path("quantized_decoder_with_past") / "openvino_model.xml"
-        grammar_corrector_pipe_int8 = get_quantized_pipeline(grammar_corrector_pipe_fp32, grammar_corrector_tokenizer, core, grammar_corrector_dir,
-                                                             quantized_model_path, device.value)
+        grammar_corrector_pipe_int8 = get_quantized_pipeline(
+            grammar_corrector_pipe_fp32,
+            grammar_corrector_tokenizer,
+            core,
+            grammar_corrector_dir,
+            quantized_model_path,
+            device.value,
+            calibration_dataset_size=CALIBRATION_DATASET_SIZE,
+        )
+
+
+
+.. parsed-literal::
+
+    Downloading readme:   0%|          | 0.00/5.94k [00:00`__\
+Test split of \ `jfleg `__\ 
 dataset is used for testing. One dataset sample consists of a text with
 errors as input and several corrected versions as labels. When measuring
 accuracy we use mean ``(1 - WER)`` against corrected text versions,
@@ -716,9 +745,9 @@ where WER is Word Error Rate metric.
 .. code:: ipython3
 
     from utils import calculate_inference_time_and_accuracy
-
+    
     TEST_SUBSET_SIZE = 50
-
+    
     if to_quantize.value:
         inference_time_fp32, accuracy_fp32 = calculate_inference_time_and_accuracy(grammar_corrector_pipe_fp32, TEST_SUBSET_SIZE)
         print(f"Evaluation results of FP32 grammar correction pipeline. Accuracy: {accuracy_fp32:.2f}%. Time: {inference_time_fp32:.2f} sec.")
@@ -737,7 +766,7 @@ where WER is Word Error Rate metric.
 
 .. parsed-literal::
 
-    Evaluation results of FP32 grammar correction pipeline. Accuracy: 58.04%. Time: 61.03 sec.
+    Evaluation results of FP32 grammar correction pipeline. Accuracy: 58.04%. Time: 62.44 sec.
 
 
 
@@ -748,11 +777,12 @@ where WER is Word Error Rate metric.
 
 .. parsed-literal::
 
-    Evaluation results of INT8 grammar correction pipeline. Accuracy: 57.46%. Time: 42.38 sec.
-    Performance speedup: 1.440
-    Accuracy drop :0.59%.
+    Evaluation results of INT8 grammar correction pipeline. Accuracy: 59.04%. Time: 40.32 sec.
+    Performance speedup: 1.549
+    Accuracy drop :-0.99%.
     Model footprint reduction: 3.989
 
+
 Interactive demo
 ----------------
 
@@ -762,18 +792,18 @@ Interactive demo
 
     import gradio as gr
     import time
-
-
+    
+    
     def correct(text, quantized, progress=gr.Progress(track_tqdm=True)):
         grammar_corrector = grammar_corrector_pipe_int8 if quantized else grammar_corrector_pipe
-
+    
         start_time = time.perf_counter()
         corrected_text = correct_text(text, grammar_checker_pipe, grammar_corrector)
         end_time = time.perf_counter()
-
+    
         return corrected_text, f"{end_time - start_time:.2f}"
-
-
+    
+    
     def create_demo_block(quantized: bool, show_model_type: bool):
         model_type = (" optimized" if quantized else " original") if show_model_type else ""
         with gr.Row():
@@ -788,17 +818,21 @@ Interactive demo
             gr.Examples(examples=[default_text], inputs=[input_text])
         with gr.Row():
             button = gr.Button(f"Run{model_type}")
-            button.click(correct, inputs=[input_text, gr.Number(quantized, visible=False)], outputs=[output_text, correction_time])
-
-
+            button.click(
+                correct,
+                inputs=[input_text, gr.Number(quantized, visible=False)],
+                outputs=[output_text, correction_time],
+            )
+    
+    
     with gr.Blocks() as demo:
         gr.Markdown("# Interactive demo")
         quantization_is_present = grammar_corrector_pipe_int8 is not None
         create_demo_block(quantized=False, show_model_type=quantization_is_present)
         if quantization_is_present:
             create_demo_block(quantized=True, show_model_type=True)
-
-
+    
+    
     # if you are launching remotely, specify server_name and server_port
     # demo.launch(server_name='your server name', server_port='server port in int')
     # Read more in the docs: https://gradio.app/docs/
@@ -806,17 +840,3 @@ Interactive demo
         demo.queue().launch(debug=False)
     except Exception:
         demo.queue().launch(share=True, debug=False)
-
-
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7860
-
-    To create a public link, set `share=True` in `launch()`.
-
-
-
-
-
-
-
diff --git a/docs/notebooks/grounded-segment-anything-with-output.rst b/docs/notebooks/grounded-segment-anything-with-output.rst
new file mode 100644
index 00000000000..fa68d1a07a0
--- /dev/null
+++ b/docs/notebooks/grounded-segment-anything-with-output.rst
@@ -0,0 +1,1663 @@
+Object detection and masking from prompts with GroundedSAM (GroundingDINO + SAM) and OpenVINO
+=============================================================================================
+
+In this notebook, we provide the OpenVINO™ optimization for the
+combination of GroundingDINO + SAM =
+`GroundedSAM `__
+on Intel® platforms.
+
+GroundedSAM aims to detect and segment anything with text inputs.
+GroundingDINO is a language-guided query selection module to enhance
+object detection using input text. It selects relevant features from
+image and text inputs and returns predicted boxes with detections. The
+Segment Anything Model (SAM) produces high quality object masks from
+input prompts such as points or boxes, and it can be used to generate
+masks for all objects in an image. We use box predictions from
+GroundingDINO to mask the original image.
+
+More details about the model can be found in the
+`paper `__, and the official
+`repository `__.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/5703039/3c19063a-c60a-4d5d-b534-e1305a854180
+   :alt: image
+
+   image
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Clone repository and install
+   requirements <#clone-repository-and-install-requirements>`__
+-  `Download checkpoints and load PyTorch
+   model <#download-checkpoints-and-load-pytorch-model>`__
+-  `Convert GroundingDINO to OpenVINO IR
+   format <#convert-groundingdino-to-openvino-ir-format>`__
+-  `Run OpenVINO optimized
+   GroundingDINO <#run-openvino-optimized-groundingdino>`__
+-  `Convert SAM to OpenVINO IR <#convert-sam-to-openvino-ir>`__
+-  `Combine GroundingDINO + SAM
+   (GroundedSAM) <#combine-groundingdino--sam-groundedsam>`__
+-  `Interactive GroundedSAM <#interactive-groundedsam>`__
+-  `Cleanup <#cleanup>`__
+
+Clone repositories and install requirements
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    %pip install -q timm --extra-index-url https://download.pytorch.org/whl/cpu  # is needed for torch
+    %pip install -q "openvino>=2024.0" "torch>=2.1" opencv-python supervision transformers yapf pycocotools addict "gradio>=4.19" tqdm
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+For faster computation and to limit RAM by default we use
+``EfficientSAM`` for segmentation, but if you wish more accurate
+segmentation you can select vanilla ``SAM``.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    sam_type_widget = widgets.Dropdown(
+        options=["EfficientSAM", "SAM"],
+        value="EfficientSAM",
+        description="Segment Anything type:",
+    )
+    sam_type_widget
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Segment Anything type:', options=('EfficientSAM', 'SAM'), value='EfficientSAM')
+
+
+
+.. code:: ipython3
+
+    use_efficient_sam = sam_type_widget.value == "EfficientSAM"
+
+.. code:: ipython3
+
+    from pathlib import Path
+    import sys
+    import os
+    
+    repo_dir = Path("Grounded-Segment-Anything")
+    ground_dino_dir = Path("GroundingDINO")
+    efficient_sam_dir = Path("EfficientSAM")
+    
+    # we use grounding dino from a fork which contains modifications that allow conversion to OpenVINO IR format
+    if not ground_dino_dir.exists():
+        !git clone https://github.com/wenyi5608/GroundingDINO/
+    if use_efficient_sam and not efficient_sam_dir.exists():
+        !git clone https://github.com/yformer/EfficientSAM
+    if not use_efficient_sam and not repo_dir.exists():
+        !git clone https://github.com/IDEA-Research/Grounded-Segment-Anything
+    
+    # append to sys.path so that modules from the repo could be imported
+    sys.path.append(str(ground_dino_dir))
+    sys.path.append(str("EfficientSAM" if use_efficient_sam else repo_dir / "segment_anything"))
+
+
+.. parsed-literal::
+
+    Cloning into 'GroundingDINO'...
+
+
+.. parsed-literal::
+
+    remote: Enumerating objects: 379, done.
+    remote: Counting objects:   0% (1/177)
+remote: Counting objects:   1% (2/177)
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+remote: Counting objects: 100% (177/177)
+remote: Counting objects: 100% (177/177), done.
+    remote: Compressing objects:   1% (1/64)
+remote: Compressing objects:   3% (2/64)
+remote: Compressing objects:   4% (3/64)
+remote: Compressing objects:   6% (4/64)
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+remote: Compressing objects:  98% (63/64)
+remote: Compressing objects: 100% (64/64)
+remote: Compressing objects: 100% (64/64), done.
+
+
+.. parsed-literal::
+
+    Receiving objects:   0% (1/379)
+
+.. parsed-literal::
+
+    Receiving objects:   1% (4/379)
+
+.. parsed-literal::
+
+    Receiving objects:   2% (8/379)
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+Receiving objects:  22% (84/379)
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+Receiving objects:  24% (91/379)
+
+.. parsed-literal::
+
+    Receiving objects:  25% (95/379)
+
+.. parsed-literal::
+
+    Receiving objects:  26% (99/379)
+
+.. parsed-literal::
+
+    Receiving objects:  27% (103/379), 9.38 MiB | 18.39 MiB/s
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+
+.. parsed-literal::
+
+    Receiving objects:  50% (190/379), 9.38 MiB | 18.39 MiB/s
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+Receiving objects:  69% (262/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  70% (266/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  71% (270/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  72% (273/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  73% (277/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  74% (281/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  75% (285/379), 9.38 MiB | 18.39 MiB/s
+remote: Total 379 (delta 137), reused 113 (delta 113), pack-reused 202
+    Receiving objects:  76% (289/379), 9.38 MiB | 18.39 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  77% (292/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  78% (296/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  79% (300/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  80% (304/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  81% (307/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  82% (311/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  83% (315/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  84% (319/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  85% (323/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  86% (326/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  87% (330/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  88% (334/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  89% (338/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  90% (342/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  91% (345/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  92% (349/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  93% (353/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  94% (357/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  95% (361/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  96% (364/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  97% (368/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  98% (372/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects:  99% (376/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects: 100% (379/379), 9.38 MiB | 18.39 MiB/s
+Receiving objects: 100% (379/379), 14.03 MiB | 19.52 MiB/s, done.
+    Resolving deltas:   0% (0/195)
+Resolving deltas:   3% (7/195)
+Resolving deltas:   6% (13/195)
+Resolving deltas:   7% (15/195)
+Resolving deltas:   8% (16/195)
+Resolving deltas:  16% (32/195)
+Resolving deltas:  17% (35/195)
+Resolving deltas:  18% (37/195)
+Resolving deltas:  19% (38/195)
+Resolving deltas:  20% (39/195)
+Resolving deltas:  21% (41/195)
+Resolving deltas:  26% (51/195)
+Resolving deltas:  41% (80/195)
+Resolving deltas:  50% (99/195)
+Resolving deltas:  52% (102/195)
+Resolving deltas:  55% (109/195)
+Resolving deltas:  57% (112/195)
+Resolving deltas:  60% (117/195)
+Resolving deltas:  61% (120/195)
+Resolving deltas:  62% (121/195)
+Resolving deltas:  68% (133/195)
+Resolving deltas:  69% (135/195)
+Resolving deltas:  73% (143/195)
+Resolving deltas:  74% (145/195)
+Resolving deltas:  75% (147/195)
+Resolving deltas:  76% (149/195)
+Resolving deltas:  78% (153/195)
+Resolving deltas:  80% (157/195)
+Resolving deltas:  81% (159/195)
+Resolving deltas: 100% (195/195)
+Resolving deltas: 100% (195/195), done.
+
+
+.. parsed-literal::
+
+    Cloning into 'EfficientSAM'...
+
+
+.. parsed-literal::
+
+    remote: Enumerating objects: 424, done.
+    remote: Counting objects:   1% (1/85)
+remote: Counting objects:   2% (2/85)
+remote: Counting objects:   3% (3/85)
+remote: Counting objects:   4% (4/85)
+remote: Counting objects:   5% (5/85)
+remote: Counting objects:   7% (6/85)
+remote: Counting objects:   8% (7/85)
+remote: Counting objects:   9% (8/85)
+remote: Counting objects:  10% (9/85)
+remote: Counting objects:  11% (10/85)
+remote: Counting objects:  12% (11/85)
+remote: Counting objects:  14% (12/85)
+remote: Counting objects:  15% (13/85)
+remote: Counting objects:  16% (14/85)
+remote: Counting objects:  17% (15/85)
+remote: Counting objects:  18% (16/85)
+remote: Counting objects:  20% (17/85)
+remote: Counting objects:  21% (18/85)
+remote: Counting objects:  22% (19/85)
+remote: Counting objects:  23% (20/85)
+remote: Counting objects:  24% (21/85)
+remote: Counting objects:  25% (22/85)
+remote: Counting objects:  27% (23/85)
+remote: Counting objects:  28% (24/85)
+remote: Counting objects:  29% (25/85)
+remote: Counting objects:  30% (26/85)
+remote: Counting objects:  31% (27/85)
+remote: Counting objects:  32% (28/85)
+remote: Counting objects:  34% (29/85)
+remote: Counting objects:  35% (30/85)
+remote: Counting objects:  36% (31/85)
+remote: Counting objects:  37% (32/85)
+remote: Counting objects:  38% (33/85)
+remote: Counting objects:  40% (34/85)
+remote: Counting objects:  41% (35/85)
+remote: Counting objects:  42% (36/85)
+remote: Counting objects:  43% (37/85)
+remote: Counting objects:  44% (38/85)
+remote: Counting objects:  45% (39/85)
+remote: Counting objects:  47% (40/85)
+remote: Counting objects:  48% (41/85)
+remote: Counting objects:  49% (42/85)
+remote: Counting objects:  50% (43/85)
+remote: Counting objects:  51% (44/85)
+remote: Counting objects:  52% (45/85)
+remote: Counting objects:  54% (46/85)
+remote: Counting objects:  55% (47/85)
+remote: Counting objects:  56% (48/85)
+remote: Counting objects:  57% (49/85)
+remote: Counting objects:  58% (50/85)
+remote: Counting objects:  60% (51/85)
+remote: Counting objects:  61% (52/85)
+remote: Counting objects:  62% (53/85)
+remote: Counting objects:  63% (54/85)
+remote: Counting objects:  64% (55/85)
+remote: Counting objects:  65% (56/85)
+remote: Counting objects:  67% (57/85)
+remote: Counting objects:  68% (58/85)
+remote: Counting objects:  69% (59/85)
+remote: Counting objects:  70% (60/85)
+remote: Counting objects:  71% (61/85)
+remote: Counting objects:  72% (62/85)
+remote: Counting objects:  74% (63/85)
+remote: Counting objects:  75% (64/85)
+remote: Counting objects:  76% (65/85)
+remote: Counting objects:  77% (66/85)
+remote: Counting objects:  78% (67/85)
+remote: Counting objects:  80% (68/85)
+remote: Counting objects:  81% (69/85)
+remote: Counting objects:  82% (70/85)
+remote: Counting objects:  83% (71/85)
+remote: Counting objects:  84% (72/85)
+remote: Counting objects:  85% (73/85)
+remote: Counting objects:  87% (74/85)
+remote: Counting objects:  88% (75/85)
+remote: Counting objects:  89% (76/85)
+remote: Counting objects:  90% (77/85)
+remote: Counting objects:  91% (78/85)
+remote: Counting objects:  92% (79/85)
+remote: Counting objects:  94% (80/85)
+remote: Counting objects:  95% (81/85)
+remote: Counting objects:  96% (82/85)
+remote: Counting objects:  97% (83/85)
+remote: Counting objects:  98% (84/85)
+remote: Counting objects: 100% (85/85)
+remote: Counting objects: 100% (85/85), done.
+    remote: Compressing objects:   3% (1/33)
+remote: Compressing objects:   6% (2/33)
+remote: Compressing objects:   9% (3/33)
+remote: Compressing objects:  12% (4/33)
+remote: Compressing objects:  15% (5/33)
+remote: Compressing objects:  18% (6/33)
+remote: Compressing objects:  21% (7/33)
+remote: Compressing objects:  24% (8/33)
+remote: Compressing objects:  27% (9/33)
+remote: Compressing objects:  30% (10/33)
+remote: Compressing objects:  33% (11/33)
+remote: Compressing objects:  36% (12/33)
+remote: Compressing objects:  39% (13/33)
+remote: Compressing objects:  42% (14/33)
+remote: Compressing objects:  45% (15/33)
+remote: Compressing objects:  48% (16/33)
+remote: Compressing objects:  51% (17/33)
+remote: Compressing objects:  54% (18/33)
+remote: Compressing objects:  57% (19/33)
+remote: Compressing objects:  60% (20/33)
+remote: Compressing objects:  63% (21/33)
+remote: Compressing objects:  66% (22/33)
+remote: Compressing objects:  69% (23/33)
+remote: Compressing objects:  72% (24/33)
+remote: Compressing objects:  75% (25/33)
+remote: Compressing objects:  78% (26/33)
+remote: Compressing objects:  81% (27/33)
+remote: Compressing objects:  84% (28/33)
+remote: Compressing objects:  87% (29/33)
+remote: Compressing objects:  90% (30/33)
+remote: Compressing objects:  93% (31/33)
+remote: Compressing objects:  96% (32/33)
+remote: Compressing objects: 100% (33/33)
+remote: Compressing objects: 100% (33/33), done.
+
+
+.. parsed-literal::
+
+    Receiving objects:   0% (1/424)
+Receiving objects:   1% (5/424)
+
+.. parsed-literal::
+
+    Receiving objects:   2% (9/424)
+Receiving objects:   3% (13/424)
+Receiving objects:   4% (17/424)
+Receiving objects:   5% (22/424)
+
+.. parsed-literal::
+
+    Receiving objects:   5% (24/424), 17.34 MiB | 17.34 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:   6% (26/424), 29.54 MiB | 19.69 MiB/s
+Receiving objects:   7% (30/424), 29.54 MiB | 19.69 MiB/s
+Receiving objects:   8% (34/424), 29.54 MiB | 19.69 MiB/s
+Receiving objects:   9% (39/424), 29.54 MiB | 19.69 MiB/s
+Receiving objects:  10% (43/424), 29.54 MiB | 19.69 MiB/s
+Receiving objects:  11% (47/424), 29.54 MiB | 19.69 MiB/s
+Receiving objects:  12% (51/424), 29.54 MiB | 19.69 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  12% (54/424), 41.72 MiB | 20.86 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  12% (54/424), 66.43 MiB | 22.08 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  13% (56/424), 66.43 MiB | 22.08 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  13% (56/424), 92.11 MiB | 22.90 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  14% (60/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  15% (64/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  16% (68/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  17% (73/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  18% (77/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  19% (81/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  20% (85/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  21% (90/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  22% (94/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  23% (98/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  24% (102/424), 105.12 MiB | 23.20 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  25% (106/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  26% (111/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  27% (115/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  28% (119/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  29% (123/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  30% (128/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  31% (132/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  32% (136/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  33% (140/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  34% (145/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  35% (149/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  36% (153/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  37% (157/424), 105.12 MiB | 23.20 MiB/s
+Receiving objects:  38% (162/424), 105.12 MiB | 23.20 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  38% (164/424), 118.14 MiB | 24.69 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  38% (164/424), 144.35 MiB | 25.23 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  38% (164/424), 170.95 MiB | 25.59 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  38% (164/424), 197.61 MiB | 26.01 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  39% (166/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  40% (170/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  41% (174/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  42% (179/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  43% (183/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  44% (187/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  45% (191/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  46% (196/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  47% (200/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  48% (204/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  49% (208/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  50% (212/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  51% (217/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  52% (221/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  53% (225/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  54% (229/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  55% (234/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  56% (238/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  57% (242/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  58% (246/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  59% (251/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  60% (255/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  61% (259/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  62% (263/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  63% (268/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  64% (272/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  65% (276/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  66% (280/424), 197.61 MiB | 26.01 MiB/s
+Receiving objects:  67% (285/424), 197.61 MiB | 26.01 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  67% (288/424), 222.04 MiB | 25.75 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  67% (288/424), 240.28 MiB | 24.07 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  68% (289/424), 240.28 MiB | 24.07 MiB/s
+Receiving objects:  69% (293/424), 240.28 MiB | 24.07 MiB/s
+Receiving objects:  70% (297/424), 240.28 MiB | 24.07 MiB/s
+Receiving objects:  71% (302/424), 240.28 MiB | 24.07 MiB/s
+Receiving objects:  72% (306/424), 240.28 MiB | 24.07 MiB/s
+Receiving objects:  73% (310/424), 240.28 MiB | 24.07 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  73% (313/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  74% (314/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  75% (318/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  76% (323/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  77% (327/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  78% (331/424), 259.66 MiB | 22.62 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  79% (335/424), 259.66 MiB | 22.62 MiB/s
+
+.. parsed-literal::
+
+    remote: Total 424 (delta 76), reused 52 (delta 52), pack-reused 339
+    Receiving objects:  80% (340/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  81% (344/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  82% (348/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  83% (352/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  84% (357/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  85% (361/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  86% (365/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  87% (369/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  88% (374/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  89% (378/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  90% (382/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  91% (386/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  92% (391/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  93% (395/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  94% (399/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  95% (403/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  96% (408/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  97% (412/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  98% (416/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects:  99% (420/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects: 100% (424/424), 259.66 MiB | 22.62 MiB/s
+Receiving objects: 100% (424/424), 262.14 MiB | 23.41 MiB/s, done.
+    Resolving deltas:   0% (0/246)
+Resolving deltas:   4% (10/246)
+Resolving deltas:   6% (17/246)
+Resolving deltas:  15% (37/246)
+Resolving deltas:  18% (46/246)
+Resolving deltas:  22% (56/246)
+Resolving deltas:  23% (57/246)
+Resolving deltas:  24% (60/246)
+Resolving deltas:  26% (64/246)
+Resolving deltas:  32% (81/246)
+Resolving deltas:  36% (90/246)
+Resolving deltas:  37% (92/246)
+Resolving deltas:  38% (94/246)
+Resolving deltas:  41% (101/246)
+
+.. parsed-literal::
+
+    Resolving deltas:  43% (108/246)
+Resolving deltas:  45% (112/246)
+
+.. parsed-literal::
+
+    Resolving deltas:  48% (119/246)
+Resolving deltas:  49% (121/246)
+Resolving deltas:  51% (127/246)
+Resolving deltas:  52% (128/246)
+Resolving deltas:  54% (133/246)
+Resolving deltas:  57% (142/246)
+Resolving deltas:  61% (152/246)
+Resolving deltas:  62% (154/246)
+Resolving deltas:  65% (162/246)
+Resolving deltas:  66% (164/246)
+Resolving deltas:  67% (165/246)
+Resolving deltas:  69% (172/246)
+Resolving deltas:  70% (174/246)
+Resolving deltas:  88% (217/246)
+Resolving deltas:  96% (237/246)
+Resolving deltas:  97% (240/246)
+Resolving deltas:  98% (243/246)
+
+.. parsed-literal::
+
+    Resolving deltas:  99% (245/246)
+
+.. parsed-literal::
+
+    Resolving deltas: 100% (246/246)
+Resolving deltas: 100% (246/246), done.
+
+
+.. code:: ipython3
+
+    import torch
+    import numpy as np
+    import supervision as sv
+    import openvino as ov
+    from PIL import Image, ImageDraw, ImageFont
+    from typing import Union, List
+    import transformers
+    
+    core = ov.Core()
+
+Download checkpoints and load PyTorch models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    IRS_PATH = Path("openvino_irs")
+    CKPT_BASE_PATH = Path("checkpoints")
+    os.makedirs(IRS_PATH, exist_ok=True)
+    os.makedirs(CKPT_BASE_PATH, exist_ok=True)
+    
+    PT_DEVICE = "cpu"
+    ov_dino_name = "openvino_grounding_dino"
+    ov_sam_name = "openvino_segment_anything"
+    
+    ground_dino_img_size = (1024, 1280)
+    
+    # GroundingDINO config and checkpoint
+    GROUNDING_DINO_CONFIG_PATH = f"{ground_dino_dir}/groundingdino/config/GroundingDINO_SwinT_OGC.py"
+    GROUNDING_DINO_CHECKPOINT_PATH = CKPT_BASE_PATH / "groundingdino_swint_ogc.pth"
+    
+    # Segment Anything checkpoint
+    SAM_CHECKPOINT_PATH = CKPT_BASE_PATH / "sam_vit_h_4b8939.pth"
+    
+    # Efficient Segment Anything checkpoint
+    EFFICIENT_SAM_CHECKPOINT_PATH = efficient_sam_dir / "weights/efficient_sam_vitt.pt"
+
+.. code:: ipython3
+
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
+    from notebook_utils import download_file
+    
+    download_file(
+        "https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth",
+        directory=CKPT_BASE_PATH,
+    )
+    if not use_efficient_sam:
+        download_file(
+            "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
+            directory=CKPT_BASE_PATH,
+        )
+
+
+
+.. parsed-literal::
+
+    checkpoints/groundingdino_swint_ogc.pth:   0%|          | 0.00/662M [00:00 torch.Tensor:
+        import groundingdino.datasets.transforms as T
+    
+        transform = T.Compose(
+            [
+                T.RandomResize([800], max_size=1333),
+                T.ToTensor(),
+                T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+            ]
+        )
+        image, _ = transform(pil_image, None)  # 3, h, w
+        return image
+    
+    
+    # detects boxes usding openvino optimized grounding dino model
+    def get_ov_grounding_output(
+        model: ov.CompiledModel,
+        pil_image: Image.Image,
+        caption: Union[str, List[str]],
+        box_threshold: float,
+        text_threshold: float,
+        dino_tokenizer: transformers.PreTrainedTokenizerBase = dino_tokenizer,
+        max_text_len: int = max_text_len,
+    ) -> (torch.Tensor, List[str], torch.Tensor):
+        #  for text prompt pre-processing we reuse existing routines from GroundignDINO repo
+        if isinstance(caption, list):
+            caption = ". ".join(caption)
+        caption = caption.lower()
+        caption = caption.strip()
+        if not caption.endswith("."):
+            caption = caption + "."
+        captions = [caption]
+    
+        tokenized = dino_tokenizer(captions, padding="longest", return_tensors="pt")
+        specical_tokens = dino_tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
+    
+        (
+            text_self_attention_masks,
+            position_ids,
+            cate_to_token_mask_list,
+        ) = generate_masks_with_special_tokens_and_transfer_map(tokenized, specical_tokens, dino_tokenizer)
+    
+        if text_self_attention_masks.shape[1] > max_text_len:
+            text_self_attention_masks = text_self_attention_masks[:, :max_text_len, :max_text_len]
+    
+            position_ids = position_ids[:, :max_text_len]
+            tokenized["input_ids"] = tokenized["input_ids"][:, :max_text_len]
+            tokenized["attention_mask"] = tokenized["attention_mask"][:, :max_text_len]
+            tokenized["token_type_ids"] = tokenized["token_type_ids"][:, :max_text_len]
+    
+        # inputs dictionary which will be fed into the ov.CompiledModel for inference
+        inputs = {}
+        inputs["attention_mask.1"] = tokenized["attention_mask"]
+        inputs["text_self_attention_masks"] = text_self_attention_masks
+        inputs["input_ids"] = tokenized["input_ids"]
+        inputs["position_ids"] = position_ids
+        inputs["token_type_ids"] = tokenized["token_type_ids"]
+    
+        # GroundingDINO fails to run with input shapes different than one used for conversion.
+        # As a workaround we resize input_image to the size used for conversion. Model does not rely
+        # on image resolution to know object sizes therefore no need to resize box_predictions
+        from torchvision.transforms.functional import resize, InterpolationMode
+    
+        input_img = resize(
+            transform_image(pil_image),
+            ground_dino_img_size,
+            interpolation=InterpolationMode.BICUBIC,
+        )[None, ...]
+        inputs["samples"] = input_img
+    
+        # OpenVINO inference
+        request = model.create_infer_request()
+        request.start_async(inputs, share_inputs=False)
+        request.wait()
+    
+        def sig(x):
+            return 1 / (1 + np.exp(-x))
+    
+        logits = torch.from_numpy(sig(np.squeeze(request.get_tensor("pred_logits").data, 0)))
+        boxes = torch.from_numpy(np.squeeze(request.get_tensor("pred_boxes").data, 0))
+    
+        # filter output
+        filt_mask = logits.max(dim=1)[0] > box_threshold
+        logits, boxes = logits[filt_mask], boxes[filt_mask]
+    
+        # get phrase and build predictions
+        tokenized = dino_tokenizer(caption)
+        pred_phrases = []
+        for logit in logits:
+            pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, dino_tokenizer)
+            pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
+    
+        return boxes, pred_phrases, logits.max(dim=1)[0]
+
+.. code:: ipython3
+
+    SOURCE_IMAGE_PATH = f"{ground_dino_dir}/.asset/demo7.jpg"
+    BOX_THRESHOLD = 0.3
+    TEXT_THRESHOLD = 0.25
+    NMS_THRESHOLD = 0.8
+    
+    pil_image = Image.open(SOURCE_IMAGE_PATH)
+    classes_prompt = ["Horse", "Cloud"]
+
+.. code:: ipython3
+
+    boxes_filt, pred_phrases, logits_filt = get_ov_grounding_output(ov_compiled_grounded_dino, pil_image, classes_prompt, BOX_THRESHOLD, TEXT_THRESHOLD)
+
+
+.. parsed-literal::
+
+    2024-04-17 23:56:30.569255: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 23:56:30.608409: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+
+
+.. parsed-literal::
+
+    2024-04-17 23:56:31.167736: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
+
+Convert predicted boxes to supervision box detections format
+
+.. code:: ipython3
+
+    source_w, source_h = pil_image.size
+    detections = Model.post_process_result(source_h=source_h, source_w=source_w, boxes=boxes_filt, logits=logits_filt)
+    
+    class_id = Model.phrases2classes(phrases=pred_phrases, classes=list(map(str.lower, classes_prompt)))
+    detections.class_id = class_id
+
+Draw box detections
+
+.. code:: ipython3
+
+    box_annotator = sv.BoxAnnotator()
+    labels = [f"{classes_prompt[class_id] if class_id is not None else 'None'} {confidence:0.2f}" for _, _, confidence, class_id, _, _ in detections]
+    annotated_frame = box_annotator.annotate(scene=np.array(pil_image).copy(), detections=detections, labels=labels)
+    
+    Image.fromarray(annotated_frame)
+
+
+.. parsed-literal::
+
+    SupervisionWarnings: BoxAnnotator is deprecated: `BoxAnnotator` is deprecated and will be removed in `supervision-0.22.0`. Use `BoundingBoxAnnotator` and `LabelAnnotator` instead
+
+
+
+
+.. image:: grounded-segment-anything-with-output_files/grounded-segment-anything-with-output_29_1.png
+
+
+
+Great! All clouds and horses are detected. Feel free to play around and
+specify other objects you wish to detect.
+
+Convert SAM to OpenVINO IR
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+And now let’s feed those detection to ``SAM`` model. We will use
+``EfficiendSAM`` for faster computation and to save ram, but feel free
+to select vanilla ``SAM`` if you wish more detailed and precise
+segmentation. First of all let’s convert ``SAM`` model to OpenVINO IR.
+
+.. code:: ipython3
+
+    ov_efficient_sam_name = "openvino_efficient_sam"
+    ov_efficient_sam_path = IRS_PATH / f"{ov_efficient_sam_name}.xml"
+    
+    # convert EfficientSAM to OpenVINO IR format
+    if not ov_efficient_sam_path.exists() and use_efficient_sam:
+        random_input_image = np.random.rand(1, 3, *pil_image.size[::-1]).astype(np.float32)
+        bounding_box = np.array([900, 100, 1000, 200]).reshape([1, 1, 2, 2])
+        bbox_labels = np.array([2, 3]).reshape([1, 1, 2])
+        efficient_sam_dummy_input = tuple(torch.from_numpy(x) for x in (random_input_image, bounding_box, bbox_labels))
+    
+        ov_efficient_sam = ov.convert_model(efficient_sam_model, example_input=efficient_sam_dummy_input)
+        ov.save_model(ov_efficient_sam, ov_efficient_sam_path)
+    elif use_efficient_sam:
+        ov_efficient_sam = core.read_model(ov_efficient_sam_path)
+
+
+.. parsed-literal::
+
+    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
+
+
+.. parsed-literal::
+
+    TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+
+
+.. parsed-literal::
+
+    TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+
+
+Below is conversion of vanilla ``SAM``. This code is not used when
+``EfficientSAM`` is selected for segmentation.
+
+.. code:: ipython3
+
+    # In order to convert to OpenVINO IR neeed to patch forward method or the torch.nn.Module for SAM
+    class SamMaskFromBoxes(torch.nn.Module):
+        def __init__(
+            self,
+            sam_predictor,
+        ) -> None:
+            super().__init__()
+            self.model = sam_predictor
+    
+        @torch.no_grad()
+        def forward(
+            self,
+            input_image: torch.Tensor,
+            transformed_boxes: torch.Tensor,
+            multimask_output: bool = False,
+            hq_token_only: bool = False,
+        ):
+            pre_processed_image = self.model.model.preprocess(input_image)
+            image_embeddings, interm_features = self.model.model.image_encoder(pre_processed_image)
+    
+            # Embed prompts
+            sparse_embeddings, dense_embeddings = self.model.model.prompt_encoder(
+                points=None,
+                boxes=transformed_boxes,
+                masks=None,
+            )
+    
+            # Predict masks
+            low_res_masks, iou_predictions = self.model.model.mask_decoder(
+                image_embeddings=image_embeddings,
+                image_pe=self.model.model.prompt_encoder.get_dense_pe(),
+                sparse_prompt_embeddings=sparse_embeddings,
+                dense_prompt_embeddings=dense_embeddings,
+                multimask_output=multimask_output,
+                hq_token_only=hq_token_only,
+                interm_embeddings=interm_features,
+            )
+    
+            return low_res_masks, iou_predictions
+
+.. code:: ipython3
+
+    ov_sam_path = IRS_PATH / f"{ov_sam_name}.xml"
+    
+    # example input for vanilla SAM
+    input_image_torch = torch.randint(0, 255, size=[1, 3, 683, 1024], dtype=torch.uint8)
+    dummy_transformed_boxes = torch.rand(1, 4, dtype=torch.float32) * 200
+    
+    # convert vanilla SAM to OpenVINO IR format
+    if not ov_sam_path.exists() and not use_efficient_sam:
+        # Load pytorch model object and prepare example input for conversion
+        exportable = SamMaskFromBoxes(sam_predictor)
+        exportable.model.model.eval()
+        for par in exportable.model.model.parameters():
+            par.requires_grad = False
+    
+        traced = torch.jit.trace(exportable, example_inputs=(input_image_torch, dummy_transformed_boxes))
+        ov_sam = ov.convert_model(traced, example_input=(input_image_torch, dummy_transformed_boxes))
+        ov.save_model(ov_sam, ov_sam_path)
+    elif not use_efficient_sam:
+        ov_sam = core.read_model(ov_sam_path)
+
+.. code:: ipython3
+
+    if use_efficient_sam:
+        compiled_efficient_sam = core.compile_model(ov_efficient_sam, device_name=device.value)
+    else:
+        compiled_vanilla_sam = core.compile_model(ov_sam, device_name=device.value)
+
+Combine GroundingDINO + SAM (GroundedSAM)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+We have OpenVINO IRs for both GroundingDINO and SAM models. Lets run the
+segmentation using predictions from GroundingDINO. Same as above, use
+``EfficientSAM`` by default.
+
+.. code:: ipython3
+
+    def predict_efficient_sam_mask(compiled_efficient_sam: ov.CompiledModel, image: Image.Image, bbox: torch.Tensor):
+        # input image is scaled so that none of the sizes is greater than 1024, same as in efficient-sam notebook
+        input_size = 1024
+        w, h = image.size[:2]
+        scale = input_size / max(w, h)
+        new_w = int(w * scale)
+        new_h = int(h * scale)
+        image = image.resize((new_w, new_h))
+    
+        numpy_image = np.array(image, dtype=np.float32) / 255.0
+        numpy_image = np.transpose(numpy_image, (2, 0, 1))[None, ...]
+    
+        scaled_points = bbox * scale
+    
+        bounding_box = scaled_points.reshape([1, 1, 2, 2])
+        bbox_labels = np.reshape(np.array([2, 3]), [1, 1, 2])
+    
+        res = compiled_efficient_sam((numpy_image, bounding_box, bbox_labels))
+    
+        predicted_logits, predicted_iou = res[0], res[1]
+    
+        all_masks = torch.ge(torch.sigmoid(torch.from_numpy(predicted_logits[0, 0, :, :, :])), 0.5).numpy()
+        predicted_iou = predicted_iou[0, 0, ...]
+    
+        # select the mask with the greatest IOU
+        max_predicted_iou = -1
+        selected_mask_using_predicted_iou = None
+        for m in range(all_masks.shape[0]):
+            curr_predicted_iou = predicted_iou[m]
+            if curr_predicted_iou > max_predicted_iou or selected_mask_using_predicted_iou is None:
+                max_predicted_iou = curr_predicted_iou
+                selected_mask_using_predicted_iou = all_masks[m]
+        return selected_mask_using_predicted_iou
+    
+    
+    # If several detections are fed to EfficientSAM, it merges them to a single mask. Therefore, we call it one by one for each detection.
+    def predict_efficient_sam_masks(compiled_efficient_sam: ov.CompiledModel, pil_image: Image.Image, transformed_boxes) -> torch.Tensor:
+        masks = []
+        for bbox in transformed_boxes:
+            mask = predict_efficient_sam_mask(compiled_efficient_sam, pil_image, bbox)
+            mask = Image.fromarray(mask).resize(pil_image.size)
+            masks.append(np.array(mask))
+        masks = torch.from_numpy(np.array(masks))
+        return masks
+
+.. code:: ipython3
+
+    def transform_boxes(sam_predictor: torch.nn.Module, boxes: torch.Tensor, size: tuple) -> torch.Tensor:
+        H, W = size[0], size[1]
+        for i in range(boxes.size(0)):
+            boxes[i] = boxes[i] * torch.Tensor([W, H, W, H])
+            boxes[i][:2] -= boxes[i][2:] / 2
+            boxes[i][2:] += boxes[i][:2]
+    
+        return sam_predictor.transform.apply_boxes_torch(boxes, size).to(PT_DEVICE)
+    
+    
+    def predict_vanilla_sam_masks(
+        compiled_vanilla_sam: ov.CompiledModel,
+        image: np.ndarray,
+        transformed_boxes: torch.Tensor,
+    ) -> torch.Tensor:
+        transfromed_image = exportable.model.transform.apply_image(image)
+        input_image_torch = torch.as_tensor(transfromed_image, device=PT_DEVICE)
+        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
+    
+        original_size = tuple(image.shape[:2])
+        input_size = tuple(input_image_torch.shape[-2:])
+    
+        low_res_masks = compiled_vanilla_sam((input_image_torch, transformed_boxes))[0]
+    
+        # Upscale the masks to the original image resolution
+        masks = exportable.model.model.postprocess_masks(torch.from_numpy(low_res_masks), input_size, original_size)
+        masks = masks > exportable.model.model.mask_threshold
+        return masks
+
+Run SAM model for the same image with the detected boxes from
+GroundingDINO.
+
+Please note that vanilla SAM and EfficientSAM have slightly different
+detection formats. But inputs for both of them originate from
+``boxes_filt`` which is result of the ``get_ov_grounding_output``. For
+EfficientSAM we use ``detections.xyxy`` boxes obtained after
+``boxes_filt`` is fed to ``Model.post_process_result``. While vanilla
+SAM has it’s own preprocessing function ``transform_boxes``.
+
+.. code:: ipython3
+
+    if use_efficient_sam:
+        masks = predict_efficient_sam_masks(compiled_efficient_sam, pil_image, detections.xyxy)
+        detections.mask = masks.numpy()
+    else:
+        transformed_boxes = transform_boxes(sam_predictor, boxes_filt, pil_image.size[::-1])
+        masks = predict_vanilla_sam_masks(compiled_vanilla_sam, np.array(pil_image), transformed_boxes)
+        detections.mask = masks[:, 0].numpy()
+
+Combine both boxes and segmentation masks and draw them.
+
+.. code:: ipython3
+
+    box_annotator = sv.BoxAnnotator()
+    mask_annotator = sv.MaskAnnotator()
+    
+    annotated_image = np.array(pil_image)
+    annotated_image = mask_annotator.annotate(scene=np.array(pil_image).copy(), detections=detections)
+    annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)
+    
+    Image.fromarray(annotated_image)
+
+
+.. parsed-literal::
+
+    SupervisionWarnings: BoxAnnotator is deprecated: `BoxAnnotator` is deprecated and will be removed in `supervision-0.22.0`. Use `BoundingBoxAnnotator` and `LabelAnnotator` instead
+
+
+
+
+.. image:: grounded-segment-anything-with-output_files/grounded-segment-anything-with-output_45_1.png
+
+
+
+Great! All detected horses and clouds are segmented as well.
+
+Interactive GroundedSAM
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Now, you can try apply grounding sam on your own images using
+interactive demo. The code below provides helper functions used in
+demonstration.
+
+.. code:: ipython3
+
+    def draw_mask(mask, draw, random_color=False):
+        import random
+    
+        if random_color:
+            color = (
+                random.randint(0, 255),
+                random.randint(0, 255),
+                random.randint(0, 255),
+                153,
+            )
+        else:
+            color = (30, 144, 255, 153)
+    
+        nonzero_coords = np.transpose(np.nonzero(mask))
+    
+        for coord in nonzero_coords:
+            draw.point(coord[::-1], fill=color)
+    
+    
+    def draw_box(box, draw, label):
+        # random color
+        color = tuple(np.random.randint(0, 255, size=3).tolist())
+    
+        draw.rectangle(((box[0], box[1]), (box[2], box[3])), outline=color, width=4)
+    
+        if label:
+            font = ImageFont.load_default(18)
+            if hasattr(font, "getbbox"):
+                bbox = draw.textbbox((box[0], box[1]), str(label), font, anchor="ld")
+            else:
+                w, h = draw.textsize(str(label), font)
+                bbox = (box[0], box[1], box[0] + w, box[1] + h)
+            draw.rectangle(bbox, fill=color)
+            draw.text((box[0], box[1]), str(label), fill="white", anchor="ld", font=font)
+
+.. code:: ipython3
+
+    """"
+    run_grounding_sam is called every time "Submit" button is clicked
+    """
+    
+    
+    def run_grounding_sam(image, task_type, text_prompt, box_threshold, text_threshold):
+        pil_image = Image.fromarray(image)
+        size = image.shape[1], image.shape[0]  # size is WH image.shape HWC
+    
+        boxes_filt, scores, pred_phrases = get_ov_grounding_output(ov_compiled_grounded_dino, pil_image, text_prompt, box_threshold, text_threshold)
+    
+        # process boxes
+        H, W = size[1], size[0]
+        for i in range(boxes_filt.size(0)):
+            boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
+            boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
+            boxes_filt[i][2:] += boxes_filt[i][:2]
+    
+        if task_type == "seg":
+            if use_efficient_sam:
+                masks = predict_efficient_sam_masks(compiled_efficient_sam, pil_image, boxes_filt.numpy())
+            else:
+                transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(PT_DEVICE)
+                masks = predict_vanilla_sam_masks(compiled_vanilla_sam, image, transformed_boxes)[:, 0]
+    
+            mask_image = Image.new("RGBA", size, color=(0, 0, 0, 0))
+            mask_draw = ImageDraw.Draw(mask_image)
+            for mask in masks:
+                draw_mask(mask.numpy(), mask_draw, random_color=True)
+    
+            image_draw = ImageDraw.Draw(pil_image)
+            for box, label in zip(boxes_filt, pred_phrases):
+                draw_box(box, image_draw, label)
+    
+            pil_image = pil_image.convert("RGBA")
+            pil_image.alpha_composite(mask_image)
+    
+            return [pil_image, mask_image]
+        if task_type == "det":
+            image_draw = ImageDraw.Draw(pil_image)
+            for box, label in zip(boxes_filt, pred_phrases):
+                draw_box(box, image_draw, label)
+            return [pil_image]
+        else:
+            gr.Warning(f"task_type:{task_type} error!")
+
+You can run interactive app with your own image and text prompts. To
+define prompt specify comma (or conjunction) separated names of objects
+you wish to segment. For demonstration, this demo already has two
+predefined examples. If many object are crowded and overlapping please
+increase threshold values in ``Advanced options``.
+
+.. code:: ipython3
+
+    import gradio as gr
+    
+    with gr.Accordion("Advanced options", open=False) as advanced:
+        box_threshold = gr.Slider(label="Box Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.05)
+        text_threshold = gr.Slider(label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.05)
+    
+    demo = gr.Interface(
+        run_grounding_sam,
+        [
+            gr.Image(),
+            gr.Dropdown(["det", "seg"], value="seg", label="task_type"),
+            gr.Textbox(value="bears", label="Text Prompt"),
+        ],
+        additional_inputs=[
+            box_threshold,
+            text_threshold,
+        ],
+        outputs=gr.Gallery(preview=True, object_fit="scale-down"),
+        examples=[
+            [f"{ground_dino_dir}/.asset/demo2.jpg", "seg", "dog, forest"],
+            [f"{ground_dino_dir}/.asset/demo7.jpg", "seg", "horses and clouds"],
+        ],
+        additional_inputs_accordion=advanced,
+    )
+    
+    try:
+        demo.launch(server_name="0.0.0.0", debug=False, height=1000)
+    except Exception:
+        demo.launch(share=True, debug=False, height=1000)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://0.0.0.0:7860
+    
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
+
+Cleanup
+~~~~~~~
+
+
+
+.. code:: ipython3
+
+    # import shutil
+    # shutil.rmtree(CKPT_BASE_PATH)
+    # shutil.rmtree(IRS_PATH)
diff --git a/docs/notebooks/288-grounded-segment-anything-with-output_files/288-grounded-segment-anything-with-output_29_0.jpg b/docs/notebooks/grounded-segment-anything-with-output_files/grounded-segment-anything-with-output_29_1.jpg
similarity index 100%
rename from docs/notebooks/288-grounded-segment-anything-with-output_files/288-grounded-segment-anything-with-output_29_0.jpg
rename to docs/notebooks/grounded-segment-anything-with-output_files/grounded-segment-anything-with-output_29_1.jpg
diff --git a/docs/notebooks/288-grounded-segment-anything-with-output_files/288-grounded-segment-anything-with-output_29_0.png b/docs/notebooks/grounded-segment-anything-with-output_files/grounded-segment-anything-with-output_29_1.png
similarity index 100%
rename from docs/notebooks/288-grounded-segment-anything-with-output_files/288-grounded-segment-anything-with-output_29_0.png
rename to docs/notebooks/grounded-segment-anything-with-output_files/grounded-segment-anything-with-output_29_1.png
diff --git a/docs/notebooks/288-grounded-segment-anything-with-output_files/288-grounded-segment-anything-with-output_45_0.jpg b/docs/notebooks/grounded-segment-anything-with-output_files/grounded-segment-anything-with-output_45_1.jpg
similarity index 100%
rename from docs/notebooks/288-grounded-segment-anything-with-output_files/288-grounded-segment-anything-with-output_45_0.jpg
rename to docs/notebooks/grounded-segment-anything-with-output_files/grounded-segment-anything-with-output_45_1.jpg
diff --git a/docs/notebooks/288-grounded-segment-anything-with-output_files/288-grounded-segment-anything-with-output_45_0.png b/docs/notebooks/grounded-segment-anything-with-output_files/grounded-segment-anything-with-output_45_1.png
similarity index 100%
rename from docs/notebooks/288-grounded-segment-anything-with-output_files/288-grounded-segment-anything-with-output_45_0.png
rename to docs/notebooks/grounded-segment-anything-with-output_files/grounded-segment-anything-with-output_45_1.png
diff --git a/docs/notebooks/209-handwritten-ocr-with-output.rst b/docs/notebooks/handwritten-ocr-with-output.rst
similarity index 89%
rename from docs/notebooks/209-handwritten-ocr-with-output.rst
rename to docs/notebooks/handwritten-ocr-with-output.rst
index da48355aa51..d91540ce5bd 100644
--- a/docs/notebooks/209-handwritten-ocr-with-output.rst
+++ b/docs/notebooks/handwritten-ocr-with-output.rst
@@ -4,7 +4,7 @@ Handwritten Chinese and Japanese OCR with OpenVINO™
 In this tutorial, we perform optical character recognition (OCR) for
 handwritten Chinese (simplified) and Japanese. An OCR tutorial using the
 Latin alphabet is available in `notebook
-208 <208-optical-character-recognition-with-output.html>`__.
+208 `__.
 This model is capable of processing only one line of symbols at a time.
 
 The models used in this notebook are
@@ -39,8 +39,9 @@ Table of contents:
 .. code:: ipython3
 
     import platform
+    
     # Install openvino-dev package
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" opencv-python tqdm
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -74,11 +75,13 @@ Imports
     import openvino as ov
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 Settings
@@ -103,9 +106,7 @@ To group files, you have to define the collection. In this case, use
 
 .. code:: ipython3
 
-    Language = namedtuple(
-        typename="Language", field_names=["model_name", "charlist_name", "demo_image_name"]
-    )
+    Language = namedtuple(typename="Language", field_names=["model_name", "charlist_name", "demo_image_name"])
     chinese_files = Language(
         model_name="handwritten-simplified-chinese-recognition-0001",
         charlist_name="chinese_charlist.txt",
@@ -156,12 +157,12 @@ model file.
 .. code:: ipython3
 
     path_to_model = download_file(
-        url=f'https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/{selected_language.model_name}/{precision}/{selected_language.model_name}.xml',
-        directory=base_models_dir
+        url=f"https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/{selected_language.model_name}/{precision}/{selected_language.model_name}.xml",
+        directory=base_models_dir,
     )
     _ = download_file(
-        url=f'https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/{selected_language.model_name}/{precision}/{selected_language.model_name}.bin',
-        directory=base_models_dir
+        url=f"https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/{selected_language.model_name}/{precision}/{selected_language.model_name}.bin",
+        directory=base_models_dir,
     )
 
 
@@ -204,8 +205,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -255,7 +256,7 @@ keep letters proportional and meet input shape.
     # Download the image from the openvino_notebooks storage based on the selected model.
     file_name = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/" + selected_language.demo_image_name,
-        directory=data_folder
+        directory=data_folder,
     )
     
     # Text detection models expect an image in grayscale format.
@@ -274,14 +275,10 @@ keep letters proportional and meet input shape.
     scale_ratio = H / image_height
     
     # Resize the image to expected input sizes.
-    resized_image = cv2.resize(
-        image, None, fx=scale_ratio, fy=scale_ratio, interpolation=cv2.INTER_AREA
-    )
+    resized_image = cv2.resize(image, None, fx=scale_ratio, fy=scale_ratio, interpolation=cv2.INTER_AREA)
     
     # Pad the image to match input size, without changing aspect ratio.
-    resized_image = np.pad(
-        resized_image, ((0, 0), (0, W - resized_image.shape[1])), mode="edge"
-    )
+    resized_image = np.pad(resized_image, ((0, 0), (0, W - resized_image.shape[1])), mode="edge")
     
     # Reshape to network input shape.
     input_image = resized_image[None, None, :, :]
@@ -308,7 +305,7 @@ After preprocessing, you can display the image.
 
 
 
-.. image:: 209-handwritten-ocr-with-output_files/209-handwritten-ocr-with-output_22_0.png
+.. image:: handwritten-ocr-with-output_files/handwritten-ocr-with-output_22_0.png
 
 
 Prepare Charlist
@@ -326,7 +323,7 @@ Chinese and Japanese models.
     # Download the image from the openvino_notebooks storage based on the selected model.
     used_charlist_file = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/text/" + selected_language.charlist_name,
-        directory=charlist_folder
+        directory=charlist_folder,
     )
 
 
@@ -426,5 +423,5 @@ the image with predicted text printed below.
 
 
 
-.. image:: 209-handwritten-ocr-with-output_files/209-handwritten-ocr-with-output_32_1.png
+.. image:: handwritten-ocr-with-output_files/handwritten-ocr-with-output_32_1.png
 
diff --git a/docs/notebooks/handwritten-ocr-with-output_files/handwritten-ocr-with-output_22_0.png b/docs/notebooks/handwritten-ocr-with-output_files/handwritten-ocr-with-output_22_0.png
new file mode 100644
index 00000000000..c712a34d825
--- /dev/null
+++ b/docs/notebooks/handwritten-ocr-with-output_files/handwritten-ocr-with-output_22_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b33a1c6c4f57f798ae3f4b31dcb638cb618363ef6108e7f60cf81f1c5bdb151
+size 53571
diff --git a/docs/notebooks/handwritten-ocr-with-output_files/handwritten-ocr-with-output_32_1.png b/docs/notebooks/handwritten-ocr-with-output_files/handwritten-ocr-with-output_32_1.png
new file mode 100644
index 00000000000..c712a34d825
--- /dev/null
+++ b/docs/notebooks/handwritten-ocr-with-output_files/handwritten-ocr-with-output_32_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b33a1c6c4f57f798ae3f4b31dcb638cb618363ef6108e7f60cf81f1c5bdb151
+size 53571
diff --git a/docs/notebooks/004-hello-detection-with-output.rst b/docs/notebooks/hello-detection-with-output.rst
similarity index 87%
rename from docs/notebooks/004-hello-detection-with-output.rst
rename to docs/notebooks/hello-detection-with-output.rst
index 989fc9866bd..9c6e2259a74 100644
--- a/docs/notebooks/004-hello-detection-with-output.rst
+++ b/docs/notebooks/hello-detection-with-output.rst
@@ -30,7 +30,7 @@ Table of contents:
 .. code:: ipython3
 
     # Install openvino package
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" opencv-python tqdm
 
 
 .. parsed-literal::
@@ -52,12 +52,14 @@ Imports
     from pathlib import Path
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file
 
 Download model weights
@@ -70,8 +72,8 @@ Download model weights
     base_model_dir = Path("./model").expanduser()
     
     model_name = "horizontal-text-detection-0001"
-    model_xml_name = f'{model_name}.xml'
-    model_bin_name = f'{model_name}.bin'
+    model_xml_name = f"{model_name}.xml"
+    model_bin_name = f"{model_name}.bin"
     
     model_xml_path = base_model_dir / model_xml_name
     model_bin_path = base_model_dir / model_bin_name
@@ -83,7 +85,7 @@ Download model weights
         download_file(model_xml_url, model_xml_name, base_model_dir)
         download_file(model_bin_url, model_bin_name, base_model_dir)
     else:
-        print(f'{model_name} already downloaded to {base_model_dir}')
+        print(f"{model_name} already downloaded to {base_model_dir}")
 
 
 
@@ -112,8 +114,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -153,7 +155,7 @@ Load an Image
     # Download the image from the openvino_notebooks storage
     image_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg",
-        directory="data"
+        directory="data",
     )
     
     # Text detection models expect an image in BGR format.
@@ -178,7 +180,7 @@ Load an Image
 
 
 
-.. image:: 004-hello-detection-with-output_files/004-hello-detection-with-output_11_1.png
+.. image:: hello-detection-with-output_files/hello-detection-with-output_11_1.png
 
 
 Do Inference
@@ -208,7 +210,10 @@ Visualize Results
         colors = {"red": (255, 0, 0), "green": (0, 255, 0)}
     
         # Fetch the image shapes to calculate a ratio.
-        (real_y, real_x), (resized_y, resized_x) = bgr_image.shape[:2], resized_image.shape[:2]
+        (real_y, real_x), (resized_y, resized_x) = (
+            bgr_image.shape[:2],
+            resized_image.shape[:2],
+        )
         ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
     
         # Convert the base image from BGR to RGB format.
@@ -220,12 +225,10 @@ Visualize Results
             conf = box[-1]
             if conf > threshold:
                 # Convert float to int and multiply corner position of each box by x and y ratio.
-                # If the bounding box is found at the top of the image, 
-                # position the upper box bar little lower to make it visible on the image. 
+                # If the bounding box is found at the top of the image,
+                # position the upper box bar little lower to make it visible on the image.
                 (x_min, y_min, x_max, y_max) = [
-                    int(max(corner_position * ratio_y, 10)) if idx % 2 
-                    else int(corner_position * ratio_x)
-                    for idx, corner_position in enumerate(box[:-1])
+                    (int(max(corner_position * ratio_y, 10)) if idx % 2 else int(corner_position * ratio_x)) for idx, corner_position in enumerate(box[:-1])
                 ]
     
                 # Draw a box based on the position, parameters in rectangle function are: image, start_point, end_point, color, thickness.
@@ -255,5 +258,5 @@ Visualize Results
 
 
 
-.. image:: 004-hello-detection-with-output_files/004-hello-detection-with-output_16_0.png
+.. image:: hello-detection-with-output_files/hello-detection-with-output_16_0.png
 
diff --git a/docs/notebooks/hello-detection-with-output_files/hello-detection-with-output_11_1.png b/docs/notebooks/hello-detection-with-output_files/hello-detection-with-output_11_1.png
new file mode 100644
index 00000000000..435c1891121
--- /dev/null
+++ b/docs/notebooks/hello-detection-with-output_files/hello-detection-with-output_11_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:82b893e29e948379dac42c19763842f7ade2ccf03853c2c07f0b28bf2d58fe17
+size 305482
diff --git a/docs/notebooks/hello-detection-with-output_files/hello-detection-with-output_16_0.png b/docs/notebooks/hello-detection-with-output_files/hello-detection-with-output_16_0.png
new file mode 100644
index 00000000000..e452c525ef0
--- /dev/null
+++ b/docs/notebooks/hello-detection-with-output_files/hello-detection-with-output_16_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7caf8b68c4e2dfd446836e5049d842227ad718a4bbde287269617e324c7d0cef
+size 457214
diff --git a/docs/notebooks/003-hello-segmentation-with-output.rst b/docs/notebooks/hello-segmentation-with-output.rst
similarity index 75%
rename from docs/notebooks/003-hello-segmentation-with-output.rst
rename to docs/notebooks/hello-segmentation-with-output.rst
index 16a5c9680c2..8eb9901014a 100644
--- a/docs/notebooks/003-hello-segmentation-with-output.rst
+++ b/docs/notebooks/hello-segmentation-with-output.rst
@@ -24,8 +24,20 @@ Table of contents:
 
 .. code:: ipython3
 
-    # Install openvino package
-    %pip install -q "openvino>=2023.1.0"
+    import platform
+    
+    # Install required packages
+    %pip install -q "openvino>=2023.1.0" opencv-python tqdm
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -46,12 +58,14 @@ Imports
     import openvino as ov
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import segmentation_map_to_image, download_file
 
 Download model weights
@@ -66,19 +80,23 @@ Download model weights
     base_model_dir = Path("./model").expanduser()
     
     model_name = "road-segmentation-adas-0001"
-    model_xml_name = f'{model_name}.xml'
-    model_bin_name = f'{model_name}.bin'
+    model_xml_name = f"{model_name}.xml"
+    model_bin_name = f"{model_name}.bin"
     
     model_xml_path = base_model_dir / model_xml_name
     
     if not model_xml_path.exists():
-        model_xml_url = "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/road-segmentation-adas-0001/FP32/road-segmentation-adas-0001.xml"
-        model_bin_url = "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/road-segmentation-adas-0001/FP32/road-segmentation-adas-0001.bin"
+        model_xml_url = (
+            "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/road-segmentation-adas-0001/FP32/road-segmentation-adas-0001.xml"
+        )
+        model_bin_url = (
+            "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/road-segmentation-adas-0001/FP32/road-segmentation-adas-0001.bin"
+        )
     
         download_file(model_xml_url, model_xml_name, base_model_dir)
         download_file(model_bin_url, model_bin_name, base_model_dir)
     else:
-        print(f'{model_name} already downloaded to {base_model_dir}')
+        print(f"{model_name} already downloaded to {base_model_dir}")
 
 
 
@@ -107,8 +125,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -141,7 +159,7 @@ Load the Model
 Load an Image
 -------------
 
-A sample image from the
+ A sample image from the
 `Mapillary Vistas `__ dataset
 is provided.
 
@@ -150,7 +168,7 @@ is provided.
     # Download the image from the openvino_notebooks storage
     image_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/empty_road_mapillary.jpg",
-        directory="data"
+        directory="data",
     )
     
     # The segmentation network expects images in BGR format.
@@ -166,9 +184,7 @@ is provided.
     resized_image = cv2.resize(image, (W, H))
     
     # Reshape to the network input shape.
-    input_image = np.expand_dims(
-        resized_image.transpose(2, 0, 1), 0
-    )  
+    input_image = np.expand_dims(resized_image.transpose(2, 0, 1), 0)
     plt.imshow(rgb_image)
 
 
@@ -182,12 +198,12 @@ is provided.
 
 .. parsed-literal::
 
-    
+    
 
 
 
 
-.. image:: 003-hello-segmentation-with-output_files/003-hello-segmentation-with-output_11_2.png
+.. image:: hello-segmentation-with-output_files/hello-segmentation-with-output_11_2.png
 
 
 Do Inference
@@ -209,12 +225,12 @@ Do Inference
 
 .. parsed-literal::
 
-    
+    
 
 
 
 
-.. image:: 003-hello-segmentation-with-output_files/003-hello-segmentation-with-output_13_1.png
+.. image:: hello-segmentation-with-output_files/hello-segmentation-with-output_13_1.png
 
 
 Prepare Data for Visualization
@@ -252,7 +268,7 @@ Visualize data
     
     # Fill the subplot.
     for ax, (name, image) in zip(axs, data.items()):
-        ax.axis('off')
+        ax.axis("off")
         ax.set_title(name)
         ax.imshow(image)
     
@@ -261,5 +277,5 @@ Visualize data
 
 
 
-.. image:: 003-hello-segmentation-with-output_files/003-hello-segmentation-with-output_17_0.png
+.. image:: hello-segmentation-with-output_files/hello-segmentation-with-output_17_0.png
 
diff --git a/docs/notebooks/hello-segmentation-with-output_files/hello-segmentation-with-output_11_2.png b/docs/notebooks/hello-segmentation-with-output_files/hello-segmentation-with-output_11_2.png
new file mode 100644
index 00000000000..12a0ec3dda0
--- /dev/null
+++ b/docs/notebooks/hello-segmentation-with-output_files/hello-segmentation-with-output_11_2.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb21264c96554435f8c9331a342b9c3a20d8129dc0725f6ff226d789779645be
+size 249032
diff --git a/docs/notebooks/hello-segmentation-with-output_files/hello-segmentation-with-output_13_1.png b/docs/notebooks/hello-segmentation-with-output_files/hello-segmentation-with-output_13_1.png
new file mode 100644
index 00000000000..ec01c58bdf8
--- /dev/null
+++ b/docs/notebooks/hello-segmentation-with-output_files/hello-segmentation-with-output_13_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:492235a08c36c9afbebabcb01c8325ac99dccff84174e7074ca321aba2ac7aac
+size 20550
diff --git a/docs/notebooks/hello-segmentation-with-output_files/hello-segmentation-with-output_17_0.png b/docs/notebooks/hello-segmentation-with-output_files/hello-segmentation-with-output_17_0.png
new file mode 100644
index 00000000000..f8d59545b65
--- /dev/null
+++ b/docs/notebooks/hello-segmentation-with-output_files/hello-segmentation-with-output_17_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62376a2e159eca912bff4ce975d169f8ed71f9d9b75c4fd09937e7552120b14d
+size 260045
diff --git a/docs/notebooks/001-hello-world-with-output.rst b/docs/notebooks/hello-world-with-output.rst
similarity index 77%
rename from docs/notebooks/001-hello-world-with-output.rst
rename to docs/notebooks/hello-world-with-output.rst
index 9104721f72f..ea0f5032f44 100644
--- a/docs/notebooks/001-hello-world-with-output.rst
+++ b/docs/notebooks/hello-world-with-output.rst
@@ -10,7 +10,7 @@ from `Open Model
 Zoo `__ is used in
 this tutorial. For more information about how OpenVINO IR models are
 created, refer to the `TensorFlow to
-OpenVINO <101-tensorflow-classification-to-openvino-with-output.html>`__
+OpenVINO `__
 tutorial.
 
 Table of contents:
@@ -26,8 +26,22 @@ Table of contents:
 
 .. code:: ipython3
 
+    import platform
+    
     # Install openvino package
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" opencv-python tqdm
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+    
+
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -50,12 +64,14 @@ Imports
     import openvino as ov
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file
 
 Download the Model and data samples
@@ -65,21 +81,21 @@ Download the Model and data samples
 
 .. code:: ipython3
 
-    base_artifacts_dir = Path('./artifacts').expanduser()
+    base_artifacts_dir = Path("./artifacts").expanduser()
     
     model_name = "v3-small_224_1.0_float"
-    model_xml_name = f'{model_name}.xml'
-    model_bin_name = f'{model_name}.bin'
+    model_xml_name = f"{model_name}.xml"
+    model_bin_name = f"{model_name}.bin"
     
     model_xml_path = base_artifacts_dir / model_xml_name
     
-    base_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/mobelinet-v3-tf/FP32/'
+    base_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/mobelinet-v3-tf/FP32/"
     
     if not model_xml_path.exists():
         download_file(base_url + model_xml_name, model_xml_name, base_artifacts_dir)
         download_file(base_url + model_bin_name, model_bin_name, base_artifacts_dir)
     else:
-        print(f'{model_name} already downloaded to {base_artifacts_dir}')
+        print(f"{model_name} already downloaded to {base_artifacts_dir}")
 
 
 
@@ -108,8 +124,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -147,7 +163,7 @@ Load an Image
     # Download the image from the openvino_notebooks storage
     image_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
-        directory="data"
+        directory="data",
     )
     
     # The MobileNet model expects images in RGB format.
@@ -168,7 +184,7 @@ Load an Image
 
 
 
-.. image:: 001-hello-world-with-output_files/001-hello-world-with-output_11_1.png
+.. image:: hello-world-with-output_files/hello-world-with-output_11_1.png
 
 
 Do Inference
@@ -185,7 +201,7 @@ Do Inference
 
     imagenet_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/datasets/imagenet/imagenet_2012.txt",
-        directory="data"
+        directory="data",
     )
     
     imagenet_classes = imagenet_filename.read_text().splitlines()
@@ -201,7 +217,7 @@ Do Inference
 
     # The model description states that for this model, class 0 is a background.
     # Therefore, a background must be added at the beginning of imagenet_classes.
-    imagenet_classes = ['background'] + imagenet_classes
+    imagenet_classes = ["background"] + imagenet_classes
     
     imagenet_classes[result_index]
 
diff --git a/docs/notebooks/hello-world-with-output_files/hello-world-with-output_11_1.png b/docs/notebooks/hello-world-with-output_files/hello-world-with-output_11_1.png
new file mode 100644
index 00000000000..cca7858e3bc
--- /dev/null
+++ b/docs/notebooks/hello-world-with-output_files/hello-world-with-output_11_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbd7b81cc8e7a73ea9bcb8be0c0575134f50b6af8f7de23ee9feed645a4cf66c
+size 387941
diff --git a/docs/notebooks/124-hugging-face-hub-with-output.rst b/docs/notebooks/hugging-face-hub-with-output.rst
similarity index 81%
rename from docs/notebooks/124-hugging-face-hub-with-output.rst
rename to docs/notebooks/hugging-face-hub-with-output.rst
index 1c844096fe2..e7ed9c3e9a7 100644
--- a/docs/notebooks/124-hugging-face-hub-with-output.rst
+++ b/docs/notebooks/hugging-face-hub-with-output.rst
@@ -65,7 +65,7 @@ Installing Requirements
 
 .. code:: ipython3
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "transformers[torch]>=4.33.0"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "transformers>=4.33.0" "torch>=2.1.0"
     %pip install -q ipywidgets
     %pip install -q "openvino>=2023.1.0"
 
@@ -127,12 +127,6 @@ tutorials `__
 to convert the model (this one is implemented in PyTorch) to OpenVINO
@@ -188,7 +184,7 @@ Note how we reuse our real ``encoded_input``, passing it to the
 
     import openvino as ov
     
-    save_model_path = Path('./models/model.xml')
+    save_model_path = Path("./models/model.xml")
     
     if not save_model_path.exists():
         ov_model = ov.convert_model(model, example_input=dict(encoded_input))
@@ -197,7 +193,7 @@ Note how we reuse our real ``encoded_input``, passing it to the
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
@@ -216,8 +212,8 @@ First, we pick a device to do the model inference
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -260,12 +256,10 @@ original model.
 This is a rather simple example as the pipeline includes just one
 encoder model. Contemporary state of the art pipelines often consist of
 several model, feel free to explore other OpenVINO tutorials: 1. `Stable
-Diffusion
-v2 `__
-2. `Zero-shot Image Classification with OpenAI
-CLIP `__
-3. `Controllable Music Generation with
-MusicGen `__
+Diffusion v2 <../stable-diffusion-v2>`__ 2. `Zero-shot Image
+Classification with OpenAI
+CLIP <../clip-zero-shot-image-classification>`__ 3. `Controllable Music
+Generation with MusicGen <../music-generation>`__
 
 The workflow for the ``diffusers`` package is exactly the same. The
 first example in the list above relies on the ``diffusers``.
@@ -341,23 +335,20 @@ documentation `__.
 
 .. parsed-literal::
 
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
-
-
-.. parsed-literal::
-
-    2024-03-12 22:53:48.040478: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:53:48.076125: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-17 23:58:07.506496: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 23:58:07.542304: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 22:53:48.672121: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-17 23:58:08.114190: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
 
 
 Initialize and Convert the Model Automatically using OVModel class
@@ -388,7 +379,7 @@ inference run.
     model = OVModelForSequenceClassification.from_pretrained(MODEL, export=True, device=device.value)
     
     # The save_pretrained() method saves the model weights to avoid conversion on the next load.
-    model.save_pretrained('./models/optimum_model')
+    model.save_pretrained("./models/optimum_model")
 
 
 .. parsed-literal::
@@ -405,13 +396,7 @@ inference run.
 
 .. parsed-literal::
 
-    Using the export variant default. Available variants are:
-        - default: The default ONNX variant.
-
-
-.. parsed-literal::
-
-    Using framework PyTorch: 2.1.0+cpu
+    Using framework PyTorch: 2.2.2+cpu
 
 
 .. parsed-literal::
@@ -431,7 +416,7 @@ inference run.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
@@ -491,7 +476,7 @@ Full list of supported arguments available via ``--help``
 
 .. parsed-literal::
 
-    2024-03-12 22:54:00.822899: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-17 23:58:19.217675: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -505,6 +490,7 @@ Full list of supported arguments available via ``--help``
                                        [--ratio RATIO] [--sym]
                                        [--group-size GROUP_SIZE]
                                        [--disable-stateful] [--convert-tokenizer]
+                                       [--library {transformers,diffusers,timm,sentence_transformers}]
                                        output
     
     optional arguments:
@@ -521,21 +507,21 @@ Full list of supported arguments available via ``--help``
       --task TASK           The task to export the model for. If not specified,
                             the task will be auto-inferred based on the model.
                             Available tasks depend on the model, but are among:
-                            ['audio-frame-classification', 'depth-estimation',
-                            'multiple-choice', 'automatic-speech-recognition',
-                            'text-to-audio', 'image-segmentation', 'feature-
-                            extraction', 'stable-diffusion-xl', 'text2text-
-                            generation', 'token-classification', 'stable-
-                            diffusion', 'audio-xvector', 'image-to-text',
-                            'semantic-segmentation', 'text-classification',
-                            'image-classification', 'sentence-similarity',
-                            'question-answering', 'mask-generation', 'object-
-                            detection', 'zero-shot-image-classification', 'image-
-                            to-image', 'fill-mask', 'zero-shot-object-detection',
-                            'conversational', 'masked-im', 'text-generation',
-                            'audio-classification']. For decoder models, use `xxx-
-                            with-past` to export the model using past key values
-                            in the decoder.
+                            ['masked-im', 'fill-mask', 'token-classification',
+                            'conversational', 'image-segmentation', 'zero-shot-
+                            image-classification', 'audio-frame-classification',
+                            'audio-classification', 'feature-extraction', 'zero-
+                            shot-object-detection', 'text2text-generation',
+                            'automatic-speech-recognition', 'text-generation',
+                            'image-to-text', 'semantic-segmentation', 'text-
+                            classification', 'sentence-similarity', 'audio-
+                            xvector', 'depth-estimation', 'object-detection',
+                            'stable-diffusion', 'image-classification', 'mask-
+                            generation', 'multiple-choice', 'stable-diffusion-xl',
+                            'image-to-image', 'text-to-audio', 'question-
+                            answering']. For decoder models, use `xxx-with-past`
+                            to export the model using past key values in the
+                            decoder.
       --cache_dir CACHE_DIR
                             Path indicating where to store cache.
       --framework {pt,tf}   The framework to use for the export. If not provided,
@@ -579,6 +565,9 @@ Full list of supported arguments available via ``--help``
                             kv-cache inputs and outputs in the model.
       --convert-tokenizer   Add converted tokenizer and detokenizer with OpenVINO
                             Tokenizers
+      --library {transformers,diffusers,timm,sentence_transformers}
+                            The library on the model. If not provided, will
+                            attempt to infer the local checkpoint's library
 
 
 The command line export for model from example above with FP16 weights
@@ -599,7 +588,7 @@ compression:
 
 .. parsed-literal::
 
-    2024-03-12 22:54:05.156908: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-17 23:58:23.821792: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -609,7 +598,8 @@ compression:
 
 .. parsed-literal::
 
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
 
 
 .. parsed-literal::
@@ -622,12 +612,6 @@ compression:
     Framework not specified. Using pt to export the model.
 
 
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
-      return self.fget.__get__(instance, owner)()
-
-
 .. parsed-literal::
 
     Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
@@ -637,16 +621,10 @@ compression:
 
 .. parsed-literal::
 
-    Using the export variant default. Available variants are:
-        - default: The default ONNX variant.
-
-
-.. parsed-literal::
-
-    Using framework PyTorch: 2.1.0+cpu
+    Using framework PyTorch: 2.2.2+cpu
     Overriding 1 configuration item(s)
     	- use_cache -> False
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
@@ -693,18 +671,15 @@ Model inference is exactly the same as for the original model!
 
 You can find more examples of using Optimum Intel here: 1. `Accelerate
 Inference of Sparse Transformer
-Models `__
-2. `Grammatical Error Correction with
-OpenVINO `__
-3. `Stable Diffusion v2.1 using Optimum-Intel
-OpenVINO `__
+Models `__ 2.
+`Grammatical Error Correction with
+OpenVINO `__ 3. `Stable
+Diffusion v2.1 using Optimum-Intel
+OpenVINO `__
 4. `Image generation with Stable Diffusion
-XL `__
-5. `Instruction following using Databricks Dolly
-2.0 `__
-6. `Create LLM-powered Chatbot using
-OpenVINO `__
-7. `Document Visual Question Answering Using Pix2Struct and
-OpenVINO `__
-8. `Automatic speech recognition using Distil-Whisper and
-OpenVINO `__
+XL <../stable-diffusion-xl>`__ 5. `Instruction following using
+Databricks Dolly 2.0 <../dolly-2-instruction-following>`__ 6. `Create
+LLM-powered Chatbot using OpenVINO <../llm-chatbot>`__ 7. `Document
+Visual Question Answering Using Pix2Struct and
+OpenVINO <../pix2struct-docvqa>`__ 8. `Automatic speech recognition
+using Distil-Whisper and OpenVINO <../distil-whisper-asr>`__
diff --git a/docs/notebooks/239-image-bind-convert-with-output.rst b/docs/notebooks/image-bind-with-output.rst
similarity index 99%
rename from docs/notebooks/239-image-bind-convert-with-output.rst
rename to docs/notebooks/image-bind-with-output.rst
index 7825b73fa90..7f7b556e952 100644
--- a/docs/notebooks/239-image-bind-convert-with-output.rst
+++ b/docs/notebooks/image-bind-with-output.rst
@@ -85,7 +85,31 @@ Table of contents:
    -  `Text-Audio classification <#text-audio-classification>`__
    -  `Image-Audio classification <#image-audio-classification>`__
 
--  `Next Steps <#next-steps>`__
+-  `Post-Training Quantization of ImageBind model with
+   NNCF <#post-training-quantization-of-imagebind-model-with-nncf>`__
+
+   -  `Prepare datasets <#prepare-datasets>`__
+   -  `Apply quantization <#apply-quantization>`__
+
+      -  `Quantize ImageBind model for vision
+         modality. <#quantize-imagebind-model-for-vision-modality->`__
+      -  `Quantize ImageBind model for text
+         modality <#quantize-imagebind-model-for-text-modality>`__
+      -  `Quantize ImageBind model for audio
+         modality <#quantize-imagebind-model-for-audio-modality>`__
+
+   -  `Compare results for the OpenVINO FP16 model and the quantized
+      model <#compare-results-for-the-openvino-fp16-model-and-the-quantized-model>`__
+
+      -  `Select inference device <#select-inference-device>`__
+
+   -  `Compare File Size <#compare-file-size>`__
+   -  `Compare inference time of the FP16 IR and quantized
+      models <#compare-inference-time-of-the-fp16-ir-and-quantized-models>`__
+
+      -  `Vision model <#vision-model>`__
+      -  `Text model <#text-model>`__
+      -  `Audio model <#audio-model>`__
 
 Prerequisites
 -------------
@@ -94,20 +118,11 @@ Prerequisites
 
 .. code:: ipython3
 
-    import sys
     import platform
     
-    %pip install -q soundfile pytorchvideo ftfy "timm>=0.6.7" einops fvcore "openvino>=2023.1.0" numpy scipy --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "torch>=2.0.1" "torchvision>=0.15.2,<0.17.0" "torchaudio>=2.0.2" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q datasets librosa soundfile pytorchvideo ftfy "timm>=0.6.7" einops fvcore "openvino>=2024.0.0" "nncf>=2.9.0" numpy scipy --extra-index-url https://download.pytorch.org/whl/cpu
     
-    if sys.version_info.minor < 8:
-        %pip install -q "decord"
-    else:
-        %pip install -q "eva-decord"
-    
-    if sys.platform != "linux":
-        %pip install -q "torch>=2.0.1" "torchvision>=0.15.2,<0.17.0" "torchaudio>=2.0.2"
-    else:
-        %pip install -q "torch>=2.0.1" "torchvision>=0.15.2,<0.17.0" "torchaudio>=2.0.2" --index-url https://download.pytorch.org/whl/cpu
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -125,12 +140,6 @@ Prerequisites
     
     %cd {repo_dir}
 
-
-.. parsed-literal::
-
-    /home/ea/work/openvino_notebooks/notebooks/239-image-bind/ImageBind
-
-
 Instantiate PyTorch model
 -------------------------
 
@@ -161,11 +170,11 @@ card `__.
 
 .. parsed-literal::
 
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.
+    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.
       warnings.warn(
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
+    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
       warnings.warn(
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
+    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
       warnings.warn(
 
 
@@ -194,8 +203,16 @@ data reading and preprocessing for each modality.
     # Prepare inputs
     
     text_list = ["A car", "A bird", "A dog"]
-    image_paths = [".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
-    audio_paths = [".assets/dog_audio.wav", ".assets/bird_audio.wav", ".assets/car_audio.wav"]
+    image_paths = [
+        ".assets/dog_image.jpg",
+        ".assets/car_image.jpg",
+        ".assets/bird_image.jpg",
+    ]
+    audio_paths = [
+        ".assets/dog_audio.wav",
+        ".assets/bird_audio.wav",
+        ".assets/car_audio.wav",
+    ]
     
     inputs = {
         ModalityType.TEXT: data.load_and_transform_text(text_list, "cpu"),
@@ -229,7 +246,7 @@ embeddings.
             super().__init__()
             self.model = model
             self.modality = modality
-        
+    
         def forward(self, data):
             return self.model({self.modality: data})
 
@@ -252,8 +269,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -264,7 +281,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
+    Dropdown(description='Device:', index=3, options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='AUTO')
 
 
 
@@ -287,23 +304,6 @@ select device from dropdown list for running inference using OpenVINO
             ov_model = core.read_model(ir_path)
         ov_modality_models[modality] = core.compile_model(ov_model, device.value)
 
-
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
-    /home/ea/work/openvino_notebooks/notebooks/239-image-bind/ImageBind/imagebind/models/multimodal_preprocessors.py:433: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if x.shape[self.time_dim] == 1:
-    /home/ea/work/openvino_notebooks/notebooks/239-image-bind/ImageBind/imagebind/models/multimodal_preprocessors.py:259: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert tokens.shape[2] == self.embed_dim
-    /home/ea/work/openvino_notebooks/notebooks/239-image-bind/ImageBind/imagebind/models/multimodal_preprocessors.py:74: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if npatch_per_img == N:
-
-
 Zero-shot classification using ImageBind and OpenVINO
 -----------------------------------------------------
 
@@ -315,7 +315,7 @@ data. In the case of ImageBind, you can classify audio, images, and
 information in the other supported modalities. We already discussed how
 to perform zero-shot image classification using the CLIP model (please
 check this
-`notebook <228-clip-zero-shot-image-classification-with-output.html>`__
+`notebook `__
 for details), capabilities of ImageBind for this task wider, because it
 allows using any combinations of supported modalities for
 classification.
@@ -357,15 +357,16 @@ they represent the same object.
     
     def visualize_prob_matrix(matrix, x_label, y_label):
         fig, ax = plt.subplots()
-        ax.matshow(matrix, cmap='winter')
+        ax.matshow(matrix, cmap="winter")
     
         for (i, j), z in np.ndenumerate(matrix):
-            ax.text(j, i, '{:0.3f}'.format(z), ha='center', va='center')
+            ax.text(j, i, "{:0.3f}".format(z), ha="center", va="center")
             ax.set_xticks(range(len(x_label)), x_label)
             ax.set_yticks(range(len(y_label)), y_label)
     
-    image_list = [img.split('/')[-1] for img in image_paths]
-    audio_list = [audio.split('/')[-1] for audio in audio_paths]
+    
+    image_list = [img.split("/")[-1] for img in image_paths]
+    audio_list = [audio.split("/")[-1] for audio in audio_paths]
 
 Text-Image classification
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -380,7 +381,7 @@ Text-Image classification
 
 
 
-.. image:: 239-image-bind-convert-with-output_files/239-image-bind-convert-with-output_20_0.png
+.. image:: image-bind-with-output_files/image-bind-with-output_20_0.png
 
 
 Text-Audio classification
@@ -396,7 +397,7 @@ Text-Audio classification
 
 
 
-.. image:: 239-image-bind-convert-with-output_files/239-image-bind-convert-with-output_22_0.png
+.. image:: image-bind-with-output_files/image-bind-with-output_22_0.png
 
 
 Image-Audio classification
@@ -412,7 +413,7 @@ Image-Audio classification
 
 
 
-.. image:: 239-image-bind-convert-with-output_files/239-image-bind-convert-with-output_24_0.png
+.. image:: image-bind-with-output_files/image-bind-with-output_24_0.png
 
 
 Putting all together, we can match text, image, and sound for our data.
@@ -421,9 +422,12 @@ Putting all together, we can match text, image, and sound for our data.
 
     import IPython.display as ipd
     from PIL import Image
+    
     text_image_ids = np.argmax(text_vision_scores, axis=0)
     text_audio_ids = np.argmax(text_audio_scores, axis=0)
-    print(f"Predicted label: {text_list[0]} \nprobability for image - {text_vision_scores[text_image_ids[0], 0]:.3f}\nprobability for audio - {text_audio_scores[0, text_audio_ids[0]]:.3f}")
+    print(
+        f"Predicted label: {text_list[0]} \nprobability for image - {text_vision_scores[text_image_ids[0], 0]:.3f}\nprobability for audio - {text_audio_scores[0, text_audio_ids[0]]:.3f}"
+    )
     display(Image.open(image_paths[text_image_ids[0]]))
     ipd.Audio(audio_paths[text_audio_ids[0]])
 
@@ -436,7 +440,7 @@ Putting all together, we can match text, image, and sound for our data.
 
 
 
-.. image:: 239-image-bind-convert-with-output_files/239-image-bind-convert-with-output_26_1.png
+.. image:: image-bind-with-output_files/image-bind-with-output_26_1.png
 
 
 
@@ -454,7 +458,9 @@ Putting all together, we can match text, image, and sound for our data.
 
 .. code:: ipython3
 
-    print(f"Predicted label: {text_list[1]} \nprobability for image - {text_vision_scores[text_image_ids[1], 1]:.3f}\nprobability for audio - {text_audio_scores[1, text_audio_ids[1]]:.3f}")
+    print(
+        f"Predicted label: {text_list[1]} \nprobability for image - {text_vision_scores[text_image_ids[1], 1]:.3f}\nprobability for audio - {text_audio_scores[1, text_audio_ids[1]]:.3f}"
+    )
     display(Image.open(image_paths[text_image_ids[1]]))
     ipd.Audio(audio_paths[text_audio_ids[1]])
 
@@ -467,7 +473,7 @@ Putting all together, we can match text, image, and sound for our data.
 
 
 
-.. image:: 239-image-bind-convert-with-output_files/239-image-bind-convert-with-output_27_1.png
+.. image:: image-bind-with-output_files/image-bind-with-output_27_1.png
 
 
 
@@ -485,7 +491,9 @@ Putting all together, we can match text, image, and sound for our data.
 
 .. code:: ipython3
 
-    print(f"Predicted label: {text_list[2]} \nprobability for image - {text_vision_scores[text_image_ids[2], 2]:.3f}\nprobability for audio - {text_audio_scores[2, text_audio_ids[2]]:.3f}")
+    print(
+        f"Predicted label: {text_list[2]} \nprobability for image - {text_vision_scores[text_image_ids[2], 2]:.3f}\nprobability for audio - {text_audio_scores[2, text_audio_ids[2]]:.3f}"
+    )
     display(Image.open(image_paths[text_image_ids[2]]))
     ipd.Audio(audio_paths[text_audio_ids[2]])
 
@@ -498,7 +506,7 @@ Putting all together, we can match text, image, and sound for our data.
 
 
 
-.. image:: 239-image-bind-convert-with-output_files/239-image-bind-convert-with-output_28_1.png
+.. image:: image-bind-with-output_files/image-bind-with-output_28_1.png
 
 
 
@@ -514,11 +522,501 @@ Putting all together, we can match text, image, and sound for our data.
 
 
 
-Next Steps
-----------
+Post-Training Quantization of ImageBind model with NNCF
+-------------------------------------------------------
 
 
 
-Open the `239-image-bind-quantize <239-image-bind-quantize-with-output.html>`__
-notebook to quantize the IR model with the Post-training Quantization
-API of NNCF and compare ``FP16`` and ``INT8`` models.
+The goal of this part of tutorial is to demonstrate how to speed up the
+model by applying 8-bit post-training quantization from
+`NNCF `__ (Neural Network
+Compression Framework) and infer quantized model via OpenVINO™ Toolkit.
+
+The optimization process contains the following steps: 1. Prepare
+quantization dataset 2. Quantize OpenVINO model with NNCF. 3. Compare
+probability matrices between converted and quantized models on input
+data examples. 4. Compare model size of converted and quantized models.
+5. Compare performance of converted and quantized models.
+
+.. code:: ipython3
+
+    modalities = [ModalityType.TEXT, ModalityType.VISION, ModalityType.AUDIO]
+    fp_model_paths = {modality: Path(f"image-bind-{modality}") / f"image-bind-{modality}.xml" for modality in modalities}
+    int8_model_paths = {modality: Path(f"image-bind-{modality}") / f"image-bind-{modality}_int8.xml" for modality in modalities}
+
+Prepare datasets
+~~~~~~~~~~~~~~~~
+
+
+
+The `Conceptual
+Captions `__ dataset
+consisting of ~3.3M images annotated with captions. Dataset is used to
+quantize image and text models.
+
+.. code:: ipython3
+
+    import imagebind.data as data
+    import os
+    import requests
+    import tempfile
+    
+    from requests.packages.urllib3.exceptions import InsecureRequestWarning
+    
+    requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
+    
+    
+    def check_text_data(data):
+        """
+        Check if the given data is text-based.
+        """
+        if isinstance(data, str):
+            return True
+        if isinstance(data, list):
+            return all(isinstance(x, str) for x in data)
+        return False
+    
+    
+    def collate_fn(examples, image_column="image_url", text_column="caption"):
+        """
+        Collates examples into a batch for processing.
+        Preprocesses each example by loading and transforming image and text data.
+        Checks if the text data in the example is valid by calling the `check_text_data` function.
+        Downloads the image specified by the URL in the image_column of the example dictionary.
+        Constructs and returns a dictionary representing the collated batch with the following keys:
+           - "pixel_values": The pixel values of the preprocessed example.
+           - "input_ids": The transformed text data of the preprocessed example.
+        """
+        assert len(examples) == 1
+        example = examples[0]
+        if not check_text_data(example[text_column]):
+            raise ValueError("Text data is not valid")
+    
+        url = example[image_column]
+        with tempfile.TemporaryDirectory() as tempdir:
+            f_name = os.path.join(tempdir, "image.jpg")
+            try:
+                response = requests.get(url, verify=False, timeout=20)
+                with open(f_name, "wb") as file:
+                    file.write(response.content)
+                pixel_values = data.load_and_transform_vision_data([f_name], "cpu")
+            except Exception:
+                print(f"Can't load image from url: {url}")
+                return None
+    
+        text = data.load_and_transform_text([example[text_column]], "cpu")
+    
+        return {"pixel_values": pixel_values, "input_ids": text}
+
+.. code:: ipython3
+
+    from datasets import load_dataset
+    import itertools
+    import torch
+    from tqdm.notebook import tqdm
+    
+    
+    def collect_vision_text_data(dataloader, init_steps):
+        """
+        This function collects vision and text data from a dataloader for a specified number of initialization steps.
+        It iterates over the dataloader, fetching batches and storing the relevant vision and text data.
+        Returns a tuple containing the collected vision_data and text_data lists.
+        """
+        text_data = []
+        vision_data = []
+        print(f"Fetching {init_steps} for the initialization...")
+        counter = 0
+        for batch in tqdm(dataloader):
+            if counter == init_steps:
+                break
+            with torch.no_grad():
+                if batch:
+                    counter += 1
+                    text_data.append(batch["input_ids"].to("cpu"))
+                    vision_data.append(batch["pixel_values"].to("cpu"))
+        return vision_data, text_data
+    
+    
+    def prepare_vision_text_dataset(opt_init_steps=50):
+        """
+        Prepares a vision-text dataset for quantization by collecting vision and text data.
+        """
+        dataset = load_dataset("conceptual_captions", streaming=False)
+        train_dataset = dataset["train"].shuffle(seed=0)
+        dataloader = torch.utils.data.DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1)
+        vision_data, text_data = collect_vision_text_data(dataloader, opt_init_steps)
+        return vision_data, text_data
+
+The `ESC-50 `__ dataset is
+used to quantize the audio modality of the ImageBind model. Dataset is a
+labeled collection of 2000 environmental audio recordings suitable for
+benchmarking methods of environmental sound classification. The dataset
+consists of 5-second-long recordings organized into 50 semantic classes.
+
+.. code:: ipython3
+
+    import numpy as np
+    import torchaudio
+    
+    
+    def collect_audio_data(dataloader, init_steps=300):
+        """
+        This function collects audio data from a dataloader for a specified number of initialization steps.
+        It iterates over the dataloader, fetching batches and storing them in a list.
+        """
+        audio_data = []
+        for _, batch in tqdm(zip(range(init_steps), itertools.islice(dataloader, 0, init_steps))):
+            with torch.no_grad():
+                audio_data.append(batch)
+        return audio_data
+    
+    
+    def prepare_audio_dataset():
+        """
+        Prepares an "ashraq/esc50" audio dataset for quantization by collecting audio data.
+        Collects audio data from the dataloader by calling the `collect_audio_data` function.
+        Returns a list containing the collected calibration audio data batches.
+        """
+        audio_dataset = load_dataset("ashraq/esc50", streaming=True)
+        train_dataset = audio_dataset["train"].shuffle(seed=42, buffer_size=1000)
+    
+        def collate_fn(examples):
+            assert len(examples) == 1
+            with tempfile.TemporaryDirectory() as tempdir:
+                f_name = os.path.join(tempdir, "audio.wav")
+                audio_data = examples[0]["audio"]["array"]
+                sample_rate = examples[0]["audio"]["sampling_rate"]
+                audio_data = torch.from_numpy(audio_data).to(torch.float32).unsqueeze(0)
+                torchaudio.save(f_name, audio_data, sample_rate)
+                return data.load_and_transform_audio_data([f_name], "cpu")
+    
+        dataloader = torch.utils.data.DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1)
+        calibration_data = collect_audio_data(dataloader)
+        return calibration_data
+
+.. code:: ipython3
+
+    vision_data, text_data = [], []
+    
+    if not int8_model_paths[ModalityType.TEXT].exists() or not int8_model_paths[ModalityType.VISION].exists():
+        vision_data, text_data = prepare_vision_text_dataset()
+
+Apply quantization
+~~~~~~~~~~~~~~~~~~
+
+ ### Apply quantization
+
+.. code:: ipython3
+
+    import logging
+    import nncf
+    import openvino as ov
+    
+    nncf.set_log_level(logging.ERROR)
+    
+    core = ov.Core()
+    
+    
+    def quantize_openvino_model(modality, calibration_data):
+        model_path = fp_model_paths[modality]
+        model = core.read_model(model_path)
+        quantized_model = nncf.quantize(
+            model=model,
+            calibration_dataset=calibration_data,
+            model_type=nncf.ModelType.TRANSFORMER,
+        )
+        ov.save_model(quantized_model, int8_model_paths[modality])
+        return quantized_model
+
+
+.. parsed-literal::
+
+    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
+
+
+Quantize ImageBind model for vision modality.
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+
+
+   **NOTE**: Quantization is time and memory consuming operation.
+   Running quantization code below may take a long time.
+
+.. code:: ipython3
+
+    if not int8_model_paths[ModalityType.VISION].exists():
+        if len(vision_data) == 0:
+            raise RuntimeError("Calibration dataset is empty. Please check internet connection and try to download images manually from the URLs above.")
+    
+        vision_dataset = nncf.Dataset(vision_data)
+        vision_quantized_model = quantize_openvino_model(modality=ModalityType.VISION, calibration_data=vision_dataset)
+
+Quantize ImageBind model for text modality
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+
+
+.. code:: ipython3
+
+    if not int8_model_paths[ModalityType.TEXT].exists():
+        text_dataset = nncf.Dataset(text_data)
+        text_quantized_model = quantize_openvino_model(modality=ModalityType.TEXT, calibration_data=text_dataset)
+
+Quantize ImageBind model for audio modality
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ #### Quantize ImageBind model
+for audio modality
+
+.. code:: ipython3
+
+    if not int8_model_paths[ModalityType.AUDIO].exists():
+        audio_calibration_data = prepare_audio_dataset()
+        audio_dataset = nncf.Dataset(audio_calibration_data)
+        audio_quantized_model = quantize_openvino_model(modality=ModalityType.AUDIO, calibration_data=audio_dataset)
+
+NNCF also supports quantization-aware training, and other algorithms
+than quantization. See the `NNCF
+documentation `__
+in the NNCF repository for more information.
+
+Compare results for the OpenVINO FP16 model and the quantized model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Compare the probability matrices for ``FP16`` and ``INT8`` models.
+
+.. code:: ipython3
+
+    # Prepare inputs
+    
+    text_list = ["A car", "A bird", "A dog"]
+    image_paths = [
+        ".assets/dog_image.jpg",
+        ".assets/car_image.jpg",
+        ".assets/bird_image.jpg",
+    ]
+    audio_paths = [
+        ".assets/dog_audio.wav",
+        ".assets/bird_audio.wav",
+        ".assets/car_audio.wav",
+    ]
+    
+    inputs = {
+        ModalityType.TEXT: data.load_and_transform_text(text_list, "cpu"),
+        ModalityType.VISION: data.load_and_transform_vision_data(image_paths, "cpu"),
+        ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, "cpu"),
+    }
+
+Select inference device
+^^^^^^^^^^^^^^^^^^^^^^^
+
+
+
+select device from dropdown list for running inference using OpenVINO
+
+.. code:: ipython3
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=3, options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='AUTO')
+
+
+
+.. code:: ipython3
+
+    embeddings = {}
+    for modality in modalities:
+        ov_model = core.compile_model(fp_model_paths[modality], device.value)
+        embeddings[modality] = ov_model(inputs[modality])[0]
+    
+    quantized_embeddings = {}
+    for modality in modalities:
+        model = core.compile_model(int8_model_paths[modality], device.value)
+        quantized_embeddings[modality] = model(inputs[modality])[0]
+
+.. code:: ipython3
+
+    def visualize_prob_matrices(fp_matrix, int_matrix, x_label, y_label):
+        fig, ax = plt.subplots(1, 2)
+        for i, matrix in enumerate([fp_matrix, int_matrix]):
+            ax[i].matshow(matrix, cmap="winter")
+    
+            for (k, j), z in np.ndenumerate(matrix):
+                ax[i].title.set_text("FP16 probs" if i == 0 else "INT8 probs")
+                ax[i].text(j, k, "{:0.3f}".format(z), ha="center", va="center")
+                ax[i].set_xticks(range(len(x_label)), x_label)
+                ax[i].set_yticks(range(len(y_label)), y_label)
+        fig.tight_layout()
+    
+    
+    image_list = [img.split("/")[-1] for img in image_paths]
+    audio_list = [audio.split("/")[-1] for audio in audio_paths]
+
+.. code:: ipython3
+
+    fp_text_vision_scores = softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, axis=-1)
+    int_text_vision_scores = softmax(
+        quantized_embeddings[ModalityType.VISION] @ quantized_embeddings[ModalityType.TEXT].T,
+        axis=-1,
+    )
+    
+    visualize_prob_matrices(fp_text_vision_scores, int_text_vision_scores, text_list, image_list)
+
+
+
+.. image:: image-bind-with-output_files/image-bind-with-output_52_0.png
+
+
+.. code:: ipython3
+
+    fp_text_audio_scores = softmax(embeddings[ModalityType.AUDIO] @ embeddings[ModalityType.TEXT].T, axis=-1)
+    int_text_audio_scores = softmax(
+        quantized_embeddings[ModalityType.AUDIO] @ quantized_embeddings[ModalityType.TEXT].T,
+        axis=-1,
+    )
+    
+    visualize_prob_matrices(fp_text_audio_scores, int_text_audio_scores, text_list, image_list)
+
+
+
+.. image:: image-bind-with-output_files/image-bind-with-output_53_0.png
+
+
+.. code:: ipython3
+
+    fp_audio_vision_scores = softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, axis=-1)
+    int_audio_vision_scores = softmax(
+        quantized_embeddings[ModalityType.VISION] @ quantized_embeddings[ModalityType.AUDIO].T,
+        axis=-1,
+    )
+    
+    visualize_prob_matrices(fp_audio_vision_scores, int_audio_vision_scores, text_list, image_list)
+
+
+
+.. image:: image-bind-with-output_files/image-bind-with-output_54_0.png
+
+
+Compare File Size
+~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    def calculate_compression_rate(modality):
+        fp16_ir_model_size = Path(fp_model_paths[modality]).with_suffix(".bin").stat().st_size / 1024
+        quantized_model_size = Path(int8_model_paths[modality]).with_suffix(".bin").stat().st_size / 1024
+        print(f"Modality: {modality}")
+        print(f"    * FP16 IR model size: {fp16_ir_model_size:.2f} KB")
+        print(f"    * INT8 model size: {quantized_model_size:.2f} KB")
+        print(f"    * Model compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
+    
+    
+    for modality in modalities:
+        calculate_compression_rate(modality)
+
+
+.. parsed-literal::
+
+    Modality: text
+        * FP16 IR model size: 691481.69 KB
+        * INT8 model size: 347006.66 KB
+        * Model compression rate: 1.993
+    Modality: vision
+        * FP16 IR model size: 1235995.15 KB
+        * INT8 model size: 620132.79 KB
+        * Model compression rate: 1.993
+    Modality: audio
+        * FP16 IR model size: 168429.15 KB
+        * INT8 model size: 84818.40 KB
+        * Model compression rate: 1.986
+
+
+Compare inference time of the FP16 IR and quantized models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+To measure the inference performance of the ``FP16`` and ``INT8``
+models, we use median inference time on calibration dataset. So we can
+approximately estimate the speed up of the dynamic quantized models.
+
+   **NOTE**: For the most accurate performance estimation, it is
+   recommended to run ``benchmark_app`` in a terminal/command prompt
+   after closing other applications with static shapes.
+
+.. code:: ipython3
+
+    import time
+    
+    
+    def calculate_inference_time(model_path, calibration_data):
+        model = core.compile_model(model_path)
+        output_layer = model.output(0)
+        inference_time = []
+        for batch in calibration_data:
+            start = time.perf_counter()
+            _ = model(batch)[output_layer]
+            end = time.perf_counter()
+            delta = end - start
+            inference_time.append(delta)
+        return np.median(inference_time)
+
+Vision model
+^^^^^^^^^^^^
+
+
+
+.. code:: ipython3
+
+    fp16_latency = calculate_inference_time(fp_model_paths[ModalityType.VISION], vision_data)
+    int8_latency = calculate_inference_time(int8_model_paths[ModalityType.VISION], vision_data)
+    print(f"Performance speed up: {fp16_latency / int8_latency:.3f}")
+
+
+.. parsed-literal::
+
+    Performance speed up: 2.375
+
+
+Text model
+^^^^^^^^^^
+
+
+
+.. code:: ipython3
+
+    fp16_latency = calculate_inference_time(fp_model_paths[ModalityType.TEXT], text_data)
+    int8_latency = calculate_inference_time(int8_model_paths[ModalityType.TEXT], text_data)
+    print(f"Performance speed up: {fp16_latency / int8_latency:.3f}")
+
+
+.. parsed-literal::
+
+    Performance speed up: 1.492
+
+
+Audio model
+^^^^^^^^^^^
+
+
+
+.. code:: ipython3
+
+    fp16_latency = calculate_inference_time(fp_model_paths[ModalityType.AUDIO], audio_calibration_data)
+    int8_latency = calculate_inference_time(int8_model_paths[ModalityType.AUDIO], audio_calibration_data)
+    print(f"Performance speed up: {fp16_latency / int8_latency:.3f}")
+
+
+.. parsed-literal::
+
+    Performance speed up: 5.770
+
diff --git a/docs/notebooks/image-bind-with-output_files/image-bind-with-output_20_0.png b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_20_0.png
new file mode 100644
index 00000000000..b61da5d71d0
--- /dev/null
+++ b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_20_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:407f4039d44322edd717fb1eba4c0e029205b2c691614606f1a5b33ed31aa047
+size 15474
diff --git a/docs/notebooks/image-bind-with-output_files/image-bind-with-output_22_0.png b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_22_0.png
new file mode 100644
index 00000000000..bf96c415a07
--- /dev/null
+++ b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_22_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:098a56bdaf58b412fe6935d327bcd810942f01789ecd5c2efe834888eba3b819
+size 13795
diff --git a/docs/notebooks/image-bind-with-output_files/image-bind-with-output_24_0.png b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_24_0.png
new file mode 100644
index 00000000000..54a9a687521
--- /dev/null
+++ b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_24_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe454683f2419970a93baaea6a5beb973dd832627217464d87c14bf2a61e8032
+size 18633
diff --git a/docs/notebooks/image-bind-with-output_files/image-bind-with-output_26_1.png b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_26_1.png
new file mode 100644
index 00000000000..6be4611dbc7
--- /dev/null
+++ b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_26_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d09352f8474421fa78d601cc5afbe88df3d0403c157f91605d424b66a2f1809a
+size 303014
diff --git a/docs/notebooks/image-bind-with-output_files/image-bind-with-output_27_1.png b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_27_1.png
new file mode 100644
index 00000000000..174dcfdcbe8
--- /dev/null
+++ b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_27_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:609e506939d69a89fb59d36622d72005d5b162afccf70c1e2463cd51d544d4dd
+size 777583
diff --git a/docs/notebooks/image-bind-with-output_files/image-bind-with-output_28_1.png b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_28_1.png
new file mode 100644
index 00000000000..a4b0b02a4d7
--- /dev/null
+++ b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_28_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7509d532217e990ed721424c57aecbadfb634d397bd1c069852f873fee8741a9
+size 572170
diff --git a/docs/notebooks/image-bind-with-output_files/image-bind-with-output_52_0.png b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_52_0.png
new file mode 100644
index 00000000000..9274858833d
--- /dev/null
+++ b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_52_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:41c01dd2ebbddd60573c560ddcb00f7671b63bf1e49ca68497be1d39fd5cb86c
+size 19998
diff --git a/docs/notebooks/image-bind-with-output_files/image-bind-with-output_53_0.png b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_53_0.png
new file mode 100644
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--- /dev/null
+++ b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_53_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:acb5ca8757899c94fa8fd68a647975ea031ffa3f4955214b9a39d097b179ad27
+size 17315
diff --git a/docs/notebooks/image-bind-with-output_files/image-bind-with-output_54_0.png b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_54_0.png
new file mode 100644
index 00000000000..f2f53fccfbd
--- /dev/null
+++ b/docs/notebooks/image-bind-with-output_files/image-bind-with-output_54_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e30a62c61037f25fa771225ab71ab9ecb407a0589103a79de2c5e0374583adf1
+size 22314
diff --git a/docs/notebooks/113-image-classification-quantization-with-output.rst b/docs/notebooks/image-classification-quantization-with-output.rst
similarity index 79%
rename from docs/notebooks/113-image-classification-quantization-with-output.rst
rename to docs/notebooks/image-classification-quantization-with-output.rst
index f237c801cd4..e4d56582cdf 100644
--- a/docs/notebooks/113-image-classification-quantization-with-output.rst
+++ b/docs/notebooks/image-classification-quantization-with-output.rst
@@ -8,7 +8,7 @@ MobileNet V2 model, trained on Cifar10 dataset. The code is designed to
 be extendable to custom models and datasets. The tutorial uses OpenVINO
 backend for performing model quantization in NNCF, if you interested how
 to apply quantization on PyTorch model, please check this
-`tutorial <112-pytorch-post-training-quantization-nncf-with-output.html>`__.
+`tutorial `__.
 
 This tutorial consists of the following steps:
 
@@ -43,8 +43,20 @@ Table of contents:
 
 .. code:: ipython3
 
-    # Install openvino package
-    %pip install -q "openvino>=2023.1.0" "nncf>=2.6.0"
+    import platform
+
+    # Install required packages
+    %pip install -q "openvino>=2023.1.0" "nncf>=2.6.0" torch torchvision tqdm --extra-index-url https://download.pytorch.org/whl/cpu
+
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -58,8 +70,8 @@ Table of contents:
 
     # Set the data and model directories
     DATA_DIR = Path("data")
-    MODEL_DIR = Path('model')
-    model_repo = 'pytorch-cifar-models'
+    MODEL_DIR = Path("model")
+    model_repo = "pytorch-cifar-models"
 
     DATA_DIR.mkdir(exist_ok=True)
     MODEL_DIR.mkdir(exist_ok=True)
@@ -364,10 +376,7 @@ Receiving objects:  63% (178/282)
 Receiving objects:  64% (181/282)
 Receiving objects:  65% (184/282)
 Receiving objects:  66% (187/282)
-
-.. parsed-literal::
-
-    Receiving objects:  67% (189/282)
+Receiving objects:  67% (189/282)
 Receiving objects:  68% (192/282)
 Receiving objects:  69% (195/282)
 Receiving objects:  70% (198/282)
@@ -384,80 +393,70 @@ Receiving objects:  75% (212/282)
 .. parsed-literal::
 
     Receiving objects:  77% (218/282)
+Receiving objects:  78% (220/282)
+Receiving objects:  79% (223/282)
+Receiving objects:  80% (226/282)
 
 .. parsed-literal::
 
-    Receiving objects:  78% (220/282)
+    Receiving objects:  81% (229/282)
+Receiving objects:  82% (232/282)
+Receiving objects:  83% (235/282)
 
 .. parsed-literal::
 
-    Receiving objects:  79% (223/282), 2.34 MiB | 4.38 MiB/s
+    Receiving objects:  84% (237/282)
+Receiving objects:  85% (240/282)
+Receiving objects:  86% (243/282)
 
 .. parsed-literal::
 
-    Receiving objects:  80% (226/282), 2.34 MiB | 4.38 MiB/s
+    Receiving objects:  87% (246/282)
 
 .. parsed-literal::
 
-    Receiving objects:  81% (229/282), 2.34 MiB | 4.38 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  82% (232/282), 2.34 MiB | 4.38 MiB/s
-Receiving objects:  83% (235/282), 2.34 MiB | 4.38 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  84% (237/282), 2.34 MiB | 4.38 MiB/s
-Receiving objects:  85% (240/282), 2.34 MiB | 4.38 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  86% (243/282), 2.34 MiB | 4.38 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  87% (246/282), 2.34 MiB | 4.38 MiB/s
-Receiving objects:  88% (249/282), 2.34 MiB | 4.38 MiB/s
-
-.. parsed-literal::
-
-    Receiving objects:  89% (251/282), 8.63 MiB | 8.35 MiB/s
+    Receiving objects:  88% (249/282)
+Receiving objects:  89% (251/282)
 remote: Total 282 (delta 135), reused 269 (delta 128), pack-reused 1
-    Receiving objects:  90% (254/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects:  91% (257/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects:  92% (260/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects:  93% (263/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects:  94% (266/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects:  95% (268/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects:  96% (271/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects:  97% (274/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects:  98% (277/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects:  99% (280/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects: 100% (282/282), 8.63 MiB | 8.35 MiB/s
-Receiving objects: 100% (282/282), 9.22 MiB | 8.26 MiB/s, done.
+    Receiving objects:  90% (254/282)
+Receiving objects:  91% (257/282)
+Receiving objects:  92% (260/282)
+Receiving objects:  93% (263/282)
+Receiving objects:  94% (266/282)
+Receiving objects:  95% (268/282)
+Receiving objects:  96% (271/282)
+Receiving objects:  97% (274/282)
+Receiving objects:  98% (277/282)
+Receiving objects:  99% (280/282)
+Receiving objects: 100% (282/282)
+Receiving objects: 100% (282/282), 9.22 MiB | 21.40 MiB/s, done.
     Resolving deltas:   0% (0/135)
-Resolving deltas:   3% (5/135)
+Resolving deltas:   2% (4/135)
+Resolving deltas:   4% (6/135)
 Resolving deltas:   5% (7/135)
-Resolving deltas:   6% (9/135)
-Resolving deltas:  13% (18/135)
-Resolving deltas:  21% (29/135)
+Resolving deltas:  14% (19/135)
+Resolving deltas:  19% (26/135)
+Resolving deltas:  20% (27/135)
+Resolving deltas:  22% (31/135)
 Resolving deltas:  25% (34/135)
+Resolving deltas:  27% (37/135)
+Resolving deltas:  28% (38/135)
 Resolving deltas:  29% (40/135)
 Resolving deltas:  30% (41/135)
 Resolving deltas:  31% (42/135)
 Resolving deltas:  32% (44/135)
-Resolving deltas:  33% (45/135)
-Resolving deltas:  34% (46/135)
+Resolving deltas:  34% (47/135)
 Resolving deltas:  40% (54/135)
 Resolving deltas:  45% (62/135)
 Resolving deltas:  46% (63/135)
-Resolving deltas:  50% (68/135)
+Resolving deltas:  51% (69/135)
 Resolving deltas:  57% (78/135)
 Resolving deltas:  58% (79/135)
-Resolving deltas:  60% (81/135)
-Resolving deltas:  62% (84/135)
-Resolving deltas:  71% (96/135)
+Resolving deltas:  59% (80/135)
+Resolving deltas:  60% (82/135)
+Resolving deltas:  61% (83/135)
+Resolving deltas:  69% (94/135)
+Resolving deltas:  71% (97/135)
 
 .. parsed-literal::
 
@@ -489,7 +488,7 @@ can be found on this
 
     model.eval()
 
-    ov_model = ov.convert_model(model, input=[1,3,32,32])
+    ov_model = ov.convert_model(model, input=[1, 3, 32, 32])
 
     ov.save_model(ov_model, MODEL_DIR / "mobilenet_v2.xml")
 
@@ -510,7 +509,12 @@ Preprocessing for model obtained from training
     from torchvision import transforms
     from torchvision.datasets import CIFAR10
 
-    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))])
+    transform = transforms.Compose(
+        [
+            transforms.ToTensor(),
+            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261)),
+        ]
+    )
     dataset = CIFAR10(root=DATA_DIR, train=False, transform=transform, download=True)
     val_loader = torch.utils.data.DataLoader(
         dataset,
@@ -534,312 +538,322 @@ Preprocessing for model obtained from training
 .. parsed-literal::
 
 
-  0%|          | 32768/170498071 [00:00<10:17, 275946.37it/s]
+  0%|          | 32768/170498071 [00:00<09:50, 288554.83it/s]
 
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-  0%|          | 65536/170498071 [00:00<10:28, 271284.88it/s]
+  0%|          | 65536/170498071 [00:00<10:02, 282666.29it/s]
 
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-  0%|          | 98304/170498071 [00:00<10:31, 269970.74it/s]
+  0%|          | 98304/170498071 [00:00<10:04, 281904.99it/s]
 
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-  0%|          | 229376/170498071 [00:00<04:50, 586473.34it/s]
+  0%|          | 229376/170498071 [00:00<04:37, 614087.82it/s]
 
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-  0%|          | 393216/170498071 [00:00<03:17, 859242.88it/s]
+  0%|          | 393216/170498071 [00:00<03:09, 898993.80it/s]
 
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-  0%|          | 786432/170498071 [00:00<01:42, 1661012.29it/s]
+  0%|          | 819200/170498071 [00:00<01:32, 1832006.56it/s]
 
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-  1%|          | 1507328/170498071 [00:00<00:55, 3048520.02it/s]
+  1%|          | 1605632/170498071 [00:00<00:49, 3430452.29it/s]
 
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-  2%|▏         | 3014656/170498071 [00:00<00:28, 5961632.12it/s]
+  2%|▏         | 3211264/170498071 [00:00<00:24, 6709167.30it/s]
 
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-  3%|▎         | 5963776/170498071 [00:01<00:14, 11664878.14it/s]
+  4%|▎         | 6324224/170498071 [00:01<00:12, 12963647.37it/s]
 
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+  6%|▌         | 9994240/170498071 [00:01<00:08, 18035329.00it/s]
 
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-  7%|▋         | 12484608/170498071 [00:01<00:08, 18713452.56it/s]
+  8%|▊         | 13107200/170498071 [00:01<00:07, 20305770.58it/s]
 
 .. parsed-literal::
 
 
-  9%|▉         | 15532032/170498071 [00:01<00:07, 20509245.76it/s]
+ 10%|▉         | 16285696/170498071 [00:01<00:06, 22104134.94it/s]
 
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- 11%|█         | 18513920/170498071 [00:01<00:07, 21529733.73it/s]
+ 11%|█▏        | 19365888/170498071 [00:01<00:06, 23124395.97it/s]
 
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- 13%|█▎        | 21692416/170498071 [00:01<00:06, 22662379.12it/s]
+ 13%|█▎        | 22478848/170498071 [00:01<00:06, 23948854.13it/s]
 
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- 15%|█▍        | 24805376/170498071 [00:01<00:06, 23237932.55it/s]
+ 15%|█▌        | 25722880/170498071 [00:01<00:05, 24802992.62it/s]
 
 .. parsed-literal::
 
 
- 16%|█▋        | 28016640/170498071 [00:01<00:05, 23916419.04it/s]
+ 17%|█▋        | 28835840/170498071 [00:01<00:05, 25083187.32it/s]
 
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- 18%|█▊        | 31195136/170498071 [00:02<00:05, 24165013.75it/s]
+ 18%|█▊        | 31358976/170498071 [00:02<00:07, 18085319.52it/s]
 
 .. parsed-literal::
 
 
- 20%|██        | 34373632/170498071 [00:02<00:05, 24570741.60it/s]
+ 21%|██        | 34963456/170498071 [00:02<00:06, 21073046.38it/s]
 
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- 22%|██▏       | 37617664/170498071 [00:02<00:05, 24839298.54it/s]
+ 22%|██▏       | 37322752/170498071 [00:02<00:08, 15989522.45it/s]
 
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+ 26%|██▌       | 44630016/170498071 [00:02<00:05, 22608262.82it/s]
 
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+
+.. parsed-literal::
+
+
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+
+.. parsed-literal::
+
+
+    100%|██████████| 170498071/170498071 [00:08<00:00, 21123642.42it/s]
 
 
 
@@ -882,10 +896,12 @@ model during quantization, in our case, to pick input tensor from pair
 
     import nncf
 
+
     def transform_fn(data_item):
         image_tensor = data_item[0]
         return image_tensor.numpy()
 
+
     quantization_dataset = nncf.Dataset(val_loader, transform_fn)
 
 
@@ -913,14 +929,14 @@ about supported parameters can be found on this
 
 .. parsed-literal::
 
-    2024-03-12 22:43:46.333703: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:43:46.368061: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-17 23:59:04.099112: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 23:59:04.131641: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 22:43:46.930366: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-17 23:59:04.768491: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -988,10 +1004,11 @@ Compare Accuracy of the Original and Quantized Models
     from tqdm.notebook import tqdm
     import numpy as np
 
+
     def test_accuracy(ov_model, data_loader):
         correct = 0
         total = 0
-        for (batch_imgs, batch_labels) in tqdm(data_loader):
+        for batch_imgs, batch_labels in tqdm(data_loader):
             result = ov_model(batch_imgs)[0]
             top_label = np.argmax(result)
             correct += top_label == batch_labels.numpy()
@@ -1012,8 +1029,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
 
@@ -1091,6 +1108,10 @@ Tool =2023.1.0" "opencv-python"
+    %pip install -q "openvino>=2023.1.0" "opencv-python" tqdm
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -49,7 +49,6 @@ Table of contents:
 
 .. code:: ipython3
 
-    import sys
     from pathlib import Path
     
     import cv2
@@ -58,8 +57,15 @@ Table of contents:
     from zipfile import ZipFile
     import openvino as ov
     
-    sys.path.append("../utils")
-    import notebook_utils as utils
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
+    from notebook_utils import download_file
 
 Download the Model
 ~~~~~~~~~~~~~~~~~~
@@ -82,12 +88,17 @@ be obtained from original model checkpoint can be found in this
     model_path = Path(f"{base_model_dir}/public/{model_name}/frozen_model.pb")
     if not model_path.exists():
         model_url = f"https://storage.openvinotoolkit.org/repositories/open_model_zoo/public/2022.1/gmcnn-places2-tf/{model_name}.zip"
-        utils.download_file(model_url, model_name, base_model_dir)
+        download_file(model_url, model_name, base_model_dir)
     else:
         print("Already downloaded")
     
-    with ZipFile(f'{base_model_dir}/{model_name}' + '', "r") as zip_ref:
-        zip_ref.extractall(path=Path(base_model_dir, 'public', ))
+    with ZipFile(f"{base_model_dir}/{model_name}" + "", "r") as zip_ref:
+        zip_ref.extractall(
+            path=Path(
+                base_model_dir,
+                "public",
+            )
+        )
 
 
 .. parsed-literal::
@@ -108,12 +119,12 @@ This step is also skipped if the model is already converted.
 
 .. code:: ipython3
 
-    model_dir = Path(base_model_dir, 'public', 'ir')
+    model_dir = Path(base_model_dir, "public", "ir")
     ir_path = Path(f"{model_dir}/frozen_model.xml")
     
     # Run model conversion API to convert model to OpenVINO IR FP32 format, if the IR file does not exist.
     if not ir_path.exists():
-        ov_model = ov.convert_model(model_path, input=[[1,512,680,3],[1,512,680,1]])
+        ov_model = ov.convert_model(model_path, input=[[1, 512, 680, 3], [1, 512, 680, 1]])
         ov.save_model(ov_model, str(ir_path))
     else:
         print(f"{ir_path} already exists.")
@@ -152,8 +163,8 @@ Only a few lines of code are required to run the model:
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -213,11 +224,13 @@ original image.
         for _ in range(number):
             start_x = np.random.randint(image_width - size_x)
             start_y = np.random.randint(image_height - size_y)
-            cv2.rectangle(img=mask,
-                          pt1=(start_x, start_y),
-                          pt2=(start_x + size_x, start_y + size_y),
-                          color=(1, 1, 1),
-                          thickness=cv2.FILLED)
+            cv2.rectangle(
+                img=mask,
+                pt1=(start_x, start_y),
+                pt2=(start_x + size_x, start_y + size_y),
+                color=(1, 1, 1),
+                thickness=cv2.FILLED,
+            )
         return mask
 
 .. code:: ipython3
@@ -230,7 +243,7 @@ original image.
 
 
 
-.. image:: 215-image-inpainting-with-output_files/215-image-inpainting-with-output_15_0.png
+.. image:: image-inpainting-with-output_files/image-inpainting-with-output_15_0.png
 
 
 Load and Resize the Image
@@ -248,8 +261,13 @@ you like. Just change the URL below.
     if not img_path.exists():
         # Download an image.
         url = "https://user-images.githubusercontent.com/29454499/281372079-fa8d84c4-8bf9-4a82-a1b9-5a74ad42ce47.png"
-        image_file = utils.download_file(
-            url, filename="laptop.png", directory="data", show_progress=False, silent=True, timeout=30
+        image_file = download_file(
+            url,
+            filename="laptop.png",
+            directory="data",
+            show_progress=False,
+            silent=True,
+            timeout=30,
         )
     
     # Read the image.
@@ -261,7 +279,7 @@ you like. Just change the URL below.
 
 
 
-.. image:: 215-image-inpainting-with-output_files/215-image-inpainting-with-output_17_0.png
+.. image:: image-inpainting-with-output_files/image-inpainting-with-output_17_0.png
 
 
 Generating the Masked Image
@@ -282,7 +300,7 @@ will be the first input to the GMCNN model.
 
 
 
-.. image:: 215-image-inpainting-with-output_files/215-image-inpainting-with-output_19_0.png
+.. image:: image-inpainting-with-output_files/image-inpainting-with-output_19_0.png
 
 
 Preprocessing
@@ -317,7 +335,7 @@ restored image.
 
 
 
-.. image:: 215-image-inpainting-with-output_files/215-image-inpainting-with-output_23_0.png
+.. image:: image-inpainting-with-output_files/image-inpainting-with-output_23_0.png
 
 
 Save the Restored Image
diff --git a/docs/notebooks/215-image-inpainting-with-output_files/215-image-inpainting-with-output_15_0.png b/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_15_0.png
similarity index 100%
rename from docs/notebooks/215-image-inpainting-with-output_files/215-image-inpainting-with-output_15_0.png
rename to docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_15_0.png
diff --git a/docs/notebooks/215-image-inpainting-with-output_files/215-image-inpainting-with-output_17_0.png b/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_17_0.png
similarity index 100%
rename from docs/notebooks/215-image-inpainting-with-output_files/215-image-inpainting-with-output_17_0.png
rename to docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_17_0.png
diff --git a/docs/notebooks/215-image-inpainting-with-output_files/215-image-inpainting-with-output_19_0.png b/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_19_0.png
similarity index 100%
rename from docs/notebooks/215-image-inpainting-with-output_files/215-image-inpainting-with-output_19_0.png
rename to docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_19_0.png
diff --git a/docs/notebooks/215-image-inpainting-with-output_files/215-image-inpainting-with-output_23_0.png b/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_23_0.png
similarity index 100%
rename from docs/notebooks/215-image-inpainting-with-output_files/215-image-inpainting-with-output_23_0.png
rename to docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_23_0.png
diff --git a/docs/notebooks/286-instant-id-with-output.rst b/docs/notebooks/instant-id-with-output.rst
similarity index 91%
rename from docs/notebooks/286-instant-id-with-output.rst
rename to docs/notebooks/instant-id-with-output.rst
index f68dc4c0641..4e1ab279c55 100644
--- a/docs/notebooks/286-instant-id-with-output.rst
+++ b/docs/notebooks/instant-id-with-output.rst
@@ -50,9 +50,11 @@ additional part demonstrates how to run optimization with
 `NNCF `__ to speed up
 pipeline.
 
+
 Table of contents:
 ^^^^^^^^^^^^^^^^^^
 
+
 - `Prerequisites <#prerequisites>`__
 - `Convert and prepare Face IdentityNet <#convert-and-prepare-face-identitynet>`__
 
@@ -110,7 +112,7 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.3.0" opencv-python transformers diffusers accelerate gdown scikit-image gradio nncf "datasets>=2.14.6"
+    %pip install -q "openvino>=2023.3.0" opencv-python transformers diffusers accelerate gdown "scikit-image>=0.19.2" "gradio>=4.19" "nncf>=2.9.0" "datasets>=2.14.6" "peft==0.6.2"
 
 Convert and prepare Face IdentityNet
 ------------------------------------
@@ -299,14 +301,10 @@ recognition results.
                 if key in self.center_cache:
                     anchor_centers = self.center_cache[key]
                 else:
-                    anchor_centers = np.stack(
-                        np.mgrid[:height, :width][::-1], axis=-1
-                    ).astype(np.float32)
+                    anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32)
                     anchor_centers = (anchor_centers * stride).reshape((-1, 2))
                     if self._num_anchors > 1:
-                        anchor_centers = np.stack(
-                            [anchor_centers] * self._num_anchors, axis=1
-                        ).reshape((-1, 2))
+                        anchor_centers = np.stack([anchor_centers] * self._num_anchors, axis=1).reshape((-1, 2))
                     if len(self.center_cache) < 100:
                         self.center_cache[key] = anchor_centers
 
@@ -371,9 +369,7 @@ recognition results.
                 if metric == "max":
                     values = area
                 else:
-                    values = (
-                        area - offset_dist_squared * 2.0
-                    )  # some extra weight on the centering
+                    values = area - offset_dist_squared * 2.0  # some extra weight on the centering
                 bindex = np.argsort(values)[::-1]  # some extra weight on the centering
                 bindex = bindex[0:max_num]
                 det = det[bindex, :]
@@ -468,14 +464,7 @@ recognition results.
             if not isinstance(imgs, list):
                 imgs = [imgs]
             input_size = self.input_size
-            blob = np.concatenate(
-                [
-                    prepare_input(
-                        cv2.resize(img, input_size), self.input_mean, self.input_std, True
-                    )
-                    for img in imgs
-                ]
-            )
+            blob = np.concatenate([prepare_input(cv2.resize(img, input_size), self.input_mean, self.input_std, True) for img in imgs])
 
             net_out = self.ov_model(blob)[0]
             return net_out
@@ -503,9 +492,7 @@ recognition results.
                 if kpss is not None:
                     kps = kpss[i]
                 embedding = self.embed_model.get(img, kps)
-                ret.append(
-                    {"bbox": bbox, "score": det_score, "kps": kps, "embedding": embedding}
-                )
+                ret.append({"bbox": bbox, "score": det_score, "kps": kps, "embedding": embedding})
             return ret
 
 Now, let’s see models inference result
@@ -513,7 +500,7 @@ Now, let’s see models inference result
 Select Inference Device for Face Recognition
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-### Select Inference Device for
+ ### Select Inference Device for
 Face Recognition
 
 .. code:: ipython3
@@ -565,6 +552,7 @@ generated image
     import PIL.Image
     from pipeline_stable_diffusion_xl_instantid import draw_kps
 
+
     def get_face_info(face_image: PIL.Image.Image):
         r"""
         Retrieve face information from the input face image.
@@ -585,7 +573,8 @@ generated image
         if len(face_info) == 0:
             raise RuntimeError("Couldn't find the face on the image")
         face_info = sorted(
-            face_info, key=lambda x: (x["bbox"][2] - x["bbox"][0]) * x["bbox"][3] - x["bbox"][1]
+            face_info,
+            key=lambda x: (x["bbox"][2] - x["bbox"][0]) * x["bbox"][3] - x["bbox"][1],
         )[
             -1
         ]  # only use the maximum face
@@ -597,9 +586,7 @@ generated image
 
     from diffusers.utils import load_image
 
-    face_image = load_image(
-        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg"
-    )
+    face_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg")
 
     face_emb, face_kps = get_face_info(face_image)
 
@@ -610,7 +597,7 @@ generated image
 
 
 
-.. image:: 286-instant-id-with-output_files/286-instant-id-with-output_15_0.png
+.. image:: instant-id-with-output_files/instant-id-with-output_15_0.png
 
 
 
@@ -621,7 +608,7 @@ generated image
 
 
 
-.. image:: 286-instant-id-with-output_files/286-instant-id-with-output_16_0.png
+.. image:: instant-id-with-output_files/instant-id-with-output_16_0.png
 
 
 
@@ -647,9 +634,7 @@ pose and IP-Adapter for adding face embeddings to prompt
         filename="ControlNetModel/diffusion_pytorch_model.safetensors",
         local_dir="./checkpoints",
     )
-    hf_hub_download(
-        repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints"
-    );
+    hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints");
 
 As it was discussed in model description, InstantID does not required
 diffusion model fine-tuning and can be applied on existing Stable
@@ -660,9 +645,8 @@ LoRA `__ to
 speedup the generation process. Previously, we already considered how to
 convert and run SDXL model for Text-to-Image and Image-to-Image
 generation using Optimum-Intel library (please check out this notebook
-for
-`details <248-stable-diffusion-xl-with-output.html>`__),
-now we will use it in combination with ControlNet and convert it using
+for `details `__ ), now
+we will use it in combination with ControlNet and convert it using
 OpenVINO Model Conversion API.
 
 .. code:: ipython3
@@ -701,9 +685,7 @@ OpenVINO Model Conversion API.
         # load IdentityNet
         controlnet = ControlNetModel.from_pretrained(controlnet_path)
 
-        pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
-            sdxl_id, controlnet=controlnet
-        )
+        pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(sdxl_id, controlnet=controlnet)
 
         # load adapter
         pipe.load_ip_adapter_instantid(face_adapter)
@@ -781,7 +763,7 @@ The pipeline consists of the following list of important parts:
 
 -  Image Projection model for getting image prompt embeddings. It is
    similar with IP-Adapter approach described in `this
-   tutorial <278-stable-diffusion-ip-adapter-with-output.html>`__,
+   tutorial `__,
    but instead of image, it uses face embeddings as input for image
    prompt encoding.
 -  Text Encoders for creating text embeddings to generate an image from
@@ -802,7 +784,7 @@ paper. It provides a framework that enables support for various spatial
 contexts such as a depth map, a segmentation map, a scribble, and key
 points that can serve as additional conditionings to Diffusion models
 such as Stable Diffusion. In this
-`tutorial <235-controlnet-stable-diffusion-with-output.html>`__
+`tutorial `__
 we already considered how to convert and use ControlNet with Stable
 Diffusion pipeline. The process of usage ControlNet for Stable Diffusion
 XL remains without changes.
@@ -838,9 +820,7 @@ XL remains without changes.
     if not ov_controlnet_path.exists():
         controlnet.forward = partial(controlnet.forward, return_dict=False)
         with torch.no_grad():
-            ov_controlnet = ov.convert_model(
-                controlnet, example_input=controlnet_example_input
-            )
+            ov_controlnet = ov.convert_model(controlnet, example_input=controlnet_example_input)
         ov_controlnet.inputs[-1].get_node().set_element_type(ov.Type.f32)
         ov_controlnet.inputs[-1].get_node().set_partial_shape(ov.PartialShape([-1, 6]))
         ov_controlnet.validate_nodes_and_infer_types()
@@ -850,9 +830,7 @@ XL remains without changes.
         gc.collect()
 
     if not ov_unet_path.exists():
-        down_block_res_samples, mid_block_res_sample = controlnet(
-            **controlnet_example_input
-        )
+        down_block_res_samples, mid_block_res_sample = controlnet(**controlnet_example_input)
     else:
         down_block_res_samples, mid_block_res_sample = None, None
 
@@ -891,9 +869,7 @@ preparing model input for Unet.
             self.sample_dtype = sample_dtype
             self.timestep_dtype = timestep_dtype
             self.encoder_hidden_states_dtype = encoder_hidden_states_dtype
-            self.down_block_additional_residuals_dtype = (
-                down_block_additional_residuals_dtype
-            )
+            self.down_block_additional_residuals_dtype = down_block_additional_residuals_dtype
             self.mid_block_additional_residual_dtype = mid_block_additional_residual_dtype
             self.text_embeds_dtype = text_embeds_dtype
             self.time_ids_dtype = time_ids_dtype
@@ -911,10 +887,7 @@ preparing model input for Unet.
             sample.to(self.sample_dtype)
             timestep.to(self.timestep_dtype)
             encoder_hidden_states.to(self.encoder_hidden_states_dtype)
-            down_block_additional_residuals = [
-                res.to(self.down_block_additional_residuals_dtype)
-                for res in down_block_additional_residuals
-            ]
+            down_block_additional_residuals = [res.to(self.down_block_additional_residuals_dtype) for res in down_block_additional_residuals]
             mid_block_additional_residual.to(self.mid_block_additional_residual_dtype)
             added_cond_kwargs = {
                 "text_embeds": text_embeds.to(self.text_embeds_dtype),
@@ -983,9 +956,7 @@ image, it means that we can skip VAE encoder part conversion.
         vae_decoder = VAEDecoderWrapper(vae)
 
         with torch.no_grad():
-            ov_vae_decoder = ov.convert_model(
-                vae_decoder, example_input=torch.zeros((1, 4, 64, 64))
-            )
+            ov_vae_decoder = ov.convert_model(vae_decoder, example_input=torch.zeros((1, 4, 64, 64)))
         ov.save_model(ov_vae_decoder, ov_vae_decoder_path)
         del ov_vae_decoder
         cleanup_torchscript_cache()
@@ -1048,9 +1019,7 @@ image prompt embeddings
 
     if not ov_image_proj_encoder_path.exists():
         with torch.no_grad():
-            ov_image_encoder = ov.convert_model(
-                image_proj_model, example_input=torch.zeros((2, 1, 512))
-            )
+            ov_image_encoder = ov.convert_model(image_proj_model, example_input=torch.zeros((2, 1, 512)))
         ov.save_model(ov_image_encoder, ov_image_proj_encoder_path)
         del ov_image_encoder
         cleanup_torchscript_cache()
@@ -1100,9 +1069,7 @@ Prepare OpenVINO InstantID Pipeline
             self.image_proj_model_in_features = 512
             self.vae_scale_factor = 8
             self.vae_scaling_factor = 0.13025
-            self.image_processor = VaeImageProcessor(
-                vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True
-            )
+            self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
             self.control_image_processor = VaeImageProcessor(
                 vae_scale_factor=self.vae_scale_factor,
                 do_convert_rgb=True,
@@ -1111,32 +1078,23 @@ Prepare OpenVINO InstantID Pipeline
             self._internal_dict = {}
             self._progress_bar_config = {}
 
-
-        def _encode_prompt_image_emb(
-            self, prompt_image_emb, num_images_per_prompt, do_classifier_free_guidance
-        ):
+        def _encode_prompt_image_emb(self, prompt_image_emb, num_images_per_prompt, do_classifier_free_guidance):
             if isinstance(prompt_image_emb, torch.Tensor):
                 prompt_image_emb = prompt_image_emb.clone().detach()
             else:
                 prompt_image_emb = torch.tensor(prompt_image_emb)
 
-            prompt_image_emb = prompt_image_emb.reshape(
-                [1, -1, self.image_proj_model_in_features]
-            )
+            prompt_image_emb = prompt_image_emb.reshape([1, -1, self.image_proj_model_in_features])
 
             if do_classifier_free_guidance:
-                prompt_image_emb = torch.cat(
-                    [torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0
-                )
+                prompt_image_emb = torch.cat([torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0)
             else:
                 prompt_image_emb = torch.cat([prompt_image_emb], dim=0)
             prompt_image_emb = self.image_proj_model(prompt_image_emb)[0]
 
             bs_embed, seq_len, _ = prompt_image_emb.shape
             prompt_image_emb = np.tile(prompt_image_emb, (1, num_images_per_prompt, 1))
-            prompt_image_emb = prompt_image_emb.reshape(
-                bs_embed * num_images_per_prompt, seq_len, -1
-            )
+            prompt_image_emb = prompt_image_emb.reshape(bs_embed * num_images_per_prompt, seq_len, -1)
 
             return prompt_image_emb
 
@@ -1300,19 +1258,11 @@ Prepare OpenVINO InstantID Pipeline
 
             do_classifier_free_guidance = guidance_scale >= 1.0
             # align format for control guidance
-            if not isinstance(control_guidance_start, list) and isinstance(
-                control_guidance_end, list
-            ):
-                control_guidance_start = len(control_guidance_end) * [
-                    control_guidance_start
-                ]
-            elif not isinstance(control_guidance_end, list) and isinstance(
-                control_guidance_start, list
-            ):
+            if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+                control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+            elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
                 control_guidance_end = len(control_guidance_start) * [control_guidance_end]
-            elif not isinstance(control_guidance_start, list) and not isinstance(
-                control_guidance_end, list
-            ):
+            elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
                 control_guidance_start, control_guidance_end = (
                     [control_guidance_start],
                     [control_guidance_end],
@@ -1347,9 +1297,7 @@ Prepare OpenVINO InstantID Pipeline
             )
 
             # 3.2 Encode image prompt
-            prompt_image_emb = self._encode_prompt_image_emb(
-                image_embeds, num_images_per_prompt, do_classifier_free_guidance
-            )
+            prompt_image_emb = self._encode_prompt_image_emb(image_embeds, num_images_per_prompt, do_classifier_free_guidance)
 
             # 4. Prepare image
             image = self.prepare_image(
@@ -1385,10 +1333,7 @@ Prepare OpenVINO InstantID Pipeline
             # 7.1 Create tensor stating which controlnets to keep
             controlnet_keep = []
             for i in range(len(timesteps)):
-                keeps = [
-                    1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
-                    for s, e in zip(control_guidance_start, control_guidance_end)
-                ]
+                keeps = [1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) for s, e in zip(control_guidance_start, control_guidance_end)]
                 controlnet_keep.append(keeps)
 
             # 7.2 Prepare added time ids & embeddings
@@ -1422,29 +1367,19 @@ Prepare OpenVINO InstantID Pipeline
                 negative_add_time_ids = add_time_ids
 
             if do_classifier_free_guidance:
-                prompt_embeds = np.concatenate(
-                    [negative_prompt_embeds, prompt_embeds], axis=0
-                )
-                add_text_embeds = np.concatenate(
-                    [negative_pooled_prompt_embeds, add_text_embeds], axis=0
-                )
+                prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds], axis=0)
+                add_text_embeds = np.concatenate([negative_pooled_prompt_embeds, add_text_embeds], axis=0)
                 add_time_ids = np.concatenate([negative_add_time_ids, add_time_ids], axis=0)
 
             add_time_ids = np.tile(add_time_ids, (batch_size * num_images_per_prompt, 1))
-            encoder_hidden_states = np.concatenate(
-                [prompt_embeds, prompt_image_emb], axis=1
-            )
+            encoder_hidden_states = np.concatenate([prompt_embeds, prompt_image_emb], axis=1)
 
             # 8. Denoising loop
             with self.progress_bar(total=num_inference_steps) as progress_bar:
                 for i, t in enumerate(timesteps):
                     # expand the latents if we are doing classifier free guidance
-                    latent_model_input = (
-                        torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                    )
-                    latent_model_input = self.scheduler.scale_model_input(
-                        latent_model_input, t
-                    )
+                    latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
                     # controlnet(s) inference
                     control_model_input = latent_model_input
@@ -1480,9 +1415,7 @@ Prepare OpenVINO InstantID Pipeline
                     # perform guidance
                     if do_classifier_free_guidance:
                         noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
-                        noise_pred = noise_pred_uncond + guidance_scale * (
-                            noise_pred_text - noise_pred_uncond
-                        )
+                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
                     # compute the previous noisy sample x_t -> x_t-1
                     latents = self.scheduler.step(
@@ -1500,9 +1433,7 @@ Prepare OpenVINO InstantID Pipeline
                 image = latents
 
             if not output_type == "latent":
-                image = self.image_processor.postprocess(
-                    torch.from_numpy(image), output_type=output_type
-                )
+                image = self.image_processor.postprocess(torch.from_numpy(image), output_type=output_type)
 
             if not return_dict:
                 return (image,)
@@ -1572,16 +1503,8 @@ Prepare OpenVINO InstantID Pipeline
                 batch_size = prompt_embeds.shape[0]
 
             # Define tokenizers and text encoders
-            tokenizers = (
-                [self.tokenizer, self.tokenizer_2]
-                if self.tokenizer is not None
-                else [self.tokenizer_2]
-            )
-            text_encoders = (
-                [self.text_encoder, self.text_encoder_2]
-                if self.text_encoder is not None
-                else [self.text_encoder_2]
-            )
+            tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+            text_encoders = [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
 
             if prompt_embeds is None:
                 prompt_2 = prompt_2 or prompt
@@ -1590,9 +1513,7 @@ Prepare OpenVINO InstantID Pipeline
                 # textual inversion: procecss multi-vector tokens if necessary
                 prompt_embeds_list = []
                 prompts = [prompt, prompt_2]
-                for prompt, tokenizer, text_encoder in zip(
-                    prompts, tokenizers, text_encoders
-                ):
+                for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
                     text_inputs = tokenizer(
                         prompt,
                         padding="max_length",
@@ -1620,11 +1541,7 @@ Prepare OpenVINO InstantID Pipeline
 
             # get unconditional embeddings for classifier free guidance
             zero_out_negative_prompt = negative_prompt is None
-            if (
-                do_classifier_free_guidance
-                and negative_prompt_embeds is None
-                and zero_out_negative_prompt
-            ):
+            if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
                 negative_prompt_embeds = np.zeros_like(prompt_embeds)
                 negative_pooled_prompt_embeds = np.zeros_like(pooled_prompt_embeds)
             elif do_classifier_free_guidance and negative_prompt_embeds is None:
@@ -1632,23 +1549,12 @@ Prepare OpenVINO InstantID Pipeline
                 negative_prompt_2 = negative_prompt_2 or negative_prompt
 
                 # normalize str to list
-                negative_prompt = (
-                    batch_size * [negative_prompt]
-                    if isinstance(negative_prompt, str)
-                    else negative_prompt
-                )
-                negative_prompt_2 = (
-                    batch_size * [negative_prompt_2]
-                    if isinstance(negative_prompt_2, str)
-                    else negative_prompt_2
-                )
+                negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+                negative_prompt_2 = batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
 
                 uncond_tokens: List[str]
                 if prompt is not None and type(prompt) is not type(negative_prompt):
-                    raise TypeError(
-                        f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                        f" {type(prompt)}."
-                    )
+                    raise TypeError(f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}.")
                 elif batch_size != len(negative_prompt):
                     raise ValueError(
                         f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
@@ -1659,9 +1565,7 @@ Prepare OpenVINO InstantID Pipeline
                     uncond_tokens = [negative_prompt, negative_prompt_2]
 
                 negative_prompt_embeds_list = []
-                for negative_prompt, tokenizer, text_encoder in zip(
-                    uncond_tokens, tokenizers, text_encoders
-                ):
+                for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
                     max_length = prompt_embeds.shape[1]
                     uncond_input = tokenizer(
                         negative_prompt,
@@ -1679,34 +1583,22 @@ Prepare OpenVINO InstantID Pipeline
 
                     negative_prompt_embeds_list.append(negative_prompt_embeds)
 
-                negative_prompt_embeds = np.concatenate(
-                    negative_prompt_embeds_list, axis=-1
-                )
+                negative_prompt_embeds = np.concatenate(negative_prompt_embeds_list, axis=-1)
 
             bs_embed, seq_len, _ = prompt_embeds.shape
             # duplicate text embeddings for each generation per prompt, using mps friendly method
             prompt_embeds = np.tile(prompt_embeds, (1, num_images_per_prompt, 1))
-            prompt_embeds = prompt_embeds.reshape(
-                bs_embed * num_images_per_prompt, seq_len, -1
-            )
+            prompt_embeds = prompt_embeds.reshape(bs_embed * num_images_per_prompt, seq_len, -1)
 
             if do_classifier_free_guidance:
                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 seq_len = negative_prompt_embeds.shape[1]
-                negative_prompt_embeds = np.tile(
-                    negative_prompt_embeds, (1, num_images_per_prompt, 1)
-                )
-                negative_prompt_embeds = negative_prompt_embeds.reshape(
-                    batch_size * num_images_per_prompt, seq_len, -1
-                )
+                negative_prompt_embeds = np.tile(negative_prompt_embeds, (1, num_images_per_prompt, 1))
+                negative_prompt_embeds = negative_prompt_embeds.reshape(batch_size * num_images_per_prompt, seq_len, -1)
 
-            pooled_prompt_embeds = np.tile(
-                pooled_prompt_embeds, (1, num_images_per_prompt)
-            ).reshape(bs_embed * num_images_per_prompt, -1)
+            pooled_prompt_embeds = np.tile(pooled_prompt_embeds, (1, num_images_per_prompt)).reshape(bs_embed * num_images_per_prompt, -1)
             if do_classifier_free_guidance:
-                negative_pooled_prompt_embeds = np.tile(
-                    negative_pooled_prompt_embeds, (1, num_images_per_prompt)
-                ).reshape(bs_embed * num_images_per_prompt, -1)
+                negative_pooled_prompt_embeds = np.tile(negative_pooled_prompt_embeds, (1, num_images_per_prompt)).reshape(bs_embed * num_images_per_prompt, -1)
 
             return (
                 prompt_embeds,
@@ -1725,9 +1617,7 @@ Prepare OpenVINO InstantID Pipeline
             do_classifier_free_guidance=False,
             guess_mode=False,
         ):
-            image = self.control_image_processor.preprocess(
-                image, height=height, width=width
-            ).to(dtype=torch.float32)
+            image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
             image_batch_size = image.shape[0]
 
             if image_batch_size == 1:
@@ -1754,7 +1644,6 @@ Prepare OpenVINO InstantID Pipeline
             add_time_ids = torch.tensor([add_time_ids])
             return add_time_ids
 
-
 Run OpenVINO pipeline inference
 -------------------------------
 
@@ -1804,7 +1693,7 @@ Select inference device for InstantID
 Create pipeline
 ~~~~~~~~~~~~~~~
 
-### Create pipeline
+ ### Create pipeline
 
 .. code:: ipython3
 
@@ -1823,7 +1712,7 @@ Create pipeline
 Run inference
 ~~~~~~~~~~~~~
 
-### Run inference
+ ### Run inference
 
 .. code:: ipython3
 
@@ -1837,7 +1726,7 @@ Run inference
         num_inference_steps=4,
         negative_prompt=negative_prompt,
         guidance_scale=0.5,
-        generator=torch.Generator(device="cpu").manual_seed(1749781188)
+        generator=torch.Generator(device="cpu").manual_seed(1749781188),
     ).images[0]
 
 
@@ -1854,7 +1743,7 @@ Run inference
 
 
 
-.. image:: 286-instant-id-with-output_files/286-instant-id-with-output_41_0.png
+.. image:: instant-id-with-output_files/instant-id-with-output_41_0.png
 
 
 
@@ -1885,10 +1774,7 @@ improve model inference speed.
 
 .. code:: ipython3
 
-    to_quantize = widgets.Checkbox(
-        value=True,
-        description='Quantization'
-    )
+    to_quantize = widgets.Checkbox(value=True, description="Quantization")
 
     to_quantize
 
@@ -1906,8 +1792,13 @@ Let’s load ``skip magic`` extension to skip quantization if
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
 
     int8_pipe = None
 
@@ -2270,7 +2161,8 @@ pipelines.
 
     import matplotlib.pyplot as plt
 
-    def visualize_results(orig_img:Image, optimized_img:Image):
+
+    def visualize_results(orig_img: Image, optimized_img: Image):
         """
         Helper function for results visualization
 
@@ -2283,7 +2175,7 @@ pipelines.
         orig_title = "FP16 pipeline"
         control_title = "INT8 pipeline"
         figsize = (20, 20)
-        fig, axs = plt.subplots(1, 2, figsize=figsize, sharex='all', sharey='all')
+        fig, axs = plt.subplots(1, 2, figsize=figsize, sharex="all", sharey="all")
         list_axes = list(axs.flat)
         for a in list_axes:
             a.set_xticklabels([])
@@ -2308,7 +2200,7 @@ pipelines.
 
 
 
-.. image:: 286-instant-id-with-output_files/286-instant-id-with-output_66_0.png
+.. image:: instant-id-with-output_files/instant-id-with-output_66_0.png
 
 
 Compare model file sizes
@@ -2411,7 +2303,7 @@ to launch the interactive demo.
 
     use_quantized_models = widgets.Checkbox(
         value=quantized_models_present,
-        description='Use quantized models',
+        description="Use quantized models",
         disabled=not quantized_models_present,
     )
 
@@ -2423,6 +2315,7 @@ to launch the interactive demo.
     from typing import Tuple
     import random
     import PIL
+    import sys
 
     sys.path.append("./InstantID/gradio_demo")
 
@@ -2439,7 +2332,7 @@ to launch the interactive demo.
         "https://huggingface.co/datasets/EnD-Diffusers/AI_Faces/resolve/main/images%207/00171-2728008415.png",
         "https://huggingface.co/datasets/EnD-Diffusers/AI_Faces/resolve/main/00003-3962843561.png",
         "https://huggingface.co/datasets/EnD-Diffusers/AI_Faces/resolve/main/00005-3104853215.png",
-        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ai_face2.png"
+        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ai_face2.png",
     ]
 
     examples_dir = Path("examples")
@@ -2449,10 +2342,10 @@ to launch the interactive demo.
         [examples_dir / "face_1.png", "photo of a business lady", "Vibrant Color", ""],
         [examples_dir / "face_2.png", "famous rock star poster", "(No style)", ""],
         [examples_dir / "face_3.png", "a person", "Neon", ""],
-        [examples_dir / "face_4.png", "a girl", "Snow", ""]
+        [examples_dir / "face_4.png", "a girl", "Snow", ""],
     ]
 
-    pipeline = int8_pipe if use_quantized_model.value else ov_pipe
+    pipeline = int8_pipe if use_quantized_models.value else ov_pipe
 
 
     if not examples_dir.exists():
@@ -2500,9 +2393,7 @@ to launch the interactive demo.
             res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
             offset_x = (max_side - w_resize_new) // 2
             offset_y = (max_side - h_resize_new) // 2
-            res[
-                offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new
-            ] = np.array(input_image)
+            res[offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new] = np.array(input_image)
             input_image = Image.fromarray(res)
         return input_image
 
@@ -2539,9 +2430,7 @@ to launch the interactive demo.
         face_info = app.get(face_image_cv2)
 
         if len(face_info) == 0:
-            raise gr.Error(
-                "Cannot find any face in the image! Please upload another person image"
-            )
+            raise gr.Error("Cannot find any face in the image! Please upload another person image")
 
         face_info = sorted(
             face_info,
@@ -2560,9 +2449,7 @@ to launch the interactive demo.
             face_info = app.get(pose_image_cv2)
 
             if len(face_info) == 0:
-                raise gr.Error(
-                    "Cannot find any face in the reference image! Please upload another person image"
-                )
+                raise gr.Error("Cannot find any face in the reference image! Please upload another person image")
 
             face_info = face_info[-1]
             face_kps = draw_kps(pose_image, face_info["kps"])
@@ -2619,9 +2506,7 @@ to launch the interactive demo.
                 face_file = gr.Image(label="Upload a photo of your face", type="pil")
 
                 # optional: upload a reference pose image
-                pose_file = gr.Image(
-                    label="Upload a reference pose image (optional)", type="pil"
-                )
+                pose_file = gr.Image(label="Upload a reference pose image (optional)", type="pil")
 
                 # prompt
                 prompt = gr.Textbox(
@@ -2632,9 +2517,7 @@ to launch the interactive demo.
                 )
 
                 submit = gr.Button("Submit", variant="primary")
-                style = gr.Dropdown(
-                    label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME
-                )
+                style = gr.Dropdown(label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
 
                 # strength
                 identitynet_strength_ratio = gr.Slider(
@@ -2658,9 +2541,7 @@ to launch the interactive demo.
                         step=1,
                         value=4,
                     )
-                    guidance_scale = gr.Slider(
-                        label="Guidance scale", minimum=0.1, maximum=10.0, step=0.1, value=0
-                    )
+                    guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=10.0, step=0.1, value=0)
                     seed = gr.Slider(
                         label="Seed",
                         minimum=0,
@@ -2700,7 +2581,7 @@ to launch the interactive demo.
 
 .. code:: ipython3
 
-    if __name__ == '__main__':
+    if __name__ == "__main__":
         try:
             demo.launch(debug=False)
         except Exception:
diff --git a/docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_15_0.jpg b/docs/notebooks/instant-id-with-output_files/instant-id-with-output_15_0.jpg
similarity index 100%
rename from docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_15_0.jpg
rename to docs/notebooks/instant-id-with-output_files/instant-id-with-output_15_0.jpg
diff --git a/docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_15_0.png b/docs/notebooks/instant-id-with-output_files/instant-id-with-output_15_0.png
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rename from docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_15_0.png
rename to docs/notebooks/instant-id-with-output_files/instant-id-with-output_15_0.png
diff --git a/docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_16_0.jpg b/docs/notebooks/instant-id-with-output_files/instant-id-with-output_16_0.jpg
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rename from docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_16_0.jpg
rename to docs/notebooks/instant-id-with-output_files/instant-id-with-output_16_0.jpg
diff --git a/docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_16_0.png b/docs/notebooks/instant-id-with-output_files/instant-id-with-output_16_0.png
similarity index 100%
rename from docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_16_0.png
rename to docs/notebooks/instant-id-with-output_files/instant-id-with-output_16_0.png
diff --git a/docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_41_0.jpg b/docs/notebooks/instant-id-with-output_files/instant-id-with-output_41_0.jpg
similarity index 100%
rename from docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_41_0.jpg
rename to docs/notebooks/instant-id-with-output_files/instant-id-with-output_41_0.jpg
diff --git a/docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_41_0.png b/docs/notebooks/instant-id-with-output_files/instant-id-with-output_41_0.png
similarity index 100%
rename from docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_41_0.png
rename to docs/notebooks/instant-id-with-output_files/instant-id-with-output_41_0.png
diff --git a/docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_66_0.png b/docs/notebooks/instant-id-with-output_files/instant-id-with-output_66_0.png
similarity index 100%
rename from docs/notebooks/286-instant-id-with-output_files/286-instant-id-with-output_66_0.png
rename to docs/notebooks/instant-id-with-output_files/instant-id-with-output_66_0.png
diff --git a/docs/notebooks/231-instruct-pix2pix-image-editing-with-output.rst b/docs/notebooks/instruct-pix2pix-image-editing-with-output.rst
similarity index 85%
rename from docs/notebooks/231-instruct-pix2pix-image-editing-with-output.rst
rename to docs/notebooks/instruct-pix2pix-image-editing-with-output.rst
index b8ca8b24102..c0c7787c271 100644
--- a/docs/notebooks/231-instruct-pix2pix-image-editing-with-output.rst
+++ b/docs/notebooks/instruct-pix2pix-image-editing-with-output.rst
@@ -60,8 +60,15 @@ Install necessary packages
 
 .. code:: ipython3
 
-    %pip install -q "transformers>=4.25.1" accelerate gradio datasets diffusers --extra-index-url https://download.pytorch.org/whl/cpu
+    import platform
+    
+    %pip install -q "transformers>=4.25.1" torch accelerate "gradio>4.19" "datasets>=2.14.6" diffusers pillow opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "openvino>=2023.1.0"
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
 
 Create Pytorch Models pipeline
 ------------------------------
@@ -71,7 +78,7 @@ Create Pytorch Models pipeline
 ``StableDiffusionInstructPix2PixPipeline`` is an end-to-end inference
 pipeline that you can use to edit images from text instructions with
 just a few lines of code provided as part
-🤗🧨\ `diffusers `__ library.
+ `diffusers `__ library.
 
 First, we load the pre-trained weights of all components of the model.
 
@@ -82,7 +89,11 @@ First, we load the pre-trained weights of all components of the model.
 .. code:: ipython3
 
     import torch
-    from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
+    from diffusers import (
+        StableDiffusionInstructPix2PixPipeline,
+        EulerAncestralDiscreteScheduler,
+    )
+    
     model_id = "timbrooks/instruct-pix2pix"
     pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float32, safety_checker=None)
     scheduler_config = pipe.scheduler.config
@@ -111,7 +122,7 @@ The InstructPix2Pix model is based on Stable Diffusion, a large-scale
 text-to-image latent diffusion model. You can find more details about
 how to run Stable Diffusion for text-to-image generation with OpenVINO
 in a separate
-`tutorial <225-stable-diffusion-text-to-image-with-output.html>`__.
+`tutorial `__.
 
 The model consists of three important parts:
 
@@ -151,6 +162,7 @@ hidden states.
     
     TEXT_ENCODER_OV_PATH = Path("text_encoder.xml")
     
+    
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -160,11 +172,11 @@ hidden states.
         torch.jit._state._clear_class_state()
     
     
-    def convert_encoder(text_encoder: torch.nn.Module, ir_path:Path):
+    def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
         """
-        Convert Text Encoder mode. 
-        Function accepts text encoder model, and prepares example inputs for conversion, 
-        Parameters: 
+        Convert Text Encoder mode.
+        Function accepts text encoder model, and prepares example inputs for conversion,
+        Parameters:
             text_encoder (torch.nn.Module): text_encoder model from Stable Diffusion pipeline
             ir_path (Path): File for storing model
         Returns:
@@ -177,12 +189,18 @@ hidden states.
         # disable gradients calculation for reducing memory consumption
         with torch.no_grad():
             # Export model to IR format
-            ov_model = ov.convert_model(text_encoder, example_input=input_ids, input=[(1,77),])
+            ov_model = ov.convert_model(
+                text_encoder,
+                example_input=input_ids,
+                input=[
+                    (1, 77),
+                ],
+            )
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        print(f'Text Encoder successfully converted to IR and saved to {ir_path}')
-        
+        print(f"Text Encoder successfully converted to IR and saved to {ir_path}")
+    
     
     if not TEXT_ENCODER_OV_PATH.exists():
         convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
@@ -229,17 +247,19 @@ into two independent models.
 
     VAE_ENCODER_OV_PATH = Path("vae_encoder.xml")
     
+    
     def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path):
         """
-        Convert VAE model for encoding to IR format. 
-        Function accepts vae model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion, 
-        Parameters: 
-            vae (torch.nn.Module): VAE model from StableDiffusio pipeline 
+        Convert VAE model for encoding to IR format.
+        Function accepts vae model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion,
+        Parameters:
+            vae (torch.nn.Module): VAE model from StableDiffusio pipeline
             ir_path (Path): File for storing model
         Returns:
             None
         """
+    
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -247,15 +267,16 @@ into two independent models.
     
             def forward(self, image):
                 return self.vae.encode(x=image)["latent_dist"].sample()
+    
         vae_encoder = VAEEncoderWrapper(vae)
         vae_encoder.eval()
         image = torch.zeros((1, 3, 512, 512))
         with torch.no_grad():
-            ov_model = ov.convert_model(vae_encoder, example_input=image, input=[((1,3,512,512),)])
+            ov_model = ov.convert_model(vae_encoder, example_input=image, input=[((1, 3, 512, 512),)])
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        print(f'VAE encoder successfully converted to IR and saved to {ir_path}')
+        print(f"VAE encoder successfully converted to IR and saved to {ir_path}")
     
     
     if not VAE_ENCODER_OV_PATH.exists():
@@ -263,19 +284,21 @@ into two independent models.
     else:
         print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH}")
     
-    VAE_DECODER_OV_PATH = Path('vae_decoder.xml')
+    VAE_DECODER_OV_PATH = Path("vae_decoder.xml")
+    
     
     def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path):
         """
-        Convert VAE model for decoding to IR format. 
-        Function accepts vae model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion, 
-        Parameters: 
+        Convert VAE model for decoding to IR format.
+        Function accepts vae model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion,
+        Parameters:
             vae (torch.nn.Module): VAE model frm StableDiffusion pipeline
             ir_path (Path): File for storing model
         Returns:
             None
         """
+    
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -283,17 +306,17 @@ into two independent models.
     
             def forward(self, latents):
                 return self.vae.decode(latents)
-        
+    
         vae_decoder = VAEDecoderWrapper(vae)
         latents = torch.zeros((1, 4, 64, 64))
     
         vae_decoder.eval()
         with torch.no_grad():
-            ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1,4,64,64),)])
+            ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1, 4, 64, 64),)])
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        print(f'VAE decoder successfully converted to IR and saved to {ir_path}')
+        print(f"VAE decoder successfully converted to IR and saved to {ir_path}")
     
     
     if not VAE_DECODER_OV_PATH.exists():
@@ -340,17 +363,14 @@ Model predicts the ``sample`` state for the next step.
     
     UNET_OV_PATH = Path("unet.xml")
     
-    dtype_mapping = {
-        torch.float32: ov.Type.f32,
-        torch.float64: ov.Type.f64
-    }
+    dtype_mapping = {torch.float32: ov.Type.f32, torch.float64: ov.Type.f64}
     
     
-    def convert_unet(unet:torch.nn.Module, ir_path:Path):
+    def convert_unet(unet: torch.nn.Module, ir_path: Path):
         """
-        Convert U-net model to IR format. 
-        Function accepts unet model, prepares example inputs for conversion, 
-        Parameters: 
+        Convert U-net model to IR format.
+        Function accepts unet model, prepares example inputs for conversion,
+        Parameters:
             unet (StableDiffusionPipeline): unet from Stable Diffusion pipeline
             ir_path (Path): File for storing model
         Returns:
@@ -374,7 +394,7 @@ Model predicts the ``sample`` state for the next step.
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        print(f'Unet successfully converted to IR and saved to {ir_path}')
+        print(f"Unet successfully converted to IR and saved to {ir_path}")
     
     
     if not UNET_OV_PATH.exists():
@@ -437,11 +457,11 @@ decoder part of the variational auto encoder.
     import cv2
     
     
-    def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
+    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
-        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio 
+        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-        
+    
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -461,7 +481,7 @@ decoder part of the variational auto encoder.
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-        
+    
         Parameters:
           image (PIL.Image.Image): input image
         Returns:
@@ -469,10 +489,8 @@ decoder part of the variational auto encoder.
            pad (Tuple[int]): pading size for each dimension for restoring image size in postprocessing
         """
         src_width, src_height = image.size
-        dst_width, dst_height = scale_fit_to_window(
-            512, 512, src_width, src_height)
-        image = np.array(image.resize((dst_width, dst_height),
-                         resample=PIL.Image.Resampling.LANCZOS))[None, :]
+        dst_width, dst_height = scale_fit_to_window(512, 512, src_width, src_height)
+        image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
         pad_width = 512 - dst_width
         pad_height = 512 - dst_height
         pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
@@ -489,7 +507,7 @@ decoder part of the variational auto encoder.
     ):
         """
         Helper function for generation random values tensor with given shape and data type
-        
+    
         Parameters:
           shape (Union[Tuple, List]): shape for filling random values
           dtype (np.dtype, *optiona*, np.float32): data type for result
@@ -505,6 +523,7 @@ decoder part of the variational auto encoder.
         """
         OpenVINO inference pipeline for InstructPix2Pix
         """
+    
         def __init__(
             self,
             tokenizer: CLIPTokenizer,
@@ -514,19 +533,26 @@ decoder part of the variational auto encoder.
             vae_encoder: ov.Model,
             unet: ov.Model,
             vae_decoder: ov.Model,
-            device: str = "AUTO"
+            device: str = "AUTO",
         ):
             super().__init__()
             self.tokenizer = tokenizer
             self.vae_scale_factor = 8
             self.scheduler = scheduler
-            self.load_models(core, device, text_encoder,
-                             vae_encoder, unet, vae_decoder)
+            self.load_models(core, device, text_encoder, vae_encoder, unet, vae_decoder)
     
-        def load_models(self, core: ov.Core, device: str, text_encoder: ov.Model, vae_encoder: ov.Model, unet: ov.Model, vae_decoder: ov.Model):
+        def load_models(
+            self,
+            core: ov.Core,
+            device: str,
+            text_encoder: ov.Model,
+            vae_encoder: ov.Model,
+            unet: ov.Model,
+            vae_decoder: ov.Model,
+        ):
             """
             Function for loading models on device using OpenVINO
-            
+    
             Parameters:
               core (Core): OpenVINO runtime Core class instance
               device (str): inference device
@@ -590,7 +616,7 @@ decoder part of the variational auto encoder.
                     [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
             Returns:
                 image ([List[Union[np.ndarray, PIL.Image.Image]]): generaited images
-                
+    
             """
     
             # 1. Define call parameters
@@ -638,18 +664,14 @@ decoder part of the variational auto encoder.
                     # Expand the latents if we are doing classifier free guidance.
                     # The latents are expanded 3 times because for pix2pix the guidance\
                     # is applied for both the text and the input image.
-                    latent_model_input = np.concatenate(
-                        [latents] * 3) if do_classifier_free_guidance else latents
+                    latent_model_input = np.concatenate([latents] * 3) if do_classifier_free_guidance else latents
     
                     # concat latents, image_latents in the channel dimension
-                    scaled_latent_model_input = self.scheduler.scale_model_input(
-                        latent_model_input, t)
-                    scaled_latent_model_input = np.concatenate(
-                        [scaled_latent_model_input, image_latents], axis=1)
+                    scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                    scaled_latent_model_input = np.concatenate([scaled_latent_model_input, image_latents], axis=1)
     
                     # predict the noise residual
-                    noise_pred = self.unet([scaled_latent_model_input, t, text_embeddings])[
-                        self.unet_out]
+                    noise_pred = self.unet([scaled_latent_model_input, t, text_embeddings])[self.unet_out]
     
                     # Hack:
                     # For karras style schedulers the model does classifier free guidance using the
@@ -662,10 +684,15 @@ decoder part of the variational auto encoder.
     
                     # perform guidance
                     if do_classifier_free_guidance:
-                        noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred[
-                            0], noise_pred[1], noise_pred[2]
+                        noise_pred_text, noise_pred_image, noise_pred_uncond = (
+                            noise_pred[0],
+                            noise_pred[1],
+                            noise_pred[2],
+                        )
                         noise_pred = (
-                            noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_image) + image_guidance_scale * (noise_pred_image - noise_pred_uncond)
+                            noise_pred_uncond
+                            + guidance_scale * (noise_pred_text - noise_pred_image)
+                            + image_guidance_scale * (noise_pred_image - noise_pred_uncond)
                         )
     
                     # For karras style schedulers the model does classifier free guidance using the
@@ -689,15 +716,18 @@ decoder part of the variational auto encoder.
             # 9. Convert to PIL
             if output_type == "pil":
                 image = self.numpy_to_pil(image)
-                image = [img.resize((orig_width, orig_height),
-                                    PIL.Image.Resampling.LANCZOS) for img in image]
+                image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
             else:
-                image = [cv2.resize(img, (orig_width, orig_width))
-                         for img in image]
+                image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
     
             return image
     
-        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True):
+        def _encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+        ):
             """
             Encodes the prompt into text encoder hidden states.
     
@@ -720,16 +750,13 @@ decoder part of the variational auto encoder.
             )
             text_input_ids = text_inputs.input_ids
     
-            text_embeddings = self.text_encoder(
-                text_input_ids)[self.text_encoder_out]
+            text_embeddings = self.text_encoder(text_input_ids)[self.text_encoder_out]
     
             # duplicate text embeddings for each generation per prompt, using mps friendly method
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(
-                    text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(
-                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
+                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
+                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
     
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
@@ -744,30 +771,30 @@ decoder part of the variational auto encoder.
                     return_tensors="np",
                 )
     
-                uncond_embeddings = self.text_encoder(uncond_input.input_ids)[
-                    self.text_encoder_out]
+                uncond_embeddings = self.text_encoder(uncond_input.input_ids)[self.text_encoder_out]
     
                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 seq_len = uncond_embeddings.shape[1]
-                uncond_embeddings = np.tile(
-                    uncond_embeddings, (1, num_images_per_prompt, 1))
-                uncond_embeddings = np.reshape(
-                    uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
+                uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
+                uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
     
                 # For classifier free guidance, you need to do two forward passes.
                 # Here, you concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
-                text_embeddings = np.concatenate(
-                    [text_embeddings, uncond_embeddings, uncond_embeddings])
+                text_embeddings = np.concatenate([text_embeddings, uncond_embeddings, uncond_embeddings])
     
             return text_embeddings
     
         def prepare_image_latents(
-            self, image, batch_size=1, num_images_per_prompt=1, do_classifier_free_guidance=True
+            self,
+            image,
+            batch_size=1,
+            num_images_per_prompt=1,
+            do_classifier_free_guidance=True,
         ):
             """
             Encodes input image to latent space using VAE Encoder
-            
+    
             Parameters:
                image (np.ndarray): input image tensor
                num_image_per_prompt (int, *optional*, 1): number of image generated for promt
@@ -784,12 +811,9 @@ decoder part of the variational auto encoder.
             if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
                 # expand image_latents for batch_size
                 additional_image_per_prompt = batch_size // image_latents.shape[0]
-                image_latents = np.concatenate(
-                    [image_latents] * additional_image_per_prompt, axis=0)
+                image_latents = np.concatenate([image_latents] * additional_image_per_prompt, axis=0)
             elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
-                raise ValueError(
-                    f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
-                )
+                raise ValueError(f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts.")
             else:
                 image_latents = np.concatenate([image_latents], axis=0)
     
@@ -799,11 +823,19 @@ decoder part of the variational auto encoder.
     
             return image_latents
     
-        def prepare_latents(self, batch_size:int, num_channels_latents:int, height:int, width:int, dtype:np.dtype = np.float32, latents:np.ndarray = None):
+        def prepare_latents(
+            self,
+            batch_size: int,
+            num_channels_latents: int,
+            height: int,
+            width: int,
+            dtype: np.dtype = np.float32,
+            latents: np.ndarray = None,
+        ):
             """
-            Preparing noise to image generation. If initial latents are not provided, they will be generated randomly, 
+            Preparing noise to image generation. If initial latents are not provided, they will be generated randomly,
             then prepared latents scaled by the standard deviation required by the scheduler
-            
+    
             Parameters:
                batch_size (int): input batch size
                num_channels_latents (int): number of channels for noise generation
@@ -814,7 +846,12 @@ decoder part of the variational auto encoder.
             Returns:
                latents (np.ndarray): scaled initial noise for diffusion
             """
-            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            shape = (
+                batch_size,
+                num_channels_latents,
+                height // self.vae_scale_factor,
+                width // self.vae_scale_factor,
+            )
             if latents is None:
                 latents = randn_tensor(shape, dtype=dtype)
             else:
@@ -824,10 +861,10 @@ decoder part of the variational auto encoder.
             latents = latents * self.scheduler.init_noise_sigma.numpy()
             return latents
     
-        def decode_latents(self, latents:np.array, pad:Tuple[int]):
+        def decode_latents(self, latents: np.array, pad: Tuple[int]):
             """
             Decode predicted image from latent space using VAE Decoder and unpad image result
-            
+    
             Parameters:
                latents (np.ndarray): image encoded in diffusion latent space
                pad (Tuple[int]): each side padding sizes obtained on preprocessing step
@@ -850,10 +887,15 @@ decoder part of the variational auto encoder.
     import matplotlib.pyplot as plt
     
     
-    def visualize_results(orig_img:PIL.Image.Image, processed_img:PIL.Image.Image, img1_title:str, img2_title:str):
+    def visualize_results(
+        orig_img: PIL.Image.Image,
+        processed_img: PIL.Image.Image,
+        img1_title: str,
+        img2_title: str,
+    ):
         """
         Helper function for results visualization
-        
+    
         Parameters:
            orig_img (PIL.Image.Image): original image
            processed_img (PIL.Image.Image): processed image after editing
@@ -865,8 +907,14 @@ decoder part of the variational auto encoder.
         im_w, im_h = orig_img.size
         is_horizontal = im_h <= im_w
         figsize = (20, 30) if is_horizontal else (30, 20)
-        fig, axs = plt.subplots(1 if is_horizontal else 2, 2 if is_horizontal else 1, figsize=figsize, sharex='all', sharey='all')
-        fig.patch.set_facecolor('white')
+        fig, axs = plt.subplots(
+            1 if is_horizontal else 2,
+            2 if is_horizontal else 1,
+            figsize=figsize,
+            sharex="all",
+            sharey="all",
+        )
+        fig.patch.set_facecolor("white")
         list_axes = list(axs.flat)
         for a in list_axes:
             a.set_xticklabels([])
@@ -876,11 +924,11 @@ decoder part of the variational auto encoder.
             a.grid(False)
         list_axes[0].imshow(np.array(orig_img))
         list_axes[1].imshow(np.array(processed_img))
-        list_axes[0].set_title(img1_title, fontsize=20) 
+        list_axes[0].set_title(img1_title, fontsize=20)
         list_axes[1].set_title(img2_title, fontsize=20)
-        fig.subplots_adjust(wspace=0.0 if is_horizontal else 0.01 , hspace=0.01 if is_horizontal else 0.0)
+        fig.subplots_adjust(wspace=0.0 if is_horizontal else 0.01, hspace=0.01 if is_horizontal else 0.0)
         fig.tight_layout()
-        fig.savefig("result.png", bbox_inches='tight')
+        fig.savefig("result.png", bbox_inches="tight")
         return fig
 
 Model tokenizer and scheduler are also important parts of the pipeline.
@@ -893,8 +941,8 @@ can provide device selecting one from available in dropdown list.
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -913,10 +961,19 @@ can provide device selecting one from available in dropdown list.
 
     from transformers import CLIPTokenizer
     
-    tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
     scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler_config)
     
-    ov_pipe = OVInstructPix2PixPipeline(tokenizer, scheduler, core, TEXT_ENCODER_OV_PATH, VAE_ENCODER_OV_PATH, UNET_OV_PATH, VAE_DECODER_OV_PATH, device=device.value)
+    ov_pipe = OVInstructPix2PixPipeline(
+        tokenizer,
+        scheduler,
+        core,
+        TEXT_ENCODER_OV_PATH,
+        VAE_ENCODER_OV_PATH,
+        UNET_OV_PATH,
+        VAE_DECODER_OV_PATH,
+        device=device.value,
+    )
 
 
 .. parsed-literal::
@@ -937,16 +994,11 @@ seed for latent state initialization and number of steps.
 
 .. code:: ipython3
 
-    style = {'description_width': 'initial'}
-    text_prompt = widgets.Text(value=" Make it in galaxy", description='your text')
-    num_steps = widgets.IntSlider(min=1, max=100, value=10, description='steps:')
-    seed = widgets.IntSlider(min=0, max=1024, description='seed: ', value=42)
-    image_widget = widgets.FileUpload(
-        accept='',
-        multiple=False,
-        description='Upload image',
-        style=style
-    )
+    style = {"description_width": "initial"}
+    text_prompt = widgets.Text(value=" Make it in galaxy", description="your text")
+    num_steps = widgets.IntSlider(min=1, max=100, value=10, description="steps:")
+    seed = widgets.IntSlider(min=0, max=1024, description="seed: ", value=42)
+    image_widget = widgets.FileUpload(accept="", multiple=False, description="Upload image", style=style)
     widgets.VBox([text_prompt, seed, num_steps, image_widget])
 
 
@@ -968,12 +1020,12 @@ seed for latent state initialization and number of steps.
     
     default_url = "https://user-images.githubusercontent.com/29454499/223343459-4ac944f0-502e-4acf-9813-8e9f0abc8a16.jpg"
     # read uploaded image
-    image = PIL.Image.open(io.BytesIO(image_widget.value[-1]['content']) if image_widget.value else requests.get(default_url, stream=True).raw)
+    image = PIL.Image.open(io.BytesIO(image_widget.value[-1]["content"]) if image_widget.value else requests.get(default_url, stream=True).raw)
     image = image.convert("RGB")
-    print('Pipeline settings')
-    print(f'Input text: {text_prompt.value}')
-    print(f'Seed: {seed.value}')
-    print(f'Number of steps: {num_steps.value}')
+    print("Pipeline settings")
+    print(f"Input text: {text_prompt.value}")
+    print(f"Seed: {seed.value}")
+    print(f"Number of steps: {num_steps.value}")
     np.random.seed(seed.value)
     processed_image = ov_pipe(text_prompt.value, image, num_steps.value)
 
@@ -999,11 +1051,16 @@ generation.
 
 .. code:: ipython3
 
-    fig = visualize_results(image, processed_image[0], img1_title="Original image", img2_title=f"Prompt: {text_prompt.value}")
+    fig = visualize_results(
+        image,
+        processed_image[0],
+        img1_title="Original image",
+        img2_title=f"Prompt: {text_prompt.value}",
+    )
 
 
 
-.. image:: 231-instruct-pix2pix-image-editing-with-output_files/231-instruct-pix2pix-image-editing-with-output_24_0.png
+.. image:: instruct-pix2pix-image-editing-with-output_files/instruct-pix2pix-image-editing-with-output_24_0.png
 
 
 Nice. As you can see, the picture has quite a high definition 🔥.
@@ -1039,7 +1096,7 @@ improve model inference speed.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -1059,8 +1116,13 @@ Let’s load ``skip magic`` extension to skip quantization if
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     %load_ext skip_kernel_extension
 
@@ -1227,7 +1289,7 @@ data.
 
 
 
-.. image:: 231-instruct-pix2pix-image-editing-with-output_files/231-instruct-pix2pix-image-editing-with-output_36_2.png
+.. image:: instruct-pix2pix-image-editing-with-output_files/instruct-pix2pix-image-editing-with-output_36_2.png
 
 
 Compare inference time of the FP16 and INT8 models
@@ -1295,7 +1357,7 @@ Interactive demo with Gradio
     pipe_precision = widgets.Dropdown(
         options=["FP16"] if not to_quantize.value else ["FP16", "INT8"],
         value="FP16",
-        description='Precision:',
+        description="Precision:",
         disabled=False,
     )
     
@@ -1313,17 +1375,21 @@ Interactive demo with Gradio
 .. code:: ipython3
 
     import gradio as gr
-    from urllib.request import urlretrieve
     from pathlib import Path
     import numpy as np
     
     default_url = "https://user-images.githubusercontent.com/29454499/223343459-4ac944f0-502e-4acf-9813-8e9f0abc8a16.jpg"
     path = Path("data/example.jpg")
     path.parent.mkdir(parents=True, exist_ok=True)
-    urlretrieve(default_url, path)
+    
+    r = requests.get(default_url)
+    
+    with path.open("wb") as f:
+        f.write(r.content)
     
     pipeline = int8_pipe if pipe_precision.value == "INT8" else ov_pipe
     
+    
     def generate(img, text, seed, num_steps, _=gr.Progress(track_tqdm=True)):
         if img is None:
             raise gr.Error("Please upload an image or choose one from the examples list")
diff --git a/docs/notebooks/231-instruct-pix2pix-image-editing-with-output_files/231-instruct-pix2pix-image-editing-with-output_24_0.png b/docs/notebooks/instruct-pix2pix-image-editing-with-output_files/instruct-pix2pix-image-editing-with-output_24_0.png
similarity index 100%
rename from docs/notebooks/231-instruct-pix2pix-image-editing-with-output_files/231-instruct-pix2pix-image-editing-with-output_24_0.png
rename to docs/notebooks/instruct-pix2pix-image-editing-with-output_files/instruct-pix2pix-image-editing-with-output_24_0.png
diff --git a/docs/notebooks/231-instruct-pix2pix-image-editing-with-output_files/231-instruct-pix2pix-image-editing-with-output_36_2.png b/docs/notebooks/instruct-pix2pix-image-editing-with-output_files/instruct-pix2pix-image-editing-with-output_36_2.png
similarity index 100%
rename from docs/notebooks/231-instruct-pix2pix-image-editing-with-output_files/231-instruct-pix2pix-image-editing-with-output_36_2.png
rename to docs/notebooks/instruct-pix2pix-image-editing-with-output_files/instruct-pix2pix-image-editing-with-output_36_2.png
diff --git a/docs/notebooks/219-knowledge-graphs-conve-with-output.rst b/docs/notebooks/knowledge-graphs-conve-with-output.rst
similarity index 88%
rename from docs/notebooks/219-knowledge-graphs-conve-with-output.rst
rename to docs/notebooks/knowledge-graphs-conve-with-output.rst
index 0fc021ffa84..76b9841d31e 100644
--- a/docs/notebooks/219-knowledge-graphs-conve-with-output.rst
+++ b/docs/notebooks/knowledge-graphs-conve-with-output.rst
@@ -51,14 +51,9 @@ Table of contents:
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" torch scikit-learn tqdm --extra-index-url https://download.pytorch.org/whl/cpu
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -130,11 +125,13 @@ Import the packages needed for successful execution
     import openvino as ov
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
-    );
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 Settings: Including path to the serialized model files and input data files
@@ -145,7 +142,7 @@ Settings: Including path to the serialized model files and input data files
 .. code:: ipython3
 
     # Path to the pretrained model checkpoint
-    modelpath = Path('models/conve.pt')
+    modelpath = Path("models/conve.pt")
     
     # Entity and relation embedding dimensions
     EMB_DIM = 300
@@ -169,19 +166,19 @@ Settings: Including path to the serialized model files and input data files
     # Download the file containing the entities and entity IDs
     entdatapath = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/text/countries_S1/kg_training_entids.txt",
-        directory=data_folder
+        directory=data_folder,
     )
     
     # Download the file containing the relations and relation IDs
     reldatapath = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/text/countries_S1/kg_training_relids.txt",
-        directory=data_folder
+        directory=data_folder,
     )
     
     # Download the test data file
     testdatapath = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/json/countries_S1/e1rel_to_e2_ranking_test.json",
-        directory=data_folder
+        directory=data_folder,
     )
 
 
@@ -210,7 +207,7 @@ Download Model Checkpoint
 
 .. code:: ipython3
 
-    model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/knowledge-graph-embeddings/conve.pt'
+    model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/knowledge-graph-embeddings/conve.pt"
     
     download_file(model_url, filename=modelpath.name, directory=modelpath.parent)
 
@@ -225,7 +222,7 @@ Download Model Checkpoint
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/219-knowledge-graphs-conve/models/conve.pt')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/knowledge-graphs-conve/models/conve.pt')
 
 
 
@@ -254,17 +251,17 @@ Defining the ConvE model class
             self.bn0 = torch.nn.BatchNorm2d(1)
             self.bn1 = torch.nn.BatchNorm2d(32)
             self.ln0 = torch.nn.LayerNorm(emb_dim)
-            self.register_parameter('b', Parameter(torch.zeros(num_entities)))
+            self.register_parameter("b", Parameter(torch.zeros(num_entities)))
             self.fc = torch.nn.Linear(16128, emb_dim)
     
         def init(self):
-            """ Initializes the model """
+            """Initializes the model"""
             # Xavier initialization
             xavier_normal_(self.emb_e.weight.data)
             xavier_normal_(self.emb_rel.weight.data)
     
         def forward(self, e1, rel):
-            """ Forward pass on the model.
+            """Forward pass on the model.
             :param e1: source entity
             :param rel: relation between the source and target entities
             Returns the model predictions for the target entities
@@ -296,7 +293,7 @@ Defining the dataloader
 
 .. code:: ipython3
 
-    class DataLoader():
+    class DataLoader:
         def __init__(self):
             super(DataLoader, self).__init__()
     
@@ -308,27 +305,27 @@ Defining the dataloader
             self.test_triples_list = self.convert_triples(data_path=self.test_file)
     
         def load_data(self, data_path):
-            """ Creates a dictionary of data items with corresponding ids """
+            """Creates a dictionary of data items with corresponding ids"""
             item_dict, ids_dict = {}, {}
             fp = open(data_path, "r")
             lines = fp.readlines()
             for line in lines:
-                name, id = line.strip().split('\t')
+                name, id = line.strip().split("\t")
                 item_dict[name] = int(id)
                 ids_dict[int(id)] = name
             fp.close()
             return item_dict, ids_dict
     
         def convert_triples(self, data_path):
-            """ Creates a triple of source entity, relation and target entities"""
+            """Creates a triple of source entity, relation and target entities"""
             triples_list = []
             dp = open(data_path, "r")
             lines = dp.readlines()
             for line in lines:
                 item_dict = json.loads(line.strip())
-                h = item_dict['e1']
-                r = item_dict['rel']
-                t = item_dict['e2_multi1'].split('\t')
+                h = item_dict["e1"]
+                r = item_dict["rel"]
+                t = item_dict["e2_multi1"].split("\t")
                 hrt_list = []
                 hrt_list.append(self.entity_ids[h])
                 hrt_list.append(self.rel_ids[r])
@@ -381,13 +378,13 @@ typical to use metrics such as Mean Reciprocal Rank, Hits@10 etc.
         pt_acc += accuracy_score(gt, pred)
     
     avg_pt_time = np.mean(pt_inf_times) * 1000
-    print(f'Average time taken for inference: {avg_pt_time} ms')
-    print(f'Mean accuracy of the model on the test dataset: {pt_acc/num_test_samples}')
+    print(f"Average time taken for inference: {avg_pt_time} ms")
+    print(f"Mean accuracy of the model on the test dataset: {pt_acc/num_test_samples}")
 
 
 .. parsed-literal::
 
-    Average time taken for inference: 0.6546974182128906 ms
+    Average time taken for inference: 0.6107787291208903 ms
     Mean accuracy of the model on the test dataset: 0.875
 
 
@@ -407,8 +404,8 @@ knowledge graph.
 
     entitynames_dict = data.ids2entities
     
-    ent = 'san_marino'
-    rel = 'locatedin'
+    ent = "san_marino"
+    rel = "locatedin"
     
     h_idx = data.entity_ids[ent]
     r_idx = data.rel_ids[rel]
@@ -418,7 +415,7 @@ knowledge graph.
     
     for j, id in enumerate(pred[0].cpu().detach().numpy()):
         pred_entity = entitynames_dict[id]
-        print(f'Source Entity: {ent}, Relation: {rel}, Target entity prediction: {pred_entity}')
+        print(f"Source Entity: {ent}, Relation: {rel}, Target entity prediction: {pred_entity}")
 
 
 .. parsed-literal::
@@ -441,7 +438,7 @@ using ``ov.save_model``.
 
 .. code:: ipython3
 
-    print('Converting the trained conve model to IR format')
+    print("Converting the trained conve model to IR format")
     
     ov_model = ov.convert_model(model, example_input=(torch.tensor(1), torch.tensor(1)))
     ov.save_model(ov_model, ir_path)
@@ -485,8 +482,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
     
@@ -513,7 +510,10 @@ select device from dropdown list for running inference using OpenVINO
     for i in range(num_test_samples):
         test_sample = triples_list[i]
         source, relation, target = test_sample
-        model_inputs = {input_layer_source: np.int64(source), input_layer_relation: np.int64(relation)}
+        model_inputs = {
+            input_layer_source: np.int64(source),
+            input_layer_relation: np.int64(relation),
+        }
         start_time = time.time()
         result = compiled_model(model_inputs)[output_layer]
         end_time = time.time()
@@ -525,13 +525,13 @@ select device from dropdown list for running inference using OpenVINO
         ov_acc += accuracy_score(gt, pred)
     
     avg_ov_time = np.mean(ov_inf_times) * 1000
-    print(f'Average time taken for inference: {avg_ov_time} ms')
-    print(f'Mean accuracy of the model on the test dataset: {ov_acc/num_test_samples}')
+    print(f"Average time taken for inference: {avg_ov_time} ms")
+    print(f"Mean accuracy of the model on the test dataset: {ov_acc/num_test_samples}")
 
 
 .. parsed-literal::
 
-    Average time taken for inference: 0.6280839443206787 ms
+    Average time taken for inference: 0.6598830223083496 ms
     Mean accuracy of the model on the test dataset: 0.10416666666666667
 
 
@@ -545,12 +545,12 @@ Determine the platform specific speedup obtained through OpenVINO graph optimiza
     # prevent division by zero
     delimiter = max(avg_ov_time, np.finfo(float).eps)
     
-    print(f'Speedup with OpenVINO optimizations: {round(float(avg_pt_time)/float(delimiter),2)} X')
+    print(f"Speedup with OpenVINO optimizations: {round(float(avg_pt_time)/float(delimiter),2)} X")
 
 
 .. parsed-literal::
 
-    Speedup with OpenVINO optimizations: 1.04 X
+    Speedup with OpenVINO optimizations: 0.93 X
 
 
 Benchmark the converted OpenVINO model using benchmark app
@@ -573,8 +573,8 @@ inference can also be obtained by looking at the benchmark app results.
 
 .. code:: ipython3
 
-    print('Benchmark OpenVINO model using the benchmark app')
-    ! benchmark_app -m $ir_path -d $device.value -api async -t 10 -shape "input.1[1],input.2[1]" 
+    print("Benchmark OpenVINO model using the benchmark app")
+    ! benchmark_app -m $ir_path -d $device.value -api async -t 10 -shape "input.1[1],input.2[1]"
 
 
 .. parsed-literal::
@@ -603,7 +603,7 @@ inference can also be obtained by looking at the benchmark app results.
 
 .. parsed-literal::
 
-    [ INFO ] Read model took 13.40 ms
+    [ INFO ] Read model took 13.96 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     e1 (node: e1) : i64 / [...] / []
@@ -623,7 +623,7 @@ inference can also be obtained by looking at the benchmark app results.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 60.07 ms
+    [ INFO ] Compile model took 82.08 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -652,21 +652,21 @@ inference can also be obtained by looking at the benchmark app results.
     [ INFO ] Fill input 'rel' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 10000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 1.33 ms
+    [ INFO ] First inference took 1.43 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            101124 iterations
-    [ INFO ] Duration:         10001.56 ms
+    [ INFO ] Count:            101412 iterations
+    [ INFO ] Duration:         10001.13 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        1.02 ms
-    [ INFO ]    Average:       1.03 ms
-    [ INFO ]    Min:           0.68 ms
-    [ INFO ]    Max:           8.62 ms
-    [ INFO ] Throughput:   10110.83 FPS
+    [ INFO ]    Median:        1.01 ms
+    [ INFO ]    Average:       1.02 ms
+    [ INFO ]    Min:           0.60 ms
+    [ INFO ]    Max:           8.57 ms
+    [ INFO ] Throughput:   10140.06 FPS
 
 
 Conclusions
diff --git a/docs/notebooks/281-kosmos2-multimodal-large-language-model-with-output.rst b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output.rst
similarity index 82%
rename from docs/notebooks/281-kosmos2-multimodal-large-language-model-with-output.rst
rename to docs/notebooks/kosmos2-multimodal-large-language-model-with-output.rst
index e60e39d5c04..8a39325ae71 100644
--- a/docs/notebooks/281-kosmos2-multimodal-large-language-model-with-output.rst
+++ b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output.rst
@@ -60,20 +60,15 @@ Install requirements
 
     %pip install --upgrade pip
     %pip install -q "openvino>=2024.0.0"
-    %pip install -q "transformers>=4.35" Pillow gradio opencv-python
+    %pip install -q "transformers>=4.35" Pillow "gradio>=4.19" opencv-python
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu torch torchvision
 
 
 .. parsed-literal::
 
-    Requirement already satisfied: pip in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (24.0)
+    Requirement already satisfied: pip in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (24.0)
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -81,7 +76,12 @@ Install requirements
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
     
 
 .. parsed-literal::
@@ -89,21 +89,6 @@ Install requirements
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -154,29 +139,29 @@ example `__
     
     # Specify `cleanup_and_extract=False` in order to see the raw model generation.
     processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
-    print(f'Raw model generation: {processed_text}')
+    print(f"Raw model generation: {processed_text}")
     # ` An image of a snowman warming himself by a fire.`
     
     # By default, the generated  text is cleanup and the entities are extracted.
     processed_text, entities = processor.post_process_generation(generated_text)
     
-    print(f'Cleaned up generated text: {processed_text=}')
+    print(f"Cleaned up generated text: {processed_text=}")
     # `An image of a snowman warming himself by a fire.`
     
-    print(f'Extracted entities: {entities}')
+    print(f"Extracted entities: {entities}")
     # `[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]`
 
 
 .. parsed-literal::
 
-    2024-03-13 00:30:30.974384: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:30:31.009208: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 00:01:02.253505: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:01:02.287838: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 00:30:31.601630: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:01:02.846922: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -237,8 +222,13 @@ draw their bounding bboxes on the image:
         text_spaces = 3
     
         for entity_name, (start, end), bboxes in entities:
-            for (x1_norm, y1_norm, x2_norm, y2_norm) in bboxes:
-                orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm * image_w), int(y1_norm * image_h), int(x2_norm * image_w), int(y2_norm * image_h)
+            for x1_norm, y1_norm, x2_norm, y2_norm in bboxes:
+                orig_x1, orig_y1, orig_x2, orig_y2 = (
+                    int(x1_norm * image_w),
+                    int(y1_norm * image_h),
+                    int(x2_norm * image_w),
+                    int(y2_norm * image_h),
+                )
                 # draw bbox
                 # random color
                 color = tuple(np.random.randint(0, 255, size=3).tolist())
@@ -255,16 +245,24 @@ draw their bounding bboxes on the image:
     
                 # add text background
                 (text_width, text_height), _ = cv2.getTextSize(f"  {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
-                text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1
+                text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = (
+                    x1,
+                    y1 - (text_height + text_offset_original + 2 * text_spaces),
+                    x1 + text_width,
+                    y1,
+                )
     
                 for prev_bbox in previous_bboxes:
                     while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox):
-                        text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces)
-                        text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces)
-                        y1 += (text_height + text_offset_original + 2 * text_spaces)
+                        text_bg_y1 += text_height + text_offset_original + 2 * text_spaces
+                        text_bg_y2 += text_height + text_offset_original + 2 * text_spaces
+                        y1 += text_height + text_offset_original + 2 * text_spaces
     
                         if text_bg_y2 >= image_h:
-                            text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces))
+                            text_bg_y1 = max(
+                                0,
+                                image_h - (text_height + text_offset_original + 2 * text_spaces),
+                            )
                             text_bg_y2 = image_h
                             y1 = image_h
                             break
@@ -282,7 +280,14 @@ draw their bounding bboxes on the image:
                             new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(np.uint8)
     
                 cv2.putText(
-                    new_image, f"  {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces), cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
+                    new_image,
+                    f"  {entity_name}",
+                    (x1, y1 - text_offset_original - 1 * text_spaces),
+                    cv2.FONT_HERSHEY_COMPLEX,
+                    text_size,
+                    (0, 0, 0),
+                    text_line,
+                    cv2.LINE_AA,
                 )
                 # previous_locations.append((x1, y1))
                 previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2))
@@ -299,7 +304,7 @@ draw their bounding bboxes on the image:
 
 
 
-.. image:: 281-kosmos2-multimodal-large-language-model-with-output_files/281-kosmos2-multimodal-large-language-model-with-output_8_0.png
+.. image:: kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.png
 
 
 Convert models to OpenVINO Intermediate representation (IR) format
@@ -357,7 +362,7 @@ file.
             with torch.no_grad():
                 converted_model = ov.convert_model(model, example_input=example_input)
             ov.save_model(converted_model, xml_path, compress_to_fp16=False)
-            
+    
             cleanup_torchscript_cache()
 
 Convert the vision model
@@ -384,15 +389,15 @@ Vision model accept ``pixel_values`` and returns ``image_embeds``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:471: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:471: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:511: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:511: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
 
 
@@ -420,7 +425,7 @@ Convert Image To Text Projection model
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:165: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:489.)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:165: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:489.)
       if a.grad is not None:
 
 
@@ -439,7 +444,9 @@ generated text by ``AutoProcessor``.
 
     from typing import Optional, List
     
-    from transformers.models.kosmos2.modeling_kosmos2 import create_position_ids_from_input_ids
+    from transformers.models.kosmos2.modeling_kosmos2 import (
+        create_position_ids_from_input_ids,
+    )
     
     
     def get_projecton_image_embeds(pixel_values):
@@ -466,7 +473,13 @@ generated text by ``AutoProcessor``.
         return flatten_inputs
     
     
-    def postprocess_converted_model(ov_model, example_input=None, input_names=None, output_names=None, dynamic_shapes=None):
+    def postprocess_converted_model(
+        ov_model,
+        example_input=None,
+        input_names=None,
+        output_names=None,
+        dynamic_shapes=None,
+    ):
         """
         Helper function for appling postprocessing on converted model with updating input names, shapes and output names
         acording to requested specification
@@ -476,7 +489,7 @@ generated text by ``AutoProcessor``.
         if input_names:
             for inp_name, m_input, input_data in zip(input_names, ov_model.inputs, flatten_example_inputs):
                 m_input.get_tensor().set_names({inp_name})
-        
+    
         if output_names:
             for out, out_name in zip(ov_model.outputs, output_names):
                 out.get_tensor().set_names({out_name})
@@ -489,15 +502,19 @@ generated text by ``AutoProcessor``.
         model.text_model.config.torchscript = True
         image_embeds = get_projecton_image_embeds(inputs["pixel_values"])
         conv_inputs = {
-            'input_ids': inputs["input_ids"],
-            'attention_mask': inputs["attention_mask"],
-            'image_embeds': image_embeds,
-            'image_embeds_position_mask': inputs["image_embeds_position_mask"],
+            "input_ids": inputs["input_ids"],
+            "attention_mask": inputs["attention_mask"],
+            "image_embeds": image_embeds,
+            "image_embeds_position_mask": inputs["image_embeds_position_mask"],
         }
         outs = model.text_model.model(**conv_inputs)
-        inputs_ = ["input_ids", 'attention_mask']
+        inputs_ = ["input_ids", "attention_mask"]
         outputs = ["logits"]
-        dynamic_shapes = {"input_ids": {1: "seq_len"}, "attention_mask": {1: "seq_len"}, "position_ids": {0: "seq_len"}}
+        dynamic_shapes = {
+            "input_ids": {1: "seq_len"},
+            "attention_mask": {1: "seq_len"},
+            "position_ids": {0: "seq_len"},
+        }
         for idx in range(len(outs[1])):
             inputs_.extend([f"past_key_values.{idx}.key", f"past_key_values.{idx}.value"])
             dynamic_shapes[inputs_[-1]] = {2: "past_sequence + sequence"}
@@ -521,39 +538,39 @@ generated text by ``AutoProcessor``.
             example_input_second_stage = {
                 "input_ids": inputs["input_ids"][:, -1:],
                 "attention_mask": inputs["input_ids"].new_ones(1, inputs["input_ids"].shape[1] + 1),
-                'position_ids': position_ids,
+                "position_ids": position_ids,
                 "past_key_values": outs[1],
             }
-            
+    
             ov_model = ov.convert_model(model.text_model.model, example_input=example_input_second_stage)
             ov_model = postprocess_converted_model(
-                ov_model, 
-                example_input=example_input_second_stage.values(), 
-                input_names=inputs_, 
-                output_names=outputs, 
-                dynamic_shapes=dynamic_shapes
+                ov_model,
+                example_input=example_input_second_stage.values(),
+                input_names=inputs_,
+                output_names=outputs,
+                dynamic_shapes=dynamic_shapes,
             )
             ov.save_model(ov_model, SECOND_STAGE_MODEL_PATH)
             del ov_model
             cleanup_torchscript_cache()
     
     
-    convert_text_model()     
+    convert_text_model()
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:810: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:810: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if max_pos > self.weights.size(0):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:1119: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:1119: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input_shape[-1] > 1:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:926: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:926: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attention_mask.size() != (batch_size, 1, seq_length, src_len):
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:1212: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:1212: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if past_key_values_length > 0:
 
 
@@ -573,8 +590,8 @@ from the dropdown list:
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -597,7 +614,7 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
 
     class WraperInternalVisionModel:
         post_layernorm = model.vision_model.model.post_layernorm
-        
+    
     
     class VisionModelWrapper(torch.nn.Module):
         def __init__(self, model_ir_path):
@@ -609,7 +626,7 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
             vision_model_output = self.vision_model(pixel_values)[0]
     
             return [torch.from_numpy(vision_model_output)]
-        
+    
     
     class ImageToTextProjectionModelWrapper(torch.nn.Module):
         def __init__(self, model_ir_path):
@@ -625,26 +642,19 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
 .. code:: ipython3
 
     from transformers.generation import GenerationConfig, GenerationMixin
-    from transformers.models.kosmos2.modeling_kosmos2 import Kosmos2ForConditionalGenerationModelOutput
+    from transformers.models.kosmos2.modeling_kosmos2 import (
+        Kosmos2ForConditionalGenerationModelOutput,
+    )
     
     
     class KosmosForCausalLMWrapper(GenerationMixin):
         def __init__(self, first_stage_model_path, second_stage_model_path, device):
-            
             self.model_stage_1 = core.compile_model(first_stage_model_path, device.value)
             self.model_stage_2 = core.read_model(second_stage_model_path)
-            self.input_names = {
-                key.get_any_name(): idx for idx, key in enumerate(self.model_stage_2.inputs)
-            }
-            self.output_names = {
-                key.get_any_name(): idx for idx, key in enumerate(self.model_stage_2.outputs)
-            }
-            self.key_value_input_names = [
-                key for key in self.input_names if "key_values" in key
-            ]
-            self.key_value_output_names = [
-                key for key in self.output_names if "present" in key
-            ]
+            self.input_names = {key.get_any_name(): idx for idx, key in enumerate(self.model_stage_2.inputs)}
+            self.output_names = {key.get_any_name(): idx for idx, key in enumerate(self.model_stage_2.outputs)}
+            self.key_value_input_names = [key for key in self.input_names if "key_values" in key]
+            self.key_value_output_names = [key for key in self.output_names if "present" in key]
             self.model_stage_2 = core.compile_model(self.model_stage_2, device.value)
     
             self.request = self.model_stage_2.create_infer_request()
@@ -653,7 +663,11 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
             self.main_input_name = "input_ids"
             self.device = torch.device("cpu")
             self.num_pkv = 2
-            self.lm_head = nn.Linear(in_features=model.text_model.config.embed_dim, out_features=model.text_model.config.vocab_size, bias=False)
+            self.lm_head = nn.Linear(
+                in_features=model.text_model.config.embed_dim,
+                out_features=model.text_model.config.vocab_size,
+                bias=False,
+            )
     
         def get_input_embeddings(self) -> nn.Module:
             return self.model.embed_tokens
@@ -682,7 +696,12 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
             **kwargs,
         ):
             return self.forward(
-                input_ids, attention_mask, image_embeds, image_embeds_position_mask, position_ids, past_key_values
+                input_ids,
+                attention_mask,
+                image_embeds,
+                image_embeds_position_mask,
+                position_ids,
+                past_key_values,
             )
     
         def forward(
@@ -693,36 +712,30 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
             image_embeds_position_mask: Optional[torch.Tensor] = None,
             position_ids=None,
             past_key_values: Optional[List[torch.FloatTensor]] = None,
-            
-            **kwargs
+            **kwargs,
         ):
             if past_key_values is None:
-                
                 outs = self.model_stage_1(
                     {
-                        'input_ids': input_ids,
-                        'attention_mask': attention_mask,
-                        'image_embeds': image_embeds,
-                        'image_embeds_position_mask': image_embeds_position_mask,
+                        "input_ids": input_ids,
+                        "attention_mask": attention_mask,
+                        "image_embeds": image_embeds,
+                        "image_embeds_position_mask": image_embeds_position_mask,
                     }
-                )            
+                )
                 lm_logits = model.text_model.lm_head(torch.from_numpy(outs[0]))
     
                 pkv = list(outs.values())[1:]
                 pkv = tuple(pkv[i : i + 2] for i in range(0, len(pkv), 2))
     
                 return Kosmos2ForConditionalGenerationModelOutput(logits=lm_logits, past_key_values=pkv)
-            
+    
             if past_key_values is not None:
-                past_key_values = tuple(
-                    past_key_value
-                    for pkv_per_layer in past_key_values
-                    for past_key_value in pkv_per_layer
-                )
+                past_key_values = tuple(past_key_value for pkv_per_layer in past_key_values for past_key_value in pkv_per_layer)
                 inputs_ = {
                     "input_ids": input_ids[:, -1].unsqueeze(-1),
                     "attention_mask": attention_mask,
-                    'position_ids': position_ids
+                    "position_ids": position_ids,
                 }
                 inputs_.update(dict(zip(self.key_value_input_names, past_key_values)))
     
@@ -734,18 +747,12 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
             logits = model.text_model.lm_head(logits)
     
             # Tuple of length equal to : number of layer * number of past_key_value per decoder layer (2 corresponds to the self-attention layer)
-            past_key_values = tuple(
-                self.request.get_tensor(key).data for key in self.key_value_output_names
-            )
+            past_key_values = tuple(self.request.get_tensor(key).data for key in self.key_value_output_names)
             # Tuple of tuple of length `n_layers`, with each tuple of length equal to 2 (k/v of self-attention)
     
-            past_key_values = tuple(
-                past_key_values[i : i + self.num_pkv]
-                for i in range(0, len(past_key_values), self.num_pkv)
-            )
-            
-            return Kosmos2ForConditionalGenerationModelOutput(logits=logits, past_key_values=past_key_values)
+            past_key_values = tuple(past_key_values[i : i + self.num_pkv] for i in range(0, len(past_key_values), self.num_pkv))
     
+            return Kosmos2ForConditionalGenerationModelOutput(logits=logits, past_key_values=past_key_values)
     
         def prepare_inputs_for_generation(
             self,
@@ -781,7 +788,11 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
                 image_embeds_position_mask = torch.cat(
                     (
                         image_embeds_position_mask,
-                        torch.zeros(size=(batch_size, seq_len - mask_len), dtype=torch.bool, device=input_ids.device),
+                        torch.zeros(
+                            size=(batch_size, seq_len - mask_len),
+                            dtype=torch.bool,
+                            device=input_ids.device,
+                        ),
                     ),
                     dim=1,
                 )
@@ -790,25 +801,29 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
                 "input_ids": input_ids,
                 "image_embeds": image_embeds,
                 "image_embeds_position_mask": image_embeds_position_mask,
-                'position_ids': position_ids,
+                "position_ids": position_ids,
                 "past_key_values": past_key_values,
                 "attention_mask": attention_mask,
             }
-        
+    
         @staticmethod
         # Copied from transformers.models.umt5.modeling_umt5.UMT5ForConditionalGeneration._reorder_cache
         def _reorder_cache(past_key_values, beam_idx):
             reordered_past = ()
             for layer_past in past_key_values:
-                reordered_past += (
-                    tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
-                )
+                reordered_past += (tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),)
             return reordered_past
     
     
     class Kosmos2ForConditionalGenerationWrapper:
-        
-        def __init__(self, vision_model_path, image_to_text_projection_model_path, first_stage_model_path, second_stage_model_path, device):
+        def __init__(
+            self,
+            vision_model_path,
+            image_to_text_projection_model_path,
+            first_stage_model_path,
+            second_stage_model_path,
+            device,
+        ):
             self.vision_model = VisionModelWrapper(vision_model_path)
             self.image_to_text_projection = ImageToTextProjectionModelWrapper(image_to_text_projection_model_path)
             self.text_model = KosmosForCausalLMWrapper(first_stage_model_path, second_stage_model_path, device)
@@ -840,7 +855,13 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
 
 .. code:: ipython3
 
-    ov_model = Kosmos2ForConditionalGenerationWrapper(VISION_MODEL_IR_PATH, IMAGE_TO_TEXT_PROJECTION_MODEL_IR_PATH, FIRST_STAGE_MODEL_PATH, SECOND_STAGE_MODEL_PATH, device)
+    ov_model = Kosmos2ForConditionalGenerationWrapper(
+        VISION_MODEL_IR_PATH,
+        IMAGE_TO_TEXT_PROJECTION_MODEL_IR_PATH,
+        FIRST_STAGE_MODEL_PATH,
+        SECOND_STAGE_MODEL_PATH,
+        device,
+    )
 
 Inference
 ---------
@@ -862,16 +883,16 @@ Inference
     
     # Specify `cleanup_and_extract=False` in order to see the raw model generation.
     processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
-    print(f'Raw model generation: {processed_text}')
+    print(f"Raw model generation: {processed_text}")
     # ` An image of a snowman warming himself by a fire.`
     
     # By default, the generated  text is cleanup and the entities are extracted.
     processed_text, entities = processor.post_process_generation(generated_text)
     
-    print(f'Cleaned up generated text: {processed_text=}')
+    print(f"Cleaned up generated text: {processed_text=}")
     # `An image of a snowman warming himself by a fire.`
     
-    print(f'Extracted entities: {entities}')
+    print(f"Extracted entities: {entities}")
     # `[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]`
 
 
@@ -889,7 +910,7 @@ Inference
 
 
 
-.. image:: 281-kosmos2-multimodal-large-language-model-with-output_files/281-kosmos2-multimodal-large-language-model-with-output_29_0.png
+.. image:: kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.png
 
 
 Interactive inference
@@ -905,7 +926,7 @@ Interactive inference
     images = {
         "snowman.png": "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.png",
         "two_dogs.jpg": "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/two_dogs.jpg",
-        "six_planes.png": "https://ydshieh-kosmos-2.hf.space/file=/home/user/app/images/six_planes.png"
+        "six_planes.png": "https://ydshieh-kosmos-2.hf.space/file=/home/user/app/images/six_planes.png",
     }
     for image_name, url in images.items():
         image = Image.open(requests.get(url, stream=True).raw)
@@ -928,7 +949,7 @@ Interactive inference
         processed_text, entities = processor.post_process_generation(generated_text)
     
         new_image = draw_entity_boxes_on_image(Image.fromarray(image), entities)
-        
+    
         return new_image, processed_text
     
     
@@ -937,13 +958,13 @@ Interactive inference
         [
             gr.Image(label="Input image"),
             gr.Textbox(label="Prompt"),
-            gr.Checkbox(label="Show bounding boxes", value=True)
+            gr.Checkbox(label="Show bounding boxes", value=True),
         ],
         ["image", "text"],
         examples=[
             ["snowman.png", "An image of"],
             ["two_dogs.jpg", "Describe this image in detail:"],
-            ["six_planes.png", "What is going on?"]
+            ["six_planes.png", "What is going on?"],
         ],
         allow_flagging="never",
     )
diff --git a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.jpg b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.jpg
new file mode 100644
index 00000000000..b400ee72fba
--- /dev/null
+++ b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c0a83eb915195973a7315333a239ea60abf597988effc7fcca7dfea302afb625
+size 117636
diff --git a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.png b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.png
new file mode 100644
index 00000000000..d0b339f53bf
--- /dev/null
+++ b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d18af510a639c26185fec562aed50889b07e16f73465e7cd071b97634995d78
+size 1151036
diff --git a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.jpg b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.jpg
new file mode 100644
index 00000000000..4208969cbb7
--- /dev/null
+++ b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9aca69f7a8aba308eed0932bb52ca703329c167786b855c33392c196775e3c7
+size 116335
diff --git a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.png b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.png
new file mode 100644
index 00000000000..4ee528c804a
--- /dev/null
+++ b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a72aac750b87ad7318f3caad198955aea63148e2bfa01db73fc7ccb9585304f8
+size 1151306
diff --git a/docs/notebooks/105-language-quantize-bert-with-output.rst b/docs/notebooks/language-quantize-bert-with-output.rst
similarity index 84%
rename from docs/notebooks/105-language-quantize-bert-with-output.rst
rename to docs/notebooks/language-quantize-bert-with-output.rst
index d6f312c4d99..555d4dc3a07 100644
--- a/docs/notebooks/105-language-quantize-bert-with-output.rst
+++ b/docs/notebooks/language-quantize-bert-with-output.rst
@@ -42,8 +42,8 @@ Table of contents:
 
 .. code:: ipython3
 
-    %pip install -q "nncf>=2.5.0" 
-    %pip install -q transformers datasets evaluate --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "nncf>=2.5.0"
+    %pip install -q torch transformers "torch>=2.1" datasets evaluate tqdm  --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "openvino>=2023.1.0"
 
 
@@ -86,24 +86,26 @@ Imports
     from transformers import BertForSequenceClassification, BertTokenizer
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 
 .. parsed-literal::
 
-    2024-03-12 22:22:23.157910: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:22:23.191787: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 00:02:10.199327: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:02:10.234239: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 22:22:23.830567: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:02:10.832965: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -173,7 +175,11 @@ PyTorch model formats are supported:
     torch_model = BertForSequenceClassification.from_pretrained(PRETRAINED_MODEL_DIR)
     torch_model.eval
     
-    input_info = [("input_ids", input_shape, np.int64),("attention_mask", input_shape, np.int64),("token_type_ids", input_shape, np.int64)]
+    input_info = [
+        ("input_ids", input_shape, np.int64),
+        ("attention_mask", input_shape, np.int64),
+        ("token_type_ids", input_shape, np.int64),
+    ]
     default_input = torch.ones(1, MAX_SEQ_LENGTH, dtype=torch.int64)
     inputs = {
         "input_ids": default_input,
@@ -189,12 +195,6 @@ PyTorch model formats are supported:
         model = core.read_model(ir_model_xml)
 
 
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
-      return self.fget.__get__(instance, owner)()
-
-
 .. parsed-literal::
 
     WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
@@ -207,17 +207,12 @@ PyTorch model formats are supported:
 
 .. parsed-literal::
 
-    WARNING:nncf:NNCF provides best results with torch==2.1.2, while current torch version is 2.1.0+cpu. If you encounter issues, consider switching to torch==2.1.2
+    WARNING:nncf:NNCF provides best results with torch==2.1.2, while current torch version is 2.2.2+cpu. If you encounter issues, consider switching to torch==2.1.2
 
 
 .. parsed-literal::
 
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
@@ -234,18 +229,20 @@ tokenizer from HuggingFace.
 .. code:: ipython3
 
     def create_data_source():
-        raw_dataset = datasets.load_dataset('glue', 'mrpc', split='validation')
+        raw_dataset = datasets.load_dataset("glue", "mrpc", split="validation")
         tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_DIR)
     
         def _preprocess_fn(examples):
-            texts = (examples['sentence1'], examples['sentence2'])
-            result = tokenizer(*texts, padding='max_length', max_length=MAX_SEQ_LENGTH, truncation=True)
-            result['labels'] = examples['label']
+            texts = (examples["sentence1"], examples["sentence2"])
+            result = tokenizer(*texts, padding="max_length", max_length=MAX_SEQ_LENGTH, truncation=True)
+            result["labels"] = examples["label"]
             return result
+    
         processed_dataset = raw_dataset.map(_preprocess_fn, batched=True, batch_size=1)
     
         return processed_dataset
     
+    
     data_source = create_data_source()
 
 Optimize model using NNCF Post-training Quantization API
@@ -268,21 +265,20 @@ The optimization process contains the following steps:
 
     INPUT_NAMES = [key for key in inputs.keys()]
     
+    
     def transform_fn(data_item):
         """
         Extract the model's input from the data item.
         The data item here is the data item that is returned from the data source per iteration.
         This function should be passed when the data item cannot be used as model's input.
         """
-        inputs = {
-            name: np.asarray([data_item[name]], dtype=np.int64) for name in INPUT_NAMES
-        }
+        inputs = {name: np.asarray([data_item[name]], dtype=np.int64) for name in INPUT_NAMES}
         return inputs
     
+    
     calibration_dataset = nncf.Dataset(data_source, transform_fn)
     # Quantize the model. By specifying model_type, we specify additional transformer patterns in the model.
-    quantized_model = nncf.quantize(model, calibration_dataset,
-                                    model_type=ModelType.TRANSFORMER)
+    quantized_model = nncf.quantize(model, calibration_dataset, model_type=ModelType.TRANSFORMER)
 
 
 
@@ -409,8 +405,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -440,7 +436,7 @@ changing ``sample_idx`` to another value (from 0 to 407).
 
     sample_idx = 5
     sample = data_source[sample_idx]
-    inputs = {k: torch.unsqueeze(torch.tensor(sample[k]), 0) for k in ['input_ids', 'token_type_ids', 'attention_mask']}
+    inputs = {k: torch.unsqueeze(torch.tensor(sample[k]), 0) for k in ["input_ids", "token_type_ids", "attention_mask"]}
     
     result = compiled_quantized_model(inputs)[output_layer]
     result = np.argmax(result)
@@ -466,33 +462,31 @@ Compare F1-score of FP32 and INT8 models
 
     def validate(model: ov.Model, dataset: Iterable[Any]) -> float:
         """
-        Evaluate the model on GLUE dataset. 
+        Evaluate the model on GLUE dataset.
         Returns F1 score metric.
         """
         compiled_model = core.compile_model(model, device_name=device.value)
         output_layer = compiled_model.output(0)
     
-        metric = evaluate.load('glue', 'mrpc')
+        metric = evaluate.load("glue", "mrpc")
         for batch in dataset:
-            inputs = [
-                np.expand_dims(np.asarray(batch[key], dtype=np.int64), 0) for key in INPUT_NAMES
-            ]
+            inputs = [np.expand_dims(np.asarray(batch[key], dtype=np.int64), 0) for key in INPUT_NAMES]
             outputs = compiled_model(inputs)[output_layer]
             predictions = outputs[0].argmax(axis=-1)
-            metric.add_batch(predictions=[predictions], references=[batch['labels']])
+            metric.add_batch(predictions=[predictions], references=[batch["labels"]])
         metrics = metric.compute()
-        f1_score = metrics['f1']
+        f1_score = metrics["f1"]
     
         return f1_score
     
     
-    print('Checking the accuracy of the original model:')
+    print("Checking the accuracy of the original model:")
     metric = validate(model, data_source)
-    print(f'F1 score: {metric:.4f}')
+    print(f"F1 score: {metric:.4f}")
     
-    print('Checking the accuracy of the quantized model:')
+    print("Checking the accuracy of the quantized model:")
     metric = validate(quantized_model, data_source)
-    print(f'F1 score: {metric:.4f}')
+    print(f"F1 score: {metric:.4f}")
 
 
 .. parsed-literal::
@@ -530,7 +524,7 @@ Frames Per Second (FPS) for images.
 
     num_samples = 50
     sample = data_source[0]
-    inputs = {k: torch.unsqueeze(torch.tensor(sample[k]), 0) for k in ['input_ids', 'token_type_ids', 'attention_mask']}
+    inputs = {k: torch.unsqueeze(torch.tensor(sample[k]), 0) for k in ["input_ids", "token_type_ids", "attention_mask"]}
     
     with torch.no_grad():
         start = time.perf_counter()
@@ -538,30 +532,21 @@ Frames Per Second (FPS) for images.
             torch_model(torch.vstack(list(inputs.values())))
         end = time.perf_counter()
         time_torch = end - start
-    print(
-        f"PyTorch model on CPU: {time_torch / num_samples:.3f} seconds per sentence, "
-        f"SPS: {num_samples / time_torch:.2f}"
-    )
+    print(f"PyTorch model on CPU: {time_torch / num_samples:.3f} seconds per sentence, " f"SPS: {num_samples / time_torch:.2f}")
     
     start = time.perf_counter()
     for _ in range(num_samples):
         compiled_model(inputs)
     end = time.perf_counter()
     time_ir = end - start
-    print(
-        f"IR FP32 model in OpenVINO Runtime/{device.value}: {time_ir / num_samples:.3f} "
-        f"seconds per sentence, SPS: {num_samples / time_ir:.2f}"
-    )
+    print(f"IR FP32 model in OpenVINO Runtime/{device.value}: {time_ir / num_samples:.3f} " f"seconds per sentence, SPS: {num_samples / time_ir:.2f}")
     
     start = time.perf_counter()
     for _ in range(num_samples):
         compiled_quantized_model(inputs)
     end = time.perf_counter()
     time_ir = end - start
-    print(
-        f"OpenVINO IR INT8 model in OpenVINO Runtime/{device.value}: {time_ir / num_samples:.3f} "
-        f"seconds per sentence, SPS: {num_samples / time_ir:.2f}"
-    )
+    print(f"OpenVINO IR INT8 model in OpenVINO Runtime/{device.value}: {time_ir / num_samples:.3f} " f"seconds per sentence, SPS: {num_samples / time_ir:.2f}")
 
 
 .. parsed-literal::
@@ -571,17 +556,17 @@ Frames Per Second (FPS) for images.
 
 .. parsed-literal::
 
-    PyTorch model on CPU: 0.073 seconds per sentence, SPS: 13.63
+    PyTorch model on CPU: 0.075 seconds per sentence, SPS: 13.34
 
 
 .. parsed-literal::
 
-    IR FP32 model in OpenVINO Runtime/AUTO: 0.020 seconds per sentence, SPS: 48.91
+    IR FP32 model in OpenVINO Runtime/AUTO: 0.020 seconds per sentence, SPS: 48.84
 
 
 .. parsed-literal::
 
-    OpenVINO IR INT8 model in OpenVINO Runtime/AUTO: 0.009 seconds per sentence, SPS: 112.60
+    OpenVINO IR INT8 model in OpenVINO Runtime/AUTO: 0.009 seconds per sentence, SPS: 113.21
 
 
 Finally, measure the inference performance of OpenVINO ``FP32`` and
@@ -622,7 +607,7 @@ in OpenVINO.
     Device with "device" name is not registered in the OpenVINO Runtime
     
     Traceback (most recent call last):
-      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 166, in main
+      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 166, in main
         supported_properties = benchmark.core.get_property(device, properties.supported_properties())
     RuntimeError: Exception from src/inference/src/cpp/core.cpp:216:
     Exception from src/inference/src/dev/core_impl.cpp:556:
@@ -655,7 +640,7 @@ in OpenVINO.
     Device with "device" name is not registered in the OpenVINO Runtime
     
     Traceback (most recent call last):
-      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 166, in main
+      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 166, in main
         supported_properties = benchmark.core.get_property(device, properties.supported_properties())
     RuntimeError: Exception from src/inference/src/cpp/core.cpp:216:
     Exception from src/inference/src/dev/core_impl.cpp:556:
diff --git a/docs/notebooks/109-latency-tricks-with-output.rst b/docs/notebooks/latency-tricks-with-output.rst
similarity index 77%
rename from docs/notebooks/109-latency-tricks-with-output.rst
rename to docs/notebooks/latency-tricks-with-output.rst
index 98ef8a83d89..cc27188b158 100644
--- a/docs/notebooks/109-latency-tricks-with-output.rst
+++ b/docs/notebooks/latency-tricks-with-output.rst
@@ -27,8 +27,7 @@ The quantization and pre-post-processing API are not included here as
 they change the precision (quantization) or processing graph
 (prepostprocessor). You can find examples of how to apply them to
 optimize performance on OpenVINO IR files in
-`111-detection-quantization <111-detection-quantization-with-output.html>`__ and
-`118-optimize-preprocessing <118-optimize-preprocessing-with-output.html>`__.
+`optimize-preprocessing <../optimize-preprocessing>`__.
 
 |image0|
 
@@ -44,7 +43,7 @@ optimize performance on OpenVINO IR files in
    result in different performance.
 
 A similar notebook focused on the throughput mode is available
-`here <109-throughput-tricks-with-output.html>`__.
+`here `__.
 
 Table of contents:
 ^^^^^^^^^^^^^^^^^^
@@ -80,7 +79,19 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0" seaborn "ultralytics<=8.0.178" onnx --extra-index-url https://download.pytorch.org/whl/cpu
+    import platform
+
+    %pip install -q "openvino>=2023.1.0" seaborn "ultralytics<=8.0.178" onnx opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
+
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -96,11 +107,13 @@ Prerequisites
     from typing import Any, List, Tuple
 
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+
+    open("notebook_utils.py", "w").write(r.text)
     import notebook_utils as utils
 
 Data
@@ -134,14 +147,14 @@ requirements of this particular object detection model.
 
 
 
-.. image:: 109-latency-tricks-with-output_files/109-latency-tricks-with-output_5_0.jpg
+.. image:: latency-tricks-with-output_files/latency-tricks-with-output_5_0.jpg
 
 
 
 
 .. parsed-literal::
 
-    
+    
 
 
 
@@ -180,7 +193,7 @@ PyTorch Hub and small enough to see the difference in performance.
 
 .. parsed-literal::
 
-    YOLOv5 🚀 2023-4-21 Python-3.8.10 torch-2.1.0+cpu CPU
+    YOLOv5 🚀 2023-4-21 Python-3.8.10 torch-2.2.2+cpu CPU
 
 
 
@@ -202,17 +215,17 @@ PyTorch Hub and small enough to see the difference in performance.
 .. parsed-literal::
 
 
-  8%|▊         | 304k/3.87M [00:00<00:01, 3.08MB/s]
+ 12%|█▏        | 480k/3.87M [00:00<00:00, 4.89MB/s]
 
 .. parsed-literal::
 
 
- 64%|██████▎   | 2.47M/3.87M [00:00<00:00, 14.6MB/s]
+ 81%|████████  | 3.14M/3.87M [00:00<00:00, 18.5MB/s]
 
 .. parsed-literal::
 
 
-   100%|██████████| 3.87M/3.87M [00:00<00:00, 18.6MB/s]
+    100%|██████████| 3.87M/3.87M [00:00<00:00, 19.5MB/s]
 
 
 
@@ -227,7 +240,7 @@ PyTorch Hub and small enough to see the difference in performance.
 
 .. parsed-literal::
 
-    YOLOv5n summary: 213 layers, 1867405 parameters, 0 gradients
+    YOLOv5n summary: 213 layers, 1867405 parameters, 0 gradients, 4.5 GFLOPs
 
 
 .. parsed-literal::
@@ -312,14 +325,86 @@ the image.
 
     # https://gist.github.com/AruniRC/7b3dadd004da04c80198557db5da4bda
     classes = [
-        "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
-        "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra",
-        "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
-        "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork",
-        "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut",
-        "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
-        "cell phone", "microwave", "oven", "oaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
-        "hair drier", "toothbrush"
+        "person",
+        "bicycle",
+        "car",
+        "motorcycle",
+        "airplane",
+        "bus",
+        "train",
+        "truck",
+        "boat",
+        "traffic light",
+        "fire hydrant",
+        "stop sign",
+        "parking meter",
+        "bench",
+        "bird",
+        "cat",
+        "dog",
+        "horse",
+        "sheep",
+        "cow",
+        "elephant",
+        "bear",
+        "zebra",
+        "giraffe",
+        "backpack",
+        "umbrella",
+        "handbag",
+        "tie",
+        "suitcase",
+        "frisbee",
+        "skis",
+        "snowboard",
+        "sports ball",
+        "kite",
+        "baseball bat",
+        "baseball glove",
+        "skateboard",
+        "surfboard",
+        "tennis racket",
+        "bottle",
+        "wine glass",
+        "cup",
+        "fork",
+        "knife",
+        "spoon",
+        "bowl",
+        "banana",
+        "apple",
+        "sandwich",
+        "orange",
+        "broccoli",
+        "carrot",
+        "hot dog",
+        "pizza",
+        "donut",
+        "cake",
+        "chair",
+        "couch",
+        "potted plant",
+        "bed",
+        "dining table",
+        "toilet",
+        "tv",
+        "laptop",
+        "mouse",
+        "remote",
+        "keyboard",
+        "cell phone",
+        "microwave",
+        "oven",
+        "oaster",
+        "sink",
+        "refrigerator",
+        "book",
+        "clock",
+        "vase",
+        "scissors",
+        "teddy bear",
+        "hair drier",
+        "toothbrush",
     ]
 
     # Colors for the classes above (Rainbow Color Map).
@@ -344,18 +429,14 @@ the image.
             score = obj[4]
             label = np.argmax(obj[5:])
             # Create a box with pixels coordinates from the box with normalized coordinates [0,1].
-            boxes.append(
-                tuple(map(int, (xmin - ww // 2, ymin - hh // 2, ww, hh)))
-            )
+            boxes.append(tuple(map(int, (xmin - ww // 2, ymin - hh // 2, ww, hh))))
             labels.append(int(label))
             scores.append(float(score))
 
         # Apply non-maximum suppression to get rid of many overlapping entities.
         # See https://paperswithcode.com/method/non-maximum-suppression
         # This algorithm returns indices of objects to keep.
-        indices = cv2.dnn.NMSBoxes(
-            bboxes=boxes, scores=scores, score_threshold=0.25, nms_threshold=0.5
-        )
+        indices = cv2.dnn.NMSBoxes(bboxes=boxes, scores=scores, score_threshold=0.25, nms_threshold=0.5)
 
         # If there are no boxes.
         if len(indices) == 0:
@@ -425,21 +506,25 @@ optimizations applied. We will treat it as our baseline.
     with torch.no_grad():
         result = pytorch_model(torch.as_tensor(input_image)).detach().numpy()[0]
         show_result(result)
-        pytorch_infer_time = benchmark_model(pytorch_model, input_data=torch.as_tensor(input_image).float(), benchmark_name="PyTorch model")
+        pytorch_infer_time = benchmark_model(
+            pytorch_model,
+            input_data=torch.as_tensor(input_image).float(),
+            benchmark_name="PyTorch model",
+        )
 
 
 
-.. image:: 109-latency-tricks-with-output_files/109-latency-tricks-with-output_15_0.jpg
+.. image:: latency-tricks-with-output_files/latency-tricks-with-output_15_0.jpg
 
 
 .. parsed-literal::
 
-    PyTorch model on CPU. First inference time: 0.0266 seconds
+    PyTorch model on CPU. First inference time: 0.0269 seconds
 
 
 .. parsed-literal::
 
-    PyTorch model on CPU: 0.0216 seconds per image (46.33 FPS)
+    PyTorch model on CPU: 0.0213 seconds per image (47.06 FPS)
 
 
 ONNX model
@@ -484,17 +569,17 @@ Representation (IR) to leverage the OpenVINO Runtime.
 
 
 
-.. image:: 109-latency-tricks-with-output_files/109-latency-tricks-with-output_18_0.jpg
+.. image:: latency-tricks-with-output_files/latency-tricks-with-output_18_0.jpg
 
 
 .. parsed-literal::
 
-    ONNX model on CPU. First inference time: 0.0164 seconds
+    ONNX model on CPU. First inference time: 0.0173 seconds
 
 
 .. parsed-literal::
 
-    ONNX model on CPU: 0.0125 seconds per image (80.25 FPS)
+    ONNX model on CPU: 0.0136 seconds per image (73.79 FPS)
 
 
 OpenVINO IR model
@@ -526,17 +611,17 @@ accuracy drop. That’s why we skip that step in this notebook.
 
 
 
-.. image:: 109-latency-tricks-with-output_files/109-latency-tricks-with-output_20_0.jpg
+.. image:: latency-tricks-with-output_files/latency-tricks-with-output_20_0.jpg
 
 
 .. parsed-literal::
 
-    OpenVINO model on CPU. First inference time: 0.0163 seconds
+    OpenVINO model on CPU. First inference time: 0.0157 seconds
 
 
 .. parsed-literal::
 
-    OpenVINO model on CPU: 0.0123 seconds per image (81.26 FPS)
+    OpenVINO model on CPU: 0.0122 seconds per image (81.74 FPS)
 
 
 OpenVINO IR model on GPU
@@ -561,7 +646,12 @@ execution.
 
         result = ov_gpu_model(input_image)[ov_gpu_model.output(0)][0]
         show_result(result)
-        ov_gpu_infer_time = benchmark_model(model=ov_gpu_model, input_data=input_image, benchmark_name="OpenVINO model", device_name="GPU")
+        ov_gpu_infer_time = benchmark_model(
+            model=ov_gpu_model,
+            input_data=input_image,
+            benchmark_name="OpenVINO model",
+            device_name="GPU",
+        )
 
         del ov_gpu_model  # release resources
 
@@ -586,23 +676,27 @@ If it is the case, don’t use it.
 
     result = ov_cpu_config_model(input_image)[ov_cpu_config_model.output(0)][0]
     show_result(result)
-    ov_cpu_config_infer_time = benchmark_model(model=ov_cpu_config_model, input_data=input_image, benchmark_name="OpenVINO model + more threads")
+    ov_cpu_config_infer_time = benchmark_model(
+        model=ov_cpu_config_model,
+        input_data=input_image,
+        benchmark_name="OpenVINO model + more threads",
+    )
 
     del ov_cpu_config_model  # release resources
 
 
 
-.. image:: 109-latency-tricks-with-output_files/109-latency-tricks-with-output_24_0.jpg
+.. image:: latency-tricks-with-output_files/latency-tricks-with-output_24_0.jpg
 
 
 .. parsed-literal::
 
-    OpenVINO model + more threads on CPU. First inference time: 0.0151 seconds
+    OpenVINO model + more threads on CPU. First inference time: 0.0157 seconds
 
 
 .. parsed-literal::
 
-    OpenVINO model + more threads on CPU: 0.0124 seconds per image (80.94 FPS)
+    OpenVINO model + more threads on CPU: 0.0123 seconds per image (81.20 FPS)
 
 
 OpenVINO IR model in latency mode
@@ -624,21 +718,26 @@ devices as well.
 
     result = ov_auto_model(input_image)[ov_auto_model.output(0)][0]
     show_result(result)
-    ov_auto_infer_time = benchmark_model(model=ov_auto_model, input_data=input_image, benchmark_name="OpenVINO model", device_name="AUTO")
+    ov_auto_infer_time = benchmark_model(
+        model=ov_auto_model,
+        input_data=input_image,
+        benchmark_name="OpenVINO model",
+        device_name="AUTO",
+    )
 
 
 
-.. image:: 109-latency-tricks-with-output_files/109-latency-tricks-with-output_26_0.jpg
+.. image:: latency-tricks-with-output_files/latency-tricks-with-output_26_0.jpg
 
 
 .. parsed-literal::
 
-    OpenVINO model on AUTO. First inference time: 0.0158 seconds
+    OpenVINO model on AUTO. First inference time: 0.0157 seconds
 
 
 .. parsed-literal::
 
-    OpenVINO model on AUTO: 0.0126 seconds per image (79.53 FPS)
+    OpenVINO model on AUTO: 0.0124 seconds per image (80.65 FPS)
 
 
 OpenVINO IR model in latency mode + shared memory
@@ -662,23 +761,28 @@ performance!
 
     result = ov_auto_model(input_tensor)[ov_auto_model.output(0)][0]
     show_result(result)
-    ov_auto_shared_infer_time = benchmark_model(model=ov_auto_model, input_data=input_tensor, benchmark_name="OpenVINO model + shared memory", device_name="AUTO")
+    ov_auto_shared_infer_time = benchmark_model(
+        model=ov_auto_model,
+        input_data=input_tensor,
+        benchmark_name="OpenVINO model + shared memory",
+        device_name="AUTO",
+    )
 
     del ov_auto_model  # release resources
 
 
 
-.. image:: 109-latency-tricks-with-output_files/109-latency-tricks-with-output_28_0.jpg
+.. image:: latency-tricks-with-output_files/latency-tricks-with-output_28_0.jpg
 
 
 .. parsed-literal::
 
-    OpenVINO model + shared memory on AUTO. First inference time: 0.0103 seconds
+    OpenVINO model + shared memory on AUTO. First inference time: 0.0113 seconds
 
 
 .. parsed-literal::
 
-    OpenVINO model + shared memory on AUTO: 0.0054 seconds per image (185.55 FPS)
+    OpenVINO model + shared memory on AUTO: 0.0054 seconds per image (186.74 FPS)
 
 
 Other tricks
@@ -689,9 +793,8 @@ Other tricks
 There are other tricks for performance improvement, such as quantization
 and pre-post-processing or dedicated to throughput mode. To get even
 more from your model, please visit
-`111-detection-quantization <111-detection-quantization-with-output.html>`__,
-`118-optimize-preprocessing <118-optimize-preprocessing>`__, and
-`109-throughput-tricks <109-throughput-tricks-with-output.html>`__.
+`optimize-preprocessing <../optimize-preprocessing>`__, and
+`throughput-tricks `__.
 
 Performance comparison
 ----------------------
@@ -710,11 +813,30 @@ steps, just skip them.
 
     from matplotlib import pyplot as plt
 
-    labels = ["PyTorch model", "ONNX model", "OpenVINO IR model", "OpenVINO IR model on GPU", "OpenVINO IR model + more inference threads",
-              "OpenVINO IR model in latency mode", "OpenVINO IR model in latency mode + shared memory"]
+    labels = [
+        "PyTorch model",
+        "ONNX model",
+        "OpenVINO IR model",
+        "OpenVINO IR model on GPU",
+        "OpenVINO IR model + more inference threads",
+        "OpenVINO IR model in latency mode",
+        "OpenVINO IR model in latency mode + shared memory",
+    ]
     # make them milliseconds
-    times = list(map(lambda x: 1000 * x, [pytorch_infer_time, onnx_infer_time, ov_cpu_infer_time, ov_gpu_infer_time, ov_cpu_config_infer_time,
-                                          ov_auto_infer_time, ov_auto_shared_infer_time]))
+    times = list(
+        map(
+            lambda x: 1000 * x,
+            [
+                pytorch_infer_time,
+                onnx_infer_time,
+                ov_cpu_infer_time,
+                ov_gpu_infer_time,
+                ov_cpu_config_infer_time,
+                ov_auto_infer_time,
+                ov_auto_shared_infer_time,
+            ],
+        )
+    )
 
     bar_colors = colors[::10] / 255.0
 
@@ -724,12 +846,12 @@ steps, just skip them.
     ax.set_ylabel("Inference time [ms]")
     ax.set_title("Performance difference")
 
-    plt.xticks(rotation='vertical')
+    plt.xticks(rotation="vertical")
     plt.show()
 
 
 
-.. image:: 109-latency-tricks-with-output_files/109-latency-tricks-with-output_31_0.png
+.. image:: latency-tricks-with-output_files/latency-tricks-with-output_31_0.png
 
 
 Conclusions
diff --git a/docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_15_0.jpg b/docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_15_0.jpg
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rename to docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_15_0.jpg
diff --git a/docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_18_0.jpg b/docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_18_0.jpg
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rename from docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_18_0.jpg
rename to docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_18_0.jpg
diff --git a/docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_20_0.jpg b/docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_20_0.jpg
similarity index 100%
rename from docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_20_0.jpg
rename to docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_20_0.jpg
diff --git a/docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_24_0.jpg b/docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_24_0.jpg
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rename from docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_24_0.jpg
rename to docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_24_0.jpg
diff --git a/docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_26_0.jpg b/docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_26_0.jpg
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rename from docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_26_0.jpg
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diff --git a/docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_28_0.jpg b/docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_28_0.jpg
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rename from docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_28_0.jpg
rename to docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_28_0.jpg
diff --git a/docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_31_0.png b/docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_31_0.png
new file mode 100644
index 00000000000..c2b9c5013c2
--- /dev/null
+++ b/docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_31_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e50d8a22c26feebfe2a2d322d21b370b0610cbf00740d0a5808a2644d62f7028
+size 57013
diff --git a/docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_5_0.jpg b/docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_5_0.jpg
similarity index 100%
rename from docs/notebooks/109-latency-tricks-with-output_files/109-latency-tricks-with-output_5_0.jpg
rename to docs/notebooks/latency-tricks-with-output_files/latency-tricks-with-output_5_0.jpg
diff --git a/docs/notebooks/263-latent-consistency-models-image-generation-with-output.rst b/docs/notebooks/latent-consistency-models-image-generation-with-output.rst
similarity index 93%
rename from docs/notebooks/263-latent-consistency-models-image-generation-with-output.rst
rename to docs/notebooks/latent-consistency-models-image-generation-with-output.rst
index 3a679731380..5f07a0906ff 100644
--- a/docs/notebooks/263-latent-consistency-models-image-generation-with-output.rst
+++ b/docs/notebooks/latent-consistency-models-image-generation-with-output.rst
@@ -80,8 +80,8 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q "torch" --index-url https://download.pytorch.org/whl/cpu
-    %pip install -q "openvino>=2023.1.0" transformers "diffusers>=0.23.1" pillow gradio "nncf>=2.7.0" datasets "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "torch>=2.1" --index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2023.1.0" transformers "diffusers>=0.23.1" pillow "gradio>=4.19" "nncf>=2.7.0" "datasets>=2.14.6" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
 
 Prepare models for OpenVINO format conversion
 ---------------------------------------------
@@ -100,9 +100,10 @@ model is also integrated into
 Diffusers is the go-to library for state-of-the-art pretrained diffusion
 models for generating images, audio, and even 3D structures of
 molecules. This allows us to compare running original Stable Diffusion
-(from this `notebook <225-stable-diffusion-text-to-image-with-output.html>`__) and
-distilled using LCD. The distillation approach efficiently converts a
-pre-trained guided diffusion model into a latent consistency model by
+(from this
+`notebook `__)
+and distilled using LCD. The distillation approach efficiently converts
+a pre-trained guided diffusion model into a latent consistency model by
 solving an augmented PF-ODE.
 
 For starting work with LCM, we should instantiate generation pipeline
@@ -156,11 +157,7 @@ provide which module should be loaded for initialization using
 
 .. code:: ipython3
 
-    skip_conversion = (
-        TEXT_ENCODER_OV_PATH.exists()
-        and UNET_OV_PATH.exists()
-        and VAE_DECODER_OV_PATH.exists()
-    )
+    skip_conversion = TEXT_ENCODER_OV_PATH.exists() and UNET_OV_PATH.exists() and VAE_DECODER_OV_PATH.exists()
     
     (
         scheduler,
@@ -548,7 +545,6 @@ decoded by the decoder part of the variational auto encoder.
             """
     
             if prompt_embeds is None:
-    
                 text_inputs = self.tokenizer(
                     prompt,
                     padding="max_length",
@@ -557,16 +553,10 @@ decoded by the decoder part of the variational auto encoder.
                     return_tensors="pt",
                 )
                 text_input_ids = text_inputs.input_ids
-                untruncated_ids = self.tokenizer(
-                    prompt, padding="longest", return_tensors="pt"
-                ).input_ids
+                untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
     
-                if untruncated_ids.shape[-1] >= text_input_ids.shape[
-                    -1
-                ] and not torch.equal(text_input_ids, untruncated_ids):
-                    removed_text = self.tokenizer.batch_decode(
-                        untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                    )
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+                    removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
                     logger.warning(
                         "The following part of your input was truncated because CLIP can only handle sequences up to"
                         f" {self.tokenizer.model_max_length} tokens: {removed_text}"
@@ -578,9 +568,7 @@ decoded by the decoder part of the variational auto encoder.
             bs_embed, seq_len, _ = prompt_embeds.shape
             # duplicate text embeddings for each generation per prompt
             prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            prompt_embeds = prompt_embeds.view(
-                bs_embed * num_images_per_prompt, seq_len, -1
-            )
+            prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
     
             # Don't need to get uncond prompt embedding because of LCM Guided Distillation
             return prompt_embeds
@@ -590,22 +578,14 @@ decoded by the decoder part of the variational auto encoder.
                 has_nsfw_concept = None
             else:
                 if torch.is_tensor(image):
-                    feature_extractor_input = self.image_processor.postprocess(
-                        image, output_type="pil"
-                    )
+                    feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
                 else:
                     feature_extractor_input = self.image_processor.numpy_to_pil(image)
-                safety_checker_input = self.feature_extractor(
-                    feature_extractor_input, return_tensors="pt"
-                )
-                image, has_nsfw_concept = self.safety_checker(
-                    images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-                )
+                safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt")
+                image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_checker_input.pixel_values.to(dtype))
             return image, has_nsfw_concept
     
-        def prepare_latents(
-            self, batch_size, num_channels_latents, height, width, dtype, latents=None
-        ):
+        def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, latents=None):
             shape = (
                 batch_size,
                 num_channels_latents,
@@ -657,7 +637,6 @@ decoded by the decoder part of the variational auto encoder.
             return_dict: bool = True,
             cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         ):
-    
             # 1. Define call parameters
             if prompt is not None and isinstance(prompt, str):
                 batch_size = 1
@@ -700,23 +679,22 @@ decoded by the decoder part of the variational auto encoder.
             # 6. LCM MultiStep Sampling Loop:
             with self.progress_bar(total=num_inference_steps) as progress_bar:
                 for i, t in enumerate(timesteps):
-    
                     ts = torch.full((bs,), t, dtype=torch.long)
     
                     # model prediction (v-prediction, eps, x)
-                    model_pred = self.unet([latents, ts, prompt_embeds, w_embedding], share_inputs=True, share_outputs=True)[0]
+                    model_pred = self.unet(
+                        [latents, ts, prompt_embeds, w_embedding],
+                        share_inputs=True,
+                        share_outputs=True,
+                    )[0]
     
                     # compute the previous noisy sample x_t -> x_t-1
-                    latents, denoised = self.scheduler.step(
-                        torch.from_numpy(model_pred), t, latents, return_dict=False
-                    )
+                    latents, denoised = self.scheduler.step(torch.from_numpy(model_pred), t, latents, return_dict=False)
                     progress_bar.update()
     
             if not output_type == "latent":
                 image = torch.from_numpy(self.vae_decoder(denoised / 0.18215, share_inputs=True, share_outputs=True)[0])
-                image, has_nsfw_concept = self.run_safety_checker(
-                    image, prompt_embeds.dtype
-                )
+                image, has_nsfw_concept = self.run_safety_checker(image, prompt_embeds.dtype)
             else:
                 image = denoised
                 has_nsfw_concept = None
@@ -726,16 +704,12 @@ decoded by the decoder part of the variational auto encoder.
             else:
                 do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
     
-            image = self.image_processor.postprocess(
-                image, output_type=output_type, do_denormalize=do_denormalize
-            )
+            image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
     
             if not return_dict:
                 return (image, has_nsfw_concept)
     
-            return StableDiffusionPipelineOutput(
-                images=image, nsfw_content_detected=has_nsfw_concept
-            )
+            return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
 
 Configure Inference Pipeline
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -836,7 +810,7 @@ Now, let’s see model in action
 
 
 
-.. image:: 263-latent-consistency-models-image-generation-with-output_files/263-latent-consistency-models-image-generation-with-output_21_0.png
+.. image:: latent-consistency-models-image-generation-with-output_files/latent-consistency-models-image-generation-with-output_21_0.png
 
 
 
@@ -875,7 +849,7 @@ improve model inference speed.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -895,14 +869,18 @@ Let’s load ``skip magic`` extension to skip quantization if
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
-    
     int8_pipe = None
     
     if to_quantize.value and "GPU" in device.value:
         to_quantize.value = False
     
+    # Fetch `skip_kernel_extension` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     %load_ext skip_kernel_extension
 
 Prepare calibration dataset
@@ -1151,7 +1129,7 @@ data.
 
 
 
-.. image:: 263-latent-consistency-models-image-generation-with-output_files/263-latent-consistency-models-image-generation-with-output_34_1.png
+.. image:: latent-consistency-models-image-generation-with-output_files/latent-consistency-models-image-generation-with-output_34_1.png
 
 
 Compare inference time of the FP16 and INT8 models
@@ -1270,6 +1248,7 @@ Interactive demo
     
     MAX_IMAGE_SIZE = 768
     
+    
     def generate(
         pipeline: OVLatentConsistencyModelPipeline,
         prompt: str,
@@ -1296,6 +1275,7 @@ Interactive demo
         ).images[0]
         return result, seed
     
+    
     generate_original = partial(generate, ov_pipe)
     generate_optimized = partial(generate, int8_pipe)
     quantized_model_present = int8_pipe is not None
@@ -1312,16 +1292,21 @@ Interactive demo
                 )
             with gr.Row():
                 with gr.Column():
-                    result = gr.Image(label="Result (Original)" if quantized_model_present else "Image", type="pil")
+                    result = gr.Image(
+                        label="Result (Original)" if quantized_model_present else "Image",
+                        type="pil",
+                    )
                     run_button = gr.Button("Run")
                 with gr.Column(visible=quantized_model_present):
-                    result_optimized = gr.Image(label="Result (Optimized)", type="pil", visible=quantized_model_present)
+                    result_optimized = gr.Image(
+                        label="Result (Optimized)",
+                        type="pil",
+                        visible=quantized_model_present,
+                    )
                     run_quantized_button = gr.Button(value="Run quantized", visible=quantized_model_present)
     
         with gr.Accordion("Advanced options", open=False):
-            seed = gr.Slider(
-                label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True
-            )
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
             randomize_seed = gr.Checkbox(label="Randomize seed across runs", value=True)
             with gr.Row():
                 width = gr.Slider(
diff --git a/docs/notebooks/263-latent-consistency-models-image-generation-with-output_files/263-latent-consistency-models-image-generation-with-output_21_0.jpg b/docs/notebooks/latent-consistency-models-image-generation-with-output_files/latent-consistency-models-image-generation-with-output_21_0.jpg
similarity index 100%
rename from docs/notebooks/263-latent-consistency-models-image-generation-with-output_files/263-latent-consistency-models-image-generation-with-output_21_0.jpg
rename to docs/notebooks/latent-consistency-models-image-generation-with-output_files/latent-consistency-models-image-generation-with-output_21_0.jpg
diff --git a/docs/notebooks/263-latent-consistency-models-image-generation-with-output_files/263-latent-consistency-models-image-generation-with-output_21_0.png b/docs/notebooks/latent-consistency-models-image-generation-with-output_files/latent-consistency-models-image-generation-with-output_21_0.png
similarity index 100%
rename from docs/notebooks/263-latent-consistency-models-image-generation-with-output_files/263-latent-consistency-models-image-generation-with-output_21_0.png
rename to docs/notebooks/latent-consistency-models-image-generation-with-output_files/latent-consistency-models-image-generation-with-output_21_0.png
diff --git a/docs/notebooks/263-latent-consistency-models-image-generation-with-output_files/263-latent-consistency-models-image-generation-with-output_34_1.jpg b/docs/notebooks/latent-consistency-models-image-generation-with-output_files/latent-consistency-models-image-generation-with-output_34_1.jpg
similarity index 100%
rename from docs/notebooks/263-latent-consistency-models-image-generation-with-output_files/263-latent-consistency-models-image-generation-with-output_34_1.jpg
rename to docs/notebooks/latent-consistency-models-image-generation-with-output_files/latent-consistency-models-image-generation-with-output_34_1.jpg
diff --git a/docs/notebooks/263-latent-consistency-models-image-generation-with-output_files/263-latent-consistency-models-image-generation-with-output_34_1.png b/docs/notebooks/latent-consistency-models-image-generation-with-output_files/latent-consistency-models-image-generation-with-output_34_1.png
similarity index 100%
rename from docs/notebooks/263-latent-consistency-models-image-generation-with-output_files/263-latent-consistency-models-image-generation-with-output_34_1.png
rename to docs/notebooks/latent-consistency-models-image-generation-with-output_files/latent-consistency-models-image-generation-with-output_34_1.png
diff --git a/docs/notebooks/263-latent-consistency-models-optimum-demo-with-output.rst b/docs/notebooks/latent-consistency-models-optimum-demo-with-output.rst
similarity index 76%
rename from docs/notebooks/263-latent-consistency-models-optimum-demo-with-output.rst
rename to docs/notebooks/latent-consistency-models-optimum-demo-with-output.rst
index c6acc725570..7f0e13ffd06 100644
--- a/docs/notebooks/263-latent-consistency-models-optimum-demo-with-output.rst
+++ b/docs/notebooks/latent-consistency-models-optimum-demo-with-output.rst
@@ -50,34 +50,19 @@ Install required packages
 
     %pip install -q "openvino>=2023.3.0"
     %pip install -q "onnx>=1.11.0"
-    %pip install -q "optimum-intel[diffusers]@git+https://github.com/huggingface/optimum-intel.git" "ipywidgets" "transformers>=4.33.0" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "optimum-intel[diffusers]@git+https://github.com/huggingface/optimum-intel.git" "ipywidgets" "torch>=2.1" "transformers>=4.33.0" --extra-index-url https://download.pytorch.org/whl/cpu
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -86,7 +71,8 @@ Install required packages
 .. code:: ipython3
 
     import warnings
-    warnings.filterwarnings('ignore')
+    
+    warnings.filterwarnings("ignore")
 
 Showing Info Available Devices
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -110,6 +96,7 @@ this
 .. code:: ipython3
 
     import openvino as ov
+    
     core = ov.Core()
     devices = core.available_devices
     
@@ -118,7 +105,6 @@ this
         print(f"{device}: {device_name}")
 
 
-
 .. parsed-literal::
 
     CPU: Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
@@ -141,17 +127,16 @@ https://huggingface.co/docs/diffusers/en/api/pipelines/latent_consistency_models
     pipeline = LatentConsistencyModelPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7")
 
 
-
 .. parsed-literal::
 
-    2024-03-13 00:06:35.851024: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:06:35.885328: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 00:04:08.916564: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:04:08.953118: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 00:06:36.470803: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:04:09.550802: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -164,9 +149,7 @@ https://huggingface.co/docs/diffusers/en/api/pipelines/latent_consistency_models
 
     prompt = "A cute squirrel in the forest, portrait, 8k"
     
-    image = pipeline(
-        prompt=prompt, num_inference_steps=4, guidance_scale=8.0
-    ).images[0]
+    image = pipeline(prompt=prompt, num_inference_steps=4, guidance_scale=8.0).images[0]
     image.save("image_standard_pipeline.png")
     image
 
@@ -179,7 +162,7 @@ https://huggingface.co/docs/diffusers/en/api/pipelines/latent_consistency_models
 
 
 
-.. image:: 263-latent-consistency-models-optimum-demo-with-output_files/263-latent-consistency-models-optimum-demo-with-output_8_1.png
+.. image:: latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.png
 
 
 
@@ -199,8 +182,8 @@ Select inference device for text-to-image generation
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
     
@@ -240,11 +223,6 @@ and there is no need to do it manually
     ov_pipeline.save_pretrained("./openvino_ir")
 
 
-.. parsed-literal::
-
-    OpenVINO Tokenizer version is not compatible with OpenVINO version. Installed OpenVINO version: 2024.0.0,OpenVINO Tokenizers requires . OpenVINO Tokenizers models will not be added during export.
-
-
 .. parsed-literal::
 
     INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
@@ -257,7 +235,7 @@ and there is no need to do it manually
 
 .. parsed-literal::
 
-    Keyword arguments {'subfolder': '', 'use_auth_token': None, 'trust_remote_code': False} are not expected by StableDiffusionPipeline and will be ignored.
+    Keyword arguments {'subfolder': '', 'trust_remote_code': False} are not expected by StableDiffusionPipeline and will be ignored.
 
 
 
@@ -268,7 +246,7 @@ and there is no need to do it manually
 
 .. parsed-literal::
 
-    Using framework PyTorch: 2.2.1+cpu
+    Using framework PyTorch: 2.2.2+cpu
 
 
 .. parsed-literal::
@@ -283,17 +261,17 @@ and there is no need to do it manually
 
 .. parsed-literal::
 
-    Using framework PyTorch: 2.2.1+cpu
+    Using framework PyTorch: 2.2.2+cpu
 
 
 .. parsed-literal::
 
-    Using framework PyTorch: 2.2.1+cpu
+    Using framework PyTorch: 2.2.2+cpu
 
 
 .. parsed-literal::
 
-    Using framework PyTorch: 2.2.1+cpu
+    Using framework PyTorch: 2.2.2+cpu
 
 
 .. code:: ipython3
@@ -314,12 +292,12 @@ and there is no need to do it manually
 
 .. parsed-literal::
 
-    Compiling the text_encoder to CPU ...
+    Compiling the vae_encoder to CPU ...
 
 
 .. parsed-literal::
 
-    Compiling the vae_encoder to CPU ...
+    Compiling the text_encoder to CPU ...
 
 
 .. code:: ipython3
@@ -339,7 +317,7 @@ and there is no need to do it manually
 
 
 
-.. image:: 263-latent-consistency-models-optimum-demo-with-output_files/263-latent-consistency-models-optimum-demo-with-output_15_1.png
+.. image:: latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.png
 
 
 
diff --git a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.jpg b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.jpg
new file mode 100644
index 00000000000..46a6211f019
--- /dev/null
+++ b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:978131b23cda8ee5f97efcd3c11e8853158abb75b93be87c596b70987c0abab4
+size 77604
diff --git a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.png b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.png
new file mode 100644
index 00000000000..cb21646d5ce
--- /dev/null
+++ b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:156d18fc303a7c9a92e356d1da39456c34ee8719291cc88cb225e6081c6c8a37
+size 1004233
diff --git a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.jpg b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.jpg
new file mode 100644
index 00000000000..96fb046ba7e
--- /dev/null
+++ b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b22f2298a9e2ddfd16462395c9d44657c7e8fd719eac2376f2dde5799e18a311
+size 74573
diff --git a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.png b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.png
new file mode 100644
index 00000000000..d801259215f
--- /dev/null
+++ b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36c4842ee6b9a0232ddc779aa24e05bcca0dfd9bfa018a4e77c9c990e59b4383
+size 1004628
diff --git a/docs/notebooks/263-lcm-lora-controlnet-with-output.rst b/docs/notebooks/lcm-lora-controlnet-with-output.rst
similarity index 89%
rename from docs/notebooks/263-lcm-lora-controlnet-with-output.rst
rename to docs/notebooks/lcm-lora-controlnet-with-output.rst
index 384ed58b961..fb487c6c282 100644
--- a/docs/notebooks/263-lcm-lora-controlnet-with-output.rst
+++ b/docs/notebooks/lcm-lora-controlnet-with-output.rst
@@ -133,12 +133,12 @@ For more details regarding Stable Diffusion work, refer to the `project
 website `__.
 There is a tutorial for Stable Diffusion Text-to-Image generation with
 OpenVINO, see the following
-`notebook <225-stable-diffusion-text-to-image-with-output.html>`__.
+`notebook `__.
 
 ControlNet
 ~~~~~~~~~~
 
-ControlNet is a neural network
+ ControlNet is a neural network
 structure to control diffusion models by adding extra conditions. Using
 this new framework, we can capture a scene, structure, object, or
 subject pose from an inputted image, and then transfer that quality to
@@ -225,7 +225,7 @@ Install required packages
 .. code:: ipython3
 
     %pip install -q "torch" transformers "diffusers>=0.22.0" "controlnet-aux>=0.0.6" "peft==0.6.2" accelerate --extra-index-url https://download.pytorch.org/whl/cpu
-    %pip install -q "openvino>=2023.2.0" pillow gradio datasets "nncf>=2.7.0"
+    %pip install -q "openvino>=2023.2.0" pillow "gradio>=4.19" "datasets>=2.14.6" "nncf>=2.7.0"
 
 Prepare PyTorch models
 
@@ -237,12 +237,12 @@ Prepare PyTorch models
     adapter_id = "latent-consistency/lcm-lora-sdv1-5"
     stable_diffusion_id = "runwayml/stable-diffusion-v1-5"
     
-    TEXT_ENCODER_OV_PATH = Path('model/text_encoder.xml')
-    UNET_OV_PATH = Path('model/unet_controlnet.xml')
-    CONTROLNET_OV_PATH = Path('model/controlnet-normalbae.xml')
-    VAE_DECODER_OV_PATH = Path('model/vae_decoder.xml')
-    TOKENIZER_PATH = Path('model/tokenizer')
-    SCHEDULER_PATH = Path('model/scheduler')
+    TEXT_ENCODER_OV_PATH = Path("model/text_encoder.xml")
+    UNET_OV_PATH = Path("model/unet_controlnet.xml")
+    CONTROLNET_OV_PATH = Path("model/controlnet-normalbae.xml")
+    VAE_DECODER_OV_PATH = Path("model/vae_decoder.xml")
+    TOKENIZER_PATH = Path("model/tokenizer")
+    SCHEDULER_PATH = Path("model/scheduler")
     
     skip_models = TEXT_ENCODER_OV_PATH.exists() and UNET_OV_PATH.exists() and CONTROLNET_OV_PATH.exists() and VAE_DECODER_OV_PATH.exists()
 
@@ -276,7 +276,7 @@ ControlNet model 3. Load LoRA weights to the pipeline using
     import gc
     
     
-    def load_original_pytorch_pipeline_components(controlnet_id:str, stable_diffusion_id:str, adapter_id:str):
+    def load_original_pytorch_pipeline_components(controlnet_id: str, stable_diffusion_id: str, adapter_id: str):
         """
         Helper function for loading Stable Diffusion ControlNet pipeline and applying LCM LoRA
     
@@ -290,7 +290,7 @@ ControlNet model 3. Load LoRA weights to the pipeline using
           unet: Stable Diffusion U-Net
           vae: Stable Diffusion Variational Autoencoder (VAE)
         """
-        
+    
         # load controlnet model
         controlnet = ControlNetModel.from_pretrained(controlnet_id)
         # load stable diffusion pipeline
@@ -344,13 +344,15 @@ color-coded image.
 
     from controlnet_aux import NormalBaeDetector
     from diffusers.utils import load_image
-    from urllib.request import urlretrieve
+    import requests
     import matplotlib.pyplot as plt
     from PIL import Image
     import numpy as np
     
     example_image_url = "https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/input.png"
-    urlretrieve(example_image_url, "example.png")
+    r = requests.get(example_image_url)
+    with open("example.png", "wb") as f:
+        f.write(r.content)
     
     processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
     
@@ -358,10 +360,15 @@ color-coded image.
     control_image = processor(image)
     
     
-    def visualize_results(orig_img:Image.Image, normal_img:Image.Image, result_img:Image.Image = None, save_fig:bool = False):
+    def visualize_results(
+        orig_img: Image.Image,
+        normal_img: Image.Image,
+        result_img: Image.Image = None,
+        save_fig: bool = False,
+    ):
         """
         Helper function for results visualization
-        
+    
         Parameters:
            orig_img (Image.Image): original image
            normal_img (Image.Image): image with bwith surface normal information
@@ -377,8 +384,14 @@ color-coded image.
         is_horizontal = im_h <= im_w
         figsize = (20, 20)
         num_images = 3 if result_img is not None else 2
-        fig, axs = plt.subplots(num_images if is_horizontal else 1, 1 if is_horizontal else num_images, figsize=figsize, sharex='all', sharey='all')
-        fig.patch.set_facecolor('white')
+        fig, axs = plt.subplots(
+            num_images if is_horizontal else 1,
+            1 if is_horizontal else num_images,
+            figsize=figsize,
+            sharex="all",
+            sharey="all",
+        )
+        fig.patch.set_facecolor("white")
         list_axes = list(axs.flat)
         for a in list_axes:
             a.set_xticklabels([])
@@ -389,15 +402,15 @@ color-coded image.
         list_axes[0].imshow(np.array(orig_img))
         list_axes[1].imshow(np.array(normal_img))
         list_axes[0].set_title(orig_title, fontsize=15)
-        list_axes[1].set_title(control_title, fontsize=15) 
+        list_axes[1].set_title(control_title, fontsize=15)
         if result_img is not None:
             list_axes[2].imshow(np.array(result_img))
             list_axes[2].set_title("Result", fontsize=15)
-        
-        fig.subplots_adjust(wspace=0.01 if is_horizontal else 0.00 , hspace=0.01 if is_horizontal else 0.1)
+    
+        fig.subplots_adjust(wspace=0.01 if is_horizontal else 0.00, hspace=0.01 if is_horizontal else 0.1)
         fig.tight_layout()
         if save_fig:
-            fig.savefig("result.png", bbox_inches='tight')
+            fig.savefig("result.png", bbox_inches="tight")
         return fig
     
     
@@ -411,7 +424,7 @@ color-coded image.
 
 
 
-.. image:: 263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_10_1.png
+.. image:: lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_10_1.png
 
 
 Convert models to OpenVINO Intermediate representation (IR) format
@@ -459,6 +472,7 @@ blocks, which serves additional context for the UNet model.
     import openvino as ov
     from functools import partial
     
+    
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -487,7 +501,7 @@ blocks, which serves additional context for the UNet model.
         torch.float32: ov.Type.f32,
         torch.float64: ov.Type.f64,
         torch.int32: ov.Type.i32,
-        torch.int64: ov.Type.i64
+        torch.int64: ov.Type.i64,
     }
     
     
@@ -512,17 +526,17 @@ blocks, which serves additional context for the UNet model.
     inputs = {
         "sample": torch.randn((1, 4, 64, 64)),
         "timestep": torch.tensor(1, dtype=torch.float32),
-        "encoder_hidden_states": torch.randn((1,77,768)),
-        "controlnet_cond": torch.randn((1,3,512,512))
+        "encoder_hidden_states": torch.randn((1, 77, 768)),
+        "controlnet_cond": torch.randn((1, 3, 512, 512)),
     }
     
     
     # Prepare conditional inputs for U-Net
-    if not UNET_OV_PATH.exists():    
+    if not UNET_OV_PATH.exists():
         controlnet.eval()
         with torch.no_grad():
             down_block_res_samples, mid_block_res_sample = controlnet(**inputs, return_dict=False)
-        
+    
     if not CONTROLNET_OV_PATH.exists():
         input_info = prepare_input_info(inputs)
         with torch.no_grad():
@@ -531,7 +545,7 @@ blocks, which serves additional context for the UNet model.
             ov.save_model(ov_model, CONTROLNET_OV_PATH)
             del ov_model
             cleanup_torchscript_cache()
-        print('ControlNet successfully converted to IR')
+        print("ControlNet successfully converted to IR")
     else:
         print(f"ControlNet will be loaded from {CONTROLNET_OV_PATH}")
     
@@ -565,15 +579,16 @@ generated by ControlNet.
 
     from typing import Tuple
     
+    
     class UnetWrapper(torch.nn.Module):
         def __init__(
-            self, 
-            unet, 
-            sample_dtype=torch.float32, 
-            timestep_dtype=torch.int64, 
-            encoder_hidden_states=torch.float32, 
-            down_block_additional_residuals=torch.float32, 
-            mid_block_additional_residual=torch.float32
+            self,
+            unet,
+            sample_dtype=torch.float32,
+            timestep_dtype=torch.int64,
+            encoder_hidden_states=torch.float32,
+            down_block_additional_residuals=torch.float32,
+            mid_block_additional_residual=torch.float32,
         ):
             super().__init__()
             self.unet = unet
@@ -584,12 +599,12 @@ generated by ControlNet.
             self.mid_block_additional_residual_dtype = mid_block_additional_residual
     
         def forward(
-            self, 
-            sample:torch.Tensor, 
-            timestep:torch.Tensor, 
-            encoder_hidden_states:torch.Tensor, 
-            down_block_additional_residuals:Tuple[torch.Tensor],  
-            mid_block_additional_residual:torch.Tensor
+            self,
+            sample: torch.Tensor,
+            timestep: torch.Tensor,
+            encoder_hidden_states: torch.Tensor,
+            down_block_additional_residuals: Tuple[torch.Tensor],
+            mid_block_additional_residual: torch.Tensor,
         ):
             sample.to(self.sample_dtype)
             timestep.to(self.timestep_dtype)
@@ -597,15 +612,14 @@ generated by ControlNet.
             down_block_additional_residuals = [res.to(self.down_block_additional_residuals_dtype) for res in down_block_additional_residuals]
             mid_block_additional_residual.to(self.mid_block_additional_residual_dtype)
             return self.unet(
-                sample, 
-                timestep, 
-                encoder_hidden_states, 
-                down_block_additional_residuals=down_block_additional_residuals, 
-                mid_block_additional_residual=mid_block_additional_residual
+                sample,
+                timestep,
+                encoder_hidden_states,
+                down_block_additional_residuals=down_block_additional_residuals,
+                mid_block_additional_residual=mid_block_additional_residual,
             )
     
     
-    
     if not UNET_OV_PATH.exists():
         inputs.pop("controlnet_cond", None)
         inputs["down_block_additional_residuals"] = down_block_res_samples
@@ -617,7 +631,7 @@ generated by ControlNet.
     
         with torch.no_grad():
             ov_model = ov.convert_model(wrapped_unet, example_input=inputs)
-            
+    
         for (input_dtype, input_shape), input_tensor in zip(input_info, ov_model.inputs):
             input_tensor.get_node().set_partial_shape(ov.PartialShape(input_shape))
             input_tensor.get_node().set_element_type(input_dtype)
@@ -628,7 +642,7 @@ generated by ControlNet.
         del wrapped_unet
         del unet
         gc.collect()
-        print('Unet successfully converted to IR')
+        print("Unet successfully converted to IR")
     else:
         del unet
         print(f"Unet will be loaded from {UNET_OV_PATH}")
@@ -668,11 +682,11 @@ hidden states.
 
 .. code:: ipython3
 
-    def convert_encoder(text_encoder:torch.nn.Module, ir_path:Path):
+    def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
         """
-        Convert Text Encoder model to OpenVINO IR. 
+        Convert Text Encoder model to OpenVINO IR.
         Function accepts text encoder model, prepares example inputs for conversion, and convert it to OpenVINO Model
-        Parameters: 
+        Parameters:
             text_encoder (torch.nn.Module): text_encoder model
             ir_path (Path): File for storing model
         Returns:
@@ -688,13 +702,13 @@ hidden states.
                 ov_model = ov.convert_model(
                     text_encoder,  # model instance
                     example_input=input_ids,  # inputs for model tracing
-                    input=([1,77],)
+                    input=([1, 77],),
                 )
                 ov.save_model(ov_model, ir_path)
                 del ov_model
             cleanup_torchscript_cache()
-            print('Text Encoder successfully converted to IR')
-        
+            print("Text Encoder successfully converted to IR")
+    
     
     if not TEXT_ENCODER_OV_PATH.exists():
         convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
@@ -735,21 +749,22 @@ process are converted back into images using the VAE decoder. During
 inference, we will see that we **only need the VAE decoder**. You can
 find instructions on how to convert the encoder part in a stable
 diffusion
-`notebook <225-stable-diffusion-text-to-image-with-output.html>`__.
+`notebook `__.
 
 .. code:: ipython3
 
     def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path):
         """
-        Convert VAE model to IR format. 
-        Function accepts pipeline, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for convert, 
-        Parameters: 
+        Convert VAE model to IR format.
+        Function accepts pipeline, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for convert,
+        Parameters:
             vae (torch.nn.Module): VAE model
             ir_path (Path): File for storing model
         Returns:
             None
         """
+    
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -768,7 +783,7 @@ diffusion
                 ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
-            print('VAE decoder successfully converted to IR')
+            print("VAE decoder successfully converted to IR")
     
     
     if not VAE_DECODER_OV_PATH.exists():
@@ -791,8 +806,8 @@ Prepare Inference pipeline
 
 We already deeply discussed how the ControlNet-guided pipeline works on
 example pose-controlled generation in `controlnet
-notebook <235-controlnet-stable-diffusion-with-output.html>`__. In our current
-example, the pipeline remains without changes. Similarly to Diffusers
+notebook <../controlnet-stable-diffusion>`__. In our current example,
+the pipeline remains without changes. Similarly to Diffusers
 ``StableDiffusionControlNetPipeline``, we define our own
 ``OVControlNetStableDiffusionPipeline`` inference pipeline based on
 OpenVINO.
@@ -805,11 +820,11 @@ OpenVINO.
     import cv2
     
     
-    def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
+    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
-        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio 
+        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-        
+    
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -823,13 +838,13 @@ OpenVINO.
         return int(im_scale * image_width), int(im_scale * image_height)
     
     
-    def preprocess(image: Image.Image, dst_height:int = 512, dst_width:int = 512):
+    def preprocess(image: Image.Image, dst_height: int = 512, dst_width: int = 512):
         """
         Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-        
+    
         Parameters:
           image (Image.Image): input image
           dst_width: destination image width
@@ -856,7 +871,7 @@ OpenVINO.
     ):
         """
         Helper function for generation random values tensor with given shape and data type
-        
+    
         Parameters:
           shape (Union[Tuple, List]): shape for filling random values
           dtype (torch.dtype, *optiona*, torch.float32): data type for result
@@ -871,6 +886,7 @@ OpenVINO.
         """
         OpenVINO inference pipeline for Stable Diffusion with ControlNet guidence
         """
+    
         def __init__(
             self,
             tokenizer: CLIPTokenizer,
@@ -880,7 +896,7 @@ OpenVINO.
             text_encoder: ov.Model,
             unet: ov.Model,
             vae_decoder: ov.Model,
-            device:str = "AUTO"
+            device: str = "AUTO",
         ):
             super().__init__()
             self.tokenizer = tokenizer
@@ -888,10 +904,18 @@ OpenVINO.
             self.scheduler = scheduler
             self.load_models(core, device, controlnet, text_encoder, unet, vae_decoder)
     
-        def load_models(self, core: ov.Core, device: str, controlnet:ov.Model, text_encoder: ov.Model, unet: ov.Model, vae_decoder: ov.Model):
+        def load_models(
+            self,
+            core: ov.Core,
+            device: str,
+            controlnet: ov.Model,
+            text_encoder: ov.Model,
+            unet: ov.Model,
+            vae_decoder: ov.Model,
+        ):
             """
             Function for loading models on device using OpenVINO
-            
+    
             Parameters:
               core (Core): OpenVINO runtime Core class instance
               device (str): inference device
@@ -913,8 +937,8 @@ OpenVINO.
             prompt: Union[str, List[str]],
             image: Image.Image,
             num_inference_steps: int = 4,
-            height:int = 512,
-            width:int = 512,
+            height: int = 512,
+            width: int = 512,
             negative_prompt: Union[str, List[str]] = None,
             guidance_scale: float = 0.5,
             controlnet_conditioning_scale: float = 1.0,
@@ -951,7 +975,7 @@ OpenVINO.
                     [PIL](https://pillow.readthedocs.io/en/stable/): `Image.Image` or `np.array`.
             Returns:
                 image ([List[Union[np.ndarray, Image.Image]]): generaited images
-                
+    
             """
     
             # 1. Define call parameters
@@ -963,7 +987,11 @@ OpenVINO.
             # corresponds to doing no classifier free guidance.
             do_classifier_free_guidance = guidance_scale > 1.0
             # 2. Encode input prompt
-            text_embeddings = self._encode_prompt(prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt)
+            text_embeddings = self._encode_prompt(
+                prompt,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                negative_prompt=negative_prompt,
+            )
     
             # 3. Preprocess image
             orig_width, orig_height = image.size
@@ -991,15 +1019,27 @@ OpenVINO.
                     # Expand the latents if we are doing classifier free guidance.
                     # The latents are expanded 3 times because for pix2pix the guidance\
                     # is applied for both the text and the input image.
-                    latent_model_input = np.concatenate(
-                        [latents] * 2) if do_classifier_free_guidance else latents
+                    latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
     
-                    result = self.controlnet([latent_model_input, t, text_embeddings, image], share_inputs=True, share_outputs=True)
+                    result = self.controlnet(
+                        [latent_model_input, t, text_embeddings, image],
+                        share_inputs=True,
+                        share_outputs=True,
+                    )
                     down_and_mid_blok_samples = [sample * controlnet_conditioning_scale for _, sample in result.items()]
     
                     # predict the noise residual
-                    noise_pred = self.unet([latent_model_input, t, text_embeddings, *down_and_mid_blok_samples], share_inputs=True, share_outputs=True)[0]
+                    noise_pred = self.unet(
+                        [
+                            latent_model_input,
+                            t,
+                            text_embeddings,
+                            *down_and_mid_blok_samples,
+                        ],
+                        share_inputs=True,
+                        share_outputs=True,
+                    )[0]
     
                     # perform guidance
                     if do_classifier_free_guidance:
@@ -1018,12 +1058,17 @@ OpenVINO.
                 image = self.numpy_to_pil(image)
                 image = [img.resize((orig_width, orig_height), Image.Resampling.LANCZOS) for img in image]
             else:
-                image = [cv2.resize(img, (orig_width, orig_width))
-                         for img in image]
+                image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
     
             return image
     
-        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True, negative_prompt:Union[str, List[str]] = None):
+        def _encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+            negative_prompt: Union[str, List[str]] = None,
+        ):
             """
             Encodes the prompt into text encoder hidden states.
     
@@ -1052,10 +1097,8 @@ OpenVINO.
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(
-                    text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(
-                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
+                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
+                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
     
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
@@ -1089,11 +1132,19 @@ OpenVINO.
     
             return text_embeddings
     
-        def prepare_latents(self, batch_size:int, num_channels_latents:int, height:int, width:int, dtype:np.dtype = torch.float32, latents:np.ndarray = None):
+        def prepare_latents(
+            self,
+            batch_size: int,
+            num_channels_latents: int,
+            height: int,
+            width: int,
+            dtype: np.dtype = torch.float32,
+            latents: np.ndarray = None,
+        ):
             """
-            Preparing noise to image generation. If initial latents are not provided, they will be generated randomly, 
+            Preparing noise to image generation. If initial latents are not provided, they will be generated randomly,
             then prepared latents scaled by the standard deviation required by the scheduler
-            
+    
             Parameters:
                batch_size (int): input batch size
                num_channels_latents (int): number of channels for noise generation
@@ -1104,7 +1155,12 @@ OpenVINO.
             Returns:
                latents (np.ndarray): scaled initial noise for diffusion
             """
-            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            shape = (
+                batch_size,
+                num_channels_latents,
+                height // self.vae_scale_factor,
+                width // self.vae_scale_factor,
+            )
             if latents is None:
                 latents = randn_tensor(shape, dtype=dtype)
             else:
@@ -1114,10 +1170,10 @@ OpenVINO.
             latents = latents * self.scheduler.init_noise_sigma
             return latents
     
-        def decode_latents(self, latents:np.array, pad:Tuple[int]):
+        def decode_latents(self, latents: np.array, pad: Tuple[int]):
             """
             Decode predicted image from latent space using VAE Decoder and unpad image result
-            
+    
             Parameters:
                latents (np.ndarray): image encoded in diffusion latent space
                pad (Tuple[int]): each side padding sizes obtained on preprocessing step
@@ -1186,8 +1242,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
     
@@ -1204,7 +1260,16 @@ select device from dropdown list for running inference using OpenVINO
 
 .. code:: ipython3
 
-    ov_pipe = OVControlNetStableDiffusionPipeline(tokenizer, scheduler, core, CONTROLNET_OV_PATH, TEXT_ENCODER_OV_PATH, UNET_OV_PATH, VAE_DECODER_OV_PATH, device=device.value)
+    ov_pipe = OVControlNetStableDiffusionPipeline(
+        tokenizer,
+        scheduler,
+        core,
+        CONTROLNET_OV_PATH,
+        TEXT_ENCODER_OV_PATH,
+        UNET_OV_PATH,
+        VAE_DECODER_OV_PATH,
+        device=device.value,
+    )
 
 Running Text-to-Image Generation with ControlNet Conditioning and OpenVINO
 --------------------------------------------------------------------------
@@ -1257,7 +1322,7 @@ Let’s see model in action
 
 
 
-.. image:: 263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_27_2.png
+.. image:: lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_27_2.png
 
 
 
@@ -1267,7 +1332,7 @@ Let’s see model in action
 
 
 
-.. image:: 263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_28_0.png
+.. image:: lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_28_0.png
 
 
 Quantization
@@ -1304,7 +1369,7 @@ improve model inference speed.
     is_gpu_device = "GPU" in device.value
     to_quantize = widgets.Checkbox(
         value=not is_gpu_device,
-        description='Quantization',
+        description="Quantization",
         disabled=is_gpu_device,
     )
     
@@ -1315,8 +1380,11 @@ Let’s load ``skip magic`` extension to skip quantization if
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     int8_pipe = None
     
@@ -1505,7 +1573,7 @@ the same input data.
 
 
 
-.. image:: 263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_42_1.png
+.. image:: lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_42_1.png
 
 
 Compare inference time of the FP16 and INT8 models
@@ -1616,6 +1684,7 @@ options for generation: ``Guidance scale``, ``Seed`` and ``Steps``.
 .. code:: ipython3
 
     import gradio as gr
+    
     MAX_SEED = np.iinfo(np.int32).max
     
     quantized_model_present = int8_pipe is not None
@@ -1626,7 +1695,7 @@ options for generation: ``Guidance scale``, ``Seed`` and ``Steps``.
             with gr.Column():
                 inp_img = gr.Image(label="Input image")
             with gr.Column(visible=True) as step1:
-                out_normal = gr.Image(label="Normal Map", type='pil', interactive=False)
+                out_normal = gr.Image(label="Normal Map", type="pil", interactive=False)
                 btn = gr.Button()
                 inp_prompt = gr.Textbox(label="Prompt")
                 inp_neg_prompt = gr.Textbox(
@@ -1657,18 +1726,34 @@ options for generation: ``Guidance scale``, ``Seed`` and ``Steps``.
         def generate(img, prompt, negative_prompt, seed, num_steps, guidance_scale):
             torch.manual_seed(seed)
             control_img = extract_normal_map(img)
-            
-            result = ov_pipe(prompt, control_img, num_steps, guidance_scale=guidance_scale, negative_prompt=negative_prompt)[0]
+    
+            result = ov_pipe(
+                prompt,
+                control_img,
+                num_steps,
+                guidance_scale=guidance_scale,
+                negative_prompt=negative_prompt,
+            )[0]
             if int8_pipe is not None:
                 torch.manual_seed(seed)
-                int8_result = int8_pipe(prompt, control_img, num_steps, guidance_scale=guidance_scale, negative_prompt=negative_prompt)[0]
+                int8_result = int8_pipe(
+                    prompt,
+                    control_img,
+                    num_steps,
+                    guidance_scale=guidance_scale,
+                    negative_prompt=negative_prompt,
+                )[0]
                 return control_img, result, int8_result
             return control_img, result
     
         output_images = [out_normal, out_result]
         if quantized_model_present:
             output_images.append(int_result)
-        btn.click(generate, [inp_img, inp_prompt, inp_neg_prompt, inp_seed, inp_steps, guidance_scale], output_images)
+        btn.click(
+            generate,
+            [inp_img, inp_prompt, inp_neg_prompt, inp_seed, inp_steps, guidance_scale],
+            output_images,
+        )
     
     
     try:
diff --git a/docs/notebooks/263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_10_1.png b/docs/notebooks/lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_10_1.png
similarity index 100%
rename from docs/notebooks/263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_10_1.png
rename to docs/notebooks/lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_10_1.png
diff --git a/docs/notebooks/263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_27_2.jpg b/docs/notebooks/lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_27_2.jpg
similarity index 100%
rename from docs/notebooks/263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_27_2.jpg
rename to docs/notebooks/lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_27_2.jpg
diff --git a/docs/notebooks/263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_27_2.png b/docs/notebooks/lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_27_2.png
similarity index 100%
rename from docs/notebooks/263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_27_2.png
rename to docs/notebooks/lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_27_2.png
diff --git a/docs/notebooks/263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_28_0.png b/docs/notebooks/lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_28_0.png
similarity index 100%
rename from docs/notebooks/263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_28_0.png
rename to docs/notebooks/lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_28_0.png
diff --git a/docs/notebooks/263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_42_1.png b/docs/notebooks/lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_42_1.png
similarity index 100%
rename from docs/notebooks/263-lcm-lora-controlnet-with-output_files/263-lcm-lora-controlnet-with-output_42_1.png
rename to docs/notebooks/lcm-lora-controlnet-with-output_files/lcm-lora-controlnet-with-output_42_1.png
diff --git a/docs/notebooks/121-legacy-mo-convert-to-openvino-with-output.rst b/docs/notebooks/legacy-mo-convert-to-openvino-with-output.rst
similarity index 95%
rename from docs/notebooks/121-legacy-mo-convert-to-openvino-with-output.rst
rename to docs/notebooks/legacy-mo-convert-to-openvino-with-output.rst
index 86231779d58..eaf936419c5 100644
--- a/docs/notebooks/121-legacy-mo-convert-to-openvino-with-output.rst
+++ b/docs/notebooks/legacy-mo-convert-to-openvino-with-output.rst
@@ -35,7 +35,7 @@ Table of contents:
 
     # Required imports. Please execute this cell first.
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu \
-    "openvino-dev>=2024.0.0" "requests" "tqdm" "transformers[onnx]>=4.21.1" "torch" "torchvision"
+    "openvino-dev>=2024.0.0" "requests" "tqdm" "transformers[onnx]>=4.21.1" "torch>=2.1" "torchvision"
 
 
 .. parsed-literal::
@@ -119,7 +119,7 @@ documentation.
                             conversion into IR. The legacy Frontend is Python
                             based and is available for TensorFlow*, ONNX*, MXNet*,
                             Caffe*, and Kaldi* models.
-      --input_model INPUT_MODEL, -m INPUT_MODEL, -w INPUT_MODEL
+      --input_model INPUT_MODEL, -w INPUT_MODEL, -m INPUT_MODEL
                             Tensorflow*: a file with a pre-trained model (binary
                             or text .pb file after freezing). Caffe*: a model
                             proto file with model weights.
@@ -714,18 +714,12 @@ NLP model from Hugging Face and export it in ONNX format:
     ONNX_NLP_MODEL_PATH = MODEL_DIRECTORY_PATH / "distilbert.onnx"
     
     # download model
-    hf_model = AutoModelForSequenceClassification.from_pretrained(
-        "distilbert-base-uncased-finetuned-sst-2-english"
-    )
+    hf_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
     # initialize tokenizer
-    tokenizer = AutoTokenizer.from_pretrained(
-        "distilbert-base-uncased-finetuned-sst-2-english"
-    )
+    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
     
     # get model onnx config function for output feature format sequence-classification
-    model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(
-        hf_model, feature="sequence-classification"
-    )
+    model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(hf_model, feature="sequence-classification")
     # fill onnx config based on pytorch model config
     onnx_config = model_onnx_config(hf_model.config)
     
@@ -741,19 +735,19 @@ NLP model from Hugging Face and export it in ONNX format:
 
 .. parsed-literal::
 
-    2024-03-12 22:51:16.046166: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:51:16.080867: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-17 23:28:06.943236: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 23:28:06.978999: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 22:51:16.593534: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-17 23:28:07.628516: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:246: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:246: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       mask, torch.tensor(torch.finfo(scores.dtype).min)
 
 
@@ -978,9 +972,7 @@ Convert PyTorch model to ONNX format:
     else:
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore")
-            torch.onnx.export(
-                model=pytorch_model, args=torch.randn(1, 3, 780, 520), f=ONNX_CV_MODEL_PATH
-            )
+            torch.onnx.export(model=pytorch_model, args=torch.randn(1, 3, 780, 520), f=ONNX_CV_MODEL_PATH)
         print(f"ONNX model exported to {ONNX_CV_MODEL_PATH}")
 
 
@@ -1022,8 +1014,8 @@ To convert a model to OpenVINO IR, use the following command:
 .. parsed-literal::
 
     [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/121-convert-to-openvino/model/distilbert.bin
+    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.xml
+    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.bin
 
 
 .. code:: ipython3
@@ -1122,8 +1114,8 @@ guide =2.1.0" "torchvision" "torchaudio" --index-url https://download.pytorch.org/whl/cpu
-    %pip install -q "openvino>=2023.2.0" "nncf>=2.7.0"  "sentencepiece" "tokenizers>=0.12.1" "transformers>=4.37.2" "gradio" "einops"
+    %pip install -q "openvino>=2023.2.0" "nncf>=2.7.0"  "sentencepiece" "tokenizers>=0.12.1" "transformers>=4.37.2" "gradio>=4.19" "einops"
 
 
 .. parsed-literal::
@@ -179,7 +179,7 @@ instruction.
         DEFAULT_IMAGE_PATCH_TOKEN,
         DEFAULT_IM_START_TOKEN,
         DEFAULT_IM_END_TOKEN,
-        DEFAULT_IMAGE_TOKEN
+        DEFAULT_IMAGE_TOKEN,
     )
     
     mm_use_im_start_end = getattr(config, "mm_use_im_start_end", False)
@@ -187,9 +187,7 @@ instruction.
     if mm_use_im_patch_token:
         tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
     if mm_use_im_start_end:
-        tokenizer.add_tokens(
-            [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True
-        )
+        tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
     
     if hasattr(config, "max_sequence_length"):
         context_len = config.max_sequence_length
@@ -264,14 +262,15 @@ on disk using ``ov.save_model``.
     from typing import Optional, Tuple, List
     import torch.nn.functional as F
     
-    warnings.filterwarnings('ignore')
+    warnings.filterwarnings("ignore")
     
     
     class ModelWrapper(torch.nn.Module):
         """
         Model wrapper class for export for spliting original forward logic on preparing multimodal data and inference using it.
-        That allows us to sperate image encoder and token embeddings model from general flow. 
+        That allows us to sperate image encoder and token embeddings model from general flow.
         """
+    
         def __init__(self, model):
             super().__init__()
             self.model = model
@@ -300,14 +299,14 @@ on disk using ``ov.save_model``.
     
             return (logits, tuple(outputs.past_key_values))
     
-        
+    
     def patch_model_forward(model):
         """
-        Helper function for patching model forward for model with past. 
-        It makes model more convinient for export to TorchScript format avoiding limitation 
+        Helper function for patching model forward for model with past.
+        It makes model more convinient for export to TorchScript format avoiding limitation
         that list of tensors can not be correctly traced as model input
         """
-        
+    
         orig_forward = model.forward
     
         @wraps(orig_forward)
@@ -317,7 +316,11 @@ on disk using ``ov.save_model``.
             attention_mask: torch.LongTensor,
         ):
             pkv_list = list(past_key_values)
-            outs = orig_forward(input_ids=input_ids, past_key_values=pkv_list, attention_mask=attention_mask,)
+            outs = orig_forward(
+                input_ids=input_ids,
+                past_key_values=pkv_list,
+                attention_mask=attention_mask,
+            )
             return outs
     
         model.forward = ts_patched_forward
@@ -347,13 +350,20 @@ on disk using ``ov.save_model``.
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
     
-    def postprocess_converted_model(ov_model, example_input=None, input_names=None, output_names=None, dynamic_shapes=None):
+    
+    def postprocess_converted_model(
+        ov_model,
+        example_input=None,
+        input_names=None,
+        output_names=None,
+        dynamic_shapes=None,
+    ):
         """
         Helper function for appling postprocessing on converted model with updating input names, shapes and output names
         acording to requested specification
         """
         flatten_example_inputs = flattenize_inputs(example_input) if example_input else []
-        
+    
         if input_names:
             for inp_name, m_input, input_data in zip(input_names, ov_model.inputs, flatten_example_inputs):
                 input_node = m_input.get_node()
@@ -365,7 +375,7 @@ on disk using ``ov.save_model``.
                         shape[k] = -1
                 input_node.set_partial_shape(ov.PartialShape(shape))
                 m_input.get_tensor().set_names({inp_name})
-        
+    
         if output_names:
             for out, out_name in zip(ov_model.outputs, output_names):
                 out.get_tensor().set_names({out_name})
@@ -373,9 +383,12 @@ on disk using ``ov.save_model``.
         return ov_model
     
     
-    def convert_llava_mpt(pt_model: torch.nn.Module, model_path: Path,
-                          image_encoder_wc_parameters: Optional[dict] = None,
-                          llava_wc_parameters: Optional[dict] = None):
+    def convert_llava_mpt(
+        pt_model: torch.nn.Module,
+        model_path: Path,
+        image_encoder_wc_parameters: Optional[dict] = None,
+        llava_wc_parameters: Optional[dict] = None,
+    ):
         """
         LLaVA MPT model conversion function
     
@@ -396,7 +409,9 @@ on disk using ``ov.save_model``.
         if not image_encoder_path.exists():
             model.forward = model.encode_images
             ov_model = ov.convert_model(
-                model, example_input=torch.zeros((1, 3, 224, 224)), input=[(-1, 3, 224, 224)]
+                model,
+                example_input=torch.zeros((1, 3, 224, 224)),
+                input=[(-1, 3, 224, 224)],
             )
             if image_encoder_wc_parameters is not None:
                 print("Applying weight compression to image encoder")
@@ -409,9 +424,7 @@ on disk using ``ov.save_model``.
     
         if not token_embedding_model_path.exists():
             model.forward = model.get_model().embed_tokens
-            ov_model = ov.convert_model(
-                model, example_input=torch.ones((1, 10), dtype=torch.long)
-            )
+            ov_model = ov.convert_model(model, example_input=torch.ones((1, 10), dtype=torch.long))
             ov.save_model(ov_model, token_embedding_model_path)
             cleanup_torchscript_cache()
             del ov_model
@@ -439,9 +452,7 @@ on disk using ``ov.save_model``.
     
         inputs.extend(["attention_mask"])
         if not first_stage_model_path.exists():
-            ov_model = ov.convert_model(
-                model_wrap, example_input=example_input_first_stage
-            )
+            ov_model = ov.convert_model(model_wrap, example_input=example_input_first_stage)
             ov_model = postprocess_converted_model(ov_model, output_names=outputs)
             if llava_wc_parameters is not None:
                 print("Applying weight compression to first stage LLava model")
@@ -450,22 +461,21 @@ on disk using ``ov.save_model``.
             cleanup_torchscript_cache()
             del ov_model
             gc.collect()
-                
     
         if not second_stage_model_path.exists():
             model_wrap = patch_model_forward(model_wrap)
             example_input_second_stage = {
                 "input_ids": torch.ones((1, 1), dtype=torch.long),
                 "past_key_values": outs[1],
-                "attention_mask": torch.ones((1, outs[1][-1][-1].shape[-2] + 1), dtype=torch.long)
+                "attention_mask": torch.ones((1, outs[1][-1][-1].shape[-2] + 1), dtype=torch.long),
             }
             ov_model = ov.convert_model(model_wrap, example_input=example_input_second_stage)
             ov_model = postprocess_converted_model(
-                ov_model, 
-                example_input=example_input_second_stage.values(), 
-                input_names=inputs, 
-                output_names=outputs, 
-                dynamic_shapes=dynamic_shapes
+                ov_model,
+                example_input=example_input_second_stage.values(),
+                input_names=inputs,
+                output_names=outputs,
+                dynamic_shapes=dynamic_shapes,
             )
             if llava_wc_parameters is not None:
                 print("Applying weight compression to second stage LLava model")
@@ -565,9 +575,9 @@ compression instead of INT8 weight compression.
     import ipywidgets as widgets
     
     compression_mode = widgets.Dropdown(
-        options=['INT4', 'INT8'],
-        value='INT4',
-        description='Compression mode:',
+        options=["INT4", "INT8"],
+        value="INT4",
+        description="Compression mode:",
         disabled=False,
     )
     
@@ -584,7 +594,7 @@ compression instead of INT8 weight compression.
 
 .. code:: ipython3
 
-    if compression_mode.value == 'INT4':
+    if compression_mode.value == "INT4":
         compressed_model_dir = Path("llava-mpt/INT4_compressed_weights")
         llava_wc_parameters = dict(mode=nncf.CompressWeightsMode.INT4_ASYM, group_size=128, ratio=0.8)
     else:
@@ -604,9 +614,12 @@ compression instead of INT8 weight compression.
     
         model.eval()
         with torch.no_grad():
-            convert_llava_mpt(model, compressed_model_dir,
-                              image_encoder_wc_parameters=dict(mode=nncf.CompressWeightsMode.INT8),
-                              llava_wc_parameters=llava_wc_parameters)
+            convert_llava_mpt(
+                model,
+                compressed_model_dir,
+                image_encoder_wc_parameters=dict(mode=nncf.CompressWeightsMode.INT8),
+                llava_wc_parameters=llava_wc_parameters,
+            )
         del model
         gc.collect();
 
@@ -804,21 +817,11 @@ documentation  CausalLMOutputWithPast:
-            return self.forward(
-                input_ids, images, attention_mask, prefix_mask, past_key_values
-            )
+            return self.forward(input_ids, images, attention_mask, prefix_mask, past_key_values)
     
         def forward(
             self,
@@ -861,11 +862,7 @@ documentation  0:
                     cur_image_features = image_features[cur_image_idx]
                     image_token_start = image_token_indices[0]
-                    if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr(
-                        self.config, "mm_use_im_start_end", False
-                    ):
+                    if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr(self.config, "mm_use_im_start_end", False):
                         embd = self.token_embed(cur_input_ids[: image_token_start - 1].unsqueeze(0))[0][0]
                         cur_new_input_embeds.append(embd)
                         embd = self.token_embed(cur_input_ids[image_token_start - 1 : image_token_start].unsqueeze(0))[0][0]
@@ -941,17 +927,13 @@ documentation  0:
-                    if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr(
-                        self.config, "mm_use_im_start_end", False
-                    ):
+                    if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr(self.config, "mm_use_im_start_end", False):
                         cur_new_input_embeds.append(self.token_embed(cur_input_ids.unsqueeze(0))[0][0])
                     else:
                         cur_new_input_embeds.append(self.token_embed(cur_input_ids.unsqueeze(0))[0][0])
@@ -979,19 +961,23 @@ documentation  Tuple[Tuple[torch.Tensor]]:
+        def _reorder_cache(self, past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
             """
             This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
             [`~PreTrainedModel.beam_sample`] is called.
@@ -1045,10 +1033,7 @@ documentation ' not in text:
-                text = text + '\n'
-            text = (text, image, 'Resize')
+            if "" not in text:
+                text = text + "\n"
+            text = (text, image, "Resize")
         conv.append_message(conv.roles[0], text)
         conv.append_message(conv.roles[1], None)
         conv.skip_next = False
@@ -1332,13 +1319,14 @@ Interactive demo
         # Initialize an empty string to store the generated text
         partial_text = ""
         for new_text in streamer:
-            if not new_text: 
+            if not new_text:
                 continue
             partial_text += new_text
             conv.messages[-1][-1] = partial_text
             history[-1][1] = partial_text
             yield history
     
+    
     with gr.Blocks(title="LLaVA") as demo:
         gr.Markdown(title_markdown)
     
@@ -1346,16 +1334,42 @@ Interactive demo
             with gr.Column():
                 imagebox = gr.Image(type="pil")
                 with gr.Accordion("Parameters", open=False, visible=True) as parameter_row:
-                    temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True, label="Temperature",)
-                    top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Top P",)
-                    max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True, label="Max output tokens",)
+                    temperature = gr.Slider(
+                        minimum=0.0,
+                        maximum=1.0,
+                        value=0.2,
+                        step=0.1,
+                        interactive=True,
+                        label="Temperature",
+                    )
+                    top_p = gr.Slider(
+                        minimum=0.0,
+                        maximum=1.0,
+                        value=0.7,
+                        step=0.1,
+                        interactive=True,
+                        label="Top P",
+                    )
+                    max_output_tokens = gr.Slider(
+                        minimum=0,
+                        maximum=1024,
+                        value=512,
+                        step=64,
+                        interactive=True,
+                        label="Max output tokens",
+                    )
     
             with gr.Column(scale=3):
                 with gr.Column(scale=6):
                     chatbot = gr.Chatbot(height=400)
                     with gr.Row():
                         with gr.Column(scale=8):
-                            textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", visible=True, container=False)
+                            textbox = gr.Textbox(
+                                show_label=False,
+                                placeholder="Enter text and press ENTER",
+                                visible=True,
+                                container=False,
+                            )
                         with gr.Column(scale=1, min_width=60):
                             submit_btn = gr.Button(value="Submit", visible=True)
                     with gr.Row(visible=True) as button_row:
@@ -1363,14 +1377,16 @@ Interactive demo
     
         gr.Markdown(tos_markdown)
     
-                
         submit_event = textbox.submit(
             fn=user,
             inputs=[textbox, chatbot],
             outputs=[textbox, chatbot],
             queue=False,
         ).then(
-            bot, [imagebox, chatbot, temperature, top_p, max_output_tokens], chatbot, queue=True
+            bot,
+            [imagebox, chatbot, temperature, top_p, max_output_tokens],
+            chatbot,
+            queue=True,
         )
         # Register listeners
         clear_btn.click(clear_history, [textbox, imagebox, chatbot], [chatbot, textbox, imagebox])
@@ -1379,7 +1395,12 @@ Interactive demo
             inputs=[textbox, chatbot],
             outputs=[textbox, chatbot],
             queue=False,
-        ).then(bot, [imagebox, chatbot, temperature, top_p, max_output_tokens], chatbot, queue=True)
+        ).then(
+            bot,
+            [imagebox, chatbot, temperature, top_p, max_output_tokens],
+            chatbot,
+            queue=True,
+        )
     
     # if you are launching remotely, specify server_name and server_port
     # demo.launch(server_name='your server name', server_port='server port in int')
diff --git a/docs/notebooks/257-llava-multimodal-chatbot-with-output_files/257-llava-multimodal-chatbot-with-output_20_1.jpg b/docs/notebooks/llava-multimodal-chatbot-with-output_files/llava-multimodal-chatbot-with-output_20_1.jpg
similarity index 100%
rename from docs/notebooks/257-llava-multimodal-chatbot-with-output_files/257-llava-multimodal-chatbot-with-output_20_1.jpg
rename to docs/notebooks/llava-multimodal-chatbot-with-output_files/llava-multimodal-chatbot-with-output_20_1.jpg
diff --git a/docs/notebooks/257-llava-multimodal-chatbot-with-output_files/257-llava-multimodal-chatbot-with-output_20_1.png b/docs/notebooks/llava-multimodal-chatbot-with-output_files/llava-multimodal-chatbot-with-output_20_1.png
similarity index 100%
rename from docs/notebooks/257-llava-multimodal-chatbot-with-output_files/257-llava-multimodal-chatbot-with-output_20_1.png
rename to docs/notebooks/llava-multimodal-chatbot-with-output_files/llava-multimodal-chatbot-with-output_20_1.png
diff --git a/docs/notebooks/llava-next-multimodal-chatbot-with-output.rst b/docs/notebooks/llava-next-multimodal-chatbot-with-output.rst
new file mode 100644
index 00000000000..510cc121ce8
--- /dev/null
+++ b/docs/notebooks/llava-next-multimodal-chatbot-with-output.rst
@@ -0,0 +1,1372 @@
+Visual-language assistant with LLaVA Next and OpenVINO
+======================================================
+
+`LLaVA-NeXT `__
+is new generation of LLaVA model family that marks breakthrough in
+advanced language reasoning over images, introducing improved OCR and
+expanded world knowledge. `LLaVA `__ (Large
+Language and Vision Assistant) is large multimodal model that aims to
+develop a general-purpose visual assistant that can follow both language
+and image instructions to complete various real-world tasks. The idea is
+to combine the power of large language models (LLMs) with vision
+encoders like CLIP to create an end-to-end trained neural assistant that
+understands and acts upon multimodal instructions.
+
+In this tutorial we consider how to convert and optimize LLaVA-NeXT
+model from Transformers library for creating multimodal chatbot. We will
+utilize the power of
+`llava-v1.6-mistral-7b `__
+model for creating multimodal chatbot, but the similar actions are also
+applicable to other models of LLaVA family compatible with HuggingFace
+transformers implementation. Additionally, we demonstrate how to apply
+stateful transformation on LLM part and model optimization techniques
+like weights compression and quantization using
+`NNCF `__
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+-  `Download PyTorch model <#download-pytorch-model>`__
+-  `Convert model to OpenVINO Intermediate
+   Representation <#convert-model-to-openvino-intermediate-representation>`__
+
+   -  `Image Encoder <#image-encoder>`__
+   -  `Text Embedding <#text-embedding>`__
+   -  `Language Model <#language-model>`__
+
+-  `Compress Language Model Weights to 4
+   bits <#compress-language-model-weights-to-4-bits>`__
+-  `Quantize Image Encoder to 8
+   bits <#quantize-image-encoder-to-8-bits>`__
+
+   -  `Prepare datasets <#prepare-datasets>`__
+   -  `Perform quantization <#perform-quantization>`__
+
+-  `Prepare model inference
+   pipeline <#prepare-model-inference-pipeline>`__
+-  `Run OpenVINO model inference <#run-openvino-model-inference>`__
+
+   -  `Select device <#select-device>`__
+
+-  `Interactive demo <#interactive-demo>`__
+
+Prerequisites
+-------------
+
+
+
+.. code:: ipython3
+
+    %pip install -q "openvino>=2024.0.0" "nncf>=2.9.0" "torch>=2.1" "transformers>=4.39.1" "accelerate" "pillow" "gradio>=4.26" "datasets>=2.14.6" "tqdm" --extra-index-url https://download.pytorch.org/whl/cpu
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. code:: ipython3
+
+    from pathlib import Path
+    
+    MODEL_DIR = Path("model")
+    IMAGE_ENCODER_PATH = MODEL_DIR / "image_encoder.xml"
+    INPUT_EMBEDDING_PATH = MODEL_DIR / "input_embeddings.xml"
+    LANGUAGE_MODEL_PATH = MODEL_DIR / "language_model.xml"
+    
+    requires_pt_model_loading = not all([p.exists() for p in [IMAGE_ENCODER_PATH, INPUT_EMBEDDING_PATH, LANGUAGE_MODEL_PATH]])
+
+Download PyTorch model
+----------------------
+
+
+
+.. code:: ipython3
+
+    from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
+    import torch
+    import gc
+    
+    processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
+    image_encoder_model, input_embedding_model, language_model = None, None, None
+    
+    
+    class ImageEncoder(torch.nn.Module):
+        def __init__(self, config, vision_tower, multi_modal_projector):
+            super().__init__()
+            self.config = config
+            self.vision_tower = vision_tower
+            self.multi_modal_projector = multi_modal_projector
+    
+        def forward(self, pixel_values):
+            batch_size, num_patches, num_channels, height, width = pixel_values.shape
+            reshaped_pixel_values = pixel_values.view(batch_size * num_patches, num_channels, height, width)
+            image_features = self.vision_tower(reshaped_pixel_values, output_hidden_states=True)
+            selected_image_feature = image_features.hidden_states[self.config.vision_feature_layer]
+            if self.config.vision_feature_select_strategy == "default":
+                selected_image_feature = selected_image_feature[:, 1:]
+            elif self.config.vision_feature_select_strategy == "full":
+                selected_image_feature = selected_image_feature
+            image_features = self.multi_modal_projector(selected_image_feature)
+            return image_features
+    
+    
+    if requires_pt_model_loading:
+        model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", low_cpu_mem_usage=True)
+        model.config.save_pretrained(MODEL_DIR)
+        image_encoder_model = ImageEncoder(model.config, model.vision_tower, model.multi_modal_projector)
+        input_embedding_model = input_embedding_model = model.get_input_embeddings()
+        language_model = model.language_model
+        del model
+        gc.collect()
+
+
+.. parsed-literal::
+
+    2024-04-04 12:27:23.875042: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-04 12:27:23.877406: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
+    2024-04-04 12:27:23.907479: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+    2024-04-04 12:27:23.907505: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
+    2024-04-04 12:27:23.907525: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+    2024-04-04 12:27:23.913713: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
+    2024-04-04 12:27:23.914384: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    2024-04-04 12:27:24.847675: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+
+
+OpenVINO## Convert model to OpenVINO Intermediate Representation 
+
+
+OpenVINO supports PyTorch models via conversion to OpenVINO Intermediate
+Representation (IR). `OpenVINO model conversion
+API `__
+should be used for these purposes. ``ov.convert_model`` function accepts
+original PyTorch model instance and example input for tracing and
+returns ``ov.Model`` representing this model in OpenVINO framework.
+Converted model can be used for saving on disk using ``ov.save_model``
+function or directly loading on device using ``core.complie_model``.
+
+LLaVA-NeXT is autoregressive transformer generative model, it means that
+each next model step depends from model output from previous step. The
+generation approach is based on the assumption that the probability
+distribution of a word sequence can be decomposed into the product of
+conditional next word distributions. In other words, model predicts the
+next token in the loop guided by previously generated tokens until the
+stop-condition will be not reached (generated sequence of maximum length
+or end of string token obtained). The way the next token will be
+selected over predicted probabilities is driven by the selected decoding
+methodology. You can find more information about the most popular
+decoding methods in this
+`blog `__. The entry point
+for the generation process for models from the Hugging Face Transformers
+library is the ``generate`` method. You can find more information about
+its parameters and configuration in the
+`documentation `__.
+To preserve flexibility in the selection decoding methodology, we will
+convert only model inference for one step.
+
+The inference flow has difference on first step and for the next. On the
+first step, model accept preprocessed input instruction and image, that
+transformed to the unified embedding space using ``input_embedding`` and
+``image_encoder`` models, after that ``language model``, LLM-based part
+of model, runs on input embeddings to predict probability of next
+generated tokens. On the next step, ``language_model`` accepts only next
+token id selected based on sampling strategy and processed by
+``input_embedding`` model and cached attention key and values. Since the
+output side is auto-regressive, an output token hidden state remains the
+same once computed for every further generation step. Therefore,
+recomputing it every time you want to generate a new token seems
+wasteful. With the cache, the model saves the hidden state once it has
+been computed. The model only computes the one for the most recently
+generated output token at each time step, re-using the saved ones for
+hidden tokens. This reduces the generation complexity from
+:math:`O(n^3)` to :math:`O(n^2)` for a transformer model. More details
+about how it works can be found in this
+`article `__.
+
+To sum up above, model consists of 3 parts:
+
+-  **Image Encoder** for encoding input images into embedding space
+-  **Input Embedding** for conversion input text tokens into embedding
+   space
+-  **Language Model** for generation answer based on input embeddings
+   provided by Image Encoder and Input Embedding models.
+
+Let’s convert each model part.
+
+Image Encoder
+~~~~~~~~~~~~~
+
+
+
+Image Encoder is represented in LLaVA by pretrained CLIP model.
+
+.. code:: ipython3
+
+    import torch
+    import openvino as ov
+    import gc
+    
+    
+    def cleanup_torchscript_cache():
+        """
+        Helper for removing cached model representation
+        """
+        torch._C._jit_clear_class_registry()
+        torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
+        torch.jit._state._clear_class_state()
+    
+    
+    if not IMAGE_ENCODER_PATH.exists():
+        ov_image_encoder = ov.convert_model(image_encoder_model, example_input=torch.zeros((1, 5, 3, 336, 336)))
+        ov.save_model(ov_image_encoder, IMAGE_ENCODER_PATH)
+        del ov_image_encoder
+        cleanup_torchscript_cache()
+    
+    del image_encoder_model
+    gc.collect();
+
+Text Embedding
+~~~~~~~~~~~~~~
+
+
+
+In LLMs, input embedding is a part of language model, but for LLaVA the
+first step hidden state produced by this model part should be integrated
+with image embeddings into common embedding space. For ability to reuse
+this model part and avoid introduction of llm model instance, we will
+use it separately.
+
+.. code:: ipython3
+
+    llm_input = None
+    
+    if not LANGUAGE_MODEL_PATH.exists():
+        llm_input = input_embedding_model(torch.ones((2, 2), dtype=torch.int64))
+    
+    if not INPUT_EMBEDDING_PATH.exists():
+        ov_input_embeddings_model = ov.convert_model(input_embedding_model, example_input=torch.ones((2, 2), dtype=torch.int64))
+        ov.save_model(ov_input_embeddings_model, INPUT_EMBEDDING_PATH)
+        del ov_input_embeddings_model
+        cleanup_torchscript_cache()
+    
+    del input_embedding_model
+    gc.collect();
+
+Language Model
+~~~~~~~~~~~~~~
+
+
+
+Language Model is responsible for generation answer in LLaVA. This part
+is very similar to standard LLM for text generation. Our model uses
+`mistralai/Mistral-7B-Instruct-v0.2 `__
+as base LLM. To optimize the generation process and use memory more
+efficiently, HuggingFace transformers API provides a mechanism for
+caching model state externally using ``use_cache=True`` parameter and
+``past_key_values`` argument in inputs and outputs. With the cache, the
+model saves the hidden state once it has been computed. The model only
+computes the one for the most recently generated output token at each
+time step, re-using the saved ones for hidden tokens. This reduces the
+generation complexity from :math:`O(n^3)` to :math:`O(n^2)` for a
+transformer model. With this option, the model gets the previous step’s
+hidden states (cached attention keys and values) as input and
+additionally provides hidden states for the current step as output. It
+means for all next iterations, it is enough to provide only a new token
+obtained from the previous step and cached key values to get the next
+token prediction.
+
+With increasing model size like in modern LLMs, we also can note an
+increase in the number of attention blocks and size past key values
+tensors respectively. The strategy for handling cache state as model
+inputs and outputs in the inference cycle may become a bottleneck for
+memory-bounded systems, especially with processing long input sequences,
+for example in a chatbot scenario. OpenVINO suggests a transformation
+that removes inputs and corresponding outputs with cache tensors from
+the model keeping cache handling logic inside the model. Such models are
+also called stateful. A stateful model is a model that implicitly
+preserves data between two consecutive inference calls. The tensors
+saved from one run are kept in an internal memory buffer called a
+``state`` or a ``variable`` and may be passed to the next run, while
+never being exposed as model output. Hiding the cache enables storing
+and updating the cache values in a more device-friendly representation.
+It helps to reduce memory consumption and additionally optimize model
+performance. More details about stateful models and working with state
+can be found in `OpenVINO
+documentation `__.
+
+.. code:: ipython3
+
+    from typing import Optional, Tuple, List
+    from openvino.runtime import opset13
+    import numpy as np
+    
+    
+    def model_has_state(ov_model: ov.Model):
+        # TODO: Provide a better way based on the variables availability, but OV Python API doesn't expose required methods
+        return len(ov_model.get_sinks()) > 0
+    
+    
+    def model_has_input_output_name(ov_model: ov.Model, name: str):
+        """
+        Helper function for checking that model has specified input or output name
+    
+        Parameters:
+          ov_model (ov.Model):   # TODO: Can we derive the dimensions from the model topology?
+          name (str):
+              name of input or output
+    
+        Returns:
+          True if input or output with requested name exists else False
+        """
+        return name in sum([list(t.get_names()) for t in ov_model.inputs + ov_model.outputs], [])
+    
+    
+    def fuse_cache_reorder(
+        ov_model: ov.Model,
+        not_kv_inputs: List[str],
+        key_value_input_names: List[str],
+        gather_dim: int,
+    ):
+        """
+        Fuses reored_cache during generate cycle into ov.Model. Used with stateful models, because we can not modify model state directly.
+    
+        Adds a new beam_idx parameter and Gather op per each kv-cache input in a given model.
+        Should be run before make_stateful. Implements optimumum's _reorder_cache
+        inside the model in the beginning of each iteration.
+        Gather works along given gather_dim dimension that may vary from model to model.
+        KV-cache inputs are identified based on names in key_value_input_names.
+        Append the new beam_idx parameter to not_kv_inputs.
+    
+        Parameters:
+          ov_model (`ov.Model`):
+              openvino model for processing
+          not_kv_inputs (`List[str]`):
+              list of input nodes in model that not related to past key values
+          key_value_input_names (`List[str]`):
+              list of names for key value input layers
+          gather_dim (int):
+              dimension for gathering cache during reorder pass
+        """
+    
+        if model_has_input_output_name(ov_model, "beam_idx"):
+            raise ValueError("Model already has fused cache")
+        input_batch = ov_model.input("inputs_embeds").get_partial_shape()[0]
+        beam_idx = opset13.parameter(name="beam_idx", dtype=ov.Type.i32, shape=ov.PartialShape([input_batch]))
+        beam_idx.output(0).get_tensor().add_names({"beam_idx"})  # why list is not accepted?
+        ov_model.add_parameters([beam_idx])
+        not_kv_inputs.append(ov_model.inputs[-1])
+        # Go over all cache parameters and fuse _reorder_cache with indices provided by the new parameter beam_idx
+        for input_name in key_value_input_names:
+            parameter_output_port = ov_model.input(input_name)
+            consumers = parameter_output_port.get_target_inputs()
+            gather = opset13.gather(parameter_output_port, beam_idx, opset13.constant(gather_dim))
+            for consumer in consumers:
+                consumer.replace_source_output(gather.output(0))
+        ov_model.validate_nodes_and_infer_types()
+    
+    
+    def build_state_initializer(ov_model: ov.Model, batch_dim: int):
+        """
+        Build initialization ShapeOf Expression for all ReadValue ops
+    
+        Parameters:
+          ov_model (ov.Model):
+              openvino model
+          batch_dim (int):
+              index of dimension corresponding to batch size
+        """
+        input_ids = ov_model.input("inputs_embeds")
+        batch = opset13.gather(
+            opset13.shape_of(input_ids, output_type="i64"),
+            opset13.constant([0]),
+            opset13.constant(0),
+        )
+        for op in ov_model.get_ops():
+            if op.get_type_name() == "ReadValue":
+                dims = [dim.min_length for dim in list(op.get_output_partial_shape(0))]
+                dims[batch_dim] = batch
+                dims = [(opset13.constant(np.array([dim], dtype=np.int64)) if isinstance(dim, int) else dim) for dim in dims]
+                shape = opset13.concat(dims, axis=0)
+                broadcast = opset13.broadcast(opset13.constant(0.0, dtype=op.get_output_element_type(0)), shape)
+                op.set_arguments([broadcast])
+        ov_model.validate_nodes_and_infer_types()
+    
+    
+    def make_stateful(
+        ov_model: ov.Model,
+        not_kv_inputs: List[str],
+        key_value_input_names: List[str],
+        key_value_output_names: List[str],
+        batch_dim: int,
+        num_attention_heads: int,
+        num_beams_and_batch: int = None,
+    ):
+        """
+        Hides kv-cache inputs and outputs inside the model as variables.
+    
+        Parameters:
+            ov_model (ov.Model):
+                openvino model
+            not_kv_inputs (`List[str]`):
+                list of input nodes in model that not related to past key values
+            key_value_input_names (`List[str]`):
+                list of names for key value input layers
+            key_value_output_names (`List[str]`):
+                list of names for key value input layers
+            batch_dim (int):
+                index of batch dimension in key value layers
+            num_attention_heads (int):
+                number of attention heads for batch dimension initialization
+            num_beams_an_batch (int):
+                precalculated number of beams and batch for shapes initialization
+        """
+        from openvino._offline_transformations import apply_make_stateful_transformation
+    
+        input_output_map = {}
+    
+        if num_beams_and_batch is not None:
+            # Set batch size for input_ids and attention mask to avoid dynamic dimension got propagated from the end of the model back to ReadValue
+            for input in not_kv_inputs:
+                shape = input.get_partial_shape()
+                if shape.rank.get_length() <= 2:  # == 1 for beam_index
+                    shape[0] = num_beams_and_batch
+                    input.get_node().set_partial_shape(shape)
+        for kv_name_pair in zip(key_value_input_names, key_value_output_names):
+            input_output_map[kv_name_pair[0]] = kv_name_pair[1]
+            if num_beams_and_batch is not None:
+                input = ov_model.input(kv_name_pair[0])
+                shape = input.get_partial_shape()
+                shape[batch_dim] = num_beams_and_batch * num_attention_heads
+                input.get_node().set_partial_shape(shape)
+    
+        if num_beams_and_batch is not None:
+            # Re-validation model if shapes are altered above
+            ov_model.validate_nodes_and_infer_types()
+    
+        apply_make_stateful_transformation(ov_model, input_output_map)
+        if num_beams_and_batch is None:
+            build_state_initializer(ov_model, batch_dim)
+    
+    
+    def patch_stateful(ov_model):
+        key_value_input_names = [key.get_any_name() for key in ov_model.inputs[2:-1]]
+        key_value_output_names = [key.get_any_name() for key in ov_model.outputs[1:]]
+        not_kv_inputs = [input for input in ov_model.inputs if not any(name in key_value_input_names for name in input.get_names())]
+        if not key_value_input_names or not key_value_output_names:
+            return
+        batch_dim = 0
+        num_attention_heads = 1
+    
+        fuse_cache_reorder(ov_model, not_kv_inputs, key_value_input_names, batch_dim)
+        make_stateful(
+            ov_model,
+            not_kv_inputs,
+            key_value_input_names,
+            key_value_output_names,
+            batch_dim,
+            num_attention_heads,
+            None,
+        )
+
+.. code:: ipython3
+
+    make_stateful_model = True
+    core = ov.Core()
+    
+    if not LANGUAGE_MODEL_PATH.exists():
+        pkv = language_model(inputs_embeds=llm_input, attention_mask=torch.ones((2, 2), dtype=torch.int64))[1]
+        model_inputs = ["attention_mask", "position_ids"]
+        model_outputs = ["logits"]
+        for idx in range(len(pkv)):
+            model_inputs.extend([f"past_key_values.{idx}.key", f"past_key_values.{idx}.value"])
+            model_outputs.extend([f"present.{idx}.key", f"present.{idx}.value"])
+        model_inputs.append("inputs_embeds")
+        language_model.config.torchscript = True
+        position_ids = torch.tensor([[2, 3], [2, 3]])
+        ov_model = ov.convert_model(
+            language_model,
+            example_input={
+                "inputs_embeds": llm_input,
+                "attention_mask": torch.ones((2, 4)),
+                "past_key_values": pkv,
+                "position_ids": position_ids,
+            },
+        )
+    
+        for input, input_name in zip(ov_model.inputs, model_inputs):
+            input.get_tensor().set_names({input_name})
+    
+        for output, output_name in zip(ov_model.outputs, model_outputs):
+            output.get_tensor().set_names({output_name})
+        if make_stateful_model:
+            patch_stateful(ov_model)
+        ov.save_model(ov_model, LANGUAGE_MODEL_PATH)
+        del ov_model
+        cleanup_torchscript_cache()
+        del language_model
+        gc.collect()
+
+Compress Language Model Weights to 4 bits
+-----------------------------------------
+
+
+
+For reducing memory consumption, weights compression optimization can be
+applied using `NNCF `__. Weight
+compression aims to reduce the memory footprint of a model. It can also
+lead to significant performance improvement for large memory-bound
+models, such as Large Language Models (LLMs). LLMs and other models,
+which require extensive memory to store the weights during inference,
+can benefit from weight compression in the following ways:
+
+-  enabling the inference of exceptionally large models that cannot be
+   accommodated in the memory of the device;
+
+-  improving the inference performance of the models by reducing the
+   latency of the memory access when computing the operations with
+   weights, for example, Linear layers.
+
+`Neural Network Compression Framework
+(NNCF) `__ provides 4-bit /
+8-bit mixed weight quantization as a compression method primarily
+designed to optimize LLMs. The main difference between weights
+compression and full model quantization (post-training quantization) is
+that activations remain floating-point in the case of weights
+compression which leads to a better accuracy. Weight compression for
+LLMs provides a solid inference performance improvement which is on par
+with the performance of the full model quantization. In addition, weight
+compression is data-free and does not require a calibration dataset,
+making it easy to use.
+
+``nncf.compress_weights`` function can be used for performing weights
+compression. The function accepts an OpenVINO model and other
+compression parameters. Compared to INT8 compression, INT4 compression
+improves performance even more, but introduces a minor drop in
+prediction quality.
+
+More details about weights compression, can be found in `OpenVINO
+documentation `__.
+
+   **Note:** weights compression process may require additional time and
+   memory for performing. You can disable it using widget below:
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    to_compress_weights = widgets.Checkbox(
+        value=True,
+        description="Weights Compression",
+        disabled=False,
+    )
+    
+    to_compress_weights
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Weights Compression')
+
+
+
+.. code:: ipython3
+
+    import nncf
+    
+    compression_configuration = {
+        "mode": nncf.CompressWeightsMode.INT4_SYM,
+        "group_size": 64,
+        "ratio": 0.6,
+    }
+    
+    LANGUAGE_MODEL_PATH_INT4 = LANGUAGE_MODEL_PATH.parent / LANGUAGE_MODEL_PATH.name.replace(".xml", "-int4.xml")
+    if to_compress_weights.value and not LANGUAGE_MODEL_PATH_INT4.exists():
+        ov_model = core.read_model(LANGUAGE_MODEL_PATH)
+        ov_compressed_model = nncf.compress_weights(ov_model, **compression_configuration)
+        ov.save_model(ov_compressed_model, LANGUAGE_MODEL_PATH_INT4)
+        del ov_compressed_model
+        del ov_model
+        gc.collect()
+
+
+.. parsed-literal::
+
+    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
+
+
+Quantize Image Encoder to 8 bits
+--------------------------------
+
+
+
+The goal of this part of tutorial is to demonstrate how to speed up the
+image encoder by applying 8-bit post-training quantization from
+`NNCF `__ (Neural Network
+Compression Framework) and infer quantized model via OpenVINO™ Toolkit.
+`NNCF `__ enables
+post-training quantization by adding quantization layers into model
+graph and then using a subset of the training dataset to initialize the
+parameters of these additional quantization layers. Quantized operations
+are executed in ``INT8`` instead of ``FP32``/``FP16`` making model
+inference faster. The optimization process contains the following steps:
+
+1. Prepare quantization dataset
+2. Quantize the converted OpenVINO model with NNCF.
+3. Save quantized model on disk for next usage.
+
+..
+
+   **Note:** quantization process may require additional time and memory
+   for performing. You can disable it using widget below:
+
+.. code:: ipython3
+
+    to_quantize = widgets.Checkbox(
+        value=True,
+        description="Quantization",
+        disabled=False,
+    )
+    
+    to_quantize
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Quantization')
+
+
+
+.. code:: ipython3
+
+    IMAGE_ENCODER_PATH_INT8 = IMAGE_ENCODER_PATH.parent / IMAGE_ENCODER_PATH.name.replace(".xml", "-int4.xml")
+    
+    
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
+    
+    %load_ext skip_kernel_extension
+
+Prepare datasets
+~~~~~~~~~~~~~~~~
+
+
+
+The `Conceptual
+Captions `__ dataset
+consisting of ~3.3M images annotated with captions is used to quantize
+model.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    import requests
+    from io import BytesIO
+    import numpy as np
+    from PIL import Image
+    from requests.packages.urllib3.exceptions import InsecureRequestWarning
+    requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
+    
+    
+    def get_pil_from_url(url):
+        """
+        Downloads and converts an image from a URL to a PIL Image object.
+        """
+        response = requests.get(url, verify=False, timeout=20)
+        image = Image.open(BytesIO(response.content))
+        return image.convert("RGB")
+    
+    def collate_fn(example, image_column="image_url"):
+        """
+        Preprocesses an example by loading and transforming image and text data.
+        Checks if the text data in the example is valid by calling the `check_text_data` function.
+        Downloads the image specified by the URL in the image_column by calling the `get_pil_from_url` function.
+        If there is any error during the download process, returns None.
+        Returns the preprocessed inputs with transformed image and text data.
+        """
+        assert len(example) == 1
+        example = example[0]
+        url = example[image_column]
+        try:
+            image = get_pil_from_url(url)
+            h, w = image.size
+            if h == 1 or w == 1:
+                return None
+        except Exception:
+            return None
+    
+        inputs = processor.image_processor(images=[image], return_tensors="pt")
+        return inputs
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    import torch
+    from datasets import load_dataset
+    from tqdm.notebook import tqdm
+    
+    def prepare_calibration_data(dataloader, init_steps):
+        """
+        This function prepares calibration data from a dataloader for a specified number of initialization steps.
+        It iterates over the dataloader, fetching batches and storing the relevant data.
+        """
+        data = []
+        print(f"Fetching {init_steps} samples for the initialization...")
+        with tqdm(total=init_steps) as pbar:
+            for batch in dataloader:
+                if len(data) == init_steps:
+                    break
+                if batch:
+                    pbar.update(1)
+                    with torch.no_grad():
+                        data.append(
+                            {
+                                "pixel_values": batch["pixel_values"].to("cpu")
+                            }
+                        )
+        return data
+    
+    
+    def prepare_dataset(opt_init_steps=50, max_train_samples=1000):
+        """
+        Prepares a vision-text dataset for quantization.
+        """
+        dataset = load_dataset("conceptual_captions")
+        train_dataset = dataset["train"].shuffle(seed=42)
+        dataloader = torch.utils.data.DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1)
+        calibration_data = prepare_calibration_data(dataloader, opt_init_steps)
+        return calibration_data
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    vcalibration_data = []
+    if not IMAGE_ENCODER_PATH_INT8.exists():
+        calibration_data = prepare_dataset()
+
+Perform quantization
+~~~~~~~~~~~~~~~~~~~~
+
+
+
+Create a quantized model from the pre-trained model.
+
+   **NOTE**: Quantization is time and memory consuming operation.
+   Running quantization code below may take some time.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    
+    if not IMAGE_ENCODER_PATH_INT8.exists():
+        if len(calibration_data) == 0:
+            raise RuntimeError(
+                'Calibration dataset is empty. Please check internet connection and try to download images manually.'
+            )
+    
+        ov_model = core.read_model(IMAGE_ENCODER_PATH)
+        calibration_dataset = nncf.Dataset(calibration_data)
+        quantized_model = nncf.quantize(
+            model=ov_model,
+            calibration_dataset=calibration_dataset,
+            model_type=nncf.ModelType.TRANSFORMER,
+            subset_size=len(calibration_data),
+            # Smooth Quant algorithm reduces activation quantization error; optimal alpha value was obtained through grid search
+            advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alpha=0.6)
+        )
+        ov.save_model(quantized_model, IMAGE_ENCODER_PATH_INT8)
+        del ov_model
+        del quantized_model
+        gc.collect()
+
+Prepare model inference pipeline
+--------------------------------
+
+
+
+|image0|
+
+``OVLlavaForCausalLM`` class provides ease-to-use interface for using
+model in generation scenario. It is based on
+``transformers.generation.GenerationMixin`` that gives us opportunity to
+reuse all reach capabilities for generation implemented in HuggingFace
+Transformers library. More details about this interface can be found in
+`HuggingFace
+documentation `__.
+
+.. |image0| image:: https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/a562e9de-5b94-4e24-ac52-532019fc92d3
+
+.. code:: ipython3
+
+    import torch
+    from transformers.generation import GenerationConfig, GenerationMixin
+    from transformers.modeling_outputs import CausalLMOutputWithPast
+    from transformers import AutoConfig
+    from transformers.models.llava_next.modeling_llava_next import (
+        get_anyres_image_grid_shape,
+        unpad_image,
+    )
+    import openvino as ov
+    
+    
+    class OVLlavaForCausalLM(GenerationMixin):
+        def __init__(
+            self,
+            core,
+            image_encoder_path,
+            input_embedding_path,
+            language_model_path,
+            device,
+        ):
+            self.image_encoder = core.compile_model(core.read_model(image_encoder_path), device)
+            self.input_embeddings = core.compile_model(core.read_model(input_embedding_path), device)
+            self.model = core.read_model(language_model_path)
+            self.input_names = {key.get_any_name(): idx for idx, key in enumerate(self.model.inputs)}
+            self.output_names = {idx: key for idx, key in enumerate(self.model.outputs)}
+            self.key_value_input_names = [key for key in list(self.input_names) if key not in ["beam_idx", "inputs_embeds", "attention_mask", "position_ids"]]
+            self.key_value_output_names = [key for key in list(self.output_names)[1:]]
+            self.stateful = len(self.key_value_input_names) == 0
+            compiled_model = core.compile_model(self.model, device)
+            self.request = compiled_model.create_infer_request()
+            self.config = AutoConfig.from_pretrained(Path(language_model_path).parent)
+            self.generation_config = GenerationConfig.from_model_config(self.config)
+            self.main_input_name = "input_ids"
+            self.device = torch.device("cpu")
+            self.num_pkv = 2
+            self.next_beam_idx = None
+            self.image_newline = torch.zeros(self.config.text_config.hidden_size, dtype=torch.float32)
+            self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+            self.past_len = 0
+    
+        def can_generate(self):
+            """Returns True to validate the check that the model using `GenerationMixin.generate()` can indeed generate."""
+            return True
+    
+        def __call__(
+            self,
+            input_ids: torch.LongTensor,
+            pixel_values: torch.Tensor,
+            attention_mask: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            image_sizes=None,
+            **kwargs,
+        ) -> CausalLMOutputWithPast:
+            return self.forward(
+                input_ids,
+                pixel_values,
+                attention_mask,
+                past_key_values,
+                position_ids,
+                image_sizes,
+                **kwargs,
+            )
+    
+        def forward(
+            self,
+            input_ids: torch.LongTensor,
+            pixel_values: torch.Tensor,
+            attention_mask: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            image_sizes=None,
+            **kwargs,
+        ) -> CausalLMOutputWithPast:
+            """General inference method"""
+            inputs = {}
+            if past_key_values is not None:
+                inputs = {}
+                if not self.stateful:
+                    past_key_values = tuple(past_key_value for pkv_per_layer in past_key_values for past_key_value in pkv_per_layer)
+                    # Add the past_key_values to the decoder inputs
+                    inputs = dict(zip(self.key_value_input_names, past_key_values))
+                # input_ids = np.array(input_ids)[:, -1:]
+                inputs_embeds = self.input_embeddings(input_ids)[0]
+                inputs["inputs_embeds"] = inputs_embeds
+                # inputs["attention_mask"] = attention_mask
+                if "beam_idx" in self.input_names:
+                    inputs["beam_idx"] = self.next_beam_idx if self.next_beam_idx is not None else np.arange(batch_size, dtype=int)
+    
+                if not self.stateful:
+                    first_layer_past_key_value = torch.from_numpy(past_key_values[0][0][:, :, :, 0])
+                else:
+                    first_layer_past_key_value = torch.from_numpy(self.request.query_state()[0].state.data[:, :, :, 0])
+    
+                # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
+                batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
+    
+                # Get the target length
+                target_length = input_ids.shape[1]
+                past_length = first_layer_past_key_value.shape[-1]
+    
+                extended_attention_mask = torch.ones(
+                    (attention_mask.shape[0], past_length),
+                    dtype=attention_mask.dtype,
+                    device=attention_mask.device,
+                )
+    
+                # Filter out only the tokens that can be un-attended, this can happen
+                # if one uses Llava + Fused modules where the cache on the
+                # first iteration is already big enough, or if one passes custom cache
+                valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
+                new_batch_index = batch_index[valid_indices]
+                new_non_attended_tokens = non_attended_tokens[valid_indices]
+    
+                # Zero-out the places where we don't need to attend
+                extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
+    
+                attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
+                position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
+                inputs["attention_mask"] = attention_mask
+                inputs["position_ids"] = position_ids
+    
+            else:
+                inputs = self.prepare_multimodal_input(input_ids, pixel_values, attention_mask, position_ids, image_sizes)
+    
+            # Run inference
+            self.request.start_async(inputs, share_inputs=True)
+            self.request.wait()
+    
+            logits = torch.from_numpy(self.request.get_tensor(self.output_names[0]).data)
+    
+            if not self.stateful:
+                # Tuple of length equal to : number of layer * number of past_key_value per decoder layer (2 corresponds to the self-attention layer)
+                past_key_values = tuple(self.request.get_tensor(key).data for key in self.key_value_output_names)
+                # Tuple of tuple of length `n_layers`, with each tuple of length equal to 2 (k/v of self-attention)
+                past_key_values = tuple(past_key_values[i : i + self.num_pkv] for i in range(0, len(past_key_values), self.num_pkv))
+            else:
+                past_key_values = ((),)
+            self.past_len += inputs["inputs_embeds"].shape[1]
+            return CausalLMOutputWithPast(logits=logits, past_key_values=past_key_values)
+    
+        def prepare_multimodal_input(self, input_ids, pixel_values, attention_mask, position_ids, image_sizes=None):
+            """Preprocessing function for embedding multimodal data"""
+            inputs = {}
+            inputs_embeds = torch.from_numpy(self.input_embeddings(input_ids)[0])
+            batch_size = input_ids.shape[0]
+            if not self.stateful:
+                for input_name in self.key_value_input_names:
+                    model_inputs = self.modeget_anyres_image_grid_shapel.input(input_name)
+                    shape = model_inputs.get_partial_shape()
+                    shape[0] = batch_size
+                    if shape[2].is_dynamic:
+                        shape[2] = 0
+                    else:
+                        shape[1] = 0
+                    inputs[input_name] = ov.Tensor(model_inputs.get_element_type(), shape.get_shape())
+            else:
+                self.past_len = 0
+                self.request.reset_state()
+                # Set initial value for the next beam_idx input that will be used at the current iteration
+                # and will be optionally updated by _reorder_cache at the next iterations if beam_search is used
+                self.next_beam_idx = np.arange(batch_size, dtype=int)
+    
+            if "beam_idx" in self.input_names:
+                inputs["beam_idx"] = self.next_beam_idx if self.next_beam_idx is not None else np.arange(batch_size, dtype=int)
+            if pixel_values is None:
+                inputs["inputs_embeds"] = inputs_embeds
+                inputs["attention_mask"] = attention_mask
+                if position_ids is None:
+                    position_ids = torch.cumsum(attention_mask, axis=1) - 1
+                    position_ids[attention_mask == 0] = 1
+                inputs["position_ids"] = position_ids
+            res = self.image_encoder(pixel_values)
+            image_features = torch.from_numpy(res[0])
+            split_sizes = [image.shape[0] for image in pixel_values]
+            image_features = torch.split(image_features, split_sizes, dim=0)
+    
+            # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
+            height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
+    
+            new_image_features = []
+            for image_idx, image_feature in enumerate(image_features):
+                if image_feature.shape[0] > 1:
+                    base_image_feature = image_feature[0]
+                    image_feature = image_feature[1:]
+    
+                    if height * width != base_image_feature.shape[0]:
+                        raise ValueError("The number of patches is not consistent with the image size.")
+                    num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+                        image_sizes[image_idx],
+                        self.config.image_grid_pinpoints,
+                        self.config.vision_config.image_size,
+                    )
+                    image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
+                    image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+                    image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+                    image_feature = unpad_image(image_feature, image_sizes[image_idx])
+                    image_feature = torch.cat(
+                        (
+                            image_feature,
+                            self.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1),
+                        ),
+                        dim=-1,
+                    )
+                    image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+                    image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+                else:
+                    image_feature = image_feature[0]
+                    image_feature = torch.cat((image_feature, self.image_newline[None]), dim=0)
+                new_image_features.append(image_feature)
+            image_features = torch.stack(new_image_features, dim=0)
+    
+            (
+                inputs_embeds,
+                attention_mask,
+                position_ids,
+            ) = self._merge_input_ids_with_image_features(image_features, inputs_embeds, input_ids, attention_mask, None)
+            inputs["inputs_embeds"] = inputs_embeds
+            inputs["attention_mask"] = attention_mask
+            inputs["position_ids"] = position_ids
+    
+            return inputs
+    
+        def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):
+            num_images, num_image_patches, embed_dim = image_features.shape
+            batch_size, sequence_length = input_ids.shape
+            left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id))
+            # 1. Create a mask to know where special image tokens are
+            special_image_token_mask = input_ids == self.config.image_token_index
+            num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
+            # Compute the maximum embed dimension
+            max_embed_dim = (num_special_image_tokens.max() * (num_image_patches - 1)) + sequence_length
+            batch_indices, non_image_indices = torch.where(input_ids != self.config.image_token_index)
+    
+            # 2. Compute the positions where text should be written
+            # Calculate new positions for text tokens in merged image-text sequence.
+            # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
+            # `torch.cumsum` computes how each image token shifts subsequent text token positions.
+            # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
+            new_token_positions = torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1) - 1
+            nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1]
+            if left_padding:
+                new_token_positions += nb_image_pad[:, None]  # offset for left padding
+            text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
+    
+            # 3. Create the full embedding, already padded to the maximum position
+            final_embedding = torch.zeros(
+                batch_size,
+                max_embed_dim,
+                embed_dim,
+                dtype=inputs_embeds.dtype,
+                device=inputs_embeds.device,
+            )
+            final_attention_mask = torch.zeros(
+                batch_size,
+                max_embed_dim,
+                dtype=attention_mask.dtype,
+                device=inputs_embeds.device,
+            )
+            # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
+            # set the corresponding tensors into their correct target device.
+            target_device = inputs_embeds.device
+            batch_indices, non_image_indices, text_to_overwrite = (
+                batch_indices.to(target_device),
+                non_image_indices.to(target_device),
+                text_to_overwrite.to(target_device),
+            )
+            attention_mask = attention_mask.to(target_device)
+    
+            # 4. Fill the embeddings based on the mask. If we have ["hey" "", "how", "are"]
+            # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
+            final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
+            final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
+            if labels is not None:
+                final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
+    
+            # 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
+            image_to_overwrite = torch.all(final_embedding == 0, dim=-1)
+            image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device)
+            if image_to_overwrite.sum() != image_features.shape[:-1].numel():
+                raise ValueError(
+                    f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while"
+                    f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation."
+                )
+    
+            final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
+            final_attention_mask |= image_to_overwrite
+            position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
+    
+            # 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
+            batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
+            indices_to_mask = new_token_positions[batch_indices, pad_indices]
+    
+            final_embedding[batch_indices, indices_to_mask] = 0
+    
+            return final_embedding, final_attention_mask, position_ids
+    
+        def prepare_inputs_for_generation(
+            self,
+            input_ids,
+            past_key_values=None,
+            inputs_embeds=None,
+            pixel_values=None,
+            image_sizes=None,
+            attention_mask=None,
+            **kwargs,
+        ):
+            if past_key_values is not None:
+                if not self.stateful:
+                    cache_length = past_length = past_key_values[0][0].shape[2]
+                else:
+                    cache_length = past_length = self.past_len
+    
+                # Keep only the unprocessed tokens:
+                # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+                # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+                # input)
+                if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                    input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+                # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+                # input_ids based on the past_length.llava
+                elif past_length < input_ids.shape[1]:
+                    input_ids = input_ids[:, past_length:]
+                # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+                elif self.config.image_token_index in input_ids:
+                    input_ids = input_ids[:, input_ids.shape[1] - 1 :]
+                # If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
+                # older attention values, as their corresponding values are not part of the input.
+                if cache_length < past_length and attention_mask is not None:
+                    attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :]
+    
+            position_ids = kwargs.get("position_ids", None)
+            if attention_mask is not None and position_ids is None:
+                # create position_ids on the fly for batch gllavaenerationsubset_siz
+                position_ids = attention_mask.long().cumsum(-1) - 1
+                position_ids.masked_fill_(attention_mask == 0, 1)
+                if past_key_values:
+                    position_ids = position_ids[:, -input_ids.shape[1] :]
+    
+            # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+            if inputs_embeds is not None and past_key_values is None:
+                model_inputs = {"inputs_embeds": inputs_embeds}
+            else:
+                model_inputs = {"input_ids": input_ids}
+    
+            model_inputs.update(
+                {
+                    "position_ids": position_ids,
+                    "past_key_values": past_key_values,
+                    "use_cache": kwargs.get("use_cache"),
+                    "attention_mask": attention_mask,
+                    "pixel_values": pixel_values,
+                    "image_sizes": image_sizes,
+                }
+            )
+            return model_inputs
+
+Run OpenVINO model inference
+----------------------------
+
+
+
+Select device
+~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    core = ov.Core()
+    
+    device = widgets.Dropdown(
+        options=core.available_devices,
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', options=('CPU', 'GPU.0', 'GPU.1'), value='CPU')
+
+
+
+.. code:: ipython3
+
+    use_int4_lang_model = widgets.Checkbox(
+        value=LANGUAGE_MODEL_PATH_INT4.exists(),
+        description="INT4 language model",
+        disabled=not LANGUAGE_MODEL_PATH_INT4.exists(),
+    )
+    
+    use_int4_lang_model
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='INT4 language model')
+
+
+
+.. code:: ipython3
+
+    use_int8_image_encoder = widgets.Checkbox(
+        value=IMAGE_ENCODER_PATH_INT8.exists(),
+        description="INT4 language model",
+        disabled=not IMAGE_ENCODER_PATH_INT8.exists(),
+    )
+    
+    use_int8_image_encoder
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='INT4 language model')
+
+
+
+.. code:: ipython3
+
+    lang_model_path = LANGUAGE_MODEL_PATH_INT4 if use_int4_lang_model.value else LANGUAGE_MODEL_PATH
+    image_encoder_path = IMAGE_ENCODER_PATH_INT8 if use_int8_image_encoder.value else IMAGE_ENCODER_PATH
+    
+    ov_llava_model = OVLlavaForCausalLM(core, image_encoder_path, INPUT_EMBEDDING_PATH, lang_model_path, device.value)
+
+.. code:: ipython3
+
+    from PIL import Image
+    import requests
+    
+    
+    from transformers import TextStreamer
+    
+    url = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/d5fbbd1a-d484-415c-88cb-9986625b7b11"
+    image = Image.open(requests.get(url, stream=True).raw)
+    question = "What is unusual on this image?"
+    prompt = f"[INST] \n{question}[/INST]"
+    streamer = TextStreamer(processor, skip_special_tokens=True, skip_prompt=True)
+    
+    inputs = processor(prompt, image, return_tensors="pt")
+    print(f"Question:\n{question}")
+    image
+
+
+.. parsed-literal::
+
+    Question:
+    What is unusual on this image?
+
+
+
+
+.. image:: llava-next-multimodal-chatbot-with-output_files/llava-next-multimodal-chatbot-with-output_32_1.png
+
+
+
+.. code:: ipython3
+
+    print("Answer:")
+    streamer = TextStreamer(processor, skip_special_tokens=True, skip_prompt=True)
+    output = ov_llava_model.generate(**inputs, max_new_tokens=49, streamer=streamer)
+
+
+.. parsed-literal::
+
+    Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
+
+
+.. parsed-literal::
+
+    Answer:
+    The image shows a cat lying on its back inside a cardboard box. What's unusual is that the cat appears to be in a relaxed and somewhat human-like pose, with its paws up in the air and its belly exposed.
+
+
+Interactive demo
+----------------
+
+
+
+.. code:: ipython3
+
+    import gradio as gr
+    from transformers import TextIteratorStreamer
+    from threading import Thread
+    from PIL import Image
+    import torch
+    
+    example_image_urls = [
+        (
+            "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/1d6a0188-5613-418d-a1fd-4560aae1d907",
+            "bee.jpg",
+        ),
+        (
+            "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/6cc7feeb-0721-4b5d-8791-2576ed9d2863",
+            "baklava.png",
+        ),
+    ]
+    for url, file_name in example_image_urls:
+        Image.open(requests.get(url, stream=True).raw).save(file_name)
+    
+    
+    def bot_streaming(message, history):
+        print(message)
+        if message["files"]:
+            image = message["files"][-1]["path"]
+        else:
+            # if there's no image uploaded for this turn, look for images in the past turns
+            # kept inside tuples, take the last one
+            for hist in history:
+                if isinstance(hist[0], tuple):
+                    image = hist[0][0]
+    
+        if image is None:
+            gr.Error("You need to upload an image for LLaVA to work.")
+        prompt = f"[INST] \n{message['text']} [/INST]"
+        image = Image.open(image).convert("RGB")
+        inputs = processor(prompt, image, return_tensors="pt")
+    
+        streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
+        generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=100)
+    
+        thread = Thread(target=ov_llava_model.generate, kwargs=generation_kwargs)
+        thread.start()
+    
+        text_prompt = f"[INST]  \n{message['text']} [/INST]"
+    
+        buffer = ""
+        for new_text in streamer:
+            buffer += new_text
+            generated_text_without_prompt = buffer[len(text_prompt) :]
+            yield generated_text_without_prompt
+    
+    
+    demo = gr.ChatInterface(
+        fn=bot_streaming,
+        title="LLaVA NeXT",
+        examples=[
+            {"text": "What is on the flower?", "files": ["./bee.jpg"]},
+            {"text": "How to make this pastry?", "files": ["./baklava.png"]},
+        ],
+        description="Try [LLaVA NeXT](https://huggingface.co/docs/transformers/main/en/model_doc/llava_next) in this demo using OpenVINO. Upload an image and start chatting about it, or simply try one of the examples below. If you don't upload an image, you will receive an error.",
+        stop_btn="Stop Generation",
+        multimodal=True,
+    )
+    
+    try:
+        demo.launch(debug=False)
+    except Exception:
+        demo.launch(debug=False, share=True)
diff --git a/docs/notebooks/llava-next-multimodal-chatbot-with-output_files/llava-next-multimodal-chatbot-with-output_32_1.jpg b/docs/notebooks/llava-next-multimodal-chatbot-with-output_files/llava-next-multimodal-chatbot-with-output_32_1.jpg
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diff --git a/docs/notebooks/llm-agent-langchain-with-output.rst b/docs/notebooks/llm-agent-langchain-with-output.rst
new file mode 100644
index 00000000000..2cb976bc13b
--- /dev/null
+++ b/docs/notebooks/llm-agent-langchain-with-output.rst
@@ -0,0 +1,640 @@
+Create LLM Agent using OpenVINO
+===============================
+
+LLM are limited to the knowledge on which they have been trained and the
+additional knowledge provided as context, as a result, if a useful piece
+of information is missing the provided knowledge, the model cannot “go
+around” and try to find it in other sources. This is the reason why we
+need to introduce the concept of Agents.
+
+The core idea of agents is to use a language model to choose a sequence
+of actions to take. In agents, a language model is used as a reasoning
+engine to determine which actions to take and in which order. Agents can
+be seen as applications powered by LLMs and integrated with a set of
+tools like search engines, databases, websites, and so on. Within an
+agent, the LLM is the reasoning engine that, based on the user input, is
+able to plan and execute a set of actions that are needed to fulfill the
+request.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/22fa5396-8381-400f-a78f-97e25d57d807
+   :alt: agent
+
+   agent
+
+`LangChain `__
+is a framework for developing applications powered by language models.
+LangChain comes with a number of built-in agents that are optimized for
+different use cases.
+
+This notebook explores how to create an AI Agent step by step using
+OpenVINO and LangChain.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+-  `Create tools <#create-tools>`__
+-  `Create prompt template <#create-prompt-template>`__
+-  `Create LLM <#create-llm>`__
+
+   -  `Download model <#select-model>`__
+   -  `Select inference device for
+      LLM <#select-inference-device-for-llm>`__
+
+-  `Create agent <#create-agent>`__
+-  `Run the agent <#run-agent>`__
+-  `Interactive Demo <#interactive-demo>`__
+
+   -  `Use built-in tool <#use-built-in-tool>`__
+
+Prerequisites
+-------------
+
+
+
+.. code:: ipython3
+
+    %pip uninstall -q -y openvino-dev openvino openvino-nightly optimum optimum-intel
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
+    "git+https://github.com/huggingface/optimum-intel.git"\
+    "git+https://github.com/openvinotoolkit/nncf.git"\
+    "torch>=2.1"\
+    "datasets"\
+    "accelerate"\
+    "openvino-nightly"\
+    "gradio"\
+    "transformers>=4.38.1" "langchain>=0.1.14" "wikipedia"
+
+Create a tools
+--------------
+
+
+
+First, we need to create some tools to call. In this example, we will
+create 3 custom functions to do basic calculation. For `more
+information `__ on
+creating custom tools.
+
+.. code:: ipython3
+
+    from langchain_core.tools import tool
+    
+    
+    @tool
+    def multiply(first_int: int, second_int: int) -> int:
+        """Multiply two integers together."""
+        return first_int * second_int
+    
+    
+    @tool
+    def add(first_int: int, second_int: int) -> int:
+        "Add two integers."
+        return first_int + second_int
+    
+    
+    @tool
+    def exponentiate(base: int, exponent: int) -> int:
+        "Exponentiate the base to the exponent power."
+        return base**exponent
+
+Tools are interfaces that an agent, chain, or LLM can use to interact
+with the world. They combine a few things:
+
+1. The name of the tool
+2. A description of what the tool is
+3. JSON schema of what the inputs to the tool are
+4. The function to call
+5. Whether the result of a tool should be returned directly to the user
+
+.. code:: ipython3
+
+    print(f"name of `multiply` tool: {multiply.name}")
+    print(f"description of `multiply` tool: {multiply.description}")
+
+
+.. parsed-literal::
+
+    name of `multiply` tool: multiply
+    description of `multiply` tool: multiply(first_int: int, second_int: int) -> int - Multiply two integers together.
+
+
+Now that we have created all of them, and we can create a list of tools
+that we will use downstream.
+
+.. code:: ipython3
+
+    tools = [multiply, add, exponentiate]
+
+Create prompt template
+----------------------
+
+
+
+A prompt for a language model is a set of instructions or input provided
+by a user to guide the model’s response, helping it understand the
+context and generate relevant and coherent language-based output, such
+as answering questions, completing sentences, or engaging in a
+conversation.
+
+Different agents have different prompting styles for reasoning. In this
+example, we will use `ReAct agent `__ with
+its typical prompt template. For a full list of built-in agents see
+`agent
+types `__.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/a83bdf7f-bb9d-4b1f-9a0a-3fe4a76ba1ae
+   :alt: react
+
+   react
+
+A ReAct prompt consists of few-shot task-solving trajectories, with
+human-written text reasoning traces and actions, as well as environment
+observations in response to actions. ReAct prompting is intuitive and
+flexible to design, and achieves state-of-the-art few-shot performances
+across a variety of tasks, from question answering to online shopping!
+
+In an prompt template for agent, ``agent_scratchpad`` should be a
+sequence of messages that contains the previous agent tool invocations
+and the corresponding tool outputs.
+
+.. code:: ipython3
+
+    from langchain.prompts import PromptTemplate
+    
+    prompt = PromptTemplate.from_template(
+        """Answer the following questions as best you can. You have access to the following tools:
+    
+        {tools}
+    
+        Use the following format:
+    
+        Question: the input question you must answer
+        Thought: you should always think about what to do
+        Action: the action to take, should be one of [{tool_names}]
+        Action Input: the input to the action\nObservation: the result of the action
+        ... (this Thought/Action/Action Input/Observation can repeat N times)
+        Thought: I now know the final answer
+        Final Answer: the final answer to the original input question
+    
+        Begin!
+    
+        Question: {input}
+        Thought:{agent_scratchpad}"""
+    )
+
+Create LLM
+----------
+
+
+
+Large Language Models (LLMs) are a core component of LangChain.
+LangChain does not serve its own LLMs, but rather provides a standard
+interface for interacting with many different LLMs. In this example, we
+select ``neural-chat-7b-v3-1`` as LLM in agent pipeline.
+
+**neural-chat-7b-v3-1** - Mistral-7b model fine-tuned using Intel Gaudi.
+The model fine-tuned on the open source dataset
+`Open-Orca/SlimOrca `__
+and aligned with `Direct Preference Optimization (DPO)
+algorithm `__. More details can be
+found in `model
+card `__ and `blog
+post `__.
+
+Download model
+~~~~~~~~~~~~~~
+
+
+
+To run LLM locally, we have to download the model in the first step. It
+is possible to `export your
+model `__
+to the OpenVINO IR format with the CLI, and load the model from local
+folder.
+
+.. code:: ipython3
+
+    from pathlib import Path
+    
+    model_id = "Intel/neural-chat-7b-v3-1"
+    model_path = "neural-chat-7b-v3-1-ov-int4"
+    
+    if not Path(model_path).exists():
+        !optimum-cli export openvino --model ${model_id} --weight-format int4 ${model_path}
+
+Select inference device for LLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import openvino as ov
+    import ipywidgets as widgets
+    
+    core = ov.Core()
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')
+
+
+
+OpenVINO models can be run locally through the ``HuggingFacePipeline``
+class in LangChain. To deploy a model with OpenVINO, you can specify the
+``backend="openvino"`` parameter to trigger OpenVINO as backend
+inference framework. For `more
+information `__.
+
+You can get additional inference speed improvement with `Dynamic
+Quantization of activations and KV-cache
+quantization `__.
+These options can be enabled with ``ov_config`` as follows:
+
+.. code:: ipython3
+
+    ov_config = {
+        "KV_CACHE_PRECISION": "u8",
+        "DYNAMIC_QUANTIZATION_GROUP_SIZE": "32",
+        "PERFORMANCE_HINT": "LATENCY",
+        "NUM_STREAMS": "1",
+        "CACHE_DIR": "",
+    }
+
+.. code:: ipython3
+
+    from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+    
+    ov_llm = HuggingFacePipeline.from_model_id(
+        model_id=model_path,
+        task="text-generation",
+        backend="openvino",
+        model_kwargs={"device": device.value, "ov_config": ov_config},
+        pipeline_kwargs={"max_new_tokens": 1024},
+    )
+
+Create agent
+------------
+
+
+
+Now that we have defined the tools, prompt template and LLM, we can
+create the agent_executor.
+
+The agent executor is the runtime for an agent. This is what actually
+calls the agent, executes the actions it chooses, passes the action
+outputs back to the agent, and repeats.
+
+.. code:: ipython3
+
+    from custom_output_parser import ReActSingleInputOutputParser
+    from langchain.agents import AgentExecutor, create_react_agent
+    
+    output_parser = ReActSingleInputOutputParser()
+    
+    agent = create_react_agent(ov_llm, tools, prompt, output_parser=output_parser)
+    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+Run the agent
+-------------
+
+
+
+We can now run the agent with a math query. Before getting the final
+answer, a agent executor will also produce intermediate steps of
+reasoning and actions. The format of these messages will follow your
+prompt template.
+
+.. code:: ipython3
+
+    agent_executor.invoke({"input": "Take 3 to the fifth power and multiply that by the sum of twelve and three"})
+
+
+.. parsed-literal::
+
+    Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
+
+
+.. parsed-literal::
+
+    
+    
+    > Entering new AgentExecutor chain...
+    Answer the following questions as best you can. You have access to the following tools:
+    
+        multiply: multiply(first_int: int, second_int: int) -> int - Multiply two integers together.
+    add: add(first_int: int, second_int: int) -> int - Add two integers.
+    exponentiate: exponentiate(base: int, exponent: int) -> int - Exponentiate the base to the exponent power.
+    
+        Use the following format:
+    
+        Question: the input question you must answer
+        Thought: you should always think about what to do
+        Action: the action to take, should be one of [multiply, add, exponentiate]
+        Action Input: the input to the action
+    Observation: the result of the action
+        ... (this Thought/Action/Action Input/Observation can repeat N times)
+        Thought: I now know the final answer
+        Final Answer: the final answer to the original input question
+    
+        Begin!
+    
+        Question: Take 3 to the fifth power and multiply that by the sum of twelve and three
+        Thought: We need to exponentiate 3 to the power of 5, then multiply the result by the sum of 12 and 3
+        Action: exponentiate
+        Action Input: base: 3, exponent: 5
+        Observation: 243
+        Action: add
+        Action Input: first_int: 12, second_int: 3
+        Observation: 15
+        Action: multiply
+        Action Input: first_int: 243, second_int: 15
+        Observation: 3645
+        Thought: I now know the final answer
+        Final Answer: 3645
+    
+    > Finished chain.
+
+
+
+
+.. parsed-literal::
+
+    {'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three',
+     'output': '3645'}
+
+
+
+Interactive Demo
+----------------
+
+
+
+Let’s create a interactive agent using
+`Gradio `__.
+
+Use built-in tool
+~~~~~~~~~~~~~~~~~
+
+
+
+LangChain has provided a list of all `built-in
+tools `__. In
+this example, we will use ``Wikipedia`` python package to query key
+words generated by agent.
+
+.. code:: ipython3
+
+    from langchain.tools import WikipediaQueryRun
+    from langchain_community.utilities import WikipediaAPIWrapper
+    
+    
+    wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
+    print(f"description of `wikipedia` tool: {wikipedia.description}")
+    
+    tools = [wikipedia]
+    
+    agent = create_react_agent(ov_llm, tools, prompt, output_parser=output_parser)
+    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+
+.. parsed-literal::
+
+    description of `wikipedia` tool: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.
+
+
+.. code:: ipython3
+
+    from threading import Thread
+    import gradio as gr
+    from transformers import TextIteratorStreamer
+    
+    examples = [
+        ["What is OpenVINO ?"],
+        ["Who is 44th presedent of USA ?"],
+        ["what is Obama's first name and who is him ?"],
+        ["How many people live in Canada ?"],
+        ["How tall is the Eiffel Tower ?"],
+    ]
+    
+    
+    def partial_text_processor(partial_text, new_text):
+        """
+        helper for updating partially generated answer, used by default
+    
+        Params:
+          partial_text: text buffer for storing previosly generated text
+          new_text: text update for the current step
+        Returns:
+          updated text string
+    
+        """
+        new_text = new_text.replace("[INST]", "").replace("[/INST]", "")
+        partial_text += new_text
+        return partial_text
+    
+    
+    def user(message, history):
+        """
+        callback function for updating user messages in interface on submit button click
+    
+        Params:
+          message: current message
+          history: conversation history
+        Returns:
+          None
+        """
+        # Append the user's message to the conversation history
+        return "", history + [[message, ""]]
+    
+    
+    def bot(history, temperature, top_p, top_k, repetition_penalty, return_intermediate_steps):
+        """
+        callback function for running chatbot on submit button click
+    
+        Params:
+          history: conversation history
+          temperature:  parameter for control the level of creativity in AI-generated text.
+                        By adjusting the `temperature`, you can influence the AI model's probability distribution, making the text more focused or diverse.
+          top_p: parameter for control the range of tokens considered by the AI model based on their cumulative probability.
+          top_k: parameter for control the range of tokens considered by the AI model based on their cumulative probability, selecting number of tokens with highest probability.
+          repetition_penalty: parameter for penalizing tokens based on how frequently they occur in the text.
+          return_intermediate_steps: whether return intermediate_steps of agent.
+    
+        """
+        streamer = TextIteratorStreamer(
+            ov_llm.pipeline.tokenizer,
+            timeout=60.0,
+            skip_prompt=True,
+            skip_special_tokens=True,
+        )
+    
+        ov_llm.pipeline._forward_params = dict(
+            max_new_tokens=512,
+            temperature=temperature,
+            do_sample=temperature > 0.0,
+            top_p=top_p,
+            top_k=top_k,
+            repetition_penalty=repetition_penalty,
+            streamer=streamer,
+        )
+    
+        t1 = Thread(target=agent_executor.invoke, args=({"input": history[-1][0]},))
+        t1.start()
+    
+        # Initialize an empty string to store the generated text
+        partial_text = ""
+        final_answer = False
+    
+        for new_text in streamer:
+            if "Answer" in new_text:
+                final_answer = True
+            if final_answer or return_intermediate_steps:
+                partial_text = partial_text_processor(partial_text, new_text)
+                history[-1][1] = partial_text
+                yield history
+    
+    
+    def request_cancel():
+        ov_llm.pipeline.model.request.cancel()
+    
+    
+    with gr.Blocks(
+        theme=gr.themes.Soft(),
+        css=".disclaimer {font-variant-caps: all-small-caps;}",
+    ) as demo:
+        gr.Markdown(f"""
OpenVINO Agent for {wikipedia.name}
""")
+        chatbot = gr.Chatbot(height=500)
+        with gr.Row():
+            with gr.Column():
+                msg = gr.Textbox(
+                    label="Chat Message Box",
+                    placeholder="Chat Message Box",
+                    show_label=False,
+                    container=False,
+                )
+            with gr.Column():
+                with gr.Row():
+                    return_cot = gr.Checkbox(value=True, label="Return intermediate steps")
+                    submit = gr.Button("Submit")
+                    stop = gr.Button("Stop")
+                    clear = gr.Button("Clear")
+        with gr.Row():
+            with gr.Accordion("Advanced Options:", open=False):
+                with gr.Row():
+                    with gr.Column():
+                        with gr.Row():
+                            temperature = gr.Slider(
+                                label="Temperature",
+                                value=0.1,
+                                minimum=0.0,
+                                maximum=1.0,
+                                step=0.1,
+                                interactive=True,
+                                info="Higher values produce more diverse outputs",
+                            )
+                    with gr.Column():
+                        with gr.Row():
+                            top_p = gr.Slider(
+                                label="Top-p (nucleus sampling)",
+                                value=1.0,
+                                minimum=0.0,
+                                maximum=1,
+                                step=0.01,
+                                interactive=True,
+                                info=(
+                                    "Sample from the smallest possible set of tokens whose cumulative probability "
+                                    "exceeds top_p. Set to 1 to disable and sample from all tokens."
+                                ),
+                            )
+                    with gr.Column():
+                        with gr.Row():
+                            top_k = gr.Slider(
+                                label="Top-k",
+                                value=50,
+                                minimum=0.0,
+                                maximum=200,
+                                step=1,
+                                interactive=True,
+                                info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
+                            )
+                    with gr.Column():
+                        with gr.Row():
+                            repetition_penalty = gr.Slider(
+                                label="Repetition Penalty",
+                                value=1.1,
+                                minimum=1.0,
+                                maximum=2.0,
+                                step=0.1,
+                                interactive=True,
+                                info="Penalize repetition — 1.0 to disable.",
+                            )
+        gr.Examples(examples, inputs=msg, label="Click on any example and press the 'Submit' button")
+    
+        submit_event = msg.submit(
+            fn=user,
+            inputs=[msg, chatbot],
+            outputs=[msg, chatbot],
+            queue=False,
+        ).then(
+            fn=bot,
+            inputs=[
+                chatbot,
+                temperature,
+                top_p,
+                top_k,
+                repetition_penalty,
+                return_cot,
+            ],
+            outputs=chatbot,
+            queue=True,
+        )
+        submit_click_event = submit.click(
+            fn=user,
+            inputs=[msg, chatbot],
+            outputs=[msg, chatbot],
+            queue=False,
+        ).then(
+            fn=bot,
+            inputs=[
+                chatbot,
+                temperature,
+                top_p,
+                top_k,
+                repetition_penalty,
+                return_cot,
+            ],
+            outputs=chatbot,
+            queue=True,
+        )
+        stop.click(
+            fn=request_cancel,
+            inputs=None,
+            outputs=None,
+            cancels=[submit_event, submit_click_event],
+            queue=False,
+        )
+        clear.click(lambda: None, None, chatbot, queue=False)
+    
+    # if you are launching remotely, specify server_name and server_port
+    #  demo.launch(server_name='your server name', server_port='server port in int')
+    # if you have any issue to launch on your platform, you can pass share=True to launch method:
+    # demo.launch(share=True)
+    # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
+    demo.launch()
+
+.. code:: ipython3
+
+    # please run this cell for stopping gradio interface
+    demo.close()
diff --git a/docs/notebooks/254-llm-chatbot-with-output.rst b/docs/notebooks/llm-chatbot-with-output.rst
similarity index 70%
rename from docs/notebooks/254-llm-chatbot-with-output.rst
rename to docs/notebooks/llm-chatbot-with-output.rst
index 01a0d26f1cf..931bfeb371b 100644
--- a/docs/notebooks/254-llm-chatbot-with-output.rst
+++ b/docs/notebooks/llm-chatbot-with-output.rst
@@ -18,7 +18,7 @@ accuracy.
 
 Previously, we already discussed how to build an instruction-following
 pipeline using OpenVINO and Optimum Intel, please check out `Dolly
-example <240-dolly-2-instruction-following-with-output.html>`__ for reference. In this
+example <../dolly-2-instruction-following>`__ for reference. In this
 tutorial, we consider how to use the power of OpenVINO for running Large
 Language Models for chat. We will use a pre-trained model from the
 `Hugging Face
@@ -43,19 +43,17 @@ Table of contents:
 
 -  `Prerequisites <#prerequisites>`__
 -  `Select model for inference <#select-model-for-inference>`__
--  `login to huggingfacehub to get access to pretrained
-   model <#login-to-huggingfacehub-to-get-access-to-pretrained-model>`__
--  `Instantiate Model using Optimum
-   Intel <#instantiate-model-using-optimum-intel>`__
+-  `Convert model using Optimum-CLI
+   tool <#convert-model-using-optimum-cli-tool>`__
 -  `Compress model weights <#compress-model-weights>`__
 
-   -  `Weights Compression using Optimum
-      Intel <#weights-compression-using-optimum-intel>`__
    -  `Weights Compression using
-      NNCF <#weights-compression-using-nncf>`__
+      Optimum-CLI <#weights-compression-using-optimum-cli>`__
 
 -  `Select device for inference and model
    variant <#select-device-for-inference-and-model-variant>`__
+-  `Instantiate Model using Optimum
+   Intel <#instantiate-model-using-optimum-intel>`__
 -  `Run Chatbot <#run-chatbot>`__
 
 Prerequisites
@@ -71,26 +69,31 @@ Install required dependencies
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
     "git+https://github.com/huggingface/optimum-intel.git"\
     "git+https://github.com/openvinotoolkit/nncf.git"\
+    "torch>=2.1"\
     "datasets" \
     "accelerate"\
     "openvino-nightly"\
-    "gradio"\
+    "gradio>=4.19"\
     "onnx" "einops" "transformers_stream_generator" "tiktoken" "transformers>=4.38.1" "bitsandbytes"
 
+.. code:: ipython3
 
-.. parsed-literal::
-
-    WARNING: Skipping openvino-dev as it is not installed.
-    WARNING: Skipping openvino as it is not installed.
-    WARNING: Skipping openvino-nightly as it is not installed.
-    WARNING: Skipping optimum as it is not installed.
-    WARNING: Skipping optimum-intel as it is not installed.
-    Note: you may need to restart the kernel to use updated packages.
+    import shutil
+    from pathlib import Path
+    import requests
     
-    [notice] A new release of pip is available: 23.1.2 -> 24.0
-    [notice] To update, run: pip install --upgrade pip
-    Note: you may need to restart the kernel to use updated packages.
-
+    # fetch model configuration
+    
+    config_shared_path = Path("../../utils/llm_config.py")
+    config_dst_path = Path("llm_config.py")
+    
+    if not config_dst_path.exists():
+        if config_shared_path.exists():
+            shutil.copy(config_shared_path, config_dst_path)
+        else:
+            r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
+            with open("llm_config.py", "w") as f:
+                f.write(r.text)
 
 Select model for inference
 --------------------------
@@ -327,7 +330,7 @@ The available options are:
 
 .. code:: ipython3
 
-    from config import SUPPORTED_LLM_MODELS
+    from llm_config import SUPPORTED_LLM_MODELS
     import ipywidgets as widgets
 
 .. code:: ipython3
@@ -370,7 +373,7 @@ The available options are:
 
 .. parsed-literal::
 
-    Dropdown(description='Model:', options=('qwen1.5-0.5b-chat', 'qwen1.5-1.8b-chat', 'qwen1.5-7b-chat', 'qwen-7b-…
+    Dropdown(description='Model:', options=('tiny-llama-1b-chat', 'gemma-2b-it', 'red-pajama-3b-chat', 'gemma-7b-i…
 
 
 
@@ -382,142 +385,83 @@ The available options are:
 
 .. parsed-literal::
 
-    Selected model qwen-7b-chat
+    Selected model tiny-llama-1b-chat
 
 
-Instantiate Model using Optimum Intel
--------------------------------------
+Convert model using Optimum-CLI tool
+------------------------------------
 
 
 
-Optimum Intel can be used to load optimized models from the `Hugging
-Face Hub `__ and
-create pipelines to run an inference with OpenVINO Runtime using Hugging
-Face APIs. The Optimum Inference models are API compatible with Hugging
-Face Transformers models. This means we just need to replace
-``AutoModelForXxx`` class with the corresponding ``OVModelForXxx``
-class.
+`Optimum Intel `__ is
+the interface between the 
+`Transformers `__ and
+`Diffusers `__ libraries
+and OpenVINO to accelerate end-to-end pipelines on Intel architectures.
+It provides ease-to-use cli interface for exporting models to `OpenVINO
+Intermediate Representation
+(IR) `__
+format.
 
-Below is an example of the RedPajama model
+The command bellow demonstrates basic command for model export with
+``optimum-cli``
 
-.. code:: diff
+::
 
-   -from transformers import AutoModelForCausalLM
-   +from optimum.intel.openvino import OVModelForCausalLM
-   from transformers import AutoTokenizer, pipeline
-
-   model_id = "togethercomputer/RedPajama-INCITE-Chat-3B-v1"
-   -model = AutoModelForCausalLM.from_pretrained(model_id)
-   +model = OVModelForCausalLM.from_pretrained(model_id, export=True)
-
-Model class initialization starts with calling ``from_pretrained``
-method. When downloading and converting Transformers model, the
-parameter ``export=True`` should be added. We can save the converted
-model for the next usage with the ``save_pretrained`` method. Tokenizer
-class and pipelines API are compatible with Optimum models.
-
-To optimize the generation process and use memory more efficiently, the
-``use_cache=True`` option is enabled. Since the output side is
-auto-regressive, an output token hidden state remains the same once
-computed for every further generation step. Therefore, recomputing it
-every time you want to generate a new token seems wasteful. With the
-cache, the model saves the hidden state once it has been computed. The
-model only computes the one for the most recently generated output token
-at each time step, re-using the saved ones for hidden tokens. This
-reduces the generation complexity from :math:`O(n^3)` to :math:`O(n^2)`
-for a transformer model. More details about how it works can be found in
-this
-`article `__.
-With this option, the model gets the previous step’s hidden states
-(cached attention keys and values) as input and additionally provides
-hidden states for the current step as output. It means for all next
-iterations, it is enough to provide only a new token obtained from the
-previous step and cached key values to get the next token prediction.
-
-In our case, MPT, Qwen and ChatGLM model currently is not covered by
-Optimum Intel, we will convert it manually and create wrapper compatible
-with Optimum Intel.
-
-.. code:: ipython3
-
-    from transformers import AutoModelForCausalLM, AutoConfig
-    from optimum.intel.openvino import OVModelForCausalLM
-    import openvino as ov
-    from pathlib import Path
-    import shutil
-    import torch
-    import logging
-    import nncf
-    import gc
-    from converter import converters, register_configs
-    
-    register_configs()
-
-
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    2024-03-07 02:52:02.115283: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-07 02:52:02.118993: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-03-07 02:52:02.161204: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
-    2024-03-07 02:52:02.161239: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
-    2024-03-07 02:52:02.161273: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
-    2024-03-07 02:52:02.169740: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-03-07 02:52:02.171079: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-03-07 02:52:03.108737: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+   optimum-cli export openvino --model  --task  
 
+where ``--model`` argument is model id from HuggingFace Hub or local
+directory with model (saved using ``.save_pretrained`` method),
+``--task`` is one of `supported
+task `__
+that exported model should solve. For LLMs it will be
+``text-generation-with-past``. If model initialization requires to use
+remote code, ``--trust-remote-code`` flag additionally should be passed.
 
 Compress model weights
 ----------------------
 
+The `Weights
+Compression `__
+algorithm is aimed at compressing the weights of the models and can be
+used to optimize the model footprint and performance of large models
+where the size of weights is relatively larger than the size of
+activations, for example, Large Language Models (LLM). Compared to INT8
+compression, INT4 compression improves performance even more, but
+introduces a minor drop in prediction quality.
 
-
-The Weights Compression algorithm is aimed at compressing the weights of
-the models and can be used to optimize the model footprint and
-performance of large models where the size of weights is relatively
-larger than the size of activations, for example, Large Language Models
-(LLM). Compared to INT8 compression, INT4 compression improves
-performance even more, but introduces a minor drop in prediction
-quality.
-
-Weights Compression using Optimum Intel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Weights Compression using Optimum-CLI
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 
 
-Optimum Intel supports weight compression via NNCF out of the box. For
-8-bit compression we pass ``load_in_8bit=True`` to ``from_pretrained()``
-method of ``OVModelForCausalLM``. For 4 bit compression we provide
-``quantization_config=OVWeightQuantizationConfig(bits=4, ...)`` argument
-containing number of bits and other compression parameters. We will
-consider how to do it on RedPajama, LLAMA and Zephyr examples.
+You can also apply fp16, 8-bit or 4-bit weight compression on the
+Linear, Convolutional and Embedding layers when exporting your model
+with the CLI by setting ``--weight-format`` to respectively fp16, int8
+or int4. This type of optimization allows to reduce the memory footprint
+and inference latency. By default the quantization scheme for int8/int4
+will be
+`asymmetric `__,
+to make it
+`symmetric `__
+you can add ``--sym``.
+
+For INT4 quantization you can also specify the following arguments : -
+The ``--group-size`` parameter will define the group size to use for
+quantization, -1 it will results in per-column quantization. - The
+``--ratio`` parameter controls the ratio between 4-bit and 8-bit
+quantization. If set to 0.9, it means that 90% of the layers will be
+quantized to int4 while 10% will be quantized to int8.
+
+Smaller group_size and ratio values usually improve accuracy at the
+sacrifice of the model size and inference latency.
 
    **Note**: There may be no speedup for INT4/INT8 compressed models on
    dGPU.
 
-Weights Compression using NNCF
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-You also can perform weights compression for OpenVINO models using NNCF
-directly. ``nncf.compress_weights`` function accepts OpenVINO model
-instance and compresses its weights for Linear and Embedding layers. We
-will consider this variant based on MPT model.
-
-   **Note**: This tutorial involves conversion model for FP16 and
-   INT4/INT8 weights compression scenarios. It may be memory and
-   time-consuming in the first run. You can manually control the
-   compression precision below.
-
 .. code:: ipython3
 
-    from IPython.display import display
+    from IPython.display import Markdown, display
     
     prepare_int4_model = widgets.Checkbox(
         value=True,
@@ -562,13 +506,10 @@ We can now save floating point and compressed model variants
 
 .. code:: ipython3
 
-    from optimum.intel import OVWeightQuantizationConfig
-    
-    nncf.set_log_level(logging.ERROR)
+    from pathlib import Path
     
     pt_model_id = model_configuration["model_id"]
     pt_model_name = model_id.value.split("-")[0]
-    model_type = AutoConfig.from_pretrained(pt_model_id, trust_remote_code=True).model_type
     fp16_model_dir = Path(model_id.value) / "FP16"
     int8_model_dir = Path(model_id.value) / "INT8_compressed_weights"
     int4_model_dir = Path(model_id.value) / "INT4_compressed_weights"
@@ -577,66 +518,28 @@ We can now save floating point and compressed model variants
     def convert_to_fp16():
         if (fp16_model_dir / "openvino_model.xml").exists():
             return
-        if not model_configuration["remote"]:
-            remote_code = model_configuration.get("remote_code", False)
-            model_kwargs = {}
-            if remote_code:
-                model_kwargs = {
-                    "trust_remote_code": True,
-                    "config": AutoConfig.from_pretrained(pt_model_id, trust_remote_code=True)
-                }
-            ov_model = OVModelForCausalLM.from_pretrained(
-                pt_model_id, export=True, compile=False, load_in_8bit=False, **model_kwargs
-            )
-            ov_model.half()
-            ov_model.save_pretrained(fp16_model_dir)
-            del ov_model
-        else:
-            model_kwargs = {}
-            if "revision" in model_configuration:
-                model_kwargs["revision"] = model_configuration["revision"]
-            model = AutoModelForCausalLM.from_pretrained(
-                model_configuration["model_id"],
-                torch_dtype=torch.float32,
-                trust_remote_code=True,
-                **model_kwargs
-            )
-            converters[pt_model_name](model, fp16_model_dir)
-            del model
-        gc.collect()
+        remote_code = model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format fp16".format(pt_model_id)
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(fp16_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
     
     
     def convert_to_int8():
         if (int8_model_dir / "openvino_model.xml").exists():
             return
         int8_model_dir.mkdir(parents=True, exist_ok=True)
-        if not model_configuration["remote"]:
-            remote_code = model_configuration.get("remote_code", False)
-            model_kwargs = {}
-            if remote_code:
-                model_kwargs = {
-                    "trust_remote_code": True,
-                    "config": AutoConfig.from_pretrained(pt_model_id, trust_remote_code=True)
-                }
-            ov_model = OVModelForCausalLM.from_pretrained(
-                pt_model_id, export=True, compile=False, load_in_8bit=True, **model_kwargs
-            )
-            ov_model.save_pretrained(int8_model_dir)
-            del ov_model
-        else:
-            convert_to_fp16()
-            ov_model = ov.Core().read_model(fp16_model_dir / "openvino_model.xml")
-            shutil.copy(fp16_model_dir / "config.json", int8_model_dir / "config.json")
-            configuration_file = fp16_model_dir / f"configuration_{model_type}.py"
-            if configuration_file.exists():
-                shutil.copy(
-                    configuration_file, int8_model_dir / f"configuration_{model_type}.py"
-                )
-            compressed_model = nncf.compress_weights(ov_model)
-            ov.save_model(compressed_model, int8_model_dir / "openvino_model.xml")
-            del ov_model
-            del compressed_model
-        gc.collect()
+        remote_code = model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int8".format(pt_model_id)
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(int8_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
     
     
     def convert_to_int4():
@@ -686,12 +589,8 @@ We can now save floating point and compressed model variants
                 "group_size": 128,
                 "ratio": 0.72,
             },
-            "qwen-7b-chat": {
-                "sym": True,
-                "group_size": 128, 
-                "ratio": 0.6
-            },
-            'red-pajama-3b-chat': {
+            "qwen-7b-chat": {"sym": True, "group_size": 128, "ratio": 0.6},
+            "red-pajama-3b-chat": {
                 "sym": False,
                 "group_size": 128,
                 "ratio": 0.5,
@@ -703,44 +602,21 @@ We can now save floating point and compressed model variants
             },
         }
     
-        model_compression_params = compression_configs.get(
-            model_id.value, compression_configs["default"]
-        )
+        model_compression_params = compression_configs.get(model_id.value, compression_configs["default"])
         if (int4_model_dir / "openvino_model.xml").exists():
             return
-        int4_model_dir.mkdir(parents=True, exist_ok=True)
-        if not model_configuration["remote"]:
-            remote_code = model_configuration.get("remote_code", False)
-            model_kwargs = {}
-            if remote_code:
-                model_kwargs = {
-                    "trust_remote_code" : True,
-                    "config": AutoConfig.from_pretrained(pt_model_id, trust_remote_code=True)
-                }
-            ov_model = OVModelForCausalLM.from_pretrained(
-                pt_model_id, export=True, compile=False,
-                quantization_config=OVWeightQuantizationConfig(bits=4, **model_compression_params),
-                **model_kwargs
-            )
-            ov_model.save_pretrained(int4_model_dir)
-            del ov_model
-        else:
-            convert_to_fp16()
-            ov_model = ov.Core().read_model(fp16_model_dir / "openvino_model.xml")
-            shutil.copy(fp16_model_dir / "config.json", int4_model_dir / "config.json")
-            configuration_file = fp16_model_dir / f"configuration_{model_type}.py"
-            if configuration_file.exists():
-                shutil.copy(
-                    configuration_file, int4_model_dir / f"configuration_{model_type}.py"
-                )
-            mode = nncf.CompressWeightsMode.INT4_SYM if model_compression_params["sym"] else \
-                nncf.CompressWeightsMode.INT4_ASYM
-            del model_compression_params["sym"]
-            compressed_model = nncf.compress_weights(ov_model, mode=mode, **model_compression_params)
-            ov.save_model(compressed_model, int4_model_dir / "openvino_model.xml")
-            del ov_model
-            del compressed_model
-        gc.collect()
+        remote_code = model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int4".format(pt_model_id)
+        int4_compression_args = " --group-size {} --ratio {}".format(model_compression_params["group_size"], model_compression_params["ratio"])
+        if model_compression_params["sym"]:
+            int4_compression_args += " --sym"
+        export_command_base += int4_compression_args
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(int4_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
     
     
     if prepare_fp16_model.value:
@@ -752,61 +628,55 @@ We can now save floating point and compressed model variants
 
 
 
-.. parsed-literal::
+**Export command:**
 
-    modeling_qwen.py:   0%|          | 0.00/55.6k [00:00 True
+    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /home/ethan/.cache/huggingface/modules/transformers_modules/Qwen/Qwen-7B-Chat/8d24619bab456ea5abe2823c1d05fc5edec19174/modeling_qwen.py:811: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if batch_size <= 0:
-    /home/ethan/.cache/huggingface/modules/transformers_modules/Qwen/Qwen-7B-Chat/8d24619bab456ea5abe2823c1d05fc5edec19174/modeling_qwen.py:835: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      elif kv_seq_len != hidden_states.size()[1]:
-    /home/ethan/.cache/huggingface/modules/transformers_modules/Qwen/Qwen-7B-Chat/8d24619bab456ea5abe2823c1d05fc5edec19174/modeling_qwen.py:1288: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
-    /home/ethan/.cache/huggingface/modules/transformers_modules/Qwen/Qwen-7B-Chat/8d24619bab456ea5abe2823c1d05fc5edec19174/modeling_qwen.py:482: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if key_size > self.seq_length and self.use_logn_attn and not self.training:
-    /home/ethan/.cache/huggingface/modules/transformers_modules/Qwen/Qwen-7B-Chat/8d24619bab456ea5abe2823c1d05fc5edec19174/modeling_qwen.py:502: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if query.size(1) == key_size:
-    /home/ethan/intel/openvino_notebooks/openvino_env/lib/python3.11/site-packages/torch/jit/_trace.py:160: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:489.)
-      if a.grad is not None:
-
+    The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class
+    The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class
+    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/optimum/exporters/openvino/model_patcher.py:311: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if sequence_length != 1:
+    Mixed-Precision assignment ━━━━━━━━━━━━━━━━━━━━ 100% 154/154 • 0:00:11 • 0:00:00;0;104;181m0:00:01181m0:00:01
+    INFO:nncf:Statistics of the bitwidth distribution:
+    ┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
+    │   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
+    ┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
+    │              8 │ 30% (42 / 156)              │ 20% (40 / 154)                         │
+    ├────────────────┼─────────────────────────────┼────────────────────────────────────────┤
+    │              4 │ 70% (114 / 156)             │ 80% (114 / 154)                        │
+    ┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙
+    Applying Weight Compression ━━━━━━━━━━━━━━━━━━━ 100% 156/156 • 0:00:26 • 0:00:00;0;104;181m0:00:01181m0:00:02
+    
 
 Let’s compare model size for different compression types
 
@@ -820,18 +690,14 @@ Let’s compare model size for different compression types
         print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
     for precision, compressed_weights in zip([8, 4], [int8_weights, int4_weights]):
         if compressed_weights.exists():
-            print(
-                f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB"
-            )
+            print(f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB")
         if compressed_weights.exists() and fp16_weights.exists():
-            print(
-                f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}"
-            )
+            print(f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}")
 
 
 .. parsed-literal::
 
-    Size of FP16 model is 14729.26 MB
+    Size of model with INT4 compressed weights is 696.19 MB
 
 
 Select device for inference and model variant
@@ -844,7 +710,10 @@ Select device for inference and model variant
 
 .. code:: ipython3
 
+    import openvino as ov
+    
     core = ov.Core()
+    
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="CPU",
@@ -859,17 +728,10 @@ Select device for inference and model variant
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')
+    Dropdown(description='Device:', options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='CPU')
 
 
 
-The cell below create ``OVMPTModel`` and ``OVCHATGLM2Model`` wrapper
-based on ``OVModelForCausalLM`` model.
-
-.. code:: ipython3
-
-    from ov_llm_model import model_classes
-
 The cell below demonstrates how to instantiate model based on selected
 variant of model weights and inference device
 
@@ -897,13 +759,50 @@ variant of model weights and inference device
 
 .. parsed-literal::
 
-    Dropdown(description='Model to run:', options=('FP16',), value='FP16')
+    Dropdown(description='Model to run:', options=('INT4',), value='INT4')
 
 
 
+Instantiate Model using Optimum Intel
+-------------------------------------
+
+
+
+Optimum Intel can be used to load optimized models from the `Hugging
+Face Hub `__ and
+create pipelines to run an inference with OpenVINO Runtime using Hugging
+Face APIs. The Optimum Inference models are API compatible with Hugging
+Face Transformers models. This means we just need to replace
+``AutoModelForXxx`` class with the corresponding ``OVModelForXxx``
+class.
+
+Below is an example of the RedPajama model
+
+.. code:: diff
+
+   -from transformers import AutoModelForCausalLM
+   +from optimum.intel.openvino import OVModelForCausalLM
+   from transformers import AutoTokenizer, pipeline
+
+   model_id = "togethercomputer/RedPajama-INCITE-Chat-3B-v1"
+   -model = AutoModelForCausalLM.from_pretrained(model_id)
+   +model = OVModelForCausalLM.from_pretrained(model_id, export=True)
+
+Model class initialization starts with calling ``from_pretrained``
+method. When downloading and converting Transformers model, the
+parameter ``export=True`` should be added (as we already converted model
+before, we do not need to provide this parameter). We can save the
+converted model for the next usage with the ``save_pretrained`` method.
+Tokenizer class and pipelines API are compatible with Optimum models.
+
+You can find more details about OpenVINO LLM inference using HuggingFace
+Optimum API in `LLM inference
+guide `__.
+
 .. code:: ipython3
 
-    from transformers import AutoTokenizer
+    from transformers import AutoConfig, AutoTokenizer
+    from optimum.intel.openvino import OVModelForCausalLM
     
     if model_to_run.value == "INT4":
         model_dir = int4_model_dir
@@ -921,15 +820,9 @@ variant of model weights and inference device
         ov_config["INFERENCE_PRECISION_HINT"] = "f32"
     
     model_name = model_configuration["model_id"]
-    class_key = model_id.value.split("-")[0]
-    tok = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    tok = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
     
-    model_class = (
-        OVModelForCausalLM
-        if not model_configuration["remote"]
-        else model_classes[class_key]
-    )
-    ov_model = model_class.from_pretrained(
+    ov_model = OVModelForCausalLM.from_pretrained(
         model_dir,
         device=device.value,
         ov_config=ov_config,
@@ -937,18 +830,6 @@ variant of model weights and inference device
         trust_remote_code=True,
     )
 
-
-.. parsed-literal::
-
-    Loading model from qwen-7b-chat/FP16
-
-
-.. parsed-literal::
-
-    The argument `trust_remote_code` is to be used along with export=True. It will be ignored.
-    Compiling the model to CPU ...
-
-
 .. code:: ipython3
 
     tokenizer_kwargs = model_configuration.get("tokenizer_kwargs", {})
@@ -960,7 +841,12 @@ variant of model weights and inference device
 
 .. parsed-literal::
 
-    2 + 2 = (1
+    Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
+
+
+.. parsed-literal::
+
+    2 + 2 = 4
 
 
 Run Chatbot
@@ -991,7 +877,7 @@ decoding methods in this
 generation updates conversation history for next conversation step. it
 makes stronger connection of next question with previously provided and
 allows user to make clarifications regarding previously provided
-answers.
+answers.https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html
 
 | There are several parameters that can control text generation quality:
   \* ``Temperature`` is a parameter used to control the level of
@@ -1041,10 +927,12 @@ answers.
    A token that has already appeared five times is penalized more
    heavily than a token that has appeared only one time. A value of 1
    means that there is no penalty and values larger than 1 discourage
-   repeated tokens.
+   repeated
+   tokens.https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html
 
 .. code:: ipython3
 
+    import torch
     from threading import Event, Thread
     from uuid import uuid4
     from typing import List, Tuple
@@ -1081,9 +969,7 @@ answers.
         ["Can you explain to me briefly what is Python programming language?"],
         ["Explain the plot of Cinderella in a sentence."],
         ["What are some common mistakes to avoid when writing code?"],
-        [
-            "Write a 100-word blog post on “Benefits of Artificial Intelligence and OpenVINO“"
-        ],
+        ["Write a 100-word blog post on “Benefits of Artificial Intelligence and OpenVINO“"],
     ]
     
     japanese_examples = [
@@ -1096,13 +982,7 @@ answers.
         ["人工知能と「OpenVINOの利点」について100語程度のブログ記事を書いてください。"],
     ]
     
-    examples = (
-        chinese_examples
-        if (model_language.value == "Chinese")
-        else japanese_examples
-        if (model_language.value == "Japanese")
-        else english_examples
-    )
+    examples = chinese_examples if (model_language.value == "Chinese") else japanese_examples if (model_language.value == "Japanese") else english_examples
     
     max_new_tokens = 256
     
@@ -1111,9 +991,7 @@ answers.
         def __init__(self, token_ids):
             self.token_ids = token_ids
     
-        def __call__(
-            self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
-        ) -> bool:
+        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
             for stop_id in self.token_ids:
                 if input_ids[0][-1] == stop_id:
                     return True
@@ -1142,9 +1020,7 @@ answers.
         return partial_text
     
     
-    text_processor = model_configuration.get(
-        "partial_text_processor", default_partial_text_processor
-    )
+    text_processor = model_configuration.get("partial_text_processor", default_partial_text_processor)
     
     
     def convert_history_to_token(history: List[Tuple[str, str]]):
@@ -1158,7 +1034,7 @@ answers.
         if pt_model_name == "baichuan2":
             system_tokens = tok.encode(start_message)
             history_tokens = []
-            for (old_query, response) in history[:-1]:
+            for old_query, response in history[:-1]:
                 round_tokens = []
                 round_tokens.append(195)
                 round_tokens.extend(tok.encode(old_query))
@@ -1180,23 +1056,11 @@ answers.
                     messages.append({"role": "user", "content": user_msg})
                 if model_msg:
                     messages.append({"role": "assistant", "content": model_msg})
-                    
-            input_token = tok.apply_chat_template(messages,
-                                                  add_generation_prompt=True,
-                                                  tokenize=True,
-                                                  return_tensors="pt")
+    
+            input_token = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_tensors="pt")
         else:
             text = start_message + "".join(
-                [
-                    "".join(
-                        [
-                            history_template.format(
-                                num=round, user=item[0], assistant=item[1]
-                            )
-                        ]
-                    )
-                    for round, item in enumerate(history[:-1])
-                ]
+                ["".join([history_template.format(num=round, user=item[0], assistant=item[1])]) for round, item in enumerate(history[:-1])]
             )
             text += "".join(
                 [
@@ -1250,9 +1114,7 @@ answers.
         if input_ids.shape[1] > 2000:
             history = [history[-1]]
             input_ids = convert_history_to_token(history)
-        streamer = TextIteratorStreamer(
-            tok, timeout=30.0, skip_prompt=True, skip_special_tokens=True
-        )
+        streamer = TextIteratorStreamer(tok, timeout=30.0, skip_prompt=True, skip_special_tokens=True)
         generate_kwargs = dict(
             input_ids=input_ids,
             max_new_tokens=max_new_tokens,
@@ -1264,8 +1126,7 @@ answers.
             streamer=streamer,
         )
         if stop_tokens is not None:
-            generate_kwargs["stopping_criteria"] = StoppingCriteriaList(
-                stop_tokens)
+            generate_kwargs["stopping_criteria"] = StoppingCriteriaList(stop_tokens)
     
         stream_complete = Event()
     
@@ -1304,8 +1165,7 @@ answers.
         css=".disclaimer {font-variant-caps: all-small-caps;}",
     ) as demo:
         conversation_id = gr.State(get_uuid)
-        gr.Markdown(
-            f"""
OpenVINO {model_id.value} Chatbot
""")
+        gr.Markdown(f"""
OpenVINO {model_id.value} Chatbot
""")
         chatbot = gr.Chatbot(height=500)
         with gr.Row():
             with gr.Column():
@@ -1370,9 +1230,7 @@ answers.
                                 interactive=True,
                                 info="Penalize repetition — 1.0 to disable.",
                             )
-        gr.Examples(
-            examples, inputs=msg, label="Click on any example and press the 'Submit' button"
-        )
+        gr.Examples(examples, inputs=msg, label="Click on any example and press the 'Submit' button")
     
         submit_event = msg.submit(
             fn=user,
@@ -1431,8 +1289,11 @@ answers.
     # please uncomment and run this cell for stopping gradio interface
     # demo.close()
 
+Next Step
+~~~~~~~~~
 
-.. parsed-literal::
-
-    Closing server running on port: 5467
-
+Besides chatbot, we can use LangChain to augmenting LLM knowledge with
+additional data, which allow you to build AI applications that can
+reason about private data or data introduced after a model’s cutoff
+date. You can find this solution in `Retrieval-augmented generation
+(RAG) example <../llm-rag-langchain/>`__.
diff --git a/docs/notebooks/275-llm-question-answering-with-output.rst b/docs/notebooks/llm-question-answering-with-output.rst
similarity index 90%
rename from docs/notebooks/275-llm-question-answering-with-output.rst
rename to docs/notebooks/llm-question-answering-with-output.rst
index a28ba2c40c0..272d5c9b7a2 100644
--- a/docs/notebooks/275-llm-question-answering-with-output.rst
+++ b/docs/notebooks/llm-question-answering-with-output.rst
@@ -67,7 +67,7 @@ Prerequisites
 .. code:: ipython3
 
     %pip uninstall -q -y openvino openvino-dev openvino-nightly optimum optimum-intel
-    %pip install -q openvino-nightly "nncf>=2.7" "transformers>=4.36.0" onnx "optimum>=1.16.1" "accelerate" "datasets" gradio "git+https://github.com/huggingface/optimum-intel.git" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "torch>=2.1" openvino-nightly "nncf>=2.7" "transformers>=4.36.0" onnx "optimum>=1.16.1" "accelerate" "datasets>=2.14.6" "gradio>=4.19" "git+https://github.com/huggingface/optimum-intel.git" --extra-index-url https://download.pytorch.org/whl/cpu
 
 Select model for inference
 --------------------------
@@ -219,7 +219,7 @@ previous step and cached key values to get the next token prediction.
 Compress model weights
 ----------------------
 
-The Weights Compression
+ The Weights Compression
 algorithm is aimed at compressing the weights of the models and can be
 used to optimize the model footprint and performance of large models
 where the size of weights is relatively larger than the size of
@@ -238,7 +238,7 @@ method of ``OVModelForCausalLM``. For 4 bit compression we provide
 ``quantization_config=OVWeightQuantizationConfig(bits=4, ...)`` argument
 containing number of bits and other compression parameters. An example
 of this approach usage you can find in `llm-chatbot
-notebook <254-llm-chatbot-with-output.html>`__
+notebook <../llm-chatbot>`__
 
 Weights Compression using NNCF
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -310,7 +310,7 @@ compression.
     from optimum.utils import NormalizedTextConfig, NormalizedConfigManager
     import gc
     
-    NormalizedConfigManager._conf['phi'] = NormalizedTextConfig
+    NormalizedConfigManager._conf["phi"] = NormalizedTextConfig
     
     nncf.set_log_level(logging.ERROR)
     
@@ -321,6 +321,7 @@ compression.
     
     core = ov.Core()
     
+    
     def convert_to_fp16():
         if (fp16_model_dir / "openvino_model.xml").exists():
             return
@@ -347,16 +348,12 @@ compression.
                 "group_size": 64,
                 "ratio": 0.6,
             },
-            'red-pajama-3b-instruct': {
+            "red-pajama-3b-instruct": {
                 "sym": False,
                 "group_size": 128,
                 "ratio": 0.5,
             },
-            "dolly-v2-3b": {
-                "sym": False,
-                "group_size": 32,
-                "ratio": 0.5
-            },
+            "dolly-v2-3b": {"sym": False, "group_size": 32, "ratio": 0.5},
             "default": {
                 "sym": False,
                 "group_size": 128,
@@ -364,14 +361,14 @@ compression.
             },
         }
     
-        model_compression_params = compression_configs.get(
-            model_id.value, compression_configs["default"]
-        )
+        model_compression_params = compression_configs.get(model_id.value, compression_configs["default"])
         if (int4_model_dir / "openvino_model.xml").exists():
             return
         ov_model = OVModelForCausalLM.from_pretrained(
-            pt_model_id, export=True, compile=False,
-            quantization_config=OVWeightQuantizationConfig(bits=4, **model_compression_params)
+            pt_model_id,
+            export=True,
+            compile=False,
+            quantization_config=OVWeightQuantizationConfig(bits=4, **model_compression_params),
         )
         ov_model.save_pretrained(int4_model_dir)
         del ov_model
@@ -410,13 +407,9 @@ Let’s compare model size for different compression types
         print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
     for precision, compressed_weights in zip([8, 4], [int8_weights, int4_weights]):
         if compressed_weights.exists():
-            print(
-                f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB"
-            )
+            print(f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB")
         if compressed_weights.exists() and fp16_weights.exists():
-            print(
-                f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}"
-            )
+            print(f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}")
 
 
 .. parsed-literal::
@@ -670,11 +663,15 @@ into model with providing additional context.
             raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
         return token_ids[0]
     
+    
     response_key = model_configuration.get("response_key")
     tokenizer_response_key = None
     
     if response_key is not None:
-        tokenizer_response_key = next((token for token in tokenizer.additional_special_tokens if token.startswith(response_key)), None)
+        tokenizer_response_key = next(
+            (token for token in tokenizer.additional_special_tokens if token.startswith(response_key)),
+            None,
+        )
     
     end_key_token_id = None
     if tokenizer_response_key:
@@ -707,10 +704,17 @@ parameter and returns model response.
 
 .. code:: ipython3
 
-    def run_generation(user_text:str, top_p:float, temperature:float, top_k:int, max_new_tokens:int, perf_text:str):
+    def run_generation(
+        user_text: str,
+        top_p: float,
+        temperature: float,
+        top_k: int,
+        max_new_tokens: int,
+        perf_text: str,
+    ):
         """
         Text generation function
-        
+    
         Parameters:
           user_text (str): User-provided instruction for a generation.
           top_p (float):  Nucleus sampling. If set to < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for a generation.
@@ -722,10 +726,10 @@ parameter and returns model response.
           model_output (str) - model-generated text
           perf_text (str) - updated perf text filed content
         """
-        
+    
         # Prepare input prompt according to model expected template
         prompt_text = prompt_template.format(instruction=user_text)
-        
+    
         # Tokenize the user text.
         model_inputs = tokenizer(prompt_text, return_tensors="pt", **tokenizer_kwargs)
     
@@ -741,7 +745,7 @@ parameter and returns model response.
             temperature=float(temperature),
             top_k=top_k,
             eos_token_id=end_key_token_id,
-            pad_token_id=pad_token_id
+            pad_token_id=pad_token_id,
         )
         t = Thread(target=ov_model.generate, kwargs=generate_kwargs)
         t.start()
@@ -770,17 +774,23 @@ elements.
 
 .. code:: ipython3
 
-    def estimate_latency(current_time:float, current_perf_text:str, new_gen_text:str, per_token_time:List[float], num_tokens:int):
+    def estimate_latency(
+        current_time: float,
+        current_perf_text: str,
+        new_gen_text: str,
+        per_token_time: List[float],
+        num_tokens: int,
+    ):
         """
         Helper function for performance estimation
-        
+    
         Parameters:
           current_time (float): This step time in seconds.
           current_perf_text (str): Current content of performance UI field.
           new_gen_text (str): New generated text.
           per_token_time (List[float]): history of performance from previous steps.
           num_tokens (int): Total number of generated tokens.
-          
+    
         Returns:
           update for performance text field
           update for a total number of tokens
@@ -790,18 +800,22 @@ elements.
         per_token_time.append(num_current_toks / current_time)
         if len(per_token_time) > 10 and len(per_token_time) % 4 == 0:
             current_bucket = per_token_time[:-10]
-            return f"Average generation speed: {np.mean(current_bucket):.2f} tokens/s. Total generated tokens: {num_tokens}", num_tokens
+            return (
+                f"Average generation speed: {np.mean(current_bucket):.2f} tokens/s. Total generated tokens: {num_tokens}",
+                num_tokens,
+            )
         return current_perf_text, num_tokens
     
-    def reset_textbox(instruction:str, response:str, perf:str):
+    
+    def reset_textbox(instruction: str, response: str, perf: str):
         """
         Helper function for resetting content of all text fields
-        
+    
         Parameters:
           instruction (str): Content of user instruction field.
           response (str): Content of model response field.
           perf (str): Content of performance info filed
-        
+    
         Returns:
           empty string for each placeholder
         """
@@ -846,7 +860,6 @@ generation parameters:
     ]
     
     
-    
     with gr.Blocks() as demo:
         gr.Markdown(
             "# Question Answering with " + model_id.value + " and OpenVINO.\n"
@@ -857,7 +870,7 @@ generation parameters:
             with gr.Column(scale=4):
                 user_text = gr.Textbox(
                     placeholder="Write an email about an alpaca that likes flan",
-                    label="User instruction"
+                    label="User instruction",
                 )
                 model_output = gr.Textbox(label="Model response", interactive=False)
                 performance = gr.Textbox(label="Performance", lines=1, interactive=False)
@@ -867,21 +880,53 @@ generation parameters:
                 gr.Examples(examples, user_text)
             with gr.Column(scale=1):
                 max_new_tokens = gr.Slider(
-                    minimum=1, maximum=1000, value=256, step=1, interactive=True, label="Max New Tokens",
+                    minimum=1,
+                    maximum=1000,
+                    value=256,
+                    step=1,
+                    interactive=True,
+                    label="Max New Tokens",
                 )
                 top_p = gr.Slider(
-                    minimum=0.05, maximum=1.0, value=0.92, step=0.05, interactive=True, label="Top-p (nucleus sampling)",
+                    minimum=0.05,
+                    maximum=1.0,
+                    value=0.92,
+                    step=0.05,
+                    interactive=True,
+                    label="Top-p (nucleus sampling)",
                 )
                 top_k = gr.Slider(
-                    minimum=0, maximum=50, value=0, step=1, interactive=True, label="Top-k",
+                    minimum=0,
+                    maximum=50,
+                    value=0,
+                    step=1,
+                    interactive=True,
+                    label="Top-k",
                 )
                 temperature = gr.Slider(
-                    minimum=0.1, maximum=5.0, value=0.8, step=0.1, interactive=True, label="Temperature",
+                    minimum=0.1,
+                    maximum=5.0,
+                    value=0.8,
+                    step=0.1,
+                    interactive=True,
+                    label="Temperature",
                 )
     
-        user_text.submit(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens, performance], [model_output, performance])
-        button_submit.click(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens, performance], [model_output, performance])
-        button_clear.click(reset_textbox, [user_text, model_output, performance], [user_text, model_output, performance])
+        user_text.submit(
+            run_generation,
+            [user_text, top_p, temperature, top_k, max_new_tokens, performance],
+            [model_output, performance],
+        )
+        button_submit.click(
+            run_generation,
+            [user_text, top_p, temperature, top_k, max_new_tokens, performance],
+            [model_output, performance],
+        )
+        button_clear.click(
+            reset_textbox,
+            [user_text, model_output, performance],
+            [user_text, model_output, performance],
+        )
     
     if __name__ == "__main__":
         demo.queue()
diff --git a/docs/notebooks/llm-rag-langchain-with-output.rst b/docs/notebooks/llm-rag-langchain-with-output.rst
new file mode 100644
index 00000000000..bed0e01fc09
--- /dev/null
+++ b/docs/notebooks/llm-rag-langchain-with-output.rst
@@ -0,0 +1,1269 @@
+Create a RAG system using OpenVINO and LangChain
+================================================
+
+**Retrieval-augmented generation (RAG)** is a technique for augmenting
+LLM knowledge with additional, often private or real-time, data. LLMs
+can reason about wide-ranging topics, but their knowledge is limited to
+the public data up to a specific point in time that they were trained
+on. If you want to build AI applications that can reason about private
+data or data introduced after a model’s cutoff date, you need to augment
+the knowledge of the model with the specific information it needs. The
+process of bringing the appropriate information and inserting it into
+the model prompt is known as Retrieval Augmented Generation (RAG).
+
+`LangChain `__
+is a framework for developing applications powered by language models.
+It has a number of components specifically designed to help build RAG
+applications. In this tutorial, we’ll build a simple question-answering
+application over a text data source.
+
+The tutorial consists of the following steps:
+
+-  Install prerequisites
+-  Download and convert the model from a public source using the
+   `OpenVINO integration with Hugging Face
+   Optimum `__.
+-  Compress model weights to 4-bit or 8-bit data types using
+   `NNCF `__
+-  Create a RAG chain pipeline
+-  Run Q&A pipeline
+
+In this example, the customized RAG pipeline consists of following
+components in order, where embedding, rerank and LLM will be deployed
+with OpenVINO to optimize their inference performance.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/0076f6c7-75e4-4c2e-9015-87b355e5ca28
+   :alt: RAG
+
+   RAG
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+-  `Select model for inference <#select-model-for-inference>`__
+-  `login to huggingfacehub to get access to pretrained
+   model <#login-to-huggingfacehub-to-get-access-to-pretrained-model>`__
+-  `Convert model and compress model
+   weights <#convert-model-and-compress-model-weights>`__
+
+   -  `Convert LLM model <#convert-llm-model>`__
+   -  `Weights Compression using
+      Optimum-CLI <#weights-compression-using-optimum-cli>`__
+   -  `Convert embedding model <#convert-embedding-model>`__
+   -  `Convert rerank model <#convert-rerank-model>`__
+
+-  `Select device for inference and model
+   variant <#select-device-for-inference-and-model-variant>`__
+
+   -  `Select device for embedding model
+      inference <#select-device-for-embedding-model-inference>`__
+   -  `Select device for rerank model
+      inference <#select-device-for-rerank-model-inference>`__
+   -  `Select device for LLM model
+      inference <#select-device-for-llm-model-inference>`__
+
+-  `Load model <#load-model>`__
+
+   -  `Load embedding model <#load-embedding-model>`__
+   -  `Load rerank model <#load-rerank-model>`__
+   -  `Load LLM model <#load-llm-model>`__
+
+-  `Run QA over Document <#run-qa-over-document>`__
+
+Prerequisites
+-------------
+
+
+
+Install required dependencies
+
+.. code:: ipython3
+
+    %pip uninstall -q -y openvino-dev openvino openvino-nightly optimum optimum-intel
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
+    "git+https://github.com/huggingface/optimum-intel.git"\
+    "git+https://github.com/openvinotoolkit/nncf.git"\
+    "datasets"\
+    "accelerate"\
+    "openvino-nightly"\
+    "gradio"\
+    "onnx" "einops" "transformers_stream_generator" "tiktoken" "transformers>=4.38.1" "bitsandbytes" "chromadb" "sentence_transformers" "langchain>=0.1.15" "langchainhub" "unstructured" "scikit-learn" "python-docx" "pdfminer.six" 
+
+.. code:: ipython3
+
+    import shutil
+    from pathlib import Path
+    import requests
+    
+    # fetch model configuration
+    
+    config_shared_path = Path("../../utils/llm_config.py")
+    config_dst_path = Path("llm_config.py")
+    
+    if not config_dst_path.exists():
+        if config_shared_path.exists():
+            shutil.copy(config_shared_path, config_dst_path)
+        else:
+            r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
+            with open("llm_config.py", "w") as f:
+                f.write(r.text)
+
+Select model for inference
+--------------------------
+
+
+
+The tutorial supports different models, you can select one from the
+provided options to compare the quality of open source LLM solutions.
+
+   **Note**: conversion of some models can require additional actions
+   from user side and at least 64GB RAM for conversion.
+
+The available embedding model options are:
+
+-  `bge-small-en-v1.5 `__
+-  `bge-small-zh-v1.5 `__
+-  `bge-large-en-v1.5 `__
+-  `bge-large-zh-v1.5 `__
+
+BGE embedding is a general Embedding Model. The model is pre-trained
+using RetroMAE and trained on large-scale pair data using contrastive
+learning.
+
+The available rerank model options are:
+
+-  `bge-reranker-large `__
+-  `bge-reranker-base `__
+
+Reranker model with cross-encoder will perform full-attention over the
+input pair, which is more accurate than embedding model (i.e.,
+bi-encoder) but more time-consuming than embedding model. Therefore, it
+can be used to re-rank the top-k documents returned by embedding model.
+
+You can also find available LLM model options in
+`llm-chatbot <../llm-chatbot/README.md>`__ notebook.
+
+.. code:: ipython3
+
+    from pathlib import Path
+    from optimum.intel.openvino import (
+        OVModelForSequenceClassification,
+        OVModelForFeatureExtraction,
+    )
+    import openvino as ov
+    import torch
+    import ipywidgets as widgets
+    from transformers import (
+        AutoTokenizer,
+        TextIteratorStreamer,
+        StoppingCriteria,
+        StoppingCriteriaList,
+    )
+
+Convert model and compress model weights
+----------------------------------------
+
+
+
+The Weights Compression algorithm is aimed at compressing the weights of
+the models and can be used to optimize the model footprint and
+performance of large models where the size of weights is relatively
+larger than the size of activations, for example, Large Language Models
+(LLM). Compared to INT8 compression, INT4 compression improves
+performance even more, but introduces a minor drop in prediction
+quality.
+
+Convert LLM model
+~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    from llm_config import (
+        SUPPORTED_EMBEDDING_MODELS,
+        SUPPORTED_RERANK_MODELS,
+        SUPPORTED_LLM_MODELS,
+    )
+    
+    model_languages = list(SUPPORTED_LLM_MODELS)
+    
+    model_language = widgets.Dropdown(
+        options=model_languages,
+        value=model_languages[0],
+        description="Model Language:",
+        disabled=False,
+    )
+    
+    model_language
+
+.. code:: ipython3
+
+    llm_model_ids = [model_id for model_id, model_config in SUPPORTED_LLM_MODELS[model_language.value].items() if model_config.get("rag_prompt_template")]
+    
+    llm_model_id = widgets.Dropdown(
+        options=llm_model_ids,
+        value=llm_model_ids[-1],
+        description="Model:",
+        disabled=False,
+    )
+    
+    llm_model_id
+
+.. code:: ipython3
+
+    llm_model_configuration = SUPPORTED_LLM_MODELS[model_language.value][llm_model_id.value]
+    print(f"Selected LLM model {llm_model_id.value}")
+
+`Optimum Intel `__ is
+the interface between the 
+`Transformers `__ and
+`Diffusers `__ libraries
+and OpenVINO to accelerate end-to-end pipelines on Intel architectures.
+It provides ease-to-use cli interface for exporting models to `OpenVINO
+Intermediate Representation
+(IR) `__
+format.
+
+The command bellow demonstrates basic command for model export with
+``optimum-cli``
+
+::
+
+   optimum-cli export openvino --model  --task  
+
+where ``--model`` argument is model id from HuggingFace Hub or local
+directory with model (saved using ``.save_pretrained`` method),
+``--task`` is one of `supported
+task `__
+that exported model should solve. For LLMs it will be
+``text-generation-with-past``. If model initialization requires to use
+remote code, ``--trust-remote-code`` flag additionally should be passed.
+
+Weights Compression using Optimum-CLI
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+You can also apply fp16, 8-bit or 4-bit weight compression on the
+Linear, Convolutional and Embedding layers when exporting your model
+with the CLI by setting ``--weight-format`` to respectively fp16, int8
+or int4. This type of optimization allows to reduce the memory footprint
+and inference latency. By default the quantization scheme for int8/int4
+will be
+`asymmetric `__,
+to make it
+`symmetric `__
+you can add ``--sym``.
+
+For INT4 quantization you can also specify the following arguments :
+
+-  The ``--group-size`` parameter will define the group size to use for
+   quantization, -1 it will results in per-column quantization.
+-  The ``--ratio`` parameter controls the ratio between 4-bit and 8-bit
+   quantization. If set to 0.9, it means that 90% of the layers will be
+   quantized to int4 while 10% will be quantized to int8.
+
+Smaller group_size and ratio values usually improve accuracy at the
+sacrifice of the model size and inference latency.
+
+   **Note**: There may be no speedup for INT4/INT8 compressed models on
+   dGPU.
+
+.. code:: ipython3
+
+    from IPython.display import Markdown, display
+    
+    prepare_int4_model = widgets.Checkbox(
+        value=True,
+        description="Prepare INT4 model",
+        disabled=False,
+    )
+    prepare_int8_model = widgets.Checkbox(
+        value=False,
+        description="Prepare INT8 model",
+        disabled=False,
+    )
+    prepare_fp16_model = widgets.Checkbox(
+        value=False,
+        description="Prepare FP16 model",
+        disabled=False,
+    )
+    
+    display(prepare_int4_model)
+    display(prepare_int8_model)
+    display(prepare_fp16_model)
+
+.. code:: ipython3
+
+    pt_model_id = llm_model_configuration["model_id"]
+    pt_model_name = llm_model_id.value.split("-")[0]
+    fp16_model_dir = Path(llm_model_id.value) / "FP16"
+    int8_model_dir = Path(llm_model_id.value) / "INT8_compressed_weights"
+    int4_model_dir = Path(llm_model_id.value) / "INT4_compressed_weights"
+    
+    
+    def convert_to_fp16():
+        if (fp16_model_dir / "openvino_model.xml").exists():
+            return
+        remote_code = llm_model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format fp16".format(pt_model_id)
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(fp16_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
+    
+    
+    def convert_to_int8():
+        if (int8_model_dir / "openvino_model.xml").exists():
+            return
+        int8_model_dir.mkdir(parents=True, exist_ok=True)
+        remote_code = llm_model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int8".format(pt_model_id)
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(int8_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
+    
+    
+    def convert_to_int4():
+        compression_configs = {
+            "zephyr-7b-beta": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "mistral-7b": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "minicpm-2b-dpo": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "gemma-2b-it": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "notus-7b-v1": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "neural-chat-7b-v3-1": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "llama-2-chat-7b": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+            "gemma-7b-it": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+            "chatglm2-6b": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.72,
+            },
+            "qwen-7b-chat": {"sym": True, "group_size": 128, "ratio": 0.6},
+            "red-pajama-3b-chat": {
+                "sym": False,
+                "group_size": 128,
+                "ratio": 0.5,
+            },
+            "default": {
+                "sym": False,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+        }
+    
+        model_compression_params = compression_configs.get(llm_model_id.value, compression_configs["default"])
+        if (int4_model_dir / "openvino_model.xml").exists():
+            return
+        remote_code = llm_model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int4".format(pt_model_id)
+        int4_compression_args = " --group-size {} --ratio {}".format(model_compression_params["group_size"], model_compression_params["ratio"])
+        if model_compression_params["sym"]:
+            int4_compression_args += " --sym"
+        export_command_base += int4_compression_args
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(int4_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
+    
+    
+    if prepare_fp16_model.value:
+        convert_to_fp16()
+    if prepare_int8_model.value:
+        convert_to_int8()
+    if prepare_int4_model.value:
+        convert_to_int4()
+
+Let’s compare model size for different compression types
+
+.. code:: ipython3
+
+    fp16_weights = fp16_model_dir / "openvino_model.bin"
+    int8_weights = int8_model_dir / "openvino_model.bin"
+    int4_weights = int4_model_dir / "openvino_model.bin"
+    
+    if fp16_weights.exists():
+        print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
+    for precision, compressed_weights in zip([8, 4], [int8_weights, int4_weights]):
+        if compressed_weights.exists():
+            print(f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB")
+        if compressed_weights.exists() and fp16_weights.exists():
+            print(f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}")
+
+Convert embedding model
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Since some embedding models can only support limited languages, we can
+filter them out according the LLM you selected.
+
+.. code:: ipython3
+
+    embedding_model_id = list(SUPPORTED_EMBEDDING_MODELS[model_language.value])
+    
+    embedding_model_id = widgets.Dropdown(
+        options=embedding_model_id,
+        value=embedding_model_id[0],
+        description="Embedding Model:",
+        disabled=False,
+    )
+    
+    embedding_model_id
+
+.. code:: ipython3
+
+    embedding_model_configuration = SUPPORTED_EMBEDDING_MODELS[model_language.value][embedding_model_id.value]
+    print(f"Selected {embedding_model_id.value} model")
+
+Optimum Intel can be used to load optimized models from the `Hugging
+Face Hub `__ and
+create pipelines to run an inference with OpenVINO Runtime using Hugging
+Face APIs. The Optimum Inference models are API compatible with Hugging
+Face Transformers models. This means we just need to replace
+``AutoModelForXxx`` class with the corresponding ``OVModelForXxx``
+class. In this example, we can use ``OVModelForFeatureExtraction`` to
+load and export embedding model.
+
+.. code:: ipython3
+
+    if not Path(embedding_model_id.value).exists():
+        ov_model = OVModelForFeatureExtraction.from_pretrained(embedding_model_configuration["model_id"], compile=False, export=True)
+        tokenizer = AutoTokenizer.from_pretrained(embedding_model_configuration["model_id"])
+        ov_model.half()
+        ov_model.save_pretrained(embedding_model_id.value)
+        tokenizer.save_pretrained(embedding_model_id.value)
+
+Convert rerank model
+~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    rerank_model_id = list(SUPPORTED_RERANK_MODELS)
+    
+    rerank_model_id = widgets.Dropdown(
+        options=rerank_model_id,
+        value=rerank_model_id[0],
+        description="Rerank Model:",
+        disabled=False,
+    )
+    
+    rerank_model_id
+
+.. code:: ipython3
+
+    rerank_model_configuration = SUPPORTED_RERANK_MODELS[rerank_model_id.value]
+    print(f"Selected {rerank_model_id.value} model")
+
+Since ``rerank`` model is sort of sentence classification task, we can
+use ``OVModelForSequenceClassification`` to load and export rerank
+model.
+
+.. code:: ipython3
+
+    if not Path(rerank_model_id.value).exists():
+        ov_model = OVModelForSequenceClassification.from_pretrained(rerank_model_configuration["model_id"], compile=False, export=True)
+        tokenizer = AutoTokenizer.from_pretrained(rerank_model_configuration["model_id"])
+        ov_model.half()
+        ov_model.save_pretrained(rerank_model_id.value)
+        tokenizer.save_pretrained(rerank_model_id.value)
+
+Select device for inference and model variant
+---------------------------------------------
+
+
+
+   **Note**: There may be no speedup for INT4/INT8 compressed models on
+   dGPU.
+
+Select device for embedding model inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    core = ov.Core()
+    embedding_device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    embedding_device
+
+.. code:: ipython3
+
+    print(f"Embedding model will be loaded to {embedding_device.value} device for text embedding")
+
+Select device for rerank model inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    rerank_device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    rerank_device
+
+.. code:: ipython3
+
+    print(f"Rerenk model will be loaded to {rerank_device.value} device for text reranking")
+
+Select device for LLM model inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    llm_device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    llm_device
+
+.. code:: ipython3
+
+    print(f"LLM model will be loaded to {llm_device.value} device for response generation")
+
+Load models
+-----------
+
+
+
+Load embedding model
+~~~~~~~~~~~~~~~~~~~~
+
+
+
+Now a Hugging Face embedding model can be supported by OpenVINO through
+`OpenVINOEmbeddings `__
+and
+`OpenVINOBgeEmbeddings `__\ classes
+of LangChain.
+
+.. code:: ipython3
+
+    from langchain_community.embeddings import OpenVINOBgeEmbeddings
+    
+    embedding_model_name = embedding_model_id.value
+    embedding_model_kwargs = {"device": embedding_device.value}
+    encode_kwargs = {
+        "mean_pooling": embedding_model_configuration["mean_pooling"],
+        "normalize_embeddings": embedding_model_configuration["normalize_embeddings"],
+    }
+    
+    embedding = OpenVINOBgeEmbeddings(
+        model_name_or_path=embedding_model_name,
+        model_kwargs=embedding_model_kwargs,
+        encode_kwargs=encode_kwargs,
+    )
+    
+    text = "This is a test document."
+    embedding_result = embedding.embed_query(text)
+    embedding_result[:3]
+
+Load rerank model
+~~~~~~~~~~~~~~~~~
+
+
+
+Now a Hugging Face embedding model can be supported by OpenVINO through
+`OpenVINOReranker `__
+class of LangChain.
+
+   **Note**: Rerank can be skipped in RAG.
+
+.. code:: ipython3
+
+    from langchain_community.document_compressors.openvino_rerank import OpenVINOReranker
+    
+    rerank_model_name = rerank_model_id.value
+    rerank_model_kwargs = {"device": rerank_device.value}
+    rerank_top_n = 2
+    
+    reranker = OpenVINOReranker(
+        model_name_or_path=rerank_model_name,
+        model_kwargs=rerank_model_kwargs,
+        top_n=rerank_top_n,
+    )
+
+Load LLM model
+~~~~~~~~~~~~~~
+
+
+
+OpenVINO models can be run locally through the ``HuggingFacePipeline``
+class. To deploy a model with OpenVINO, you can specify the
+``backend="openvino"`` parameter to trigger OpenVINO as backend
+inference framework.
+
+.. code:: ipython3
+
+    available_models = []
+    if int4_model_dir.exists():
+        available_models.append("INT4")
+    if int8_model_dir.exists():
+        available_models.append("INT8")
+    if fp16_model_dir.exists():
+        available_models.append("FP16")
+    
+    model_to_run = widgets.Dropdown(
+        options=available_models,
+        value=available_models[0],
+        description="Model to run:",
+        disabled=False,
+    )
+    
+    model_to_run
+
+OpenVINO models can be run locally through the ``HuggingFacePipeline``
+class in
+`LangChain `__.
+To deploy a model with OpenVINO, you can specify the
+``backend="openvino"`` parameter to trigger OpenVINO as backend
+inference framework.
+
+.. code:: ipython3
+
+    from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+    
+    if model_to_run.value == "INT4":
+        model_dir = int4_model_dir
+    elif model_to_run.value == "INT8":
+        model_dir = int8_model_dir
+    else:
+        model_dir = fp16_model_dir
+    print(f"Loading model from {model_dir}")
+    
+    ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
+    
+    # On a GPU device a model is executed in FP16 precision. For red-pajama-3b-chat model there known accuracy
+    # issues caused by this, which we avoid by setting precision hint to "f32".
+    if llm_model_id.value == "red-pajama-3b-chat" and "GPU" in core.available_devices and llm_device.value in ["GPU", "AUTO"]:
+        ov_config["INFERENCE_PRECISION_HINT"] = "f32"
+    
+    llm = HuggingFacePipeline.from_model_id(
+        model_id=str(model_dir),
+        task="text-generation",
+        backend="openvino",
+        model_kwargs={
+            "device": llm_device.value,
+            "ov_config": ov_config,
+            "trust_remote_code": True,
+        },
+        pipeline_kwargs={"max_new_tokens": 2},
+    )
+    
+    llm.invoke("2 + 2 =")
+
+Run QA over Document
+--------------------
+
+
+
+Now, when model created, we can setup Chatbot interface using
+`Gradio `__.
+
+A typical RAG application has two main components:
+
+-  **Indexing**: a pipeline for ingesting data from a source and
+   indexing it. This usually happen offline.
+
+-  **Retrieval and generation**: the actual RAG chain, which takes the
+   user query at run time and retrieves the relevant data from the
+   index, then passes that to the model.
+
+The most common full sequence from raw data to answer looks like:
+
+**Indexing**
+
+1. ``Load``: First we need to load our data. We’ll use DocumentLoaders
+   for this.
+2. ``Split``: Text splitters break large Documents into smaller chunks.
+   This is useful both for indexing data and for passing it in to a
+   model, since large chunks are harder to search over and won’t in a
+   model’s finite context window.
+3. ``Store``: We need somewhere to store and index our splits, so that
+   they can later be searched over. This is often done using a
+   VectorStore and Embeddings model.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/dfed2ba3-0c3a-4e0e-a2a7-01638730486a
+   :alt: Indexing pipeline
+
+   Indexing pipeline
+
+**Retrieval and generation**
+
+1. ``Retrieve``: Given a user input, relevant splits are retrieved from
+   storage using a Retriever.
+2. ``Generate``: A LLM produces an answer using a prompt that includes
+   the question and the retrieved data.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/f0545ddc-c0cd-4569-8c86-9879fdab105a
+   :alt: Retrieval and generation pipeline
+
+   Retrieval and generation pipeline
+
+.. code:: ipython3
+
+    from typing import List
+    from langchain.text_splitter import (
+        CharacterTextSplitter,
+        RecursiveCharacterTextSplitter,
+        MarkdownTextSplitter,
+    )
+    from langchain.document_loaders import (
+        CSVLoader,
+        EverNoteLoader,
+        PDFMinerLoader,
+        TextLoader,
+        UnstructuredEPubLoader,
+        UnstructuredHTMLLoader,
+        UnstructuredMarkdownLoader,
+        UnstructuredODTLoader,
+        UnstructuredPowerPointLoader,
+        UnstructuredWordDocumentLoader,
+    )
+    
+    
+    class ChineseTextSplitter(CharacterTextSplitter):
+        def __init__(self, pdf: bool = False, **kwargs):
+            super().__init__(**kwargs)
+            self.pdf = pdf
+    
+        def split_text(self, text: str) -> List[str]:
+            if self.pdf:
+                text = re.sub(r"\n{3,}", "\n", text)
+                text = text.replace("\n\n", "")
+            sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))')
+            sent_list = []
+            for ele in sent_sep_pattern.split(text):
+                if sent_sep_pattern.match(ele) and sent_list:
+                    sent_list[-1] += ele
+                elif ele:
+                    sent_list.append(ele)
+            return sent_list
+    
+    
+    TEXT_SPLITERS = {
+        "Character": CharacterTextSplitter,
+        "RecursiveCharacter": RecursiveCharacterTextSplitter,
+        "Markdown": MarkdownTextSplitter,
+        "Chinese": ChineseTextSplitter,
+    }
+    
+    
+    LOADERS = {
+        ".csv": (CSVLoader, {}),
+        ".doc": (UnstructuredWordDocumentLoader, {}),
+        ".docx": (UnstructuredWordDocumentLoader, {}),
+        ".enex": (EverNoteLoader, {}),
+        ".epub": (UnstructuredEPubLoader, {}),
+        ".html": (UnstructuredHTMLLoader, {}),
+        ".md": (UnstructuredMarkdownLoader, {}),
+        ".odt": (UnstructuredODTLoader, {}),
+        ".pdf": (PDFMinerLoader, {}),
+        ".ppt": (UnstructuredPowerPointLoader, {}),
+        ".pptx": (UnstructuredPowerPointLoader, {}),
+        ".txt": (TextLoader, {"encoding": "utf8"}),
+    }
+
+We can build a RAG pipeline of LangChain through
+`create_retrieval_chain `__,
+which will help to create a chain to connect RAG components including:
+
+-  `Vector stores `__\ ，
+-  `Retrievers `__
+-  `LLM `__
+-  `Embedding `__
+
+.. code:: ipython3
+
+    from langchain.prompts import PromptTemplate
+    from langchain.vectorstores import Chroma
+    from langchain.chains.retrieval import create_retrieval_chain
+    from langchain.chains.combine_documents import create_stuff_documents_chain
+    from langchain.docstore.document import Document
+    from langchain.retrievers import ContextualCompressionRetriever
+    from threading import Thread
+    import gradio as gr
+    
+    stop_tokens = llm_model_configuration.get("stop_tokens")
+    
+    
+    class StopOnTokens(StoppingCriteria):
+        def __init__(self, token_ids):
+            self.token_ids = token_ids
+    
+        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+            for stop_id in self.token_ids:
+                if input_ids[0][-1] == stop_id:
+                    return True
+            return False
+    
+    
+    if stop_tokens is not None:
+        if isinstance(stop_tokens[0], str):
+            stop_tokens = llm.pipeline.tokenizer.convert_tokens_to_ids(stop_tokens)
+    
+        stop_tokens = [StopOnTokens(stop_tokens)]
+    
+    
+    def load_single_document(file_path: str) -> List[Document]:
+        """
+        helper for loading a single document
+    
+        Params:
+          file_path: document path
+        Returns:
+          documents loaded
+    
+        """
+        ext = "." + file_path.rsplit(".", 1)[-1]
+        if ext in LOADERS:
+            loader_class, loader_args = LOADERS[ext]
+            loader = loader_class(file_path, **loader_args)
+            return loader.load()
+    
+        raise ValueError(f"File does not exist '{ext}'")
+    
+    
+    def default_partial_text_processor(partial_text: str, new_text: str):
+        """
+        helper for updating partially generated answer, used by default
+    
+        Params:
+          partial_text: text buffer for storing previosly generated text
+          new_text: text update for the current step
+        Returns:
+          updated text string
+    
+        """
+        partial_text += new_text
+        return partial_text
+    
+    
+    text_processor = llm_model_configuration.get("partial_text_processor", default_partial_text_processor)
+    
+    
+    def create_vectordb(
+        docs,
+        spliter_name,
+        chunk_size,
+        chunk_overlap,
+        vector_search_top_k,
+        vector_search_top_n,
+        run_rerank,
+    ):
+        """
+        Initialize a vector database
+    
+        Params:
+          doc: orignal documents provided by user
+          chunk_size:  size of a single sentence chunk
+          chunk_overlap: overlap size between 2 chunks
+          vector_search_top_k: Vector search top k
+    
+        """
+        documents = []
+        for doc in docs:
+            documents.extend(load_single_document(doc.name))
+    
+        text_splitter = TEXT_SPLITERS[spliter_name](chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+    
+        texts = text_splitter.split_documents(documents)
+    
+        global db
+        db = Chroma.from_documents(texts, embedding)
+    
+        global retriever
+        retriever = db.as_retriever(search_kwargs={"k": vector_search_top_k})
+        if run_rerank:
+            reranker.top_n = vector_search_top_n
+            retriever = ContextualCompressionRetriever(base_compressor=reranker, base_retriever=retriever)
+        prompt = PromptTemplate.from_template(llm_model_configuration["rag_prompt_template"])
+    
+        global combine_docs_chain
+        combine_docs_chain = create_stuff_documents_chain(llm, prompt)
+        global rag_chain
+        rag_chain = create_retrieval_chain(retriever, combine_docs_chain)
+    
+        return "Vector database is Ready"
+    
+    
+    def update_retriever(vector_search_top_k, vector_rerank_top_n, run_rerank, search_method):
+        """
+        Update retriever
+    
+        Params:
+          vector_search_top_k: size of searching results
+          vector_rerank_top_n:  size of rerank results
+          run_rerank: whether run rerank step
+          search_method: search method used by vector store
+    
+        """
+        global retriever
+        global db
+        global rag_chain
+        global combine_docs_chain
+    
+        retriever = db.as_retriever(search_kwargs={"k": vector_search_top_k}, search_type=search_method)
+        if run_rerank:
+            retriever = ContextualCompressionRetriever(base_compressor=reranker, base_retriever=retriever)
+            reranker.top_n = vector_rerank_top_n
+        rag_chain = create_retrieval_chain(retriever, combine_docs_chain)
+    
+    
+    def user(message, history):
+        """
+        callback function for updating user messages in interface on submit button click
+    
+        Params:
+          message: current message
+          history: conversation history
+        Returns:
+          None
+        """
+        # Append the user's message to the conversation history
+        return "", history + [[message, ""]]
+    
+    
+    def bot(history, temperature, top_p, top_k, repetition_penalty, hide_full_prompt):
+        """
+        callback function for running chatbot on submit button click
+    
+        Params:
+          history: conversation history
+          temperature:  parameter for control the level of creativity in AI-generated text.
+                        By adjusting the `temperature`, you can influence the AI model's probability distribution, making the text more focused or diverse.
+          top_p: parameter for control the range of tokens considered by the AI model based on their cumulative probability.
+          top_k: parameter for control the range of tokens considered by the AI model based on their cumulative probability, selecting number of tokens with highest probability.
+          repetition_penalty: parameter for penalizing tokens based on how frequently they occur in the text.
+          hide_full_prompt: whether to show searching results in promopt.
+    
+        """
+        streamer = TextIteratorStreamer(
+            llm.pipeline.tokenizer,
+            timeout=60.0,
+            skip_prompt=hide_full_prompt,
+            skip_special_tokens=True,
+        )
+        llm.pipeline._forward_params = dict(
+            max_new_tokens=512,
+            temperature=temperature,
+            do_sample=temperature > 0.0,
+            top_p=top_p,
+            top_k=top_k,
+            repetition_penalty=repetition_penalty,
+            streamer=streamer,
+        )
+        if stop_tokens is not None:
+            llm.pipeline._forward_params["stopping_criteria"] = StoppingCriteriaList(stop_tokens)
+    
+        t1 = Thread(target=rag_chain.invoke, args=({"input": history[-1][0]},))
+        t1.start()
+    
+        # Initialize an empty string to store the generated text
+        partial_text = ""
+        for new_text in streamer:
+            partial_text = text_processor(partial_text, new_text)
+            history[-1][1] = partial_text
+            yield history
+    
+    
+    def request_cancel():
+        llm.pipeline.model.request.cancel()
+    
+    
+    with gr.Blocks(
+        theme=gr.themes.Soft(),
+        css=".disclaimer {font-variant-caps: all-small-caps;}",
+    ) as demo:
+        gr.Markdown("""
QA over Document
""")
+        gr.Markdown(f"""
Powered by OpenVINO and {llm_model_id.value} 
""")
+        with gr.Row():
+            with gr.Column(scale=1):
+                docs = gr.File(
+                    label="Step 1: Load text files",
+                    file_count="multiple",
+                    file_types=[
+                        ".csv",
+                        ".doc",
+                        ".docx",
+                        ".enex",
+                        ".epub",
+                        ".html",
+                        ".md",
+                        ".odt",
+                        ".pdf",
+                        ".ppt",
+                        ".pptx",
+                        ".txt",
+                    ],
+                )
+                load_docs = gr.Button("Step 2: Build Vector Store")
+                db_argument = gr.Accordion("Vector Store Configuration", open=False)
+                with db_argument:
+                    spliter = gr.Dropdown(
+                        ["Character", "RecursiveCharacter", "Markdown", "Chinese"],
+                        value="RecursiveCharacter",
+                        label="Text Spliter",
+                        info="Method used to splite the documents",
+                        multiselect=False,
+                    )
+    
+                    chunk_size = gr.Slider(
+                        label="Chunk size",
+                        value=700,
+                        minimum=100,
+                        maximum=2000,
+                        step=50,
+                        interactive=True,
+                        info="Size of sentence chunk",
+                    )
+    
+                    chunk_overlap = gr.Slider(
+                        label="Chunk overlap",
+                        value=100,
+                        minimum=0,
+                        maximum=400,
+                        step=10,
+                        interactive=True,
+                        info=("Overlap between 2 chunks"),
+                    )
+    
+                langchain_status = gr.Textbox(
+                    label="Vector Store Status",
+                    value="Vector Store is Not ready",
+                    interactive=False,
+                )
+                with gr.Accordion("Generation Configuration", open=False):
+                    with gr.Row():
+                        with gr.Column():
+                            with gr.Row():
+                                temperature = gr.Slider(
+                                    label="Temperature",
+                                    value=0.1,
+                                    minimum=0.0,
+                                    maximum=1.0,
+                                    step=0.1,
+                                    interactive=True,
+                                    info="Higher values produce more diverse outputs",
+                                )
+                        with gr.Column():
+                            with gr.Row():
+                                top_p = gr.Slider(
+                                    label="Top-p (nucleus sampling)",
+                                    value=1.0,
+                                    minimum=0.0,
+                                    maximum=1,
+                                    step=0.01,
+                                    interactive=True,
+                                    info=(
+                                        "Sample from the smallest possible set of tokens whose cumulative probability "
+                                        "exceeds top_p. Set to 1 to disable and sample from all tokens."
+                                    ),
+                                )
+                        with gr.Column():
+                            with gr.Row():
+                                top_k = gr.Slider(
+                                    label="Top-k",
+                                    value=50,
+                                    minimum=0.0,
+                                    maximum=200,
+                                    step=1,
+                                    interactive=True,
+                                    info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
+                                )
+                        with gr.Column():
+                            with gr.Row():
+                                repetition_penalty = gr.Slider(
+                                    label="Repetition Penalty",
+                                    value=1.1,
+                                    minimum=1.0,
+                                    maximum=2.0,
+                                    step=0.1,
+                                    interactive=True,
+                                    info="Penalize repetition — 1.0 to disable.",
+                                )
+            with gr.Column(scale=4):
+                chatbot = gr.Chatbot(
+                    height=600,
+                    label="Step 3: Input Query",
+                )
+                with gr.Row():
+                    with gr.Column():
+                        with gr.Row():
+                            msg = gr.Textbox(
+                                label="QA Message Box",
+                                placeholder="Chat Message Box",
+                                show_label=False,
+                                container=False,
+                            )
+                    with gr.Column():
+                        with gr.Row():
+                            submit = gr.Button("Submit")
+                            stop = gr.Button("Stop")
+                            clear = gr.Button("Clear")
+                retriever_argument = gr.Accordion("Retriever Configuration", open=True)
+                with retriever_argument:
+                    with gr.Row():
+                        with gr.Row():
+                            do_rerank = gr.Checkbox(
+                                value=True,
+                                label="Rerank searching result",
+                                interactive=True,
+                            )
+                            hide_context = gr.Checkbox(
+                                value=True,
+                                label="Hide searching result in prompt",
+                                interactive=True,
+                            )
+                        with gr.Row():
+                            search_method = gr.Dropdown(
+                                ["similarity", "mmr"],
+                                value="similarity",
+                                label="Searching Method",
+                                info="Method used to search vector store",
+                                multiselect=False,
+                                interactive=True,
+                            )
+                        with gr.Row():
+                            vector_rerank_top_n = gr.Slider(
+                                1,
+                                10,
+                                value=2,
+                                step=1,
+                                label="Rerank top n",
+                                info="Number of rerank results",
+                                interactive=True,
+                            )
+                        with gr.Row():
+                            vector_search_top_k = gr.Slider(
+                                1,
+                                50,
+                                value=10,
+                                step=1,
+                                label="Search top k",
+                                info="Number of searching results, must >= Rerank top n",
+                                interactive=True,
+                            )
+        load_docs.click(
+            create_vectordb,
+            inputs=[
+                docs,
+                spliter,
+                chunk_size,
+                chunk_overlap,
+                vector_search_top_k,
+                vector_rerank_top_n,
+                do_rerank,
+            ],
+            outputs=[langchain_status],
+            queue=False,
+        )
+        submit_event = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+            bot,
+            [chatbot, temperature, top_p, top_k, repetition_penalty, hide_context],
+            chatbot,
+            queue=True,
+        )
+        submit_click_event = submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+            bot,
+            [chatbot, temperature, top_p, top_k, repetition_penalty, hide_context],
+            chatbot,
+            queue=True,
+        )
+        stop.click(
+            fn=request_cancel,
+            inputs=None,
+            outputs=None,
+            cancels=[submit_event, submit_click_event],
+            queue=False,
+        )
+        clear.click(lambda: None, None, chatbot, queue=False)
+        vector_search_top_k.change(
+            update_retriever,
+            [vector_search_top_k, vector_rerank_top_n, do_rerank, search_method],
+        )
+        vector_rerank_top_n.change(
+            update_retriever,
+            [vector_search_top_k, vector_rerank_top_n, do_rerank, search_method],
+        )
+        do_rerank.change(
+            update_retriever,
+            [vector_search_top_k, vector_rerank_top_n, do_rerank, search_method],
+        )
+        search_method.change(
+            update_retriever,
+            [vector_search_top_k, vector_rerank_top_n, do_rerank, search_method],
+        )
+    
+    
+    demo.queue()
+    # if you are launching remotely, specify server_name and server_port
+    #  demo.launch(server_name='your server name', server_port='server port in int')
+    # if you have any issue to launch on your platform, you can pass share=True to launch method:
+    # demo.launch(share=True)
+    # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
+    demo.launch()
+
+.. code:: ipython3
+
+    # please run this cell for stopping gradio interface
+    demo.close()
diff --git a/docs/notebooks/125-lraspp-segmentation-with-output.rst b/docs/notebooks/lraspp-segmentation-with-output.rst
similarity index 77%
rename from docs/notebooks/125-lraspp-segmentation-with-output.rst
rename to docs/notebooks/lraspp-segmentation-with-output.rst
index 2920db91b5e..ddd384c280c 100644
--- a/docs/notebooks/125-lraspp-segmentation-with-output.rst
+++ b/docs/notebooks/lraspp-segmentation-with-output.rst
@@ -61,14 +61,29 @@ Prerequisites
 
 .. parsed-literal::
 
-    Note: you may need to restart the kernel to use updated packages.
-
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
 
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -90,23 +105,26 @@ First of all lets get a test image from an open dataset.
 
 .. code:: ipython3
 
-    import urllib.request
+    import requests
     
     from torchvision.io import read_image
     import torchvision.transforms as transforms
     
     
-    img_path = 'cats_image.jpeg'
-    urllib.request.urlretrieve(
-        url='https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg',
-        filename=img_path
+    img_path = "cats_image.jpeg"
+    r = requests.get(
+        url="https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg",
     )
+    
+    with open(img_path, "wb") as f:
+        f.write(r.content)
+    
     image = read_image(img_path)
     display(transforms.ToPILImage()(image))
 
 
 
-.. image:: 125-lraspp-segmentation-with-output_files/125-lraspp-segmentation-with-output_5_0.png
+.. image:: lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_5_0.png
 
 
 Download and prepare a model
@@ -191,7 +209,7 @@ Run an inference on the PyTorch model
 
     model.eval()
     with torch.no_grad():
-        result_torch = model(torch.as_tensor(input_data).float())['out']
+        result_torch = model(torch.as_tensor(input_data).float())["out"]
 
 Convert the original model to OpenVINO IR Format
 ------------------------------------------------
@@ -205,7 +223,7 @@ directory. For more information on how to convert models, see this
 
 .. code:: ipython3
 
-    ov_model_xml_path = Path('models/ov_lraspp_model.xml')
+    ov_model_xml_path = Path("models/ov_lraspp_model.xml")
     
     
     if not ov_model_xml_path.exists():
@@ -230,8 +248,8 @@ Select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -277,7 +295,7 @@ visualize the image with a ``cat`` mask for the PyTorch model.
     import torchvision.transforms.functional as F
     
     
-    plt.rcParams["savefig.bbox"] = 'tight'
+    plt.rcParams["savefig.bbox"] = "tight"
     
     
     def show(imgs):
@@ -295,21 +313,39 @@ Prepare and display a cat mask.
 .. code:: ipython3
 
     sem_classes = [
-        '__background__', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
-        'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
-        'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
+        "__background__",
+        "aeroplane",
+        "bicycle",
+        "bird",
+        "boat",
+        "bottle",
+        "bus",
+        "car",
+        "cat",
+        "chair",
+        "cow",
+        "diningtable",
+        "dog",
+        "horse",
+        "motorbike",
+        "person",
+        "pottedplant",
+        "sheep",
+        "sofa",
+        "train",
+        "tvmonitor",
     ]
     sem_class_to_idx = {cls: idx for (idx, cls) in enumerate(sem_classes)}
     
     normalized_mask = torch.nn.functional.softmax(result_torch, dim=1)
     
-    cat_mask = normalized_mask[0, sem_class_to_idx['cat']]
+    cat_mask = normalized_mask[0, sem_class_to_idx["cat"]]
     
     show(cat_mask)
 
 
 
-.. image:: 125-lraspp-segmentation-with-output_files/125-lraspp-segmentation-with-output_28_0.png
+.. image:: lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_28_0.png
 
 
 The
@@ -322,7 +358,7 @@ following:
 .. code:: ipython3
 
     class_dim = 1
-    boolean_cat_mask = (normalized_mask.argmax(class_dim) == sem_class_to_idx['cat'])
+    boolean_cat_mask = normalized_mask.argmax(class_dim) == sem_class_to_idx["cat"]
 
 And now we can plot a boolean mask on top of the original image.
 
@@ -330,11 +366,11 @@ And now we can plot a boolean mask on top of the original image.
 
     from torchvision.utils import draw_segmentation_masks
     
-    show(draw_segmentation_masks(image, masks=boolean_cat_mask, alpha=0.7, colors='yellow'))
+    show(draw_segmentation_masks(image, masks=boolean_cat_mask, alpha=0.7, colors="yellow"))
 
 
 
-.. image:: 125-lraspp-segmentation-with-output_files/125-lraspp-segmentation-with-output_32_0.png
+.. image:: lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_32_0.png
 
 
 Show results for the OpenVINO IR model
@@ -345,10 +381,10 @@ Show results for the OpenVINO IR model
 .. code:: ipython3
 
     normalized_mask = torch.nn.functional.softmax(torch.from_numpy(res_ir), dim=1)
-    boolean_cat_mask = (normalized_mask.argmax(class_dim) == sem_class_to_idx['cat'])
-    show(draw_segmentation_masks(image, masks=boolean_cat_mask, alpha=0.7, colors='yellow'))
+    boolean_cat_mask = normalized_mask.argmax(class_dim) == sem_class_to_idx["cat"]
+    show(draw_segmentation_masks(image, masks=boolean_cat_mask, alpha=0.7, colors="yellow"))
 
 
 
-.. image:: 125-lraspp-segmentation-with-output_files/125-lraspp-segmentation-with-output_34_0.png
+.. image:: lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_34_0.png
 
diff --git a/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_28_0.png b/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_28_0.png
new file mode 100644
index 00000000000..fcd66628777
--- /dev/null
+++ b/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_28_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95d793138fdc24454e9f7499ddbe5eea3e6a4d0b5700e5ed0d643839c3ff1996
+size 87576
diff --git a/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_32_0.png b/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_32_0.png
new file mode 100644
index 00000000000..3601a35a2bf
--- /dev/null
+++ b/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_32_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1cb37ba3ed5d8529d6669019dfdbf3f2287890603f347fad9699da417c9068fd
+size 385522
diff --git a/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_34_0.png b/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_34_0.png
new file mode 100644
index 00000000000..d8b1d0fe5ef
--- /dev/null
+++ b/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_34_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5474002d82c8e70a73650967a1cc02dc57bf6c0c8fb31b415cba9edfcadebb3
+size 385520
diff --git a/docs/notebooks/125-lraspp-segmentation-with-output_files/125-lraspp-segmentation-with-output_5_0.jpg b/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_5_0.jpg
similarity index 100%
rename from docs/notebooks/125-lraspp-segmentation-with-output_files/125-lraspp-segmentation-with-output_5_0.jpg
rename to docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_5_0.jpg
diff --git a/docs/notebooks/125-lraspp-segmentation-with-output_files/125-lraspp-segmentation-with-output_5_0.png b/docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_5_0.png
similarity index 100%
rename from docs/notebooks/125-lraspp-segmentation-with-output_files/125-lraspp-segmentation-with-output_5_0.png
rename to docs/notebooks/lraspp-segmentation-with-output_files/lraspp-segmentation-with-output_5_0.png
diff --git a/docs/notebooks/221-machine-translation-with-output.rst b/docs/notebooks/machine-translation-with-output.rst
similarity index 80%
rename from docs/notebooks/221-machine-translation-with-output.rst
rename to docs/notebooks/machine-translation-with-output.rst
index c7431e97707..ba5b6d33ee2 100644
--- a/docs/notebooks/221-machine-translation-with-output.rst
+++ b/docs/notebooks/machine-translation-with-output.rst
@@ -31,25 +31,15 @@ Table of contents:
 .. code:: ipython3
 
     # # Install requirements
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" tqdm
     %pip install -q tokenizers
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -58,14 +48,20 @@ Table of contents:
 .. code:: ipython3
 
     import time
-    import sys
     import openvino as ov
     import numpy as np
     import itertools
     from pathlib import Path
     from tokenizers import SentencePieceBPETokenizer
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 Downloading model
@@ -89,8 +85,16 @@ Make sure you have run ``pip install openvino-dev`` beforehand.
     src_tok_dir.mkdir(exist_ok=True)
     target_tok_dir.mkdir(exist_ok=True)
     
-    download_file(base_url + f'/{model_name}/{precision}/{model_name}.xml', f"{model_name}.xml", model_base_dir)
-    download_file(base_url + f'/{model_name}/{precision}/{model_name}.bin', f"{model_name}.bin", model_base_dir)
+    download_file(
+        base_url + f"/{model_name}/{precision}/{model_name}.xml",
+        f"{model_name}.xml",
+        model_base_dir,
+    )
+    download_file(
+        base_url + f"/{model_name}/{precision}/{model_name}.bin",
+        f"{model_name}.bin",
+        model_base_dir,
+    )
     download_file(f"{base_url}/{model_name}/tokenizer_src/merges.txt", "merges.txt", src_tok_dir)
     download_file(f"{base_url}/{model_name}/tokenizer_tgt/merges.txt", "merges.txt", target_tok_dir)
     download_file(f"{base_url}/{model_name}/tokenizer_src/vocab.json", "vocab.json", src_tok_dir)
@@ -165,8 +169,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -205,14 +209,8 @@ Initialize the tokenizer for the input ``src_tokenizer`` and the output
 
 .. code:: ipython3
 
-    src_tokenizer = SentencePieceBPETokenizer.from_file(
-        str(src_tok_dir / 'vocab.json'),
-        str(src_tok_dir / 'merges.txt')
-    )
-    tgt_tokenizer = SentencePieceBPETokenizer.from_file(
-        str(target_tok_dir / 'vocab.json'),
-        str(target_tok_dir / 'merges.txt')
-    )
+    src_tokenizer = SentencePieceBPETokenizer.from_file(str(src_tok_dir / "vocab.json"), str(src_tok_dir / "merges.txt"))
+    tgt_tokenizer = SentencePieceBPETokenizer.from_file(str(target_tok_dir / "vocab.json"), str(target_tok_dir / "merges.txt"))
 
 Perform translation
 -------------------
@@ -236,14 +234,13 @@ The following function translates a sentence in English to German.
         assert len(sentence) > 0
         tokens = src_tokenizer.encode(sentence).ids
         # Transform the tokenized sentence into the model's input format
-        tokens = [src_tokenizer.token_to_id('')] + \
-            tokens + [src_tokenizer.token_to_id('')]
+        tokens = [src_tokenizer.token_to_id("")] + tokens + [src_tokenizer.token_to_id("")]
         pad_length = max_tokens - len(tokens)
     
         # If the sentence size is less than the maximum allowed tokens,
         # fill the remaining tokens with ''.
         if pad_length > 0:
-            tokens = tokens + [src_tokenizer.token_to_id('')] * pad_length
+            tokens = tokens + [src_tokenizer.token_to_id("")] * pad_length
         assert len(tokens) == max_tokens, "input sentence is too long"
         encoded_sentence = np.array(tokens).reshape(1, -1)
     
@@ -257,8 +254,8 @@ The following function translates a sentence in English to German.
     
         # Remove  tokens, as well as '' and '' tokens which mark the
         # beginning and ending of the sentence.
-        for s in ['', '', '']:
-            sentence = sentence.replace(s, '')
+        for s in ["", "", ""]:
+            sentence = sentence.replace(s, "")
     
         # Transform sentence into lower case and join words by a white space
         sentence = sentence.lower().split()
@@ -289,8 +286,8 @@ The following function is a basic loop that translates sentences.
             start_time = time.perf_counter()
             translated = translate(input_sentence)
             end_time = time.perf_counter()
-            print(f'Translated: {translated}')
-            print(f'Time: {end_time - start_time:.2f}s')
+            print(f"Translated: {translated}")
+            print(f"Time: {end_time - start_time:.2f}s")
 
 .. code:: ipython3
 
@@ -308,7 +305,7 @@ German
 .. code:: ipython3
 
     sentence = "My name is openvino"
-    print(f'Translated: {translate(sentence)}')
+    print(f"Translated: {translate(sentence)}")
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/magika-content-type-recognition-with-output.rst b/docs/notebooks/magika-content-type-recognition-with-output.rst
new file mode 100644
index 00000000000..690fe008efa
--- /dev/null
+++ b/docs/notebooks/magika-content-type-recognition-with-output.rst
@@ -0,0 +1,392 @@
+Magika: AI powered fast and efficient file type identification using OpenVINO
+=============================================================================
+
+Magika is a novel AI powered file type detection tool that relies on the
+recent advance of deep learning to provide accurate detection. Under the
+hood, Magika employs a custom, highly optimized model that only weighs
+about 1MB, and enables precise file identification within milliseconds,
+even when running on a single CPU.
+
+Why identifying file type is difficult
+--------------------------------------
+
+Since the early days of computing, accurately detecting file types has
+been crucial in determining how to process files. Linux comes equipped
+with ``libmagic`` and the ``file`` utility, which have served as the de
+facto standard for file type identification for over 50 years. Today web
+browsers, code editors, and countless other software rely on file-type
+detection to decide how to properly render a file. For example, modern
+code editors use file-type detection to choose which syntax coloring
+scheme to use as the developer starts typing in a new file.
+
+Accurate file-type detection is a notoriously difficult problem because
+each file format has a different structure, or no structure at all. This
+is particularly challenging for textual formats and programming
+languages as they have very similar constructs. So far, ``libmagic`` and
+most other file-type-identification software have been relying on a
+handcrafted collection of heuristics and custom rules to detect each
+file format.
+
+This manual approach is both time consuming and error prone as it is
+hard for humans to create generalized rules by hand. In particular for
+security applications, creating dependable detection is especially
+challenging as attackers are constantly attempting to confuse detection
+with adversarially-crafted payloads.
+
+To address this issue and provide fast and accurate file-type detection
+Magika was developed. More details about approach and model can be found
+in original `repo `__ and `Google’s
+blog
+post `__.
+
+In this tutorial we consider how to bring OpenVINO power into Magika.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+-  `Define model loading class <#define-model-loading-class>`__
+-  `Run OpenVINO model inference <#run-openvino-model-inference>`__
+
+   -  `Select device <#select-device>`__
+   -  `Create model <#create-model>`__
+   -  `Run inference on bytes input <#run-inference-on-bytes-input>`__
+   -  `Run inference on file input <#run-inference-on-file-input>`__
+
+-  `Interactive demo <#interactive-demo>`__
+
+Prerequisites
+-------------
+
+ ## Prerequisites
+
+.. code:: ipython3
+
+    %pip uninstall -q -y openvino-dev openvino openvino-nightly
+    %pip install -q magika openvino-nightly "gradio>=4.19"
+
+
+.. parsed-literal::
+
+    WARNING: Skipping openvino-nightly as it is not installed.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+
+
+.. parsed-literal::
+
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you have numpy 1.24.4 which is incompatible.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Define model loading class
+--------------------------
+
+
+
+At inference time Magika uses ONNX as an inference engine to ensure
+files are identified in a matter of milliseconds, almost as fast as a
+non-AI tool even on CPU. The code below extending original Magika
+inference class with OpenVINO API. The provided code is fully compatible
+with original `Magika Python
+API `__.
+
+.. code:: ipython3
+
+    import time
+    from pathlib import Path
+    from functools import partial
+    from typing import List, Tuple, Optional, Dict
+
+    from magika import Magika
+    from magika.types import ModelFeatures, ModelOutput, MagikaResult
+    from magika.prediction_mode import PredictionMode
+    import numpy.typing as npt
+    import numpy as np
+
+    import openvino as ov
+
+
+    class OVMagika(Magika):
+        def __init__(
+            self,
+            model_dir: Optional[Path] = None,
+            prediction_mode: PredictionMode = PredictionMode.HIGH_CONFIDENCE,
+            no_dereference: bool = False,
+            verbose: bool = False,
+            debug: bool = False,
+            use_colors: bool = False,
+            device="CPU",
+        ) -> None:
+            self._device = device
+            super().__init__(model_dir, prediction_mode, no_dereference, verbose, debug, use_colors)
+
+        def _init_onnx_session(self):
+            # overload model loading using OpenVINO
+            start_time = time.time()
+            core = ov.Core()
+            ov_model = core.compile_model(self._model_path, self._device.upper())
+            elapsed_time = 1000 * (time.time() - start_time)
+            self._log.debug(f'ONNX DL model "{self._model_path}" loaded in {elapsed_time:.03f} ms on {self._device}')
+            return ov_model
+
+        def _get_raw_predictions(self, features: List[Tuple[Path, ModelFeatures]]) -> npt.NDArray:
+            """
+            Given a list of (path, features), return a (files_num, features_size)
+            matrix encoding the predictions.
+            """
+
+            dataset_format = self._model_config["train_dataset_info"]["dataset_format"]
+            assert dataset_format == "int-concat/one-hot"
+            start_time = time.time()
+            X_bytes = []
+            for _, fs in features:
+                sample_bytes = []
+                if self._input_sizes["beg"] > 0:
+                    sample_bytes.extend(fs.beg[: self._input_sizes["beg"]])
+                if self._input_sizes["mid"] > 0:
+                    sample_bytes.extend(fs.mid[: self._input_sizes["mid"]])
+                if self._input_sizes["end"] > 0:
+                    sample_bytes.extend(fs.end[-self._input_sizes["end"] :])
+                X_bytes.append(sample_bytes)
+            X = np.array(X_bytes).astype(np.float32)
+            elapsed_time = time.time() - start_time
+            self._log.debug(f"DL input prepared in {elapsed_time:.03f} seconds")
+
+            start_time = time.time()
+            raw_predictions_list = []
+            samples_num = X.shape[0]
+
+            max_internal_batch_size = 1000
+            batches_num = samples_num // max_internal_batch_size
+            if samples_num % max_internal_batch_size != 0:
+                batches_num += 1
+
+            for batch_idx in range(batches_num):
+                self._log.debug(f"Getting raw predictions for (internal) batch {batch_idx+1}/{batches_num}")
+                start_idx = batch_idx * max_internal_batch_size
+                end_idx = min((batch_idx + 1) * max_internal_batch_size, samples_num)
+                batch_raw_predictions = self._onnx_session({"bytes": X[start_idx:end_idx, :]})["target_label"]
+                raw_predictions_list.append(batch_raw_predictions)
+            elapsed_time = time.time() - start_time
+            self._log.debug(f"DL raw prediction in {elapsed_time:.03f} seconds")
+            return np.concatenate(raw_predictions_list)
+
+        def _get_topk_model_outputs_from_features(self, all_features: List[Tuple[Path, ModelFeatures]], k: int = 5) -> List[Tuple[Path, List[ModelOutput]]]:
+            """
+            Helper function for getting top k the highest ranked model results for each feature
+            """
+            raw_preds = self._get_raw_predictions(all_features)
+            top_preds_idxs = np.argsort(raw_preds, axis=1)[:, -k:][:, ::-1]
+            scores = [raw_preds[i, idx] for i, idx in enumerate(top_preds_idxs)]
+            results = []
+            for (path, _), scores, top_idxes in zip(all_features, raw_preds, top_preds_idxs):
+                model_outputs_for_path = []
+                for idx in top_idxes:
+                    ct_label = self._target_labels_space_np[idx]
+                    score = scores[idx]
+                    model_outputs_for_path.append(ModelOutput(ct_label=ct_label, score=float(score)))
+                results.append((path, model_outputs_for_path))
+            return results
+
+        def _get_results_from_features_topk(self, all_features: List[Tuple[Path, ModelFeatures]], top_k=5) -> Dict[str, MagikaResult]:
+            """
+            Helper function for getting top k the highest ranked model results for each feature
+            """
+            # We now do inference for those files that need it.
+
+            if len(all_features) == 0:
+                # nothing to be done
+                return {}
+
+            outputs: Dict[str, MagikaResult] = {}
+
+            for path, model_output in self._get_topk_model_outputs_from_features(all_features, top_k):
+                # In additional to the content type label from the DL model, we
+                # also allow for other logic to overwrite such result. For
+                # debugging and information purposes, the JSON output stores
+                # both the raw DL model output and the final output we return to
+                # the user.
+                results = []
+                for out in model_output:
+                    output_ct_label = self._get_output_ct_label_from_dl_result(out.ct_label, out.score)
+
+                    results.append(
+                        self._get_result_from_labels_and_score(
+                            path,
+                            dl_ct_label=out.ct_label,
+                            output_ct_label=output_ct_label,
+                            score=out.score,
+                        )
+                    )
+                outputs[str(path)] = results
+
+            return outputs
+
+        def identify_bytes_topk(self, content: bytes, top_k=5) -> MagikaResult:
+            # Helper function for getting topk results from bytes
+            _get_results_from_features = self._get_results_from_features
+            self._get_results_from_features = partial(self._get_results_from_features_topk, top_k=top_k)
+            result = super().identify_bytes(content)
+            self._get_results_from_features = _get_results_from_features
+            return result
+
+Run OpenVINO model inference
+----------------------------
+
+
+
+Now let’s check model inference result.
+
+Select device
+~~~~~~~~~~~~~
+
+
+
+For starting work, please, select one of represented devices from
+dropdown list.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+
+    core = ov.Core()
+
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+Create model
+~~~~~~~~~~~~
+
+
+
+As we discussed above, our OpenVINO extended ``OVMagika`` class has the
+same API like original one. Let’s try to create interface instance and
+launch it on different input formats
+
+.. code:: ipython3
+
+    ov_magika = OVMagika(device=device.value)
+
+Run inference on bytes input
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    result = ov_magika.identify_bytes(b"# Example\nThis is an example of markdown!")
+    print(f"Content type: {result.output.ct_label} - {result.output.score * 100:.4}%")
+
+
+.. parsed-literal::
+
+    Content type: markdown - 99.29%
+
+
+Run inference on file input
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import requests
+
+    input_file = Path("./README.md")
+    if not input_file.exists():
+        r = requests.get("https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/README.md")
+        with open("README.md", "w") as f:
+            f.write(r.text)
+    result = ov_magika.identify_path(input_file)
+    print(f"Content type: {result.output.ct_label} - {result.output.score * 100:.4}%")
+
+
+.. parsed-literal::
+
+    Content type: markdown - 100.0%
+
+
+Interactive demo
+----------------
+
+
+
+Now, you can try model on own files. Upload file into input file window,
+click submit button and look on predicted file types.
+
+.. code:: ipython3
+
+    import gradio as gr
+
+
+    def classify(file_path):
+        """Classify file using classes listing.
+        Args:
+            file_path): path to input file
+        Returns:
+            (dict): Mapping between class labels and class probabilities.
+        """
+        results = ov_magika.identify_bytes_topk(file_path)
+
+        return {result.dl.ct_label: float(result.output.score) for result in results}
+
+
+    demo = gr.Interface(
+        classify,
+        [
+            gr.File(label="Input file", type="binary"),
+        ],
+        gr.Label(label="Result"),
+        examples=[["./README.md"]],
+        allow_flagging="never",
+    )
+    try:
+        demo.launch(debug=False)
+    except Exception:
+        demo.launch(share=True, debug=False)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
diff --git a/docs/notebooks/203-meter-reader-with-output.rst b/docs/notebooks/meter-reader-with-output.rst
similarity index 85%
rename from docs/notebooks/203-meter-reader-with-output.rst
rename to docs/notebooks/meter-reader-with-output.rst
index bd4cc0975af..7bf9b2c083f 100644
--- a/docs/notebooks/203-meter-reader-with-output.rst
+++ b/docs/notebooks/meter-reader-with-output.rst
@@ -46,7 +46,7 @@ Table of contents:
     import platform
     
     # Install openvino package
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" opencv-python tqdm
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -72,7 +72,6 @@ Import
 .. code:: ipython3
 
     import os
-    import sys
     from pathlib import Path
     import numpy as np
     import math
@@ -81,13 +80,20 @@ Import
     import matplotlib.pyplot as plt
     import openvino as ov
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file, segmentation_map_to_image
 
 Prepare the Model and Test Image
 --------------------------------
 
-Download PPYOLOv2 and
+ Download PPYOLOv2 and
 DeepLabV3P pre-trained models from PaddlePaddle community.
 
 .. code:: ipython3
@@ -108,7 +114,7 @@ DeepLabV3P pre-trained models from PaddlePaddle community.
     file = tarfile.open(f"model/{DET_FILE_NAME}")
     res = file.extractall("model")
     if not res:
-        print(f"Detection Model Extracted to \"./{MODEL_DIR}\".")
+        print(f'Detection Model Extracted to "./{MODEL_DIR}".')
     else:
         print("Error Extracting the Detection model. Please check the network.")
     
@@ -116,13 +122,13 @@ DeepLabV3P pre-trained models from PaddlePaddle community.
     file = tarfile.open(f"model/{SEG_FILE_NAME}")
     res = file.extractall("model")
     if not res:
-        print(f"Segmentation Model Extracted to \"./{MODEL_DIR}\".")
+        print(f'Segmentation Model Extracted to "./{MODEL_DIR}".')
     else:
         print("Error Extracting the Segmentation model. Please check the network.")
     
     download_file(IMG_LINK, directory=DATA_DIR, show_progress=True)
     if IMG_PATH.is_file():
-        print(f"Test Image Saved to \"./{DATA_DIR}\".")
+        print(f'Test Image Saved to "./{DATA_DIR}".')
     else:
         print("Error Downloading the Test Image. Please check the network.")
 
@@ -163,13 +169,13 @@ DeepLabV3P pre-trained models from PaddlePaddle community.
 Configuration
 -------------
 
-Add parameter configuration for
+ Add parameter configuration for
 reading calculation.
 
 .. code:: ipython3
 
-    METER_SHAPE = [512, 512] 
-    CIRCLE_CENTER = [256, 256] 
+    METER_SHAPE = [512, 512]
+    CIRCLE_CENTER = [256, 256]
     CIRCLE_RADIUS = 250
     PI = math.pi
     RECTANGLE_HEIGHT = 120
@@ -178,22 +184,17 @@ reading calculation.
     COLORMAP = np.array([[28, 28, 28], [238, 44, 44], [250, 250, 250]])
     
     # There are 2 types of meters in test image datasets
-    METER_CONFIG = [{
-        'scale_interval_value': 25.0 / 50.0,
-        'range': 25.0,
-        'unit': "(MPa)"
-    }, {
-        'scale_interval_value': 1.6 / 32.0,
-        'range': 1.6,
-        'unit': "(MPa)"
-    }]
+    METER_CONFIG = [
+        {"scale_interval_value": 25.0 / 50.0, "range": 25.0, "unit": "(MPa)"},
+        {"scale_interval_value": 1.6 / 32.0, "range": 1.6, "unit": "(MPa)"},
+    ]
     
-    SEG_LABEL = {'background': 0, 'pointer': 1, 'scale': 2}
+    SEG_LABEL = {"background": 0, "pointer": 1, "scale": 2}
 
 Load the Models
 ---------------
 
-Define a common class for model
+ Define a common class for model
 loading and inference
 
 .. code:: ipython3
@@ -207,11 +208,12 @@ loading and inference
         This class represents a OpenVINO model object.
     
         """
+    
         def __init__(self, model_path, new_shape, device="CPU"):
             """
             Initialize the model object
-            
-            Param: 
+    
+            Param:
                 model_path (string): path of inference model
                 new_shape (dict): new shape of model input
     
@@ -224,10 +226,10 @@ loading and inference
         def predict(self, input_image):
             """
             Run inference
-            
-            Param: 
+    
+            Param:
                 input_image (np.array): input data
-                
+    
             Retuns:
                 result (np.array)): model output data
             """
@@ -237,7 +239,7 @@ loading and inference
 Data Process
 ------------
 
-Including the preprocessing and
+ Including the preprocessing and
 postprocessing tasks of each model.
 
 .. code:: ipython3
@@ -246,12 +248,12 @@ postprocessing tasks of each model.
         """
         Preprocessing the input data for detection task
     
-        Param: 
+        Param:
             input_image (np.array): input data
             size (int): the image size required by model input layer
         Retuns:
             img.astype (np.array): preprocessed image
-        
+    
         """
         img = cv2.resize(input_image, (target_size, target_size))
         img = np.transpose(img, [2, 0, 1]) / 255
@@ -273,7 +275,7 @@ postprocessing tasks of each model.
     
         Retuns：
             filtered_results (list[dict]): filter detection results
-        
+    
         """
         filtered_results = []
         for i in range(len(det_results)):
@@ -295,14 +297,19 @@ postprocessing tasks of each model.
         Retuns：
             roi_imgs (list[np.array]): the list of meter images
             loc (list[int]): the list of meter locations
-        
+    
         """
         roi_imgs = []
         loc = []
         for result in results:
             bbox = result[2:]
-            xmin, ymin, xmax, ymax = [int(bbox[0] * scale_x), int(bbox[1] * scale_y), int(bbox[2] * scale_x), int(bbox[3] * scale_y)]
-            sub_img = image[ymin:(ymax + 1), xmin:(xmax + 1), :]
+            xmin, ymin, xmax, ymax = [
+                int(bbox[0] * scale_x),
+                int(bbox[1] * scale_y),
+                int(bbox[2] * scale_x),
+                int(bbox[3] * scale_y),
+            ]
+            sub_img = image[ymin : (ymax + 1), xmin : (xmax + 1), :]
             roi_imgs.append(sub_img)
             loc.append([xmin, ymin, xmax, ymax])
         return roi_imgs, loc
@@ -321,7 +328,7 @@ postprocessing tasks of each model.
         Retuns：
             img_list (list[np.array])：the list of processed images
             resize_img (list[np.array]): for visualization
-        
+    
         """
         img_list = list()
         resize_list = list()
@@ -350,7 +357,7 @@ postprocessing tasks of each model.
     
         Return：
             eroded_results (list[dict])： the lab map of eroded_results
-            
+    
         """
         kernel = np.ones((erode_kernel, erode_kernel), np.uint8)
         eroded_results = seg_results
@@ -379,7 +386,7 @@ postprocessing tasks of each model.
             for row in range(RECTANGLE_HEIGHT):
                 for col in range(RECTANGLE_WIDTH):
                     theta = PI * 2 * (col + 1) / RECTANGLE_WIDTH
-                    
+    
                     # The radius of meter circle will be mapped to the height of rectangle image
                     rho = CIRCLE_RADIUS - row - 1
                     y = int(CIRCLE_CENTER[0] + rho * math.cos(theta) + 0.5)
@@ -409,9 +416,9 @@ postprocessing tasks of each model.
             line_pointer = np.zeros((width), dtype=np.uint8)
             for col in range(width):
                 for row in range(height):
-                    if rectangle_meter[row, col] == SEG_LABEL['pointer']:
+                    if rectangle_meter[row, col] == SEG_LABEL["pointer"]:
                         line_pointer[col] += 1
-                    elif rectangle_meter[row, col] == SEG_LABEL['scale']:
+                    elif rectangle_meter[row, col] == SEG_LABEL["scale"]:
                         line_scale[col] += 1
             line_scales.append(line_scale)
             line_pointers.append(line_pointer)
@@ -505,7 +512,7 @@ postprocessing tasks of each model.
                         pointer_start = j
                         find_start = True
                 if find_start:
-                    if line_pointer[j] == 0 and line_pointer[j + 1] == 0 :
+                    if line_pointer[j] == 0 and line_pointer[j + 1] == 0:
                         pointer_end = j - 1
                         location = (pointer_start + pointer_end) / 2
                         find_start = False
@@ -526,18 +533,17 @@ postprocessing tasks of each model.
             pointed_scales (list[dict])： a list of dict with:
                                          'num_scales': total number of scales
                                          'pointed_scale': predicted number of scales
-                
+    
         """
         pointed_scales = list()
-        for scale_location, pointer_location in zip(scale_locations,
-                                                    pointer_locations):
+        for scale_location, pointer_location in zip(scale_locations, pointer_locations):
             num_scales = len(scale_location)
             pointed_scale = -1
             if num_scales > 0:
                 for i in range(num_scales - 1):
                     if scale_location[i] <= pointer_location < scale_location[i + 1]:
                         pointed_scale = i + (pointer_location - scale_location[i]) / (scale_location[i + 1] - scale_location[i] + 1e-05) + 1
-            result = {'num_scales': num_scales, 'pointed_scale': pointed_scale}
+            result = {"num_scales": num_scales, "pointed_scale": pointed_scale}
             pointed_scales.append(result)
         return pointed_scales
     
@@ -551,17 +557,17 @@ postprocessing tasks of each model.
     
         Return：
             readings (list[float])： the list of values read from meter
-                
+    
         """
         readings = list()
         batch_size = len(pointed_scales)
         for i in range(batch_size):
             pointed_scale = pointed_scales[i]
             # find the type of meter according the total number of scales
-            if pointed_scale['num_scales'] > TYPE_THRESHOLD:
-                reading = pointed_scale['pointed_scale'] * METER_CONFIG[0]['scale_interval_value']
+            if pointed_scale["num_scales"] > TYPE_THRESHOLD:
+                reading = pointed_scale["pointed_scale"] * METER_CONFIG[0]["scale_interval_value"]
             else:
-                reading = pointed_scale['pointed_scale'] * METER_CONFIG[1]['scale_interval_value']
+                reading = pointed_scale["pointed_scale"] * METER_CONFIG[1]["scale_interval_value"]
             readings.append(reading)
         return readings
 
@@ -583,8 +589,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -612,9 +618,13 @@ bounds of input batch size.
 
     img_file = f"{DATA_DIR}/{IMG_FILE_NAME}"
     det_model_path = f"{MODEL_DIR}/meter_det_model/model.pdmodel"
-    det_model_shape = {'image': [1, 3, 608, 608], 'im_shape': [1, 2], 'scale_factor': [1, 2]}
+    det_model_shape = {
+        "image": [1, 3, 608, 608],
+        "im_shape": [1, 2],
+        "scale_factor": [1, 2],
+    }
     seg_model_path = f"{MODEL_DIR}/meter_seg_model/model.pdmodel"
-    seg_model_shape = {'image': [ov.Dimension(1, 2), 3, 512, 512]}
+    seg_model_shape = {"image": [ov.Dimension(1, 2), 3, 512, 512]}
     
     erode_kernel = 4
     score_threshold = 0.5
@@ -635,27 +645,27 @@ bounds of input batch size.
 
 .. parsed-literal::
 
-    
+    
 
 
 
 
-.. image:: 203-meter-reader-with-output_files/203-meter-reader-with-output_16_1.png
+.. image:: meter-reader-with-output_files/meter-reader-with-output_16_1.png
 
 
 Run meter detection model
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Detect the location of the
+ Detect the location of the
 meter and prepare the ROI images for segmentation.
 
 .. code:: ipython3
 
     # Prepare the input data for meter detection model
-    im_shape = np.array([[input_shape, input_shape]]).astype('float32')
-    scale_factor = np.array([[1, 2]]).astype('float32')
+    im_shape = np.array([[input_shape, input_shape]]).astype("float32")
+    scale_factor = np.array([[1, 2]]).astype("float32")
     input_image = det_preprocess(image, input_shape)
-    inputs_dict = {'image': input_image, "im_shape": im_shape, "scale_factor": scale_factor}
+    inputs_dict = {"image": input_image, "im_shape": im_shape, "scale_factor": scale_factor}
     
     # Run meter detection model
     det_results = detector.predict(inputs_dict)
@@ -675,7 +685,7 @@ meter and prepare the ROI images for segmentation.
     roi_stack = np.hstack(resize_imgs)
     
     if cv2.imwrite(f"{DATA_DIR}/detection_results.jpg", roi_stack):
-        print("The detection result image has been saved as \"detection_results.jpg\" in data")
+        print('The detection result image has been saved as "detection_results.jpg" in data')
         plt.imshow(cv2.cvtColor(roi_stack, cv2.COLOR_BGR2RGB))
 
 
@@ -685,13 +695,13 @@ meter and prepare the ROI images for segmentation.
 
 
 
-.. image:: 203-meter-reader-with-output_files/203-meter-reader-with-output_18_1.png
+.. image:: meter-reader-with-output_files/meter-reader-with-output_18_1.png
 
 
 Run meter segmentation model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Get the results of segmentation
+ Get the results of segmentation
 task on detected ROI.
 
 .. code:: ipython3
@@ -707,7 +717,7 @@ task on detected ROI.
         seg_results.extend(seg_result)
     results = []
     for i in range(len(seg_results)):
-        results.append(np.argmax(seg_results[i], axis=0)) 
+        results.append(np.argmax(seg_results[i], axis=0))
     seg_results = erode(results, erode_kernel)
     
     # Create the pictures of segmentation results
@@ -716,7 +726,7 @@ task on detected ROI.
     mask_stack = np.hstack(mask_list)
     
     if cv2.imwrite(f"{DATA_DIR}/segmentation_results.jpg", cv2.cvtColor(mask_stack, cv2.COLOR_RGB2BGR)):
-        print("The segmentation result image has been saved as \"segmentation_results.jpg\" in data")
+        print('The segmentation result image has been saved as "segmentation_results.jpg" in data')
         plt.imshow(mask_stack)
 
 
@@ -726,18 +736,18 @@ task on detected ROI.
 
 
 
-.. image:: 203-meter-reader-with-output_files/203-meter-reader-with-output_20_1.png
+.. image:: meter-reader-with-output_files/meter-reader-with-output_20_1.png
 
 
 Postprocess the models result and calculate the final readings
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Use OpenCV function to find the
+ Use OpenCV function to find the
 location of the pointer in a scale map.
 
 .. code:: ipython3
 
-    # Find the pointer location in scale map and calculate the meters reading 
+    # Find the pointer location in scale map and calculate the meters reading
     rectangle_meters = circle_to_rectangle(seg_results)
     line_scales, line_pointers = rectangle_to_line(rectangle_meters)
     binaried_scales = mean_binarization(line_scales)
@@ -753,8 +763,11 @@ location of the pointer in a scale map.
         rectangle_list.append(segmentation_map_to_image(rectangle_meters[i], COLORMAP))
     rectangle_meters_stack = np.hstack(rectangle_list)
     
-    if cv2.imwrite(f"{DATA_DIR}/rectangle_meters.jpg", cv2.cvtColor(rectangle_meters_stack, cv2.COLOR_RGB2BGR)):
-        print("The rectangle_meters result image has been saved as \"rectangle_meters.jpg\" in data")
+    if cv2.imwrite(
+        f"{DATA_DIR}/rectangle_meters.jpg",
+        cv2.cvtColor(rectangle_meters_stack, cv2.COLOR_RGB2BGR),
+    ):
+        print('The rectangle_meters result image has been saved as "rectangle_meters.jpg" in data')
         plt.imshow(rectangle_meters_stack)
 
 
@@ -764,7 +777,7 @@ location of the pointer in a scale map.
 
 
 
-.. image:: 203-meter-reader-with-output_files/203-meter-reader-with-output_22_1.png
+.. image:: meter-reader-with-output_files/meter-reader-with-output_22_1.png
 
 
 Get the reading result on the meter picture
@@ -777,15 +790,30 @@ Get the reading result on the meter picture
     # Create a final result photo with reading
     for i in range(len(meter_readings)):
         print("Meter {}: {:.3f}".format(i + 1, meter_readings[i]))
-        
+    
     result_image = image.copy()
     for i in range(len(loc)):
-        cv2.rectangle(result_image,(loc[i][0], loc[i][1]), (loc[i][2], loc[i][3]), (0, 150, 0), 3)
+        cv2.rectangle(result_image, (loc[i][0], loc[i][1]), (loc[i][2], loc[i][3]), (0, 150, 0), 3)
         font = cv2.FONT_HERSHEY_SIMPLEX
-        cv2.rectangle(result_image, (loc[i][0], loc[i][1]), (loc[i][0] + 100, loc[i][1] + 40), (0, 150, 0), -1)
-        cv2.putText(result_image, "#{:.3f}".format(meter_readings[i]), (loc[i][0],loc[i][1] + 25), font, 0.8, (255, 255, 255), 2, cv2.LINE_AA)
+        cv2.rectangle(
+            result_image,
+            (loc[i][0], loc[i][1]),
+            (loc[i][0] + 100, loc[i][1] + 40),
+            (0, 150, 0),
+            -1,
+        )
+        cv2.putText(
+            result_image,
+            "#{:.3f}".format(meter_readings[i]),
+            (loc[i][0], loc[i][1] + 25),
+            font,
+            0.8,
+            (255, 255, 255),
+            2,
+            cv2.LINE_AA,
+        )
     if cv2.imwrite(f"{DATA_DIR}/reading_results.jpg", result_image):
-        print("The reading results image has been saved as \"reading_results.jpg\" in data")
+        print('The reading results image has been saved as "reading_results.jpg" in data')
         plt.imshow(cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB))
 
 
@@ -797,7 +825,7 @@ Get the reading result on the meter picture
 
 
 
-.. image:: 203-meter-reader-with-output_files/203-meter-reader-with-output_24_1.png
+.. image:: meter-reader-with-output_files/meter-reader-with-output_24_1.png
 
 
 Try it with your meter photos!
diff --git a/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_16_1.png b/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_16_1.png
new file mode 100644
index 00000000000..f5f465e5e0d
--- /dev/null
+++ b/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_16_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5277177823d4b99e277b1ecd207f67b850c5fd312974c2e691e260e016811526
+size 170121
diff --git a/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_18_1.png b/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_18_1.png
new file mode 100644
index 00000000000..373f323c93b
--- /dev/null
+++ b/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_18_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:889b083b05c7dd518506e68c76a9c7e78d2cbc1273606e1edbd3c2f308a49d9e
+size 190271
diff --git a/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_20_1.png b/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_20_1.png
new file mode 100644
index 00000000000..6c3df0677c7
--- /dev/null
+++ b/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_20_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8594e7ed5ce58de7b10de8aa066fa4f9adc43308be46e2ef4dd208da4913301e
+size 26914
diff --git a/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_22_1.png b/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_22_1.png
new file mode 100644
index 00000000000..20a9bb7513c
--- /dev/null
+++ b/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_22_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eafe2bfb1d91093d1208523063def5d5b4d13285153568d173c302b3d600adfa
+size 8966
diff --git a/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_24_1.png b/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_24_1.png
new file mode 100644
index 00000000000..4647a76e34c
--- /dev/null
+++ b/docs/notebooks/meter-reader-with-output_files/meter-reader-with-output_24_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:91a9b23ec86373699c0dbbb252a2cb1b9351ebb08b771a79a4fec4bffbb1787d
+size 170338
diff --git a/docs/notebooks/255-mms-massively-multilingual-speech-with-output.rst b/docs/notebooks/mms-massively-multilingual-speech-with-output.rst
similarity index 99%
rename from docs/notebooks/255-mms-massively-multilingual-speech-with-output.rst
rename to docs/notebooks/mms-massively-multilingual-speech-with-output.rst
index a8443cb6c78..46f9e8619d8 100644
--- a/docs/notebooks/255-mms-massively-multilingual-speech-with-output.rst
+++ b/docs/notebooks/mms-massively-multilingual-speech-with-output.rst
@@ -73,8 +73,8 @@ Prerequisites
 .. code:: ipython3
 
     %pip install -q --upgrade pip
-    %pip install -q "transformers>=4.33.1" "openvino>=2023.1.0" "numpy>=1.21.0,<=1.24" "nncf>=2.7.0" 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu torch datasets accelerate soundfile librosa gradio jiwer
+    %pip install -q "transformers>=4.33.1" "torch>=2.1" "openvino>=2023.1.0" "numpy>=1.21.0" "nncf>=2.9.0"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu torch "datasets>=2.14.6" accelerate soundfile librosa "gradio>=4.19" jiwer
 
 .. code:: ipython3
 
@@ -103,9 +103,9 @@ Choose one of them.
     
     
     SAMPLE_LANG = widgets.Dropdown(
-        options=['german', 'dutch', 'french', 'spanish', 'italian', 'portuguese', 'polish'],
-        value='german',
-        description='Dataset language:',
+        options=["german", "dutch", "french", "spanish", "italian", "portuguese", "polish"],
+        value="german",
+        description="Dataset language:",
         disabled=False,
     )
     
@@ -150,8 +150,8 @@ transcription.
 
     import IPython.display as ipd
     
-    print(example['text'])
-    ipd.Audio(example['audio']['array'], rate=16_000)
+    print(example["text"])
+    ipd.Audio(example["audio"]["array"], rate=16_000)
 
 
 .. parsed-literal::
@@ -201,7 +201,7 @@ Use the original model to run an inference
 
 .. code:: ipython3
 
-    inputs = lid_processor(example['audio']['array'], sampling_rate=16_000, return_tensors="pt")
+    inputs = lid_processor(example["audio"]["array"], sampling_rate=16_000, return_tensors="pt")
     
     with torch.no_grad():
         outputs = lid_model(**inputs).logits
@@ -229,8 +229,8 @@ Select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -251,14 +251,15 @@ Convert model to OpenVINO format and compile it
 
     MAX_SEQ_LENGTH = 30480
     
-    lid_model_xml_path = Path('models/ov_lid_model.xml')
+    lid_model_xml_path = Path("models/ov_lid_model.xml")
+    
     
     def get_lid_model(model_path, compiled=True):
         input_values = torch.zeros([1, MAX_SEQ_LENGTH], dtype=torch.float)
     
         if not model_path.exists() and model_path == lid_model_xml_path:
             lid_model_xml_path.parent.mkdir(parents=True, exist_ok=True)
-            converted_model = ov.convert_model(lid_model, example_input={'input_values': input_values})
+            converted_model = ov.convert_model(lid_model, example_input={"input_values": input_values})
             ov.save_model(converted_model, lid_model_xml_path)
             if not compiled:
                 return converted_model
@@ -266,6 +267,7 @@ Convert model to OpenVINO format and compile it
             return core.compile_model(model_path, device_name=device.value)
         return core.read_model(model_path)
     
+    
     compiled_lid_model = get_lid_model(lid_model_xml_path)
 
 
@@ -283,17 +285,17 @@ Now it is possible to run an inference.
 
     def detect_language(compiled_model, audio_data):
         inputs = lid_processor(audio_data, sampling_rate=16_000, return_tensors="pt")
-        
-        outputs = compiled_model(inputs['input_values'])[0]
-        
+    
+        outputs = compiled_model(inputs["input_values"])[0]
+    
         lang_id = torch.argmax(torch.from_numpy(outputs), dim=-1)[0].item()
         detected_lang = lid_model.config.id2label[lang_id]
-        
+    
         return detected_lang
 
 .. code:: ipython3
 
-    detect_language(compiled_lid_model, example['audio']['array'])
+    detect_language(compiled_lid_model, example["audio"]["array"])
 
 
 
@@ -309,9 +311,9 @@ Let’s check another language.
 .. code:: ipython3
 
     SAMPLE_LANG = widgets.Dropdown(
-        options=['german', 'dutch', 'french', 'spanish', 'italian', 'portuguese', 'polish'],
-        value='french',
-        description='Dataset language:',
+        options=["german", "dutch", "french", "spanish", "italian", "portuguese", "polish"],
+        value="french",
+        description="Dataset language:",
         disabled=False,
     )
     
@@ -332,8 +334,8 @@ Let’s check another language.
     mls_dataset = iter(mls_dataset)
     
     example = next(mls_dataset)
-    print(example['text'])
-    ipd.Audio(example['audio']['array'], rate=16_000)
+    print(example["text"])
+    ipd.Audio(example["audio"]["array"], rate=16_000)
 
 
 .. parsed-literal::
@@ -356,7 +358,7 @@ Let’s check another language.
 
 .. code:: ipython3
 
-    language_id = detect_language(compiled_lid_model, example['audio']['array'])
+    language_id = detect_language(compiled_lid_model, example["audio"]["array"])
     print(language_id)
 
 
@@ -388,6 +390,7 @@ language later.
 .. code:: ipython3
 
     from transformers import Wav2Vec2ForCTC, AutoProcessor
+    
     model_id = "facebook/mms-1b-all"
     
     asr_processor = AutoProcessor.from_pretrained(model_id)
@@ -434,7 +437,7 @@ Use the original model for inference
 
 .. code:: ipython3
 
-    inputs = asr_processor(example['audio']['array'], sampling_rate=16_000, return_tensors="pt")
+    inputs = asr_processor(example["audio"]["array"], sampling_rate=16_000, return_tensors="pt")
     
     with torch.no_grad():
         outputs = asr_model(**inputs).logits
@@ -461,7 +464,7 @@ for these purposes.
 
 .. code:: ipython3
 
-    asr_model_xml_path_template = 'models/ov_asr_{}_model.xml'
+    asr_model_xml_path_template = "models/ov_asr_{}_model.xml"
     
     
     def get_asr_model(model_path_template, language_id, compiled=True):
@@ -471,9 +474,9 @@ for these purposes.
         asr_processor.tokenizer.set_target_lang(language_id)
         if not model_path.exists() and model_path_template == asr_model_xml_path_template:
             asr_model.load_adapter(language_id)
-            
+    
             model_path.parent.mkdir(parents=True, exist_ok=True)
-            converted_model = ov.convert_model(asr_model, example_input={'input_values': input_values})
+            converted_model = ov.convert_model(asr_model, example_input={"input_values": input_values})
             ov.save_model(converted_model, model_path)
             if not compiled:
                 return converted_model
@@ -500,16 +503,16 @@ Run inference.
 
     def recognize_audio(compiled_model, src_audio):
         inputs = asr_processor(src_audio, sampling_rate=16_000, return_tensors="pt")
-        outputs = compiled_model(inputs['input_values'])[0]
-        
+        outputs = compiled_model(inputs["input_values"])[0]
+    
         ids = torch.argmax(torch.from_numpy(outputs), dim=-1)[0]
         transcription = asr_processor.decode(ids)
     
         return transcription
     
     
-    transcription = recognize_audio(compiled_asr_model, example['audio']['array'])
-    print("Original text:", example['text'])
+    transcription = recognize_audio(compiled_asr_model, example["audio"]["array"])
+    print("Original text:", example["text"])
     print("Transcription:", transcription)
 
 
@@ -550,7 +553,7 @@ The optimization process contains the following steps:
     
     to_quantize = widgets.Checkbox(
         value=False,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -570,8 +573,13 @@ not selected
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     %load_ext skip_kernel_extension
 
@@ -883,16 +891,18 @@ data is sampled to 16000 kHz.
     import time
     
     
-    title = 'MMS with Gradio'
-    description = 'Gradio Demo for MMS and OpenVINO™. Upload a source audio, then click the "Submit" button to detect a language ID and a transcription. ' \
-                  'Make sure that the audio data is sampled to 16000 kHz. If this language has not been used before, it may take some time to prepare the ASR model.' \
-                  '\n' \
-                  '> Note: In order to run quantized model to transcribe some language, first the quantized model for that specific language must be prepared.'
+    title = "MMS with Gradio"
+    description = (
+        'Gradio Demo for MMS and OpenVINO™. Upload a source audio, then click the "Submit" button to detect a language ID and a transcription. '
+        "Make sure that the audio data is sampled to 16000 kHz. If this language has not been used before, it may take some time to prepare the ASR model."
+        "\n"
+        "> Note: In order to run quantized model to transcribe some language, first the quantized model for that specific language must be prepared."
+    )
     
     
     current_state = {
         "fp32": {"model": None, "language": None},
-        "int8": {"model": None, "language": None}
+        "int8": {"model": None, "language": None},
     }
     
     
@@ -909,21 +919,24 @@ data is sampled to 16000 kHz.
         if detected_language_id != state["language"]:
             template_path = quantized_asr_model_xml_path_template if quantized else asr_model_xml_path_template
             try:
-                gr.Info(f"Loading {'quantized' if quantized else ''} ASR model for '{detected_language_id}' language. "
-                        "This will take some time.")
+                gr.Info(f"Loading {'quantized' if quantized else ''} ASR model for '{detected_language_id}' language. " "This will take some time.")
                 state["model"] = get_asr_model(template_path, detected_language_id)
                 state["language"] = detected_language_id
             except RuntimeError as e:
                 if "Unable to read the model:" in str(e) and quantized:
-                    raise gr.Error(f"There is no quantized ASR model for '{detected_language_id}' language. "
-                                   "Please run quantization for this language first.")
+                    raise gr.Error(f"There is no quantized ASR model for '{detected_language_id}' language. " "Please run quantization for this language first.")
     
         start_time = time.perf_counter()
         transcription = recognize_audio(state["model"], src_audio)
         end_time = time.perf_counter()
         transcription_delta_time = f"{end_time - start_time:.2f}"
     
-        return detected_language_id, transcription, identification_delta_time, transcription_delta_time
+        return (
+            detected_language_id,
+            transcription,
+            identification_delta_time,
+            transcription_delta_time,
+        )
     
     
     with gr.Blocks() as demo:
@@ -943,7 +956,7 @@ data is sampled to 16000 kHz.
             with gr.Row():
                 with gr.Column():
                     if not quantized:
-                        audio = gr.Audio(label="Source Audio", type='filepath')
+                        audio = gr.Audio(label="Source Audio", type="filepath")
                     run_button_name = "Run INT8" if quantized else "Run FP32" if to_quantize.value else "Run"
                     run_button[quantized] = gr.Button(value=run_button_name)
                 with gr.Column():
@@ -952,13 +965,27 @@ data is sampled to 16000 kHz.
                     identification_time[quantized] = gr.Textbox(label=f"Identification time{' (Quantized)' if quantized else ''}")
                     transcription_time[quantized] = gr.Textbox(label=f"Transcription time{' (Quantized)' if quantized else ''}")
     
-        run_button[False].click(infer,
-                                inputs=[audio, gr.Number(0, visible=False)],
-                                outputs=[detected_language[False], transcription[False], identification_time[False], transcription_time[False]])
+        run_button[False].click(
+            infer,
+            inputs=[audio, gr.Number(0, visible=False)],
+            outputs=[
+                detected_language[False],
+                transcription[False],
+                identification_time[False],
+                transcription_time[False],
+            ],
+        )
         if to_quantize.value:
-            run_button[True].click(infer,
-                                   inputs=[audio, gr.Number(1, visible=False)],
-                                   outputs=[detected_language[True], transcription[True], identification_time[True], transcription_time[True]])
+            run_button[True].click(
+                infer,
+                inputs=[audio, gr.Number(1, visible=False)],
+                outputs=[
+                    detected_language[True],
+                    transcription[True],
+                    identification_time[True],
+                    transcription_time[True],
+                ],
+            )
     
     
     try:
diff --git a/docs/notebooks/289-mobileclip-video-search-with-output.rst b/docs/notebooks/mobileclip-video-search-with-output.rst
similarity index 82%
rename from docs/notebooks/289-mobileclip-video-search-with-output.rst
rename to docs/notebooks/mobileclip-video-search-with-output.rst
index 2a3840f83a3..41af1e469da 100644
--- a/docs/notebooks/289-mobileclip-video-search-with-output.rst
+++ b/docs/notebooks/mobileclip-video-search-with-output.rst
@@ -47,14 +47,14 @@ Table of contents:
 Prerequisites
 -------------
 
-## Prerequisites
+ ## Prerequisites
 
 .. code:: ipython3
 
     from pathlib import Path
     
     repo_dir = Path("./ml-mobileclip")
-                    
+    
     if not repo_dir.exists():
         !git clone https://github.com/apple/ml-mobileclip.git
 
@@ -155,11 +155,11 @@ Unpacking objects:   4% (2/45)
 Unpacking objects:   6% (3/45)
 Unpacking objects:   8% (4/45)
 Unpacking objects:  11% (5/45)
+Unpacking objects:  13% (6/45)
 
 .. parsed-literal::
 
-    Unpacking objects:  13% (6/45)
-Unpacking objects:  15% (7/45)
+    Unpacking objects:  15% (7/45)
 Unpacking objects:  17% (8/45)
 Unpacking objects:  20% (9/45)
 Unpacking objects:  22% (10/45)
@@ -202,26 +202,28 @@ Unpacking objects:  93% (42/45)
 Unpacking objects:  95% (43/45)
 Unpacking objects:  97% (44/45)
 Unpacking objects: 100% (45/45)
-Unpacking objects: 100% (45/45), 428.50 KiB | 2.86 MiB/s, done.
+Unpacking objects: 100% (45/45), 428.50 KiB | 3.04 MiB/s, done.
 
 
 .. code:: ipython3
 
-    %pip install -q "./ml-mobileclip" --extra-index-url "https://download.pytorch.org/whl/cpu"
-    %pip install -q "openvino>=2024.0.0" "gradio" "matplotlib" "Pillow"  "altair" "pandas" "opencv-python"
+    %pip install -q "./ml-mobileclip" --no-deps
+    
+    %pip install -q "clip-benchmark>=1.4.0" "datasets>=2.8.0" "open-clip-torch>=2.20.0" "timm>=0.9.5" "torch>=1.13.1" "torchvision>=0.14.1" --extra-index-url https://download.pytorch.org/whl/cpu
+    
+    %pip install -q "openvino>=2024.0.0" "gradio>=4.19" "matplotlib" "Pillow"  "altair" "pandas" "opencv-python" "tqdm"
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
+    Note: you may need to restart the kernel to use updated packages.
+
 
 .. parsed-literal::
 
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    pyannote-audio 2.0.1 requires torchaudio<1.0,>=0.10, but you have torchaudio 2.2.1+cpu which is incompatible.
-    pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 4.25.3 which is incompatible.
-    torchaudio 2.2.1+cpu requires torch==2.2.1, but you have torch 1.13.1+cpu which is incompatible.
+    mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.2.2+cpu which is incompatible.
+    mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.17.2+cpu which is incompatible.
     
 
 .. parsed-literal::
@@ -231,7 +233,7 @@ Unpacking objects: 100% (45/45), 428.50 KiB | 2.86 MiB/s, done.
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    WARNING: typer 0.12.3 does not provide the extra 'all'
     
 
 .. parsed-literal::
@@ -288,23 +290,71 @@ comparison purposes, you can select different models among:
     
     supported_models = {
         "MobileCLIP": {
-            "mobileclip_s0": {"model_name": "mobileclip_s0", "pretrained": model_dir / "mobileclip_s0.pt", "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_s0.pt", "image_size": 256},
-            "mobileclip_s1": {"model_name": "mobileclip_s1", "pretrained": model_dir / "mobileclip_s1.pt", "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_s1.pt", "image_size": 256},
-            "mobileclip_s2": {"model_name": "mobileclip_s0", "pretrained": model_dir / "mobileclip_s2.pt", "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_s2.pt", "image_size": 256},
-            "mobileclip_b": {"model_name": "mobileclip_b", "pretrained": model_dir / "mobileclip_b.pt", "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_b.pt", "image_size": 224},
-            "mobileclip_blt": {"model_name": "mobileclip_b", "pretrained": model_dir / "mobileclip_blt.pt", "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_blt.pt", "image_size": 224},
+            "mobileclip_s0": {
+                "model_name": "mobileclip_s0",
+                "pretrained": model_dir / "mobileclip_s0.pt",
+                "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_s0.pt",
+                "image_size": 256,
+            },
+            "mobileclip_s1": {
+                "model_name": "mobileclip_s1",
+                "pretrained": model_dir / "mobileclip_s1.pt",
+                "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_s1.pt",
+                "image_size": 256,
+            },
+            "mobileclip_s2": {
+                "model_name": "mobileclip_s0",
+                "pretrained": model_dir / "mobileclip_s2.pt",
+                "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_s2.pt",
+                "image_size": 256,
+            },
+            "mobileclip_b": {
+                "model_name": "mobileclip_b",
+                "pretrained": model_dir / "mobileclip_b.pt",
+                "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_b.pt",
+                "image_size": 224,
+            },
+            "mobileclip_blt": {
+                "model_name": "mobileclip_b",
+                "pretrained": model_dir / "mobileclip_blt.pt",
+                "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_blt.pt",
+                "image_size": 224,
+            },
         },
         "CLIP": {
-            "clip-vit-b-32": {"model_name": "ViT-B-32", "pretrained": "laion2b_s34b_b79k", "image_size": 224},
-            "clip-vit-b-16": {"image_name": "ViT-B-16", "pretrained": "openai", "image_size": 224},
-            "clip-vit-l-14": {"image_name": "ViT-L-14", "pretrained": "datacomp_xl_s13b_b90k", "image_size": 224},
-            "clip-vit-h-14": {"image_name": "ViT-H-14", "pretrained": "laion2b_s32b_b79k", "image_size": 224},
+            "clip-vit-b-32": {
+                "model_name": "ViT-B-32",
+                "pretrained": "laion2b_s34b_b79k",
+                "image_size": 224,
+            },
+            "clip-vit-b-16": {
+                "image_name": "ViT-B-16",
+                "pretrained": "openai",
+                "image_size": 224,
+            },
+            "clip-vit-l-14": {
+                "image_name": "ViT-L-14",
+                "pretrained": "datacomp_xl_s13b_b90k",
+                "image_size": 224,
+            },
+            "clip-vit-h-14": {
+                "image_name": "ViT-H-14",
+                "pretrained": "laion2b_s32b_b79k",
+                "image_size": 224,
+            },
         },
         "SigLIP": {
-            "siglip-vit-b-16": {"model_name": "ViT-B-16-SigLIP", "pretrained": "webli", "image_size": 224},
-            "siglip-vit-l-16": {"model_name": "ViT-L-16-SigLIP-256", "pretrained": "webli", "image_size": 256},
-        }
-        
+            "siglip-vit-b-16": {
+                "model_name": "ViT-B-16-SigLIP",
+                "pretrained": "webli",
+                "image_size": 224,
+            },
+            "siglip-vit-l-16": {
+                "model_name": "ViT-L-16-SigLIP-256",
+                "pretrained": "webli",
+                "image_size": 256,
+            },
+        },
     }
     
     
@@ -324,7 +374,11 @@ comparison purposes, you can select different models among:
 
     available_models = supported_models[model_type.value]
     
-    model_checkpoint = widgets.Dropdown(options=available_models.keys(), default=list(available_models), description="Model:")
+    model_checkpoint = widgets.Dropdown(
+        options=available_models.keys(),
+        default=list(available_models),
+        description="Model:",
+    )
     
     model_checkpoint
 
@@ -339,12 +393,14 @@ comparison purposes, you can select different models among:
 
 .. code:: ipython3
 
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file
     
     model_config = available_models[model_checkpoint.value]
@@ -383,7 +439,7 @@ Prepare image gallery
     from PIL import Image
     
     
-    def visualize_result(images:List, query:str = "", selected:List[int] = None):
+    def visualize_result(images: List, query: str = "", selected: List[int] = None):
         """
         Utility function for visualization classification results
         params:
@@ -393,9 +449,9 @@ Prepare image gallery
         returns:
           matplotlib.Figure
         """
-        figsize = (20, 5) 
-        fig, axs = plt.subplots(1, 4, figsize=figsize, sharex='all', sharey='all')
-        fig.patch.set_facecolor('white')
+        figsize = (20, 5)
+        fig, axs = plt.subplots(1, 4, figsize=figsize, sharex="all", sharey="all")
+        fig.patch.set_facecolor("white")
         list_axes = list(axs.flat)
         if query:
             fig.suptitle(query, fontsize=20)
@@ -408,15 +464,15 @@ Prepare image gallery
             a.imshow(images[idx])
             if selected is not None and idx not in selected:
                 mask = np.ones_like(np.array(images[idx]))
-                a.imshow(mask, 'jet', interpolation='none', alpha=0.75)
+                a.imshow(mask, "jet", interpolation="none", alpha=0.75)
         return fig
     
     
     images_urls = [
-        "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/282ce53e-912d-41aa-ab48-2a001c022d74", 
+        "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/282ce53e-912d-41aa-ab48-2a001c022d74",
         "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/9bb40168-82b5-4b11-ada6-d8df104c736c",
         "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/0747b6db-12c3-4252-9a6a-057dcf8f3d4e",
-        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bricks.png"
+        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bricks.png",
     ]
     image_names = ["red_panda.png", "cat.png", "raccoon.png", "dog.png"]
     sample_path = Path("data")
@@ -429,7 +485,7 @@ Prepare image gallery
             download_file(image_url, filename=image_name, directory=sample_path)
         images.append(Image.open(image_path).convert("RGB").resize((640, 420)))
     
-    input_labels = ['cat']
+    input_labels = ["cat"]
     text_descriptions = [f"This is a photo of a {label}" for label in input_labels]
     
     visualize_result(images, "image gallery");
@@ -460,7 +516,7 @@ Prepare image gallery
 
 
 
-.. image:: 289-mobileclip-video-search-with-output_files/289-mobileclip-video-search-with-output_10_4.png
+.. image:: mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_10_4.png
 
 
 Prepare model
@@ -511,7 +567,6 @@ Perform search
     
     
     with torch.no_grad():
-    
         # calculate image embeddings
         image_encoding_start = time.perf_counter()
         image_features = model.encode_image(image_tensor)
@@ -536,12 +591,12 @@ Perform search
 
 .. parsed-literal::
 
-    Image encoding took 0.101 ms
-    Text encoding took 0.0102 ms
+    Image encoding took 0.109 ms
+    Text encoding took 0.0108 ms
 
 
 
-.. image:: 289-mobileclip-video-search-with-output_files/289-mobileclip-video-search-with-output_14_1.png
+.. image:: mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_14_1.png
 
 
 Convert Model to OpenVINO Intermediate Representation format
@@ -591,7 +646,11 @@ be used separately. Let’s convert each part to OpenVINO.
         if "mobileclip_s" in model_name:
             model.image_encoder.model.conv_exp.se.forward = types.MethodType(se_block_forward, model.image_encoder.model.conv_exp.se)
         model.forward = model.encode_image
-        ov_image_encoder = ov.convert_model(model, example_input=image_tensor, input=[-1, 3, image_tensor.shape[2], image_tensor.shape[3]])
+        ov_image_encoder = ov.convert_model(
+            model,
+            example_input=image_tensor,
+            input=[-1, 3, image_tensor.shape[2], image_tensor.shape[3]],
+        )
         ov.save_model(ov_image_encoder, image_encoder_path)
         del ov_image_encoder
         gc.collect()
@@ -611,7 +670,7 @@ be used separately. Let’s convert each part to OpenVINO.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/mobileclip/modules/common/transformer.py:125: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/mobileclip/modules/common/transformer.py:125: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if seq_len != self.num_embeddings:
 
 
@@ -633,8 +692,8 @@ Select device for image encoder
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -703,12 +762,12 @@ Perform search
 
 .. parsed-literal::
 
-    Image encoding took 0.0306 ms
-    Text encoding took 0.0058 ms
+    Image encoding took 0.0309 ms
+    Text encoding took 0.00586 ms
 
 
 
-.. image:: 289-mobileclip-video-search-with-output_files/289-mobileclip-video-search-with-output_25_1.png
+.. image:: mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_25_1.png
 
 
 Interactive Demo
@@ -745,8 +804,16 @@ searching frames in the video by text query or image. Upload v
     available_converted_models = [model_file.name.split("_im_encoder")[0] for model_file in ov_models_dir.glob("*_im_encoder.xml")]
     available_devices = list(core.available_devices) + ["AUTO"]
     
-    download_file("https://github.com/intel-iot-devkit/sample-videos/raw/master/car-detection.mp4", directory=sample_path)
-    download_file("https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/Coco%20Walking%20in%20Berkeley.mp4", directory=sample_path, filename="coco.mp4")
+    download_file(
+        "https://github.com/intel-iot-devkit/sample-videos/raw/master/car-detection.mp4",
+        directory=sample_path,
+    )
+    download_file(
+        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/Coco%20Walking%20in%20Berkeley.mp4",
+        directory=sample_path,
+        filename="coco.mp4",
+    )
+    
     
     def get_preprocess_and_tokenizer(model_name):
         if "mobileclip" in model_name:
@@ -770,7 +837,7 @@ searching frames in the video by text query or image. Upload v
             tokenizer = open_clip.get_tokenizer(model_configs[model_name]["model_name"])
     
         return preprocess, tokenizer
-            
+    
     
     def run(
         path: str,
@@ -782,11 +849,8 @@ searching frames in the video by text query or image. Upload v
         stride: int,
         batch_size: int,
     ):
-    
         assert path, "An input video should be provided"
-        assert (
-            text_search is not None or image_search is not None
-        ), "A text or image query should be provided"
+        assert text_search is not None or image_search is not None, "A text or image query should be provided"
         global current_model
         global current_device
         global preprocess
@@ -794,7 +858,6 @@ searching frames in the video by text query or image. Upload v
         global ov_compiled_image_encoder
         global ov_compiled_text_encoder
     
-    
         if current_model != model_name or device != current_device:
             ov_compiled_image_encoder = core.compile_model(ov_models_dir / f"{model_name}_im_encoder.xml", device)
             ov_compiled_text_encoder = core.compile_model(ov_models_dir / f"{model_name}_text_encoder.xml", device)
@@ -803,10 +866,8 @@ searching frames in the video by text query or image. Upload v
             current_device = device
         # Load video
         dataset = LoadVideo(path, transforms=preprocess, vid_stride=stride)
-        dataloader = DataLoader(
-            dataset, batch_size=batch_size, shuffle=False, num_workers=0
-        )
-            
+        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0)
+    
         # Get image query features
         if image_search:
             image = preprocess(image_search).unsqueeze(0)
@@ -862,21 +923,20 @@ searching frames in the video by text query or image. Upload v
     
         selected_frames = np.argsort(-1 * np.array(matches_probs))[:20]
         matched_sorted_frames = [matches[idx] for idx in selected_frames]
-        
     
-        return lines + rule, matched_sorted_frames  # Only return up to 20 images to not crash the UI
+        return (
+            lines + rule,
+            matched_sorted_frames,
+        )  # Only return up to 20 images to not crash the UI
     
     
     class LoadVideo(Dataset):
         def __init__(self, path, transforms, vid_stride=1):
-    
             self.transforms = transforms
             self.vid_stride = vid_stride
             self.cur_frame = 0
             self.cap = cv2.VideoCapture(path)
-            self.total_frames = int(
-                self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride
-            )
+            self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
     
         def __getitem__(self, _):
             # Read video
@@ -900,9 +960,8 @@ searching frames in the video by text query or image. Upload v
     
         def __len__(self):
             return self.total_frames
-                
-                    
-            
+    
+    
     desc_text = """
     Search the content's of a video with a text description.
     __Note__: Long videos (over a few minutes) may cause UI performance issues.
@@ -914,7 +973,11 @@ searching frames in the video by text query or image. Upload v
             gr.Video(label="Video"),
             gr.Textbox(label="Text Search Query"),
             gr.Image(label="Image Search Query", visible=False),
-            gr.Dropdown(label="Model", choices=available_converted_models, value=model_checkpoint.value),
+            gr.Dropdown(
+                label="Model",
+                choices=available_converted_models,
+                value=model_checkpoint.value,
+            ),
             gr.Dropdown(label="Device", choices=available_devices, value=device.value),
             gr.Slider(label="Threshold", maximum=1.0, value=0.2),
             gr.Slider(label="Frame-rate Stride", value=4, step=1),
@@ -922,7 +985,7 @@ searching frames in the video by text query or image. Upload v
         ],
         outputs=[
             gr.Plot(label="Similarity Plot"),
-            gr.Gallery(label="Matched Frames", columns=2, object_fit="contain", height="auto")
+            gr.Gallery(label="Matched Frames", columns=2, object_fit="contain", height="auto"),
         ],
         examples=[[sample_path / "car-detection.mp4", "white car"]],
         allow_flagging="never",
@@ -939,7 +1002,11 @@ searching frames in the video by text query or image. Upload v
             gr.Video(label="Video"),
             gr.Textbox(label="Text Search Query", visible=False),
             gr.Image(label="Image Search Query", type="pil"),
-            gr.Dropdown(label="Model", choices=available_converted_models, value=model_checkpoint.value),
+            gr.Dropdown(
+                label="Model",
+                choices=available_converted_models,
+                value=model_checkpoint.value,
+            ),
             gr.Dropdown(label="Device", choices=available_devices, value=device.value),
             gr.Slider(label="Threshold", maximum=1.0, value=0.2),
             gr.Slider(label="Frame-rate Stride", value=4, step=1),
@@ -947,18 +1014,18 @@ searching frames in the video by text query or image. Upload v
         ],
         outputs=[
             gr.Plot(label="Similarity Plot"),
-            gr.Gallery(label="Matched Frames", columns=2, object_fit="contain", height="auto")
+            gr.Gallery(label="Matched Frames", columns=2, object_fit="contain", height="auto"),
         ],
         allow_flagging="never",
-        examples=[[sample_path / "coco.mp4", None, sample_path / "dog.png"]]
+        examples=[[sample_path / "coco.mp4", None, sample_path / "dog.png"]],
     )
     demo = gr.TabbedInterface(
         interface_list=[text_app, image_app],
         tab_names=["Text Query Search", "Image Query Search"],
         title="CLIP Video Content Search",
     )
-        
-        
+    
+    
     try:
         demo.launch(debug=False)
     except Exception:
diff --git a/docs/notebooks/mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_10_4.png b/docs/notebooks/mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_10_4.png
new file mode 100644
index 00000000000..3097711be91
--- /dev/null
+++ b/docs/notebooks/mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_10_4.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:012ab44a6e4292be32171ccb588f72c75c17a662e04cf27f271e5ddd33c89b99
+size 627462
diff --git a/docs/notebooks/mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_14_1.png b/docs/notebooks/mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_14_1.png
new file mode 100644
index 00000000000..7d60b0ba72d
--- /dev/null
+++ b/docs/notebooks/mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_14_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc816c6ac9360e9432eb81eca13dd8f4afa0c0ad90312c876fad89dbbb80a65e
+size 449871
diff --git a/docs/notebooks/mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_25_1.png b/docs/notebooks/mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_25_1.png
new file mode 100644
index 00000000000..7d60b0ba72d
--- /dev/null
+++ b/docs/notebooks/mobileclip-video-search-with-output_files/mobileclip-video-search-with-output_25_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc816c6ac9360e9432eb81eca13dd8f4afa0c0ad90312c876fad89dbbb80a65e
+size 449871
diff --git a/docs/notebooks/279-mobilevlm-language-assistant-with-output.rst b/docs/notebooks/mobilevlm-language-assistant-with-output.rst
similarity index 85%
rename from docs/notebooks/279-mobilevlm-language-assistant-with-output.rst
rename to docs/notebooks/mobilevlm-language-assistant-with-output.rst
index 2299f32c7f6..d20f84bfff8 100644
--- a/docs/notebooks/279-mobilevlm-language-assistant-with-output.rst
+++ b/docs/notebooks/mobilevlm-language-assistant-with-output.rst
@@ -48,15 +48,10 @@ Install requirements
 
 .. code:: ipython3
 
-    %pip install -q "torch>=2.0.1" "timm>=0.9.12" --extra-index-url "https://download.pytorch.org/whl/cpu"
-    %pip install -q "transformers>=4.33.1,<4.35.0" accelerate "sentencepiece>=0.1.99" "openvino>=2023.2.0" "nncf>=2.7.0" ipywidgets numpy gradio
+    %pip install -q "torch>=2.1.0" "timm>=0.9.12" --extra-index-url "https://download.pytorch.org/whl/cpu"
+    %pip install -q "transformers>=4.33.1,<4.35.0" accelerate "sentencepiece>=0.1.99" "openvino>=2023.2.0" "nncf>=2.7.0" ipywidgets numpy "gradio>=4.19"
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -64,16 +59,15 @@ Install requirements
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    WARNING: typer 0.12.3 does not provide the extra 'all'
     
 
 .. parsed-literal::
 
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    datasets 2.18.0 requires huggingface-hub>=0.19.4, but you have huggingface-hub 0.17.3 which is incompatible.
-    diffusers 0.26.3 requires huggingface-hub>=0.20.2, but you have huggingface-hub 0.17.3 which is incompatible.
-    optimum-intel 1.16.0.dev0 requires transformers<4.39.0,>=4.36.0, but you have transformers 4.34.1 which is incompatible.
-    pyannote-audio 2.0.1 requires torchaudio<1.0,>=0.10, but you have torchaudio 2.2.1+cpu which is incompatible.
+    mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.2.2+cpu which is incompatible.
+    mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.17.2+cpu which is incompatible.
+    optimum-intel 1.17.0.dev0+aca2b6c requires transformers<4.40.0,>=4.36.0, but you have transformers 4.33.3 which is incompatible.
     
 
 .. parsed-literal::
@@ -90,6 +84,7 @@ Clone MobileVLM repository
 
     from pathlib import Path
     import sys
+    
     MOBILEVLM_REPO_DIR = Path("./MobileVLM")
     if not MOBILEVLM_REPO_DIR.exists():
         !git clone -q "https://github.com/Meituan-AutoML/MobileVLM.git"
@@ -128,26 +123,26 @@ Import required packages
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-      torch.utils._pytree._register_pytree_node(
-
-
-.. parsed-literal::
-
-    2024-03-13 00:18:55.454952: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:18:55.489108: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 00:08:44.504815: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:08:44.539140: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 00:18:55.998695: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:08:45.045243: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
 
 
 .. parsed-literal::
@@ -158,7 +153,7 @@ Import required packages
 .. code:: ipython3
 
     MODELS_DIR = Path("./models")
-    MODEL_PATH = 'mtgv/MobileVLM-1.7B'
+    MODEL_PATH = "mtgv/MobileVLM-1.7B"
     
     TEMPERATURE = 0.2
     TOP_P = None
@@ -179,13 +174,19 @@ image processor to convert images to appropriate tensors.
 
 .. code:: ipython3
 
-    model_name = MODEL_PATH.split('/')[-1]
+    model_name = MODEL_PATH.split("/")[-1]
     disable_torch_init()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
         tokenizer, model, image_processor, _ = load_pretrained_model(MODEL_PATH, device="cpu")
     model = model.to(dtype=torch.float32)
 
+
+.. parsed-literal::
+
+    You are resizing the embedding layer without providing a `pad_to_multiple_of` parameter. This means that the new embedding dimension will be 32000. This might induce some performance reduction as *Tensor Cores* will not be available. For more details about this, or help on choosing the correct value for resizing, refer to this guide: https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc
+
+
 Convert model to OpenVINO Intermediate Representation (IR)
 ----------------------------------------------------------
 
@@ -243,9 +244,9 @@ compression instead of INT8 weight compression.
 .. code:: ipython3
 
     compression_mode = widgets.Dropdown(
-        options=['INT4', 'INT8'],
-        value='INT4',
-        description='Compression mode:',
+        options=["INT4", "INT8"],
+        value="INT4",
+        description="Compression mode:",
         disabled=False,
     )
     
@@ -267,7 +268,7 @@ compression instead of INT8 weight compression.
 
 .. code:: ipython3
 
-    if compression_mode.value == 'INT4':
+    if compression_mode.value == "INT4":
         wc_parameters = dict(mode=nncf.CompressWeightsMode.INT4_ASYM, group_size=128, ratio=0.8)
     else:
         wc_parameters = dict(mode=nncf.CompressWeightsMode.INT8)
@@ -284,13 +285,13 @@ compression instead of INT8 weight compression.
             input_ids: torch.LongTensor = None,
             attention_mask: Optional[torch.Tensor] = None,
             past_key_values: Optional[List[torch.FloatTensor]] = None,
-            inputs_embeds: Optional[torch.FloatTensor] = None
+            inputs_embeds: Optional[torch.FloatTensor] = None,
         ):
             outputs = self.model.model(
                 input_ids=input_ids,
                 attention_mask=attention_mask,
                 past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds
+                inputs_embeds=inputs_embeds,
             )
             hidden_states = outputs[0]
             logits = self.model.lm_head(hidden_states)
@@ -303,10 +304,7 @@ compression instead of INT8 weight compression.
         input_names = [
             "input_ids",
             "attention_mask",
-            *itertools.chain.from_iterable(
-                [f"past_key_values.{idx}.key", f"past_key_values.{idx}.value"]
-                for idx, _ in enumerate(past_key_values)
-            ),
+            *itertools.chain.from_iterable([f"past_key_values.{idx}.key", f"past_key_values.{idx}.value"] for idx, _ in enumerate(past_key_values)),
         ]
         assert len(input_names) == len(model.inputs)
         for _input, input_name in zip(model.inputs, input_names):
@@ -317,10 +315,7 @@ compression instead of INT8 weight compression.
     def set_output_names(model, past_key_values):
         output_names = [
             "logits",
-            *itertools.chain.from_iterable(
-                [f"present.{idx}.key", f"present.{idx}.value"]
-                for idx, _ in enumerate(past_key_values)
-            ),
+            *itertools.chain.from_iterable([f"present.{idx}.key", f"present.{idx}.value"] for idx, _ in enumerate(past_key_values)),
         ]
         assert len(output_names) == len(model.outputs)
         for out, out_name in zip(ov_model.outputs, output_names):
@@ -358,20 +353,20 @@ compression instead of INT8 weight compression.
 
 .. parsed-literal::
 
-    WARNING:nncf:NNCF provides best results with torch==2.1.2, while current torch version is 2.2.1+cpu. If you encounter issues, consider switching to torch==2.1.2
+    WARNING:nncf:NNCF provides best results with torch==2.1.2, while current torch version is 2.2.2+cpu. If you encounter issues, consider switching to torch==2.1.2
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:808: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:595: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input_shape[-1] > 1:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:146: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:119: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if seq_len > self.max_seq_len_cached:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:375: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:348: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:382: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:355: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:392: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:365: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
 
 
@@ -454,7 +449,7 @@ compression instead of INT8 weight compression.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:165: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:489.)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:165: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:489.)
       if a.grad is not None:
 
 
@@ -591,11 +586,7 @@ documentation `__, including its
 `post-installation `__
 steps, on your development system. To verify installation, test it,
@@ -120,7 +120,7 @@ image and a message.
 Step 2: Preparing a Model Repository
 ------------------------------------
 
-The models need to be placed
+ The models need to be placed
 and mounted in a particular directory structure and according to the
 following rules:
 
@@ -170,18 +170,27 @@ following rules:
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0"
+    import platform
+    
+    %pip install -q "openvino>=2023.1.0" opencv-python tqdm
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
 
 .. code:: ipython3
 
     import os
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
     
     dedicated_dir = "models"
@@ -192,8 +201,12 @@ following rules:
     XML_PATH = "horizontal-text-detection-0001.xml"
     BIN_PATH = "horizontal-text-detection-0001.bin"
     os.makedirs(MODEL_DIR, exist_ok=True)
-    model_xml_url = "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/horizontal-text-detection-0001/FP32/horizontal-text-detection-0001.xml"
-    model_bin_url = "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/horizontal-text-detection-0001/FP32/horizontal-text-detection-0001.bin"
+    model_xml_url = (
+        "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/horizontal-text-detection-0001/FP32/horizontal-text-detection-0001.xml"
+    )
+    model_bin_url = (
+        "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/horizontal-text-detection-0001/FP32/horizontal-text-detection-0001.bin"
+    )
     
     download_file(model_xml_url, XML_PATH, MODEL_DIR)
     download_file(model_bin_url, BIN_PATH, MODEL_DIR)
@@ -215,14 +228,14 @@ following rules:
 
 .. parsed-literal::
 
-    PosixPath('/home/ethan/intel/openvino_notebooks/notebooks/117-model-server/models/detection/1/horizontal-text-detection-0001.bin')
+    PosixPath('/home/ethan/intel/openvino_notebooks/notebooks/model-server/models/detection/1/horizontal-text-detection-0001.bin')
 
 
 
 Step 3: Start the Model Server Container
 ----------------------------------------
 
-Pull and start the container:
+ Pull and start the container:
 
 Searching for an available serving port in local.
 
@@ -231,13 +244,13 @@ Searching for an available serving port in local.
     import socket
     
     sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-    sock.bind(('localhost', 0))
+    sock.bind(("localhost", 0))
     sock.listen(1)
     port = sock.getsockname()[1]
     sock.close()
     print(f"Port {port} is available")
     
-    os.environ['port'] = str(port)
+    os.environ["port"] = str(port)
 
 
 .. parsed-literal::
@@ -692,7 +705,7 @@ available port on your system. For example:\ ``-p 9020:9000``
 Step 4: Prepare the Example Client Components
 ---------------------------------------------
 
-OpenVINO Model Server exposes
+ OpenVINO Model Server exposes
 two sets of APIs: one compatible with ``TensorFlow Serving`` and another
 one, with ``KServe API``, for inference. Both APIs work on ``gRPC`` and
 ``REST``\ interfaces. Supporting two sets of APIs makes OpenVINO Model
@@ -775,7 +788,7 @@ Load input image
     # Download the image from the openvino_notebooks storage
     image_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg",
-        directory="data"
+        directory="data",
     )
     
     # Text detection models expect an image in BGR format.
@@ -783,7 +796,7 @@ Load input image
     fp_image = image.astype("float32")
     
     # Resize the image to meet network expected input sizes.
-    input_shape = model_metadata['inputs']['image']['shape']
+    input_shape = model_metadata["inputs"]["image"]["shape"]
     height, width = input_shape[2], input_shape[3]
     resized_image = cv2.resize(fp_image, (height, width))
     
@@ -807,7 +820,7 @@ Load input image
 
 
 
-.. image:: 117-model-server-with-output_files/117-model-server-with-output_23_2.png
+.. image:: model-server-with-output_files/model-server-with-output_23_2.png
 
 
 Request Prediction on a Numpy Array
@@ -820,7 +833,7 @@ Request Prediction on a Numpy Array
     inputs = {"image": input_image}
     
     # Run inference on model server and receive the result data
-    boxes = client.predict(inputs=inputs, model_name=model_name)['boxes']
+    boxes = client.predict(inputs=inputs, model_name=model_name)["boxes"]
     
     # Remove zero only boxes.
     boxes = boxes[~np.all(boxes == 0, axis=1)]
@@ -851,7 +864,10 @@ Visualization
         colors = {"red": (255, 0, 0), "green": (0, 255, 0)}
     
         # Fetch the image shapes to calculate a ratio.
-        (real_y, real_x), (resized_y, resized_x) = bgr_image.shape[:2], resized_image.shape[:2]
+        (real_y, real_x), (resized_y, resized_x) = (
+            bgr_image.shape[:2],
+            resized_image.shape[:2],
+        )
         ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
     
         # Convert the base image from BGR to RGB format.
@@ -863,12 +879,10 @@ Visualization
             conf = box[-1]
             if conf > threshold:
                 # Convert float to int and multiply corner position of each box by x and y ratio.
-                # If the bounding box is found at the top of the image, 
-                # position the upper box bar little lower to make it visible on the image. 
+                # If the bounding box is found at the top of the image,
+                # position the upper box bar little lower to make it visible on the image.
                 (x_min, y_min, x_max, y_max) = [
-                    int(max(corner_position * ratio_y, 10)) if idx % 2 
-                    else int(corner_position * ratio_x)
-                    for idx, corner_position in enumerate(box[:-1])
+                    (int(max(corner_position * ratio_y, 10)) if idx % 2 else int(corner_position * ratio_x)) for idx, corner_position in enumerate(box[:-1])
                 ]
     
                 # Draw a box based on the position, parameters in rectangle function are: image, start_point, end_point, color, thickness.
@@ -906,7 +920,7 @@ Visualization
 
 
 
-.. image:: 117-model-server-with-output_files/117-model-server-with-output_28_1.png
+.. image:: model-server-with-output_files/model-server-with-output_28_1.png
 
 
 To stop and remove the model server container, you can use the following
diff --git a/docs/notebooks/117-model-server-with-output_files/117-model-server-with-output_23_2.png b/docs/notebooks/model-server-with-output_files/model-server-with-output_23_2.png
similarity index 100%
rename from docs/notebooks/117-model-server-with-output_files/117-model-server-with-output_23_2.png
rename to docs/notebooks/model-server-with-output_files/model-server-with-output_23_2.png
diff --git a/docs/notebooks/117-model-server-with-output_files/117-model-server-with-output_28_1.png b/docs/notebooks/model-server-with-output_files/model-server-with-output_28_1.png
similarity index 100%
rename from docs/notebooks/117-model-server-with-output_files/117-model-server-with-output_28_1.png
rename to docs/notebooks/model-server-with-output_files/model-server-with-output_28_1.png
diff --git a/docs/notebooks/model-tools-with-output.rst b/docs/notebooks/model-tools-with-output.rst
new file mode 100644
index 00000000000..c11484d170c
--- /dev/null
+++ b/docs/notebooks/model-tools-with-output.rst
@@ -0,0 +1,1025 @@
+Working with Open Model Zoo Models
+==================================
+
+This tutorial shows how to download a model from `Open Model
+Zoo `__, convert it
+to OpenVINO™ IR format, show information about the model, and benchmark
+the model.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `OpenVINO and Open Model Zoo
+   Tools <#openvino-and-open-model-zoo-tools>`__
+-  `Preparation <#preparation>`__
+
+   -  `Model Name <#model-name>`__
+   -  `Imports <#imports>`__
+   -  `Settings and Configuration <#settings-and-configuration>`__
+
+-  `Download a Model from Open Model
+   Zoo <#download-a-model-from-open-model-zoo>`__
+-  `Convert a Model to OpenVINO IR
+   format <#convert-a-model-to-openvino-ir-format>`__
+-  `Get Model Information <#get-model-information>`__
+-  `Run Benchmark Tool <#run-benchmark-tool>`__
+
+   -  `Benchmark with Different
+      Settings <#benchmark-with-different-settings>`__
+
+OpenVINO and Open Model Zoo Tools
+---------------------------------
+
+
+
+OpenVINO and Open Model Zoo tools are listed in the table below.
+
++------------+--------------+-----------------------------------------+
+| Tool       | Command      | Description                             |
++============+==============+=========================================+
+| Model      | ``omz_downlo | Download models from Open Model Zoo.    |
+| Downloader | ader``       |                                         |
++------------+--------------+-----------------------------------------+
+| Model      | ``omz_conver | Convert Open Model Zoo models to        |
+| Converter  | ter``        | OpenVINO’s IR format.                   |
++------------+--------------+-----------------------------------------+
+| Info       | ``omz_info_d | Print information about Open Model Zoo  |
+| Dumper     | umper``      | models.                                 |
++------------+--------------+-----------------------------------------+
+| Benchmark  | ``benchmark_ | Benchmark model performance by          |
+| Tool       | app``        | computing inference time.               |
++------------+--------------+-----------------------------------------+
+
+.. code:: ipython3
+
+    # Install openvino package
+    %pip install -q "openvino-dev>=2024.0.0" torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Preparation
+-----------
+
+
+
+Model Name
+~~~~~~~~~~
+
+
+
+Set ``model_name`` to the name of the Open Model Zoo model to use in
+this notebook. Refer to the list of
+`public `__
+and
+`Intel `__
+pre-trained models for a full list of models that can be used. Set
+``model_name`` to the model you want to use.
+
+.. code:: ipython3
+
+    # model_name = "resnet-50-pytorch"
+    model_name = "mobilenet-v2-pytorch"
+
+Imports
+~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import json
+    from pathlib import Path
+
+    import openvino as ov
+    from IPython.display import Markdown, display
+
+    # Fetch `notebook_utils` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+
+    open("notebook_utils.py", "w").write(r.text)
+    from notebook_utils import DeviceNotFoundAlert, NotebookAlert
+
+Settings and Configuration
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Set the file and directory paths. By default, this notebook downloads
+models from Open Model Zoo to the ``open_model_zoo_models`` directory in
+your ``$HOME`` directory. On Windows, the $HOME directory is usually
+``c:\users\username``, on Linux ``/home/username``. To change the
+folder, change ``base_model_dir`` in the cell below.
+
+The following settings can be changed:
+
+-  ``base_model_dir``: Models will be downloaded into the ``intel`` and
+   ``public`` folders in this directory.
+-  ``omz_cache_dir``: Cache folder for Open Model Zoo. Specifying a
+   cache directory is not required for Model Downloader and Model
+   Converter, but it speeds up subsequent downloads.
+-  ``precision``: If specified, only models with this precision will be
+   downloaded and converted.
+
+.. code:: ipython3
+
+    base_model_dir = Path("model")
+    omz_cache_dir = Path("cache")
+    precision = "FP16"
+
+    # Check if an GPU is available on this system to use with Benchmark App.
+    core = ov.Core()
+    gpu_available = "GPU" in core.available_devices
+
+    print(f"base_model_dir: {base_model_dir}, omz_cache_dir: {omz_cache_dir}, gpu_availble: {gpu_available}")
+
+
+.. parsed-literal::
+
+    base_model_dir: model, omz_cache_dir: cache, gpu_availble: False
+
+
+Download a Model from Open Model Zoo
+------------------------------------
+
+
+
+Specify, display and run the Model Downloader command to download the
+model.
+
+.. code:: ipython3
+
+    ## Uncomment the next line to show help in omz_downloader which explains the command-line options.
+
+    # !omz_downloader --help
+
+.. code:: ipython3
+
+    download_command = f"omz_downloader --name {model_name} --output_dir {base_model_dir} --cache_dir {omz_cache_dir}"
+    display(Markdown(f"Download command: `{download_command}`"))
+    display(Markdown(f"Downloading {model_name}..."))
+    ! $download_command
+
+
+
+Download command:
+``omz_downloader --name mobilenet-v2-pytorch --output_dir model --cache_dir cache``
+
+
+
+Downloading mobilenet-v2-pytorch…
+
+
+.. parsed-literal::
+
+    ################|| Downloading mobilenet-v2-pytorch ||################
+
+    ========== Downloading model/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth
+
+
+.. parsed-literal::
+
+    ... 0%, 32 KB, 954 KB/s, 0 seconds passed
+
+.. parsed-literal::
+
+    ... 0%, 64 KB, 986 KB/s, 0 seconds passed
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+
+.. parsed-literal::
+
+    ... 2%, 288 KB, 2214 KB/s, 0 seconds passed
+... 2%, 320 KB, 2450 KB/s, 0 seconds passed
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+
+.. parsed-literal::
+
+    ... 4%, 608 KB, 3723 KB/s, 0 seconds passed
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+
+.. parsed-literal::
+
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+
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+
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+
+.. parsed-literal::
+
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+
+.. parsed-literal::
+
+    ... 91%, 12768 KB, 35223 KB/s, 0 seconds passed
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+
+
+
+
+Convert a Model to OpenVINO IR format
+-------------------------------------
+
+
+
+Specify, display and run the Model Converter command to convert the
+model to OpenVINO IR format. Model conversion may take a while. The
+output of the Model Converter command will be displayed. When the
+conversion is successful, the last lines of the output will include:
+``[ SUCCESS ] Generated IR version 11 model.`` For downloaded models
+that are already in OpenVINO IR format, conversion will be skipped.
+
+.. code:: ipython3
+
+    ## Uncomment the next line to show Help in omz_converter which explains the command-line options.
+
+    # !omz_converter --help
+
+.. code:: ipython3
+
+    convert_command = f"omz_converter --name {model_name} --precisions {precision} --download_dir {base_model_dir} --output_dir {base_model_dir}"
+    display(Markdown(f"Convert command: `{convert_command}`"))
+    display(Markdown(f"Converting {model_name}..."))
+
+    ! $convert_command
+
+
+
+Convert command:
+``omz_converter --name mobilenet-v2-pytorch --precisions FP16 --download_dir model --output_dir model``
+
+
+
+Converting mobilenet-v2-pytorch…
+
+
+.. parsed-literal::
+
+    ========== Converting mobilenet-v2-pytorch to ONNX
+    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-name=mobilenet_v2 --weights=model/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth --import-module=torchvision.models --input-shape=1,3,224,224 --output-file=model/public/mobilenet-v2-pytorch/mobilenet-v2.onnx --input-names=data --output-names=prob
+
+
+
+.. parsed-literal::
+
+    ONNX check passed successfully.
+
+
+.. parsed-literal::
+
+
+    ========== Converting mobilenet-v2-pytorch to IR (FP16)
+    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/mobilenet-v2-pytorch/FP16 --model_name=mobilenet-v2-pytorch --input=data '--mean_values=data[123.675,116.28,103.53]' '--scale_values=data[58.624,57.12,57.375]' --reverse_input_channels --output=prob --input_model=model/public/mobilenet-v2-pytorch/mobilenet-v2.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 224, 224]' --compress_to_fp16=True
+
+
+
+.. parsed-literal::
+
+    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
+    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
+    [ SUCCESS ] Generated IR version 11 model.
+    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/model-tools/model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml
+    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/model-tools/model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.bin
+
+
+
+
+
+Get Model Information
+---------------------
+
+
+
+The Info Dumper prints the following information for Open Model Zoo
+models:
+
+-  Model name
+-  Description
+-  Framework that was used to train the model
+-  License URL
+-  Precisions supported by the model
+-  Subdirectory: the location of the downloaded model
+-  Task type
+
+This information can be shown by running
+``omz_info_dumper --name model_name`` in a terminal. The information can
+also be parsed and used in scripts.
+
+In the next cell, run Info Dumper and use ``json`` to load the
+information in a dictionary.
+
+.. code:: ipython3
+
+    model_info_output = %sx omz_info_dumper --name $model_name
+    model_info = json.loads(model_info_output.get_nlstr())
+
+    if len(model_info) > 1:
+        NotebookAlert(
+            f"There are multiple IR files for the {model_name} model. The first model in the "
+            "omz_info_dumper output will be used for benchmarking. Change "
+            "`selected_model_info` in the cell below to select a different model from the list.",
+            "warning",
+        )
+
+    model_info
+
+
+
+
+.. parsed-literal::
+
+    [{'name': 'mobilenet-v2-pytorch',
+      'composite_model_name': None,
+      'description': 'MobileNet V2 is image classification model pre-trained on ImageNet dataset. This is a PyTorch* implementation of MobileNetV2 architecture as described in the paper "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation" .\nThe model input is a blob that consists of a single image of "1, 3, 224, 224" in "RGB" order.\nThe model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.',
+      'framework': 'pytorch',
+      'license_url': 'https://raw.githubusercontent.com/pytorch/vision/master/LICENSE',
+      'accuracy_config': '/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/mobilenet-v2-pytorch/accuracy-check.yml',
+      'model_config': '/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/mobilenet-v2-pytorch/model.yml',
+      'precisions': ['FP16', 'FP32'],
+      'subdirectory': 'public/mobilenet-v2-pytorch',
+      'task_type': 'classification',
+      'input_info': [{'name': 'data',
+        'shape': [1, 3, 224, 224],
+        'layout': 'NCHW'}],
+      'model_stages': []}]
+
+
+
+Having information of the model in a JSON file enables extraction of the
+path to the model directory, and building the path to the OpenVINO IR
+file.
+
+.. code:: ipython3
+
+    selected_model_info = model_info[0]
+    model_path = base_model_dir / Path(selected_model_info["subdirectory"]) / Path(f"{precision}/{selected_model_info['name']}.xml")
+    print(model_path, "exists:", model_path.exists())
+
+
+.. parsed-literal::
+
+    model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml exists: True
+
+
+Run Benchmark Tool
+------------------
+
+
+
+By default, Benchmark Tool runs inference for 60 seconds in asynchronous
+mode on CPU. It returns inference speed as latency (milliseconds per
+image) and throughput values (frames per second).
+
+.. code:: ipython3
+
+    ## Uncomment the next line to show Help in benchmark_app which explains the command-line options.
+    # !benchmark_app --help
+
+.. code:: ipython3
+
+    benchmark_command = f"benchmark_app -m {model_path} -t 15"
+    display(Markdown(f"Benchmark command: `{benchmark_command}`"))
+    display(Markdown(f"Benchmarking {model_name} on CPU with async inference for 15 seconds..."))
+
+    ! $benchmark_command
+
+
+
+Benchmark command:
+``benchmark_app -m model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml -t 15``
+
+
+
+Benchmarking mobilenet-v2-pytorch on CPU with async inference for 15
+seconds…
+
+
+.. parsed-literal::
+
+    [Step 1/11] Parsing and validating input arguments
+    [ INFO ] Parsing input parameters
+    [Step 2/11] Loading OpenVINO Runtime
+
+
+.. parsed-literal::
+
+    [ INFO ] OpenVINO:
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
+    [ INFO ]
+    [ INFO ] Device info:
+    [ INFO ] CPU
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
+    [ INFO ]
+    [ INFO ]
+    [Step 3/11] Setting device configuration
+    [ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT.
+    [Step 4/11] Reading model files
+    [ INFO ] Loading model files
+
+
+.. parsed-literal::
+
+    [ INFO ] Read model took 28.84 ms
+    [ INFO ] Original model I/O parameters:
+    [ INFO ] Model inputs:
+    [ INFO ]     data (node: data) : f32 / [N,C,H,W] / [1,3,224,224]
+    [ INFO ] Model outputs:
+    [ INFO ]     prob (node: prob) : f32 / [...] / [1,1000]
+    [Step 5/11] Resizing model to match image sizes and given batch
+    [ INFO ] Model batch size: 1
+    [Step 6/11] Configuring input of the model
+    [ INFO ] Model inputs:
+    [ INFO ]     data (node: data) : u8 / [N,C,H,W] / [1,3,224,224]
+    [ INFO ] Model outputs:
+    [ INFO ]     prob (node: prob) : f32 / [...] / [1,1000]
+    [Step 7/11] Loading the model to the device
+
+
+.. parsed-literal::
+
+    [ INFO ] Compile model took 154.21 ms
+    [Step 8/11] Querying optimal runtime parameters
+    [ INFO ] Model:
+    [ INFO ]   NETWORK_NAME: main_graph
+    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
+    [ INFO ]   NUM_STREAMS: 6
+    [ INFO ]   AFFINITY: Affinity.CORE
+    [ INFO ]   INFERENCE_NUM_THREADS: 24
+    [ INFO ]   PERF_COUNT: NO
+    [ INFO ]   INFERENCE_PRECISION_HINT: 
+    [ INFO ]   PERFORMANCE_HINT: THROUGHPUT
+    [ INFO ]   EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
+    [ INFO ]   PERFORMANCE_HINT_NUM_REQUESTS: 0
+    [ INFO ]   ENABLE_CPU_PINNING: True
+    [ INFO ]   SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
+    [ INFO ]   ENABLE_HYPER_THREADING: True
+    [ INFO ]   EXECUTION_DEVICES: ['CPU']
+    [ INFO ]   CPU_DENORMALS_OPTIMIZATION: False
+    [ INFO ]   LOG_LEVEL: Level.NO
+    [ INFO ]   CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
+    [ INFO ]   DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]   KV_CACHE_PRECISION: 
+    [Step 9/11] Creating infer requests and preparing input tensors
+    [ WARNING ] No input files were given for input 'data'!. This input will be filled with random values!
+    [ INFO ] Fill input 'data' with random values
+    [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
+    [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
+    [ INFO ] First inference took 6.50 ms
+
+
+.. parsed-literal::
+
+    [Step 11/11] Dumping statistics report
+    [ INFO ] Execution Devices:['CPU']
+    [ INFO ] Count:            20394 iterations
+    [ INFO ] Duration:         15004.33 ms
+    [ INFO ] Latency:
+    [ INFO ]    Median:        4.29 ms
+    [ INFO ]    Average:       4.29 ms
+    [ INFO ]    Min:           2.36 ms
+    [ INFO ]    Max:           12.12 ms
+    [ INFO ] Throughput:   1359.21 FPS
+
+
+Benchmark with Different Settings
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+The ``benchmark_app`` tool displays logging information that is not
+always necessary. A more compact result is achieved when the output is
+parsed with ``json``.
+
+The following cells show some examples of ``benchmark_app`` with
+different parameters. Below are some useful parameters:
+
+-  ``-d`` A device to use for inference. For example: CPU, GPU, MULTI.
+   Default: CPU.
+-  ``-t`` Time expressed in number of seconds to run inference. Default:
+   60.
+-  ``-api`` Use asynchronous (async) or synchronous (sync) inference.
+   Default: async.
+-  ``-b`` Batch size. Default: 1.
+
+Run ``! benchmark_app --help`` to get an overview of all possible
+command-line parameters.
+
+In the next cell, define the ``benchmark_model()`` function that calls
+``benchmark_app``. This makes it easy to try different combinations. In
+the cell below that, you display available devices on the system.
+
+   **Note**: In this notebook, ``benchmark_app`` runs for 15 seconds to
+   give a quick indication of performance. For more accurate
+   performance, it is recommended to run inference for at least one
+   minute by setting the ``t`` parameter to 60 or higher, and run
+   ``benchmark_app`` in a terminal/command prompt after closing other
+   applications. Copy the **benchmark command** and paste it in a
+   command prompt where you have activated the ``openvino_env``
+   environment.
+
+.. code:: ipython3
+
+    def benchmark_model(model_xml, device="CPU", seconds=60, api="async", batch=1):
+        core = ov.Core()
+        model_path = Path(model_xml)
+        if ("GPU" in device) and ("GPU" not in core.available_devices):
+            DeviceNotFoundAlert("GPU")
+        else:
+            benchmark_command = f"benchmark_app -m {model_path} -d {device} -t {seconds} -api {api} -b {batch}"
+            display(Markdown(f"**Benchmark {model_path.name} with {device} for {seconds} seconds with {api} inference**"))
+            display(Markdown(f"Benchmark command: `{benchmark_command}`"))
+
+            benchmark_output = %sx $benchmark_command
+            print("command ended")
+            benchmark_result = [line for line in benchmark_output if not (line.startswith(r"[") or line.startswith("      ") or line == "")]
+            print("\n".join(benchmark_result))
+
+.. code:: ipython3
+
+    core = ov.Core()
+
+    # Show devices available for OpenVINO Runtime
+    for device in core.available_devices:
+        device_name = core.get_property(device, "FULL_DEVICE_NAME")
+        print(f"{device}: {device_name}")
+
+
+.. parsed-literal::
+
+    CPU: Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
+
+
+You can select inference device using device widget
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')
+
+
+
+.. code:: ipython3
+
+    benchmark_model(model_path, device=device.value, seconds=15, api="async")
+
+
+
+**Benchmark mobilenet-v2-pytorch.xml with CPU for 15 seconds with async
+inference**
+
+
+
+Benchmark command:
+``benchmark_app -m model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml -d CPU -t 15 -api async -b 1``
+
+
+.. parsed-literal::
+
+    command ended
+
+
diff --git a/docs/notebooks/music-generation-with-output.rst b/docs/notebooks/music-generation-with-output.rst
new file mode 100644
index 00000000000..53702235dbd
--- /dev/null
+++ b/docs/notebooks/music-generation-with-output.rst
@@ -0,0 +1,717 @@
+Controllable Music Generation with MusicGen and OpenVINO
+========================================================
+
+MusicGen is a single-stage auto-regressive Transformer model capable of
+generating high-quality music samples conditioned on text descriptions
+or audio prompts. The text prompt is passed to a text encoder model (T5)
+to obtain a sequence of hidden-state representations. These hidden
+states are fed to MusicGen, which predicts discrete audio tokens (audio
+codes). Finally, audio tokens are then decoded using an audio
+compression model (EnCodec) to recover the audio waveform.
+
+.. figure:: https://user-images.githubusercontent.com/76463150/260439306-81c81c8d-1f9c-41d0-b881-9491766def8e.png
+   :alt: pipeline
+
+   pipeline
+
+`The MusicGen model `__ does not
+require a self-supervised semantic representation of the text/audio
+prompts; it operates over several streams of compressed discrete music
+representation with efficient token interleaving patterns, thus
+eliminating the need to cascade multiple models to predict a set of
+codebooks (e.g. hierarchically or upsampling). Unlike prior models
+addressing music generation, it is able to generate all the codebooks in
+a single forward pass.
+
+In this tutorial, we consider how to run the MusicGen model using
+OpenVINO.
+
+We will use a model implementation from the `Hugging Face
+Transformers `__
+library.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+
+   -  `Install requirements <#install-requirements>`__
+   -  `Imports <#imports>`__
+
+-  `MusicGen in HF Transformers <#musicgen-in-hf-transformers>`__
+
+   -  `Original Pipeline Inference <#original-pipeline-inference>`__
+
+-  `Convert models to OpenVINO Intermediate representation (IR)
+   format <#convert-models-to-openvino-intermediate-representation-ir-format>`__
+
+   -  `0. Set Up Variables <#0--set-up-variables>`__
+   -  `1. Convert Text Encoder <#1--convert-text-encoder>`__
+   -  `2. Convert MusicGen Language
+      Model <#2--convert-musicgen-language-model>`__
+   -  `3. Convert Audio Decoder <#3--convert-audio-decoder>`__
+
+-  `Embedding the converted models into the original
+   pipeline <#embedding-the-converted-models-into-the-original-pipeline>`__
+
+   -  `Select inference device <#select-inference-device>`__
+   -  `Adapt OpenVINO models to the original
+      pipeline <#adapt-openvino-models-to-the-original-pipeline>`__
+
+-  `Try out the converted pipeline <#try-out-the-converted-pipeline>`__
+
+Prerequisites
+-------------
+
+
+
+Install requirements
+~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    %pip install -q "openvino>=2023.3.0"
+    %pip install -q "torch>=2.1" "gradio>=4.19" transformers --extra-index-url https://download.pytorch.org/whl/cpu
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Imports
+~~~~~~~
+
+
+
+.. code:: ipython3
+
+    from collections import namedtuple
+    from functools import partial
+    import gc
+    from pathlib import Path
+    from typing import Optional, Tuple
+    import warnings
+    
+    from IPython.display import Audio
+    import openvino as ov
+    import numpy as np
+    import torch
+    from torch.jit import TracerWarning
+    from transformers import AutoProcessor, MusicgenForConditionalGeneration
+    from transformers.modeling_outputs import (
+        BaseModelOutputWithPastAndCrossAttentions,
+        CausalLMOutputWithCrossAttentions,
+    )
+    
+    # Ignore tracing warnings
+    warnings.filterwarnings("ignore", category=TracerWarning)
+
+
+.. parsed-literal::
+
+    2024-04-18 00:11:58.655998: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:11:58.690632: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+
+
+.. parsed-literal::
+
+    2024-04-18 00:11:59.179380: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
+
+
+MusicGen in HF Transformers
+---------------------------
+
+
+
+To work with
+`MusicGen `__ by Meta
+AI, we will use `Hugging Face Transformers
+package `__. Transformers
+package exposes the ``MusicgenForConditionalGeneration`` class,
+simplifying the model instantiation and weights loading. The code below
+demonstrates how to create a ``MusicgenForConditionalGeneration`` and
+generate a text-conditioned music sample.
+
+.. code:: ipython3
+
+    # Load the pipeline
+    model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small", torchscript=True, return_dict=False)
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+      warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+
+
+In the cell below user is free to change the desired music sample
+length.
+
+.. code:: ipython3
+
+    sample_length = 8  # seconds
+    
+    n_tokens = sample_length * model.config.audio_encoder.frame_rate + 3
+    sampling_rate = model.config.audio_encoder.sampling_rate
+    print("Sampling rate is", sampling_rate, "Hz")
+    
+    model.to("cpu")
+    model.eval();
+
+
+.. parsed-literal::
+
+    Sampling rate is 32000 Hz
+
+
+Original Pipeline Inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Text Preprocessing prepares the text prompt to be fed into the model,
+the ``processor`` object abstracts this step for us. Text tokenization
+is performed under the hood, it assigning tokens or IDs to the words; in
+other words, token IDs are just indices of the words in the model
+vocabulary. It helps the model understand the context of a sentence.
+
+.. code:: ipython3
+
+    processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
+    
+    inputs = processor(
+        text=["80s pop track with bassy drums and synth"],
+        return_tensors="pt",
+    )
+    
+    audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=n_tokens)
+    
+    Audio(audio_values[0].cpu().numpy(), rate=sampling_rate)
+
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+Convert models to OpenVINO Intermediate representation (IR) format
+------------------------------------------------------------------
+
+
+
+Model conversion API enables direct conversion of PyTorch models. We
+will utilize the ``openvino.convert_model`` method to acquire OpenVINO
+IR versions of the models. The method requires a model object and
+example input for model tracing. Under the hood, the converter will use
+the PyTorch JIT compiler, to build a frozen model graph.
+
+The pipeline consists of three important parts:
+
+-  The `T5 text encoder `__
+   that translates user prompts into vectors in the latent space that
+   the next model - the MusicGen decoder can utilize.
+-  The `MusicGen Language
+   Model `__
+   that auto-regressively generates audio tokens (codes).
+-  The `EnCodec model `__
+   (we will use only the decoder part of it) is used to decode the audio
+   waveform from the audio tokens predicted by the MusicGen Language
+   Model.
+
+Let us convert each model step by step.
+
+0. Set Up Variables
+~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    models_dir = Path("./models")
+    t5_ir_path = models_dir / "t5.xml"
+    musicgen_0_ir_path = models_dir / "mg_0.xml"
+    musicgen_ir_path = models_dir / "mg.xml"
+    audio_decoder_ir_path = models_dir / "encodec.xml"
+
+1. Convert Text Encoder
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+The text encoder is responsible for converting the input prompt, such as
+“90s rock song with loud guitars and heavy drums” into an embedding
+space that can be fed to the next model. Typically, it is a
+transformer-based encoder that maps a sequence of input tokens to a
+sequence of text embeddings.
+
+The input for the text encoder consists of a tensor ``input_ids``, which
+contains token indices from the text processed by the tokenizer and
+``attention_mask`` that we will ignore as we will process one prompt at
+a time and this vector will just consist of ones.
+
+We use OpenVINO Converter (OVC) below to convert the PyTorch model to
+the OpenVINO Intermediate Representation format (IR), which you can
+infer later with `OpenVINO
+runtime `__
+
+.. code:: ipython3
+
+    if not t5_ir_path.exists():
+        t5_ov = ov.convert_model(model.text_encoder, example_input={"input_ids": inputs["input_ids"]})
+    
+        ov.save_model(t5_ov, t5_ir_path)
+        del t5_ov
+        gc.collect()
+
+
+.. parsed-literal::
+
+    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
+
+
+.. parsed-literal::
+
+    [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
+
+
+2. Convert MusicGen Language Model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+This model is the central part of the whole pipeline, it takes the
+embedded text representation and generates audio codes that can be then
+decoded into actual music. The model outputs several streams of audio
+codes - tokens sampled from the pre-trained codebooks representing music
+efficiently with a lower frame rate. The model employs innovative codes
+intervaling strategy, that makes single-stage generation possible.
+
+On the 0th generation step the model accepts ``input_ids`` representing
+the indices of audio codes, ``encoder_hidden_states`` and
+``encoder_attention_mask`` that were provided by the text encoder.
+
+.. code:: ipython3
+
+    # Set model config `torchscript` to True, so the model returns a tuple as output
+    model.decoder.config.torchscript = True
+    
+    if not musicgen_0_ir_path.exists():
+        decoder_input = {
+            "input_ids": torch.ones(8, 1, dtype=torch.int64),
+            "encoder_hidden_states": torch.ones(2, 12, 1024, dtype=torch.float32),
+            "encoder_attention_mask": torch.ones(2, 12, dtype=torch.int64),
+        }
+        mg_ov_0_step = ov.convert_model(model.decoder, example_input=decoder_input)
+    
+        ov.save_model(mg_ov_0_step, musicgen_0_ir_path)
+        del mg_ov_0_step
+        gc.collect()
+
+On further iterations, the model is also provided with a
+``past_key_values`` argument that contains previous outputs of the
+attention block, it allows us to save on computations. But for us, it
+means that the signature of the model’s ``forward`` method changed.
+Models in OpenVINO IR have frozen calculation graphs and do not allow
+optional arguments, that is why the MusicGen model must be converted a
+second time, with an increased number of inputs.
+
+.. code:: ipython3
+
+    # Add additional argument to the example_input dict
+    if not musicgen_ir_path.exists():
+        # Add `past_key_values` to the converted model signature
+        decoder_input["past_key_values"] = tuple(
+            [
+                (
+                    torch.ones(2, 16, 1, 64, dtype=torch.float32),
+                    torch.ones(2, 16, 1, 64, dtype=torch.float32),
+                    torch.ones(2, 16, 12, 64, dtype=torch.float32),
+                    torch.ones(2, 16, 12, 64, dtype=torch.float32),
+                )
+            ]
+            * 24
+        )
+    
+        mg_ov = ov.convert_model(model.decoder, example_input=decoder_input)
+        for input in mg_ov.inputs[3:]:
+            input.get_node().set_partial_shape(ov.PartialShape([-1, 16, -1, 64]))
+            input.get_node().set_element_type(ov.Type.f32)
+    
+        mg_ov.validate_nodes_and_infer_types()
+    
+        ov.save_model(mg_ov, musicgen_ir_path)
+        del mg_ov
+        gc.collect()
+
+3. Convert Audio Decoder
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+The audio decoder which is a part of the EnCodec model is used to
+recover the audio waveform from the audio tokens predicted by the
+MusicGen decoder. To learn more about the model please refer to the
+corresponding `OpenVINO example <../encodec-audio-compression>`__.
+
+.. code:: ipython3
+
+    if not audio_decoder_ir_path.exists():
+    
+        class AudioDecoder(torch.nn.Module):
+            def __init__(self, model):
+                super().__init__()
+                self.model = model
+    
+            def forward(self, output_ids):
+                return self.model.decode(output_ids, [None])
+    
+        audio_decoder_input = {"output_ids": torch.ones((1, 1, 4, n_tokens - 3), dtype=torch.int64)}
+    
+        with torch.no_grad():
+            audio_decoder_ov = ov.convert_model(AudioDecoder(model.audio_encoder), example_input=audio_decoder_input)
+        ov.save_model(audio_decoder_ov, audio_decoder_ir_path)
+        del audio_decoder_ov
+        gc.collect()
+
+Embedding the converted models into the original pipeline
+---------------------------------------------------------
+
+
+
+OpenVINO™ Runtime Python API is used to compile the model in OpenVINO IR
+format. The
+`Core `__
+class provides access to the OpenVINO Runtime API. The ``core`` object,
+which is an instance of the ``Core`` class represents the API and it is
+used to compile the model.
+
+.. code:: ipython3
+
+    core = ov.Core()
+
+Select inference device
+^^^^^^^^^^^^^^^^^^^^^^^
+
+
+
+Select device that will be used to do models inference using OpenVINO
+from the dropdown list:
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+Adapt OpenVINO models to the original pipeline
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Here we create wrapper classes for all three OpenVINO models that we
+want to embed in the original inference pipeline. Here are some of the
+things to consider when adapting an OV model: - Make sure that
+parameters passed by the original pipeline are forwarded to the compiled
+OV model properly; sometimes the OV model uses only a portion of the
+input arguments and some are ignored, sometimes you need to convert the
+argument to another data type or unwrap some data structures such as
+tuples or dictionaries. - Guarantee that the wrapper class returns
+results to the pipeline in an expected format. In the example below you
+can see how we pack OV model outputs into special classes declared in
+the HF repo. - Pay attention to the model method used in the original
+pipeline for calling the model - it may be not the ``forward`` method!
+Refer to the ``AudioDecoderWrapper`` to see how we wrap OV model
+inference into the ``decode`` method.
+
+.. code:: ipython3
+
+    class TextEncoderWrapper(torch.nn.Module):
+        def __init__(self, encoder_ir, config):
+            super().__init__()
+            self.encoder = core.compile_model(encoder_ir, device.value)
+            self.config = config
+    
+        def forward(self, input_ids, **kwargs):
+            last_hidden_state = self.encoder(input_ids)[self.encoder.outputs[0]]
+            last_hidden_state = torch.tensor(last_hidden_state)
+            return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=last_hidden_state)
+    
+    
+    class MusicGenWrapper(torch.nn.Module):
+        def __init__(
+            self,
+            music_gen_lm_0_ir,
+            music_gen_lm_ir,
+            config,
+            num_codebooks,
+            build_delay_pattern_mask,
+            apply_delay_pattern_mask,
+        ):
+            super().__init__()
+            self.music_gen_lm_0 = core.compile_model(music_gen_lm_0_ir, device.value)
+            self.music_gen_lm = core.compile_model(music_gen_lm_ir, device.value)
+            self.config = config
+            self.num_codebooks = num_codebooks
+            self.build_delay_pattern_mask = build_delay_pattern_mask
+            self.apply_delay_pattern_mask = apply_delay_pattern_mask
+    
+        def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            encoder_hidden_states: torch.FloatTensor = None,
+            encoder_attention_mask: torch.LongTensor = None,
+            past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
+            **kwargs
+        ):
+            if past_key_values is None:
+                model = self.music_gen_lm_0
+                arguments = (input_ids, encoder_hidden_states, encoder_attention_mask)
+            else:
+                model = self.music_gen_lm
+                arguments = (
+                    input_ids,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    *past_key_values,
+                )
+    
+            output = model(arguments)
+            return CausalLMOutputWithCrossAttentions(
+                logits=torch.tensor(output[model.outputs[0]]),
+                past_key_values=tuple([output[model.outputs[i]] for i in range(1, 97)]),
+            )
+    
+    
+    class AudioDecoderWrapper(torch.nn.Module):
+        def __init__(self, decoder_ir, config):
+            super().__init__()
+            self.decoder = core.compile_model(decoder_ir, device.value)
+            self.config = config
+            self.output_type = namedtuple("AudioDecoderOutput", ["audio_values"])
+    
+        def decode(self, output_ids, audio_scales):
+            output = self.decoder(output_ids)[self.decoder.outputs[0]]
+            return self.output_type(audio_values=torch.tensor(output))
+
+Now we initialize the wrapper objects and load them to the HF pipeline
+
+.. code:: ipython3
+
+    text_encode_ov = TextEncoderWrapper(t5_ir_path, model.text_encoder.config)
+    musicgen_decoder_ov = MusicGenWrapper(
+        musicgen_0_ir_path,
+        musicgen_ir_path,
+        model.decoder.config,
+        model.decoder.num_codebooks,
+        model.decoder.build_delay_pattern_mask,
+        model.decoder.apply_delay_pattern_mask,
+    )
+    audio_encoder_ov = AudioDecoderWrapper(audio_decoder_ir_path, model.audio_encoder.config)
+    
+    del model.text_encoder
+    del model.decoder
+    del model.audio_encoder
+    gc.collect()
+    
+    model.text_encoder = text_encode_ov
+    model.decoder = musicgen_decoder_ov
+    model.audio_encoder = audio_encoder_ov
+    
+    
+    def prepare_inputs_for_generation(
+        self,
+        decoder_input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        head_mask=None,
+        decoder_attention_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
+        use_cache=None,
+        encoder_outputs=None,
+        decoder_delay_pattern_mask=None,
+        guidance_scale=None,
+        **kwargs,
+    ):
+        if decoder_delay_pattern_mask is None:
+            (
+                decoder_input_ids,
+                decoder_delay_pattern_mask,
+            ) = self.decoder.build_delay_pattern_mask(
+                decoder_input_ids,
+                self.generation_config.pad_token_id,
+                max_length=self.generation_config.max_length,
+            )
+    
+        # apply the delay pattern mask
+        decoder_input_ids = self.decoder.apply_delay_pattern_mask(decoder_input_ids, decoder_delay_pattern_mask)
+    
+        if guidance_scale is not None and guidance_scale > 1:
+            # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these
+            # before sampling)
+            decoder_input_ids = decoder_input_ids.repeat((2, 1))
+            if decoder_attention_mask is not None:
+                decoder_attention_mask = decoder_attention_mask.repeat((2, 1))
+    
+        if past_key_values is not None:
+            # cut decoder_input_ids if past is used
+            decoder_input_ids = decoder_input_ids[:, -1:]
+    
+        return {
+            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
+            "encoder_outputs": encoder_outputs,
+            "past_key_values": past_key_values,
+            "decoder_input_ids": decoder_input_ids,
+            "attention_mask": attention_mask,
+            "decoder_attention_mask": decoder_attention_mask,
+            "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
+            "use_cache": use_cache,
+        }
+    
+    
+    model.prepare_inputs_for_generation = partial(prepare_inputs_for_generation, model)
+
+We can now infer the pipeline backed by OpenVINO models.
+
+.. code:: ipython3
+
+    processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
+    
+    inputs = processor(
+        text=["80s pop track with bassy drums and synth"],
+        return_tensors="pt",
+    )
+    
+    audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=n_tokens)
+    
+    Audio(audio_values[0].cpu().numpy(), rate=sampling_rate)
+
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+Try out the converted pipeline
+------------------------------
+
+
+
+The demo app below is created using `Gradio
+package `__
+
+.. code:: ipython3
+
+    def _generate(prompt):
+        inputs = processor(
+            text=[
+                prompt,
+            ],
+            padding=True,
+            return_tensors="pt",
+        )
+        audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=n_tokens)
+        waveform = audio_values[0].cpu().squeeze() * 2**15
+        return (sampling_rate, waveform.numpy().astype(np.int16))
+
+.. code:: ipython3
+
+    import gradio as gr
+    
+    demo = gr.Interface(
+        _generate,
+        inputs=[
+            gr.Textbox(label="Text Prompt"),
+        ],
+        outputs=["audio"],
+        examples=[
+            ["80s pop track with bassy drums and synth"],
+            ["Earthy tones, environmentally conscious, ukulele-infused, harmonic, breezy, easygoing, organic instrumentation, gentle grooves"],
+            ["90s rock song with loud guitars and heavy drums"],
+            ["Heartful EDM with beautiful synths and chords"],
+        ],
+    )
+    try:
+        demo.launch(debug=False)
+    except Exception:
+        demo.launch(share=True, debug=False)
+    
+    # If you are launching remotely, specify server_name and server_port
+    # EXAMPLE: `demo.launch(server_name='your server name', server_port='server port in int')`
+    # To learn more please refer to the Gradio docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+    
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
diff --git a/docs/notebooks/named-entity-recognition-with-output.rst b/docs/notebooks/named-entity-recognition-with-output.rst
new file mode 100644
index 00000000000..8572c7a0968
--- /dev/null
+++ b/docs/notebooks/named-entity-recognition-with-output.rst
@@ -0,0 +1,440 @@
+Named entity recognition with OpenVINO™
+=======================================
+
+The Named Entity Recognition(NER) is a natural language processing
+method that involves the detecting of key information in the
+unstructured text and categorizing it into pre-defined categories. These
+categories or named entities refer to the key subjects of text, such as
+names, locations, companies and etc.
+
+NER is a good method for the situations when a high-level overview of a
+large amount of text is needed. NER can be helpful with such task as
+analyzing key information in unstructured text or automates the
+information extraction of large amounts of data.
+
+This tutorial shows how to perform named entity recognition using
+OpenVINO. We will use the pre-trained model
+`elastic/distilbert-base-cased-finetuned-conll03-english `__.
+It is DistilBERT based model, trained on
+`conll03 english dataset `__.
+The model can recognize four named entities in text: persons, locations,
+organizations and names of miscellaneous entities that do not belong to
+the previous three groups. The model is sensitive to capital letters.
+
+To simplify the user experience, the `Hugging Face
+Optimum `__ library is used to
+convert the model to OpenVINO™ IR format and quantize it.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+-  `Download the NER model <#download-the-ner-model>`__
+-  `Quantize the model, using Hugging Face Optimum
+   API <#quantize-the-model-using-hugging-face-optimum-api>`__
+-  `Compare the Original and Quantized
+   Models <#compare-the-original-and-quantized-models>`__
+
+   -  `Compare performance <#compare-performance>`__
+   -  `Compare size of the models <#compare-size-of-the-models>`__
+
+-  `Prepare demo for Named Entity Recognition OpenVINO
+   Runtime <#prepare-demo-for-named-entity-recognition-openvino-runtime>`__
+
+Prerequisites
+-------------
+
+
+
+.. code:: ipython3
+
+    %pip install -q "diffusers>=0.17.1" "openvino>=2023.1.0" "nncf>=2.5.0" "gradio>=4.19" "onnx>=1.11.0" "transformers>=4.33.0" "torch>=2.1" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "git+https://github.com/huggingface/optimum-intel.git"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Download the NER model
+----------------------
+
+
+
+We load the
+`distilbert-base-cased-finetuned-conll03-english `__
+model from the `Hugging Face Hub `__ with
+`Hugging Face Transformers
+library `__\ and Optimum
+Intel with OpenVINO integration.
+
+``OVModelForTokenClassification`` is represent model class for Named
+Entity Recognition task in Optimum Intel. Model class initialization
+starts with calling ``from_pretrained`` method. For conversion original
+PyTorch model to OpenVINO format on the fly, ``export=True`` parameter
+should be used. To easily save the model, you can use the
+``save_pretrained()`` method. After saving the model on disk, we can use
+pre-converted model for next usage, and speedup deployment process.
+
+.. code:: ipython3
+
+    from pathlib import Path
+    from transformers import AutoTokenizer
+    from optimum.intel import OVModelForTokenClassification
+    
+    original_ner_model_dir = Path("original_ner_model")
+    
+    model_id = "elastic/distilbert-base-cased-finetuned-conll03-english"
+    if not original_ner_model_dir.exists():
+        model = OVModelForTokenClassification.from_pretrained(model_id, export=True)
+    
+        model.save_pretrained(original_ner_model_dir)
+    else:
+        model = OVModelForTokenClassification.from_pretrained(model_id, export=True)
+    
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+
+.. parsed-literal::
+
+    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
+
+
+.. parsed-literal::
+
+    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
+    2024-04-05 18:35:04.594311: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-05 18:35:04.596755: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
+    2024-04-05 18:35:04.628293: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+    2024-04-05 18:35:04.628326: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
+    2024-04-05 18:35:04.628349: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+    2024-04-05 18:35:04.634704: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
+    2024-04-05 18:35:04.635314: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    2024-04-05 18:35:05.607762: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /home/ea/miniconda3/lib/python3.11/site-packages/transformers/utils/import_utils.py:519: FutureWarning: `is_torch_tpu_available` is deprecated and will be removed in 4.41.0. Please use the `is_torch_xla_available` instead.
+      warnings.warn(
+    Framework not specified. Using pt to export the model.
+    Using the export variant default. Available variants are:
+        - default: The default ONNX variant.
+    Using framework PyTorch: 2.1.2+cpu
+    /home/ea/miniconda3/lib/python3.11/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+      warnings.warn(
+    /home/ea/miniconda3/lib/python3.11/site-packages/nncf/torch/dynamic_graph/wrappers.py:80: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      op1 = operator(\*args, \*\*kwargs)
+    Compiling the model to CPU ...
+
+
+Quantize the model, using Hugging Face Optimum API
+--------------------------------------------------
+
+
+
+Post-training static quantization introduces an additional calibration
+step where data is fed through the network in order to compute the
+activations quantization parameters. For quantization it will be used
+`Hugging Face Optimum Intel
+API `__.
+
+To handle the NNCF quantization process we use class
+`OVQuantizer `__.
+The quantization with Hugging Face Optimum Intel API contains the next
+steps: \* Model class initialization starts with calling
+``from_pretrained()`` method. \* Next we create calibration dataset with
+``get_calibration_dataset()`` to use for the post-training static
+quantization calibration step. \* After we quantize a model and save the
+resulting model in the OpenVINO IR format to save_directory with
+``quantize()`` method. \* Then we load the quantized model. The Optimum
+Inference models are API compatible with Hugging Face Transformers
+models and we can just replace ``AutoModelForXxx`` class with the
+corresponding ``OVModelForXxx`` class. So we use
+``OVModelForTokenClassification`` to load the model.
+
+.. code:: ipython3
+
+    from functools import partial
+    from optimum.intel import OVQuantizer, OVConfig, OVQuantizationConfig
+    
+    from optimum.intel import OVModelForTokenClassification
+    
+    
+    def preprocess_fn(data, tokenizer):
+        examples = []
+        for data_chunk in data["tokens"]:
+            examples.append(" ".join(data_chunk))
+    
+        return tokenizer(examples, padding=True, truncation=True, max_length=128)
+    
+    
+    quantizer = OVQuantizer.from_pretrained(model)
+    calibration_dataset = quantizer.get_calibration_dataset(
+        "conll2003",
+        preprocess_function=partial(preprocess_fn, tokenizer=tokenizer),
+        num_samples=100,
+        dataset_split="train",
+        preprocess_batch=True,
+    )
+    
+    # The directory where the quantized model will be saved
+    quantized_ner_model_dir = "quantized_ner_model"
+    
+    # Apply static quantization and save the resulting model in the OpenVINO IR format
+    ov_config = OVConfig(quantization_config=OVQuantizationConfig(num_samples=len(calibration_dataset)))
+    quantizer.quantize(
+        calibration_dataset=calibration_dataset,
+        save_directory=quantized_ner_model_dir,
+        ov_config=ov_config,
+    )
+
+
+.. parsed-literal::
+
+    /home/ea/miniconda3/lib/python3.11/site-packages/datasets/load.py:2516: FutureWarning: 'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.
+    You can remove this warning by passing 'token=' instead.
+      warnings.warn(
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. parsed-literal::
+
+    INFO:nncf:18 ignored nodes were found by name in the NNCFGraph
+    INFO:nncf:25 ignored nodes were found by name in the NNCFGraph
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    import openvino as ov
+    
+    core = ov.Core()
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=3, options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='AUTO')
+
+
+
+.. code:: ipython3
+
+    # Load the quantized model
+    optimized_model = OVModelForTokenClassification.from_pretrained(quantized_ner_model_dir, device=device.value)
+
+
+.. parsed-literal::
+
+    Compiling the model to AUTO ...
+
+
+Compare the Original and Quantized Models
+-----------------------------------------
+
+
+
+Compare the original
+`distilbert-base-cased-finetuned-conll03-english `__
+model with quantized and converted to OpenVINO IR format models to see
+the difference.
+
+Compare performance
+~~~~~~~~~~~~~~~~~~~
+
+
+
+As the Optimum Inference models are API compatible with Hugging Face
+Transformers models, we can just use ``pipleine()`` from `Hugging Face
+Transformers API `__ for
+inference.
+
+.. code:: ipython3
+
+    from transformers import pipeline
+    
+    ner_pipeline_optimized = pipeline("token-classification", model=optimized_model, tokenizer=tokenizer)
+    
+    ner_pipeline_original = pipeline("token-classification", model=model, tokenizer=tokenizer)
+
+.. code:: ipython3
+
+    import time
+    import numpy as np
+    
+    
+    def calc_perf(ner_pipeline):
+        inference_times = []
+    
+        for data in calibration_dataset:
+            text = " ".join(data["tokens"])
+            start = time.perf_counter()
+            ner_pipeline(text)
+            end = time.perf_counter()
+            inference_times.append(end - start)
+    
+        return np.median(inference_times)
+    
+    
+    print(f"Median inference time of quantized model: {calc_perf(ner_pipeline_optimized)} ")
+    
+    print(f"Median inference time of original model: {calc_perf(ner_pipeline_original)} ")
+
+
+.. parsed-literal::
+
+    Median inference time of quantized model: 0.0063508255407214165 
+    Median inference time of original model: 0.007429798366501927 
+
+
+Compare size of the models
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    from pathlib import Path
+    
+    fp_model_file = Path(original_ner_model_dir) / "openvino_model.bin"
+    print(f"Size of original model in Bytes is {fp_model_file.stat().st_size}")
+    print(f'Size of quantized model in Bytes is {Path(quantized_ner_model_dir, "openvino_model.bin").stat().st_size}')
+
+
+.. parsed-literal::
+
+    Size of original model in Bytes is 260795516
+    Size of quantized model in Bytes is 65802712
+
+
+Prepare demo for Named Entity Recognition OpenVINO Runtime
+----------------------------------------------------------
+
+
+
+Now, you can try NER model on own text. Put your sentence to input text
+box, click Submit button, the model label the recognized entities in the
+text.
+
+.. code:: ipython3
+
+    import gradio as gr
+    
+    examples = [
+        "My name is Wolfgang and I live in Berlin.",
+    ]
+    
+    
+    def run_ner(text):
+        output = ner_pipeline_optimized(text)
+        return {"text": text, "entities": output}
+    
+    
+    demo = gr.Interface(
+        run_ner,
+        gr.Textbox(placeholder="Enter sentence here...", label="Input Text"),
+        gr.HighlightedText(label="Output Text"),
+        examples=examples,
+        allow_flagging="never",
+    )
+    
+    if __name__ == "__main__":
+        try:
+            demo.launch(debug=False)
+        except Exception:
+            demo.launch(share=True, debug=False)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
diff --git a/docs/notebooks/notebook_utils-with-output.rst b/docs/notebooks/notebook_utils-with-output.rst
deleted file mode 100644
index fe056cc382f..00000000000
--- a/docs/notebooks/notebook_utils-with-output.rst
+++ /dev/null
@@ -1,564 +0,0 @@
-Notebook Utils
-==============
-
-This notebook contains helper functions and classes for use with
-OpenVINO™ Notebooks. The code is synchronized with the
-``notebook_utils.py`` file in the same directory as this notebook.
-
-There are four categories:
-
--  `Files <#files>`__
--  `Images <#images>`__
--  `Videos <#videos>`__
--  `Visualization <#visualization>`__
--  `Checks and Alerts <#checks-and-alerts>`__
-
-Each category contains a test cell that also shows how to use the
-functions in the section. #### Table of contents:
-
--  `Files <#files>`__
-
-   -  `Test File Functions <#test-file-functions>`__
-
--  `Images <#images>`__
-
-   -  `Convert Pixel Data <#convert-pixel-data>`__
-   -  `Test Data Conversion
-      Functions <#test-data-conversion-functions>`__
-
--  `Videos <#videos>`__
-
-   -  `Video Player <#video-player>`__
-   -  `Test Video Player <#test-video-player>`__
-
--  `Visualization <#visualization>`__
-
-   -  `Segmentation <#segmentation>`__
-   -  `Network Results <#network-results>`__
-   -  `Test Visualization Functions <#test-visualization-functions>`__
-
--  `Checks and Alerts <#checks-and-alerts>`__
-
-   -  `Test Alerts <#test-alerts>`__
-
-.. code:: ipython3
-
-    # Install requirements
-    %pip install -q "openvino>=2023.1.0" opencv-python
-    %pip install -q pillow tqdm requests matplotlib
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Files
------
-
-
-
-Load an image, download a file, download an OpenVINO IR model, and
-create a progress bar to show download progress.
-
-.. code:: ipython3
-
-    import os
-    import shutil
-    from PIL import Image
-    from notebook_utils import load_image, download_file, download_ir_model
-
-.. code:: ipython3
-
-    ??load_image
-
-.. code:: ipython3
-
-    ??download_file
-
-.. code:: ipython3
-
-    ??download_ir_model
-
-Test File Functions
-~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/segmentation.xml"
-    download_ir_model(model_url, "model")
-    
-    assert os.path.exists("model/segmentation.xml")
-    assert os.path.exists("model/segmentation.bin")
-
-
-
-.. parsed-literal::
-
-    model/segmentation.bin:   0%|          | 0.00/1.09M [00:00Hello, world!
-
-
-
-.. raw:: html
-
-    
Running this cell requires a GPU device, which is not available on this system. The following device is available: CPU
-
-
-.. code:: ipython3
-
-    assert check_device("CPU")
-
-.. code:: ipython3
-
-    if check_device("HELLOWORLD"):
-        print("Hello World device found.")
-
-
-
-.. raw:: html
-
-    
Running this cell requires a HELLOWORLD device, which is not available on this system. The following device is available: CPU
-
-
-.. code:: ipython3
-
-    check_openvino_version("2022.1");
-
-
-
-.. raw:: html
-
-    
This notebook requires OpenVINO 2022.1. The version on your system is: 2024.0.0-14509-34caeefd078-releases/2024/0.
Please run pip install --upgrade -r requirements.txt in the openvino_env environment to install this version. See the OpenVINO Notebooks README for detailed instructions
-
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diff --git a/docs/notebooks/notebooks_tags.json b/docs/notebooks/notebooks_tags.json
index f9bbba854d7..fc712c32db8 100644
--- a/docs/notebooks/notebooks_tags.json
+++ b/docs/notebooks/notebooks_tags.json
@@ -1,630 +1,11 @@
 {
-    "002-openvino-api": [
-        "ONNX",
-        "Pytorch",
-        "Reshape Model",
-        "Torchvision"
-    ],
-    "101-tensorflow-classification-to-openvino": [
-        "Tensorflow"
-    ],
-    "102-pytorch-onnx-to-openvino": [
-        "ONNX",
-        "Pytorch",
-        "Torchvision"
-    ],
-    "102-pytorch-to-openvino": [
-        "Pytorch",
-        "Torchvision"
-    ],
-    "103-paddle-to-openvino-classification": [
-        "Paddle"
-    ],
-    "104-model-tools": [
-        "Benchmark Model",
-        "Convert Model",
-        "Download Model",
-        "OMZ Info Dumper",
-        "ONNX"
-    ],
-    "105-language-quantize-bert": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "106-auto-device": [
-        "Async Inference",
-        "Pytorch",
-        "Torchvision"
-    ],
-    "107-speech-recognition-quantization-data2vec": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "107-speech-recognition-quantization-wav2vec2": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "108-gpu-device": [
-        "Async Inference",
-        "Benchmark Model",
-        "Optimize Model"
-    ],
-    "109-latency-tricks": [
-        "Benchmark Model",
-        "ONNX",
-        "Pytorch"
-    ],
-    "109-throughput-tricks": [
-        "Async Inference",
-        "Benchmark Model",
-        "ONNX",
-        "Pytorch"
-    ],
-    "110-ct-scan-live-inference": [
-        "Async Inference",
-        "Benchmark Model"
-    ],
-    "110-ct-segmentation-quantize-nncf": [
-        "Benchmark Model",
-        "NNCF",
-        "ONNX",
-        "Pytorch"
-    ],
-    "112-pytorch-post-training-quantization-nncf": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Torchvision"
-    ],
-    "113-image-classification-quantization": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Torchvision"
-    ],
-    "115-async-api": [
-        "Async Inference",
-        "Download Model"
-    ],
-    "116-sparsity-optimization": [
-        "Benchmark Model",
-        "Transformers"
-    ],
-    "117-model-server": [
-        "ONNX"
-    ],
-    "118-optimize-preprocessing": [
-        "Tensorflow"
-    ],
-    "119-tflite-to-openvino": [
-        "Benchmark Model"
-    ],
-    "121-convert-to-openvino": [
-        "Dynamic Shape",
-        "ONNX",
-        "Pytorch",
-        "Tensorflow",
-        "Torchvision",
-        "Transformers"
-    ],
-    "121-legacy-mo-convert-to-openvino": [
-        "ONNX",
-        "Pytorch",
-        "Torchvision",
-        "Transformers"
-    ],
-    "122-speech-recognition-quantization-wav2vec2": [
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "122-yolov8-quantization-with-accuracy-control": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch"
-    ],
-    "123-detectron2-to-openvino": [
-        "Pytorch"
-    ],
-    "124-hugging-face-hub": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "125-convnext-classification": [
-        "Pytorch",
-        "Torchvision"
-    ],
-    "125-lraspp-segmentation": [
-        "Pytorch",
-        "Torchvision"
-    ],
-    "126-tensorflow-hub": [
-        "Tensorflow"
-    ],
-    "127-tensorflow-bit-image-classification-nncf-quantization": [
-        "NNCF",
-        "Reshape Model",
-        "Tensorflow",
-        "Train Model"
-    ],
-    "128-opnevino-tokenizers": [
-        "Async Inference",
-        "Transformers"
-    ],
-    "203-meter-reader": [
-        "Dynamic Shape",
-        "Reshape Model"
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-    "204-segmenter-semantic-segmentation": [
-        "Benchmark Model",
-        "Pytorch",
-        "Torchvision"
-    ],
-    "205-vision-background-removal": [
-        "Pytorch"
-    ],
-    "206-vision-paddlegan-anime": [
-        "ONNX",
-        "Paddle"
-    ],
-    "207-vision-paddlegan-superresolution": [
-        "ONNX",
-        "Paddle"
-    ],
-    "208-optical-character-recognition": [
+    "3D-pose-estimation": [
         "Convert Model",
         "Download Model",
         "ONNX"
     ],
-    "210-slowfast-video-recognition": [
-        "Pytorch"
-    ],
-    "211-speech-to-text": [
-        "Convert Model",
-        "Download Model",
-        "Dynamic Shape",
-        "Pytorch",
-        "Reshape Model"
-    ],
-    "212-pyannote-speaker-diarization": [
-        "ONNX",
-        "Pytorch"
-    ],
-    "214-grammar-correction": [
-        "Async Inference",
-        "Transformers"
-    ],
-    "216-attention-center": [
-        "Tensorflow"
-    ],
-    "219-knowledge-graphs-conve": [
-        "Benchmark Model",
-        "Pytorch"
-    ],
-    "220-cross-lingual-books-alignment": [
-        "Async Inference",
-        "Pytorch",
-        "Transformers"
-    ],
-    "222-vision-image-colorization": [
-        "Convert Model",
-        "Download Model"
-    ],
-    "223-text-prediction": [
-        "Dynamic Shape",
-        "Pytorch",
-        "Transformers"
-    ],
-    "224-3D-segmentation-point-clouds": [
+    "3D-segmentation-point-clouds": [
         "Dynamic Shape",
         "ONNX"
-    ],
-    "225-stable-diffusion-text-to-image": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "226-yolov7-optimization": [
-        "Benchmark Model",
-        "NNCF",
-        "ONNX",
-        "Pytorch"
-    ],
-    "227-whisper-convert": [
-        "Pytorch"
-    ],
-    "227-whisper-nncf-quantize": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "228-clip-zero-shot-classification": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "229-distilbert-sequence-classification": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "230-yolov8-instance-segmentation": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Reshape Model"
-    ],
-    "230-yolov8-keypoint-detection": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Reshape Model"
-    ],
-    "230-yolov8-object-detection": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Reshape Model"
-    ],
-    "231-instruct-pix2pix-image-editing": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "232-clip-language-saliency-map": [
-        "Async Inference",
-        "Pytorch",
-        "Transformers"
-    ],
-    "233-blip-visual-language-processing": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "234-encodec-audio-compression": [
-        "Pytorch"
-    ],
-    "235-controlnet-stable-diffusion": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Reshape Model",
-        "Transformers"
-    ],
-    "236-stable-diffusion-v2-infinite-zoom": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "236-stable-diffusion-v2-text-to-image-demo": [
-        "Transformers"
-    ],
-    "236-stable-diffusion-v2-text-to-image": [
-        "Benchmark Model",
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-        "Pytorch",
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-    "237-segment-anything": [
-        "Benchmark Model",
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-        "Pytorch",
-        "Torchvision"
-    ],
-    "238-deep-floyd-if-convert": [
-        "Pytorch",
-        "Reshape Model"
-    ],
-    "238-deep-floyd-if-optimize": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Reshape Model"
-    ],
-    "239-image-bind-convert": [
-        "Pytorch"
-    ],
-    "239-image-bind-quantize": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch"
-    ],
-    "240-dolly-2-instruction-following": [
-        "Transformers"
-    ],
-    "241-riffusion-text-to-music": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "242-freevc-voice-conversion": [
-        "ONNX",
-        "Pytorch"
-    ],
-    "244-named-entity-recognition": [
-        "Transformers"
-    ],
-    "245-typo-detector": [
-        "Transformers"
-    ],
-    "246-depth-estimation-videpth": [
-        "Pytorch",
-        "Torchvision"
-    ],
-    "247-code-language-id": [
-        "ONNX",
-        "Transformers"
-    ],
-    "248-segmind-vegart": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "248-ssd-b1": [
-        "Pytorch"
-    ],
-    "248-stable-diffusion-xl": [
-        "Transformers"
-    ],
-    "249-oneformer-segmentation": [
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "250-music-generation": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "251-tiny-sd-image-generation": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "252-fastcomposer-image-generation": [
-        "Pytorch",
-        "Torchvision",
-        "Transformers"
-    ],
-    "253-zeroscope-text2video": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "254-llm-chatbot": [
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "254-rag-chatbot": [
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "255-mms-massively-multilingual-speech": [
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "256-bark-text-to-audio": [
-        "Dynamic Shape",
-        "Pytorch"
-    ],
-    "257-llava-multimodal-chatbot": [
-        "Async Inference",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "257-videollava-multimodal-chatbot": [
-        "Async Inference",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "258-blip-diffusion-subject-generation": [
-        "Dynamic Shape",
-        "Pytorch"
-    ],
-    "259-decidiffusion-image-generation": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "260-pix2struct-docvqa": [
-        "Async Inference",
-        "Transformers"
-    ],
-    "261-fast-segment-anything": [
-        "NNCF",
-        "ONNX",
-        "Pytorch"
-    ],
-    "262-softvc-voice-conversion": [
-        "Pytorch"
-    ],
-    "263-latent-consistency-models-image-generation": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "263-lcm-lora-controlnet": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "264-qrcode-monster": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "265-wuerstchen-image-generation": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "266-speculative-sampling": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "267-distil-whisper-asr": [
-        "Async Inference",
-        "NNCF",
-        "Transformers"
-    ],
-    "268-table-question-answering": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "269-film-slowmo": [
-        "Tensorflow"
-    ],
-    "270-sound-generation-audioldm2": [
-        "Pytorch"
-    ],
-    "271-sdxl-turbo": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "272-paint-by-example": [
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "273-stable-zephyr-3b-chatbot": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "274-efficient-sam": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch"
-    ],
-    "275-llm-question-answering": [
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-        "Transformers"
-    ],
-    "276-stable-diffusion-torchdynamo-backend": [
-        "Pytorch"
-    ],
-    "277-amused-lightweight-text-to-image": [
-        "Pytorch"
-    ],
-    "278-stable-diffusion-ip-adapter": [
-        "Pytorch",
-        "Transformers"
-    ],
-    "279-mobilevlm-language-assistant": [
-        "Async Inference",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
-    ],
-    "280-depth-anything": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Torchvision"
-    ],
-    "281-kosmos2-multimodal-large-language-model": [
-        "Async Inference",
-        "Pytorch",
-        "Transformers"
-    ],
-    "282-siglip-zero-shot-image-classification": [
-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Transformers"
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-    "283-photo-maker": [
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-    ],
-    "284-openvoice": [
-        "Pytorch"
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-    "285-surya-line-level-text-detection": [
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-        "NNCF",
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-        "Pytorch",
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-        "Pytorch",
-        "Reshape Model"
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-        "Async Inference",
-        "Pytorch",
-        "Torchvision",
-        "Transformers"
-    ],
-    "289-mobileclip-video-search": [
-        "Pytorch",
-        "Torchvision"
-    ],
-    "301-tensorflow-training-openvino-nncf": [
-        "Benchmark Model",
-        "NNCF",
-        "Tensorflow"
-    ],
-    "301-tensorflow-training-openvino": [
-        "Tensorflow",
-        "Train Model"
-    ],
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-        "Benchmark Model",
-        "NNCF",
-        "Pytorch",
-        "Torchvision",
-        "Train Model"
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-        "Benchmark Model",
-        "NNCF",
-        "Tensorflow",
-        "Train Model"
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-    "401-object-detection": [
-        "Optimize Model"
-    ],
-    "402-pose-estimation": [
-        "Dynamic Shape"
-    ],
-    "404-style-transfer": [
-        "ONNX"
-    ],
-    "405-paddle-ocr-webcam": [
-        "Dynamic Shape",
-        "Paddle",
-        "Reshape Model"
-    ],
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-        "Convert Model",
-        "Download Model",
-        "ONNX"
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-    "407-person-tracking": [
-        "Download Model",
-        "Dynamic Shape",
-        "Reshape Model"
     ]
 }
diff --git a/docs/notebooks/notebooks_with_binder_buttons.txt b/docs/notebooks/notebooks_with_binder_buttons.txt
index 91ef87ec6f6..1b2c6975e35 100644
--- a/docs/notebooks/notebooks_with_binder_buttons.txt
+++ b/docs/notebooks/notebooks_with_binder_buttons.txt
@@ -1,55 +1,49 @@
-001-hello-world
-002-openvino-api
-003-hello-segmentation
-004-hello-detection
-101-tensorflow-classification-to-openvino
-102-pytorch-onnx-to-openvino
-102-pytorch-onnx-to-openvino
-103-paddle-to-openvino-classification
-104-model-tools
-106-auto-device
-110-ct-scan-live-inference
-110-ct-scan-live-inference
-110-ct-scan-live-inference
-110-ct-scan-live-inference
-113-image-classification-quantization
-115-async-api
-120-tensorflow-instance-segmentation-to-openvino
-120-tensorflow-object-detection-to-openvino
-120-tensorflow-instance-segmentation-to-openvino
-120-tensorflow-object-detection-to-openvino
-121-convert-to-openvino
-121-convert-to-openvino
-123-detectron2-to-openvino
-124-hugging-face-hub
-128-opnevino-tokenizers
-201-vision-monodepth
-202-vision-superresolution-image
-202-vision-superresolution-video
-202-vision-superresolution-image
-202-vision-superresolution-video
-203-meter-reader
-205-vision-background-removal
-209-handwritten-ocr
-211-speech-to-text
-213-question-answering
-215-image-inpainting
-218-vehicle-detection-and-recognition
-219-knowledge-graphs-conve
-220-cross-lingual-books-alignment
-221-machine-translation
-222-vision-image-colorization
-224-3D-segmentation-point-clouds
-229-distilbert-sequence-classification
-243-tflite-selfie-segmentation
-250-music-generation
-261-fast-segment-anything
-280-depth-anything
-284-openvoice
-401-object-detection
-402-pose-estimation
-403-action-recognition-webcam
-404-style-transfer
-405-paddle-ocr-webcam
-406-3D-pose-estimation
-407-person-tracking
+3D-pose-estimation
+3D-segmentation-point-clouds
+action-recognition-webcam
+async-api
+auto-device
+convert-to-openvino
+cross-lingual-books-alignment
+ct-scan-live-inference
+depth-anything
+detectron2-to-openvino
+distilbert-sequence-classification
+fast-segment-anything
+handwritten-ocr
+hello-detection
+hello-segmentation
+hello-world
+hugging-face-hub
+image-classification-quantization
+image-inpainting
+knowledge-graphs-conve
+machine-translation
+magika-content-type-recognition
+meter-reader
+model-tools
+music-generation
+object-detection
+openvino-api
+openvino-tokenizers
+openvoice
+paddle-ocr-webcam
+paddle-to-openvino-classification
+person-tracking
+pose-estimation
+pytorch-onnx-to-openvino
+question-answering
+291-rmbg-background-removal
+speech-to-text
+style-transfer
+tensorflow-classification-to-openvino
+tensorflow-hub
+tensorflow-instance-segmentation-to-openvino
+tensorflow-object-detection-to-openvino
+tflite-selfie-segmentation
+vehicle-detection-and-recognition
+vision-background-removal
+vision-image-colorization
+vision-monodepth
+vision-superresolution-image
+vision-superresolution-video
diff --git a/docs/notebooks/notebooks_with_colab_buttons.txt b/docs/notebooks/notebooks_with_colab_buttons.txt
index b91884e8f10..7c144834866 100644
--- a/docs/notebooks/notebooks_with_colab_buttons.txt
+++ b/docs/notebooks/notebooks_with_colab_buttons.txt
@@ -1,87 +1,72 @@
-001-hello-world
-002-openvino-api
-003-hello-segmentation
-004-hello-detection
-101-tensorflow-classification-to-openvino
-102-pytorch-onnx-to-openvino
-102-pytorch-to-openvino
-102-pytorch-onnx-to-openvino
-102-pytorch-to-openvino
-103-paddle-to-openvino-classification
-104-model-tools
-105-language-quantize-bert
-106-auto-device
-107-speech-recognition-quantization-data2vec
-107-speech-recognition-quantization-data2vec
-109-latency-tricks
-109-throughput-tricks
-109-latency-tricks
-109-throughput-tricks
-113-image-classification-quantization
-115-async-api
-116-sparsity-optimization
-118-optimize-preprocessing
-119-tflite-to-openvino
-120-tensorflow-instance-segmentation-to-openvino
-120-tensorflow-object-detection-to-openvino
-120-tensorflow-instance-segmentation-to-openvino
-120-tensorflow-object-detection-to-openvino
-121-convert-to-openvino
-121-convert-to-openvino
-123-detectron2-to-openvino
-124-hugging-face-hub
-126-tensorflow-hub
-128-opnevino-tokenizers
-201-vision-monodepth
-202-vision-superresolution-image
-202-vision-superresolution-video
-202-vision-superresolution-image
-202-vision-superresolution-video
-204-segmenter-semantic-segmentation
-205-vision-background-removal
-206-vision-paddlegan-anime
-207-vision-paddlegan-superresolution
-208-optical-character-recognition
-209-handwritten-ocr
-211-speech-to-text
-213-question-answering
-216-attention-center
-219-knowledge-graphs-conve
-220-cross-lingual-books-alignment
-221-machine-translation
-223-text-prediction
-224-3D-segmentation-point-clouds
-227-whisper-convert
-227-whisper-convert
-229-distilbert-sequence-classification
-230-yolov8-instance-segmentation
-230-yolov8-keypoint-detection
-230-yolov8-object-detection
-230-yolov8-instance-segmentation
-230-yolov8-keypoint-detection
-230-yolov8-object-detection
-230-yolov8-instance-segmentation
-230-yolov8-keypoint-detection
-230-yolov8-object-detection
-232-clip-language-saliency-map
-243-tflite-selfie-segmentation
-244-named-entity-recognition
-250-music-generation
-251-tiny-sd-image-generation
-260-pix2struct-docvqa
-261-fast-segment-anything
-262-softvc-voice-conversion
-266-speculative-sampling
-268-table-question-answering
-277-amused-lightweight-text-to-image
-280-depth-anything
-282-siglip-zero-shot-image-classification
-284-openvoice
-285-surya-line-level-text-detection
-288-grounded-segment-anything
-289-mobileclip-video-search
-305-tensorflow-quantization-aware-training
-401-object-detection
-404-style-transfer
-405-paddle-ocr-webcam
-407-person-tracking
+3D-segmentation-point-clouds
+amused-lightweight-text-to-image
+async-api
+attention-center
+auto-device
+clip-language-saliency-map
+convert-to-openvino
+cross-lingual-books-alignment
+depth-anything
+detectron2-to-openvino
+distilbert-sequence-classification
+fast-segment-anything
+grounded-segment-anything
+handwritten-ocr
+hello-detection
+hello-segmentation
+hello-world
+hugging-face-hub
+image-classification-quantization
+knowledge-graphs-conve
+language-quantize-bert
+machine-translation
+magika-content-type-recognition
+mobileclip-video-search
+model-tools
+music-generation
+named-entity-recognition
+object-detection
+openvino-api
+openvino-tokenizers
+optical-character-recognition
+optimize-preprocessing
+paddle-ocr-webcam
+paddle-to-openvino-classification
+latency-tricks
+throughput-tricks
+person-tracking
+pix2struct-docvqa
+pytorch-onnx-to-openvino
+pytorch-to-openvino
+question-answering
+291-rmbg-background-removal
+segmenter-semantic-segmentation
+siglip-zero-shot-image-classification
+softvc-voice-conversion
+sparsity-optimization
+speculative-sampling
+speech-recognition-quantization-data2vec
+speech-to-text
+style-transfer
+surya-line-level-text-detection
+table-question-answering
+tensorflow-classification-to-openvino
+tensorflow-hub
+tensorflow-instance-segmentation-to-openvino
+tensorflow-object-detection-to-openvino
+tensorflow-quantization-aware-training
+text-prediction
+tflite-selfie-segmentation
+tflite-to-openvino
+tiny-sd-image-generation
+vision-background-removal
+vision-monodepth
+vision-paddlegan-anime
+vision-paddlegan-superresolution
+vision-superresolution-image
+vision-superresolution-video
+whisper-convert
+yolov8-instance-segmentation
+yolov8-keypoint-detection
+yolov8-obb
+yolov8-object-detection
diff --git a/docs/notebooks/401-object-detection-with-output.rst b/docs/notebooks/object-detection-with-output.rst
similarity index 79%
rename from docs/notebooks/401-object-detection-with-output.rst
rename to docs/notebooks/object-detection-with-output.rst
index e41434f9b75..95510bc8bb0 100644
--- a/docs/notebooks/401-object-detection-with-output.rst
+++ b/docs/notebooks/object-detection-with-output.rst
@@ -56,17 +56,14 @@ Install requirements
     %pip install -q opencv-python requests tqdm
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     
+    open("notebook_utils.py", "w").write(r.text)
+
 
 .. parsed-literal::
 
@@ -75,7 +72,11 @@ Install requirements
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    magika 0.5.1 requires numpy<2.0,>=1.24; python_version >= "3.8" and python_version < "3.9", but you have numpy 1.23.5 which is incompatible.
+    mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.2.2+cpu which is incompatible.
+    mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.17.2+cpu which is incompatible.
+    optimum-intel 1.17.0.dev0+aca2b6c requires transformers<4.40.0,>=4.36.0, but you have transformers 4.33.3 which is incompatible.
     
 
 .. parsed-literal::
@@ -83,11 +84,6 @@ Install requirements
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -97,7 +93,7 @@ Install requirements
 
 .. parsed-literal::
 
-    ('notebook_utils.py', )
+    21503
 
 
 
@@ -193,11 +189,11 @@ converted, this step is skipped.
     trans_config_path = Path(ov_tf_front.__file__).parent / "ssd_v2_support.json"
     if not converted_model_path.exists():
         ov_model = mo.convert_model(
-            tf_model_path, 
-            compress_to_fp16=(precision == 'FP16'), 
+            tf_model_path,
+            compress_to_fp16=(precision == "FP16"),
             transformations_config=trans_config_path,
-            tensorflow_object_detection_api_pipeline_config=tf_model_path.parent / "pipeline.config", 
-            reverse_input_channels=True
+            tensorflow_object_detection_api_pipeline_config=tf_model_path.parent / "pipeline.config",
+            reverse_input_channels=True,
         )
         ov.save_model(ov_model, converted_model_path)
         del ov_model
@@ -230,8 +226,8 @@ best performance. For that purpose, just use ``AUTO``.
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -300,18 +296,98 @@ threshold (0.5). Finally, draw boxes and labels inside them.
 
     # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
     classes = [
-        "background", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train",
-        "truck", "boat", "traffic light", "fire hydrant", "street sign", "stop sign",
-        "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant",
-        "bear", "zebra", "giraffe", "hat", "backpack", "umbrella", "shoe", "eye glasses",
-        "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
-        "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
-        "plate", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
-        "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair",
-        "couch", "potted plant", "bed", "mirror", "dining table", "window", "desk", "toilet",
-        "door", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
-        "toaster", "sink", "refrigerator", "blender", "book", "clock", "vase", "scissors",
-        "teddy bear", "hair drier", "toothbrush", "hair brush"
+        "background",
+        "person",
+        "bicycle",
+        "car",
+        "motorcycle",
+        "airplane",
+        "bus",
+        "train",
+        "truck",
+        "boat",
+        "traffic light",
+        "fire hydrant",
+        "street sign",
+        "stop sign",
+        "parking meter",
+        "bench",
+        "bird",
+        "cat",
+        "dog",
+        "horse",
+        "sheep",
+        "cow",
+        "elephant",
+        "bear",
+        "zebra",
+        "giraffe",
+        "hat",
+        "backpack",
+        "umbrella",
+        "shoe",
+        "eye glasses",
+        "handbag",
+        "tie",
+        "suitcase",
+        "frisbee",
+        "skis",
+        "snowboard",
+        "sports ball",
+        "kite",
+        "baseball bat",
+        "baseball glove",
+        "skateboard",
+        "surfboard",
+        "tennis racket",
+        "bottle",
+        "plate",
+        "wine glass",
+        "cup",
+        "fork",
+        "knife",
+        "spoon",
+        "bowl",
+        "banana",
+        "apple",
+        "sandwich",
+        "orange",
+        "broccoli",
+        "carrot",
+        "hot dog",
+        "pizza",
+        "donut",
+        "cake",
+        "chair",
+        "couch",
+        "potted plant",
+        "bed",
+        "mirror",
+        "dining table",
+        "window",
+        "desk",
+        "toilet",
+        "door",
+        "tv",
+        "laptop",
+        "mouse",
+        "remote",
+        "keyboard",
+        "cell phone",
+        "microwave",
+        "oven",
+        "toaster",
+        "sink",
+        "refrigerator",
+        "blender",
+        "book",
+        "clock",
+        "vase",
+        "scissors",
+        "teddy bear",
+        "hair drier",
+        "toothbrush",
+        "hair brush",
     ]
     
     # Colors for the classes above (Rainbow Color Map).
@@ -331,18 +407,14 @@ threshold (0.5). Finally, draw boxes and labels inside them.
         scores = []
         for _, label, score, xmin, ymin, xmax, ymax in results:
             # Create a box with pixels coordinates from the box with normalized coordinates [0,1].
-            boxes.append(
-                tuple(map(int, (xmin * w, ymin * h, (xmax - xmin) * w, (ymax - ymin) * h)))
-            )
+            boxes.append(tuple(map(int, (xmin * w, ymin * h, (xmax - xmin) * w, (ymax - ymin) * h))))
             labels.append(int(label))
             scores.append(float(score))
     
         # Apply non-maximum suppression to get rid of many overlapping entities.
         # See https://paperswithcode.com/method/non-maximum-suppression
         # This algorithm returns indices of objects to keep.
-        indices = cv2.dnn.NMSBoxes(
-            bboxes=boxes, scores=scores, score_threshold=thresh, nms_threshold=0.6
-        )
+        indices = cv2.dnn.NMSBoxes(bboxes=boxes, scores=scores, score_threshold=thresh, nms_threshold=0.6)
     
         # If there are no boxes.
         if len(indices) == 0:
@@ -390,16 +462,12 @@ file.
         player = None
         try:
             # Create a video player to play with target fps.
-            player = utils.VideoPlayer(
-                source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames
-            )
+            player = utils.VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
             # Start capturing.
             player.start()
             if use_popup:
                 title = "Press ESC to Exit"
-                cv2.namedWindow(
-                    winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE
-                )
+                cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
     
             processing_times = collections.deque()
             while True:
@@ -420,9 +488,7 @@ file.
                     )
     
                 # Resize the image and change dims to fit neural network input.
-                input_img = cv2.resize(
-                    src=frame, dsize=(width, height), interpolation=cv2.INTER_AREA
-                )
+                input_img = cv2.resize(src=frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
                 # Create a batch of images (size = 1).
                 input_img = input_img[np.newaxis, ...]
     
@@ -467,9 +533,7 @@ file.
                         break
                 else:
                     # Encode numpy array to jpg.
-                    _, encoded_img = cv2.imencode(
-                        ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100]
-                    )
+                    _, encoded_img = cv2.imencode(ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                     # Create an IPython image.
                     i = display.Image(data=encoded_img)
                     # Display the image in this notebook.
@@ -531,7 +595,7 @@ Run the object detection:
 
 
 
-.. image:: 401-object-detection-with-output_files/401-object-detection-with-output_19_0.png
+.. image:: object-detection-with-output_files/object-detection-with-output_19_0.png
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/object-detection-with-output_files/object-detection-with-output_19_0.png b/docs/notebooks/object-detection-with-output_files/object-detection-with-output_19_0.png
new file mode 100644
index 00000000000..600e198bfd5
--- /dev/null
+++ b/docs/notebooks/object-detection-with-output_files/object-detection-with-output_19_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b5fec4c23a0eb6bd04219e80f3a8d1abaacb24d50b44a6df123cc577e33fef5
+size 175119
diff --git a/docs/notebooks/249-oneformer-segmentation-with-output.rst b/docs/notebooks/oneformer-segmentation-with-output.rst
similarity index 88%
rename from docs/notebooks/249-oneformer-segmentation-with-output.rst
rename to docs/notebooks/oneformer-segmentation-with-output.rst
index b809244c88f..0a356d389e9 100644
--- a/docs/notebooks/249-oneformer-segmentation-with-output.rst
+++ b/docs/notebooks/oneformer-segmentation-with-output.rst
@@ -54,7 +54,7 @@ Install required libraries
 
     import platform
     
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "transformers>=4.26.0" "openvino>=2023.1.0" "nncf>=2.7.0" gradio torch scipy ipywidgets Pillow
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "transformers>=4.26.0" "openvino>=2023.1.0" "nncf>=2.7.0" "gradio>=4.19" "torch>=2.1" scipy ipywidgets Pillow tqdm
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -80,10 +80,11 @@ variables.
     import warnings
     from collections import defaultdict
     from pathlib import Path
-    import sys
     
     from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
-    from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
+    from transformers.models.oneformer.modeling_oneformer import (
+        OneFormerForUniversalSegmentationOutput,
+    )
     import torch
     import matplotlib.pyplot as plt
     import matplotlib.patches as mpatches
@@ -92,13 +93,20 @@ variables.
     
     import openvino
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 .. code:: ipython3
 
     IR_PATH = Path("oneformer.xml")
-    OUTPUT_NAMES = ['class_queries_logits', 'masks_queries_logits']
+    OUTPUT_NAMES = ["class_queries_logits", "masks_queries_logits"]
 
 Load OneFormer fine-tuned on COCO for universal segmentation
 ------------------------------------------------------------
@@ -139,7 +147,7 @@ images and post-process model outputs for visualization.
     shape = (800, 800)
     dummy_input = {
         "pixel_values": torch.randn(1, 3, *shape),
-        "task_inputs": torch.randn(1, task_seq_length)
+        "task_inputs": torch.randn(1, task_seq_length),
     }
 
 Convert the model to OpenVINO IR format
@@ -195,8 +203,8 @@ Select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -224,8 +232,8 @@ images and text to solve image segmentation.
         image = ImageOps.pad(image, shape)
         inputs = processor(image, [task], return_tensors="pt")
         converted = {
-            'pixel_values': inputs['pixel_values'],
-            'task_inputs': inputs['task_inputs']
+            "pixel_values": inputs["pixel_values"],
+            "task_inputs": inputs["task_inputs"],
         }
         return converted
 
@@ -233,9 +241,7 @@ images and text to solve image segmentation.
 
     def process_output(d):
         """Convert OpenVINO model output to HuggingFace representation for visualization"""
-        hf_kwargs = {
-            output_name: torch.tensor(d[output_name]) for output_name in OUTPUT_NAMES
-        }
+        hf_kwargs = {output_name: torch.tensor(d[output_name]) for output_name in OUTPUT_NAMES}
     
         return OneFormerForUniversalSegmentationOutput(**hf_kwargs)
 
@@ -259,15 +265,15 @@ the inference results.
         @staticmethod
         def extract_legend(handles):
             fig = plt.figure()
-            fig.legend(handles=handles, ncol=len(handles) // 20 + 1, loc='center')
+            fig.legend(handles=handles, ncol=len(handles) // 20 + 1, loc="center")
             fig.tight_layout()
             return fig
-        
+    
         @staticmethod
         def predicted_semantic_map_to_figure(predicted_map):
             segmentation = predicted_map[0]
             # get the used color map
-            viridis = plt.get_cmap('viridis', max(1, torch.max(segmentation)))
+            viridis = plt.get_cmap("viridis", max(1, torch.max(segmentation)))
             # get all the unique numbers
             labels_ids = torch.unique(segmentation).tolist()
             fig, ax = plt.subplots()
@@ -281,13 +287,13 @@ the inference results.
             fig_legend = Visualizer.extract_legend(handles=handles)
             fig.tight_layout()
             return fig, fig_legend
-            
+    
         @staticmethod
         def predicted_instance_map_to_figure(predicted_map):
-            segmentation = predicted_map[0]['segmentation']
-            segments_info = predicted_map[0]['segments_info']
+            segmentation = predicted_map[0]["segmentation"]
+            segments_info = predicted_map[0]["segments_info"]
             # get the used color map
-            viridis = plt.get_cmap('viridis', max(torch.max(segmentation), 1))
+            viridis = plt.get_cmap("viridis", max(torch.max(segmentation), 1))
             fig, ax = plt.subplots()
             ax.imshow(segmentation)
             ax.set_axis_off()
@@ -295,24 +301,24 @@ the inference results.
             handles = []
             # for each segment, draw its legend
             for segment in segments_info:
-                segment_id = segment['id']
-                segment_label_id = segment['label_id']
+                segment_id = segment["id"]
+                segment_label_id = segment["label_id"]
                 segment_label = id2label[segment_label_id]
                 label = f"{segment_label}-{instances_counter[segment_label_id]}"
                 instances_counter[segment_label_id] += 1
                 color = viridis(segment_id)
                 handles.append(mpatches.Patch(color=color, label=label))
-                
+    
             fig_legend = Visualizer.extract_legend(handles)
             fig.tight_layout()
             return fig, fig_legend
     
         @staticmethod
         def predicted_panoptic_map_to_figure(predicted_map):
-            segmentation = predicted_map[0]['segmentation']
-            segments_info = predicted_map[0]['segments_info']
+            segmentation = predicted_map[0]["segmentation"]
+            segments_info = predicted_map[0]["segments_info"]
             # get the used color map
-            viridis = plt.get_cmap('viridis', max(torch.max(segmentation), 1))
+            viridis = plt.get_cmap("viridis", max(torch.max(segmentation), 1))
             fig, ax = plt.subplots()
             ax.imshow(segmentation)
             ax.set_axis_off()
@@ -320,21 +326,24 @@ the inference results.
             handles = []
             # for each segment, draw its legend
             for segment in segments_info:
-                segment_id = segment['id']
-                segment_label_id = segment['label_id']
+                segment_id = segment["id"]
+                segment_label_id = segment["label_id"]
                 segment_label = id2label[segment_label_id]
                 label = f"{segment_label}-{instances_counter[segment_label_id]}"
                 instances_counter[segment_label_id] += 1
                 color = viridis(segment_id)
                 handles.append(mpatches.Patch(color=color, label=label))
-                
+    
             fig_legend = Visualizer.extract_legend(handles)
             fig.tight_layout()
             return fig, fig_legend
     
         @staticmethod
         def figures_to_images(fig, fig_legend, name_suffix=""):
-            seg_filename, leg_filename = f"segmentation{name_suffix}.png", f"legend{name_suffix}.png"
+            seg_filename, leg_filename = (
+                f"segmentation{name_suffix}.png",
+                f"legend{name_suffix}.png",
+            )
             fig.savefig(seg_filename, bbox_inches="tight")
             fig_legend.savefig(leg_filename, bbox_inches="tight")
             segmentation = Image.open(seg_filename)
@@ -358,9 +367,7 @@ the inference results.
         inputs = prepare_inputs(img, task)
         outputs = model(inputs)
         hf_output = process_output(outputs)
-        predicted_map = getattr(processor, f"post_process_{task}_segmentation")(
-            hf_output, target_sizes=[img.size[::-1]]
-        )
+        predicted_map = getattr(processor, f"post_process_{task}_segmentation")(hf_output, target_sizes=[img.size[::-1]])
         return getattr(Visualizer, f"predicted_{task}_map_to_figure")(predicted_map)
 
 .. code:: ipython3
@@ -378,7 +385,7 @@ the inference results.
 
 
 
-.. image:: 249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_23_1.png
+.. image:: oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_23_1.png
 
 
 
@@ -411,14 +418,17 @@ Inference
 .. code:: ipython3
 
     import matplotlib
+    
     matplotlib.use("Agg")  # disable showing figures
     
+    
     def stack_images_horizontally(img1: Image, img2: Image):
-        res = Image.new("RGB", (img1.width + img2.width, max(img1.height, img2.height)), (255, 255,255))
+        res = Image.new("RGB", (img1.width + img2.width, max(img1.height, img2.height)), (255, 255, 255))
         res.paste(img1, (0, 0))
         res.paste(img2, (img1.width, 0))
         return res
     
+    
     segmentation_fig, legend_fig = segment(compiled_model, image, task.value)
     segmentation_image, legend_image = Visualizer.figures_to_images(segmentation_fig, legend_fig)
     plt.close("all")
@@ -428,7 +438,7 @@ Inference
 
 
 
-.. image:: 249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_27_0.png
+.. image:: oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_27_0.png
 
 
 
@@ -461,7 +471,7 @@ improve model inference speed.
     
     to_quantize = widgets.Checkbox(
         value=False,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -481,8 +491,11 @@ not selected
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     %load_ext skip_kernel_extension
 
@@ -624,7 +637,7 @@ Let’s see quantized model prediction next to original model prediction.
 
 
 
-.. image:: 249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_39_1.png
+.. image:: oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_39_1.png
 
 
 .. parsed-literal::
@@ -633,7 +646,7 @@ Let’s see quantized model prediction next to original model prediction.
 
 
 
-.. image:: 249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_39_3.png
+.. image:: oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_39_3.png
 
 
 Compare model size and performance
@@ -725,6 +738,7 @@ Interactive Demo
         if quantized_model_present:
             compiled_quantized_model = core.compile_model(model=quantized_model, device_name=device)
     
+    
     def segment_wrapper(image, task, run_quantized=False):
         current_model = compiled_quantized_model if run_quantized else compiled_model
     
@@ -743,43 +757,44 @@ Interactive Demo
         with gr.Row():
             with gr.Column():
                 inp_img = gr.Image(label="Image", type="pil")
-                inp_task = gr.Radio(
-                    ["semantic", "instance", "panoptic"], label="Task", value="semantic"
-                )
-                inp_device = gr.Dropdown(
-                    label="Device", choices=core.available_devices + ["AUTO"], value="AUTO"
-                )
+                inp_task = gr.Radio(["semantic", "instance", "panoptic"], label="Task", value="semantic")
+                inp_device = gr.Dropdown(label="Device", choices=core.available_devices + ["AUTO"], value="AUTO")
             with gr.Column():
                 out_result = gr.Image(label="Result (Original)" if quantized_model_present else "Result")
                 inference_time = gr.Textbox(label="Time (seconds)")
                 out_result_quantized = gr.Image(label="Result (Quantized)", visible=quantized_model_present)
                 inference_time_quantized = gr.Textbox(label="Time (seconds)", visible=quantized_model_present)
         run_button = gr.Button(value="Run")
-        run_button.click(segment_wrapper, [inp_img, inp_task, gr.Number(0, visible=False)], [out_result, inference_time])
-        run_quantized_button = gr.Button(value="Run quantized", visible=quantized_model_present)
-        run_quantized_button.click(segment_wrapper, [inp_img, inp_task, gr.Number(1, visible=False)], [out_result_quantized, inference_time_quantized])
-        gr.Examples(
-            examples=[["sample.jpg", "semantic"]], inputs=[inp_img, inp_task]
+        run_button.click(
+            segment_wrapper,
+            [inp_img, inp_task, gr.Number(0, visible=False)],
+            [out_result, inference_time],
         )
-    
+        run_quantized_button = gr.Button(value="Run quantized", visible=quantized_model_present)
+        run_quantized_button.click(
+            segment_wrapper,
+            [inp_img, inp_task, gr.Number(1, visible=False)],
+            [out_result_quantized, inference_time_quantized],
+        )
+        gr.Examples(examples=[["sample.jpg", "semantic"]], inputs=[inp_img, inp_task])
     
         def on_device_change_begin():
             return (
                 run_button.update(value="Changing device...", interactive=False),
                 run_quantized_button.update(value="Changing device...", interactive=False),
-                inp_device.update(interactive=False)
+                inp_device.update(interactive=False),
             )
     
         def on_device_change_end():
             return (
                 run_button.update(value="Run", interactive=True),
                 run_quantized_button.update(value="Run quantized", interactive=True),
-                inp_device.update(interactive=True)
+                inp_device.update(interactive=True),
             )
     
-        inp_device.change(on_device_change_begin, outputs=[run_button, run_quantized_button, inp_device]).then(
-            compile_model, inp_device
-        ).then(on_device_change_end, outputs=[run_button, run_quantized_button, inp_device])
+        inp_device.change(on_device_change_begin, outputs=[run_button, run_quantized_button, inp_device]).then(compile_model, inp_device).then(
+            on_device_change_end, outputs=[run_button, run_quantized_button, inp_device]
+        )
     
     try:
         demo.launch(debug=False)
diff --git a/docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_23_1.jpg b/docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_23_1.jpg
similarity index 100%
rename from docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_23_1.jpg
rename to docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_23_1.jpg
diff --git a/docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_23_1.png b/docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_23_1.png
similarity index 100%
rename from docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_23_1.png
rename to docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_23_1.png
diff --git a/docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_27_0.jpg b/docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_27_0.jpg
similarity index 100%
rename from docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_27_0.jpg
rename to docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_27_0.jpg
diff --git a/docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_27_0.png b/docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_27_0.png
similarity index 100%
rename from docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_27_0.png
rename to docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_27_0.png
diff --git a/docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_39_1.jpg b/docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_39_1.jpg
similarity index 100%
rename from docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_39_1.jpg
rename to docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_39_1.jpg
diff --git a/docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_39_1.png b/docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_39_1.png
similarity index 100%
rename from docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_39_1.png
rename to docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_39_1.png
diff --git a/docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_39_3.jpg b/docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_39_3.jpg
similarity index 100%
rename from docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_39_3.jpg
rename to docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_39_3.jpg
diff --git a/docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_39_3.png b/docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_39_3.png
similarity index 100%
rename from docs/notebooks/249-oneformer-segmentation-with-output_files/249-oneformer-segmentation-with-output_39_3.png
rename to docs/notebooks/oneformer-segmentation-with-output_files/oneformer-segmentation-with-output_39_3.png
diff --git a/docs/notebooks/002-openvino-api-with-output.rst b/docs/notebooks/openvino-api-with-output.rst
similarity index 87%
rename from docs/notebooks/002-openvino-api-with-output.rst
rename to docs/notebooks/openvino-api-with-output.rst
index e88ae2e7add..7e24190b738 100644
--- a/docs/notebooks/002-openvino-api-with-output.rst
+++ b/docs/notebooks/openvino-api-with-output.rst
@@ -41,16 +41,18 @@ Table of contents:
 .. code:: ipython3
 
     # Required imports. Please execute this cell first.
-    %pip install -q "openvino>=2023.1.0" 
+    %pip install -q "openvino>=2023.1.0"
     %pip install -q requests tqdm ipywidgets
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file
 
 
@@ -110,7 +112,7 @@ inference using this widget
     device = widgets.Dropdown(
         options=core.available_devices,
         value=core.available_devices[0],
-        description='Device:',
+        description="Device:",
         disabled=False,
     )
     
@@ -165,19 +167,19 @@ to be more suitable for inference, for example, by alternating input
 shapes, embedding preprocessing and cutting training parts off. For
 information on how to convert your existing TensorFlow, PyTorch or ONNX
 model to OpenVINO IR format with model conversion API, refer to the
-`tensorflow-to-openvino <101-tensorflow-classification-to-openvino-with-output.html>`__
+`tensorflow-to-openvino `__
 and
-`pytorch-onnx-to-openvino <102-pytorch-onnx-to-openvino-with-output.html>`__
+`pytorch-onnx-to-openvino `__
 notebooks.
 
 .. code:: ipython3
 
-    ir_model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/'
-    ir_model_name_xml = 'classification.xml'
-    ir_model_name_bin = 'classification.bin'
+    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
+    ir_model_name_xml = "classification.xml"
+    ir_model_name_bin = "classification.bin"
     
-    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory='model')
-    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory='model')
+    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory="model")
+    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory="model")
 
 
 
@@ -196,7 +198,7 @@ notebooks.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/002-openvino-api/model/classification.bin')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.bin')
 
 
 
@@ -229,10 +231,10 @@ points to the filename of an ONNX model.
 
 .. code:: ipython3
 
-    onnx_model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/segmentation.onnx'
-    onnx_model_name = 'segmentation.onnx'
+    onnx_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/segmentation.onnx"
+    onnx_model_name = "segmentation.onnx"
     
-    download_file(onnx_model_url, filename=onnx_model_name, directory='model')
+    download_file(onnx_model_url, filename=onnx_model_name, directory="model")
 
 
 
@@ -245,7 +247,7 @@ points to the filename of an ONNX model.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/002-openvino-api/model/segmentation.onnx')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvino-api/model/segmentation.onnx')
 
 
 
@@ -277,12 +279,16 @@ without any conversion step. Pass the filename with extension to
 
 .. code:: ipython3
 
-    paddle_model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/'
-    paddle_model_name = 'inference.pdmodel'
-    paddle_params_name = 'inference.pdiparams'
+    paddle_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
+    paddle_model_name = "inference.pdmodel"
+    paddle_params_name = "inference.pdiparams"
     
-    download_file(paddle_model_url + paddle_model_name, filename=paddle_model_name, directory='model')
-    download_file(paddle_model_url + paddle_params_name, filename=paddle_params_name, directory='model')
+    download_file(paddle_model_url + paddle_model_name, filename=paddle_model_name, directory="model")
+    download_file(
+        paddle_model_url + paddle_params_name,
+        filename=paddle_params_name,
+        directory="model",
+    )
 
 
 
@@ -301,7 +307,7 @@ without any conversion step. Pass the filename with extension to
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/002-openvino-api/model/inference.pdiparams')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvino-api/model/inference.pdiparams')
 
 
 
@@ -310,7 +316,7 @@ without any conversion step. Pass the filename with extension to
     import openvino as ov
     
     core = ov.Core()
-    paddle_model_path = 'model/inference.pdmodel'
+    paddle_model_path = "model/inference.pdmodel"
     
     model_paddle = core.read_model(model=paddle_model_path)
     compiled_model_paddle = core.compile_model(model=model_paddle, device_name=device.value)
@@ -329,10 +335,10 @@ TensorFlow models saved in frozen graph format can also be passed to
 
 .. code:: ipython3
 
-    pb_model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/classification.pb'
-    pb_model_name = 'classification.pb'
+    pb_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/classification.pb"
+    pb_model_name = "classification.pb"
     
-    download_file(pb_model_url, filename=pb_model_name, directory='model')
+    download_file(pb_model_url, filename=pb_model_name, directory="model")
 
 
 
@@ -345,7 +351,7 @@ TensorFlow models saved in frozen graph format can also be passed to
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/002-openvino-api/model/classification.pb')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.pb')
 
 
 
@@ -381,10 +387,14 @@ It is pre-trained model optimized to work with TensorFlow Lite.
 
     from pathlib import Path
     
-    tflite_model_url = 'https://www.kaggle.com/models/tensorflow/inception/frameworks/tfLite/variations/v4-quant/versions/1?lite-format=tflite'
-    tflite_model_path = Path('model/classification.tflite')
+    tflite_model_url = "https://www.kaggle.com/models/tensorflow/inception/frameworks/tfLite/variations/v4-quant/versions/1?lite-format=tflite"
+    tflite_model_path = Path("model/classification.tflite")
     
-    download_file(tflite_model_url, filename=tflite_model_path.name, directory=tflite_model_path.parent)
+    download_file(
+        tflite_model_url,
+        filename=tflite_model_path.name,
+        directory=tflite_model_path.parent,
+    )
 
 
 
@@ -397,7 +407,7 @@ It is pre-trained model optimized to work with TensorFlow Lite.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/002-openvino-api/model/classification.tflite')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.tflite')
 
 
 
@@ -422,8 +432,8 @@ PyTorch Model
 `PyTorch `__ models can not be directly passed to
 ``core.read_model``. ``ov.Model`` for model objects from this framework
 can be obtained using ``ov.convert_model`` API. You can find more
-details in `pytorch-to-openvino <102-pytorch-to-openvino-with-output.html>`__
-notebook. In this tutorial we will use
+details in `pytorch-to-openvino <../pytorch-to-openvino>`__ notebook. In
+this tutorial we will use
 `resnet18 `__
 model form torchvision library. After conversion model using
 ``ov.convert_model``, it can be compiled on device using
@@ -460,12 +470,12 @@ Information about the inputs and outputs of the model are in
 
 .. code:: ipython3
 
-    ir_model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/'
-    ir_model_name_xml = 'classification.xml'
-    ir_model_name_bin = 'classification.bin'
+    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
+    ir_model_name_xml = "classification.xml"
+    ir_model_name_bin = "classification.bin"
     
-    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory='model')
-    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory='model')
+    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory="model")
+    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory="model")
 
 
 .. parsed-literal::
@@ -482,7 +492,7 @@ Information about the inputs and outputs of the model are in
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/002-openvino-api/model/classification.bin')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.bin')
 
 
 
@@ -634,7 +644,7 @@ Doing Inference on a Model
 
    **NOTE** this notebook demonstrates only the basic synchronous
    inference API. For an async inference example, please refer to `Async
-   API notebook <115-async-api-with-output.html>`__
+   API notebook `__
 
 The diagram below shows a typical inference pipeline with OpenVINO
 
@@ -670,12 +680,12 @@ produced data as values.
 
 .. code:: ipython3
 
-    ir_model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/'
-    ir_model_name_xml = 'classification.xml'
-    ir_model_name_bin = 'classification.bin'
+    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
+    ir_model_name_xml = "classification.xml"
+    ir_model_name_bin = "classification.bin"
     
-    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory='model')
-    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory='model')
+    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory="model")
+    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory="model")
 
 
 .. parsed-literal::
@@ -692,7 +702,7 @@ produced data as values.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/002-openvino-api/model/classification.bin')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.bin')
 
 
 
@@ -719,7 +729,7 @@ the input layout of the network.
     
     image_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_hollywood.jpg",
-        directory="data"
+        directory="data",
     )
     image = cv2.imread(str(image_filename))
     image.shape
@@ -835,7 +845,7 @@ from the output, use the ``.data`` parameter.
 The output shape is (1,1001), which is the expected output shape. This
 shape indicates that the network returns probabilities for 1001 classes.
 To learn more about this notion, refer to the `hello world
-notebook <001-hello-world-with-output.html>`__.
+notebook `__.
 
 Reshaping and Resizing
 ----------------------
@@ -857,12 +867,12 @@ input shape.
 
 .. code:: ipython3
 
-    ir_model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/'
-    ir_model_name_xml = 'segmentation.xml'
-    ir_model_name_bin = 'segmentation.bin'
+    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
+    ir_model_name_xml = "segmentation.xml"
+    ir_model_name_bin = "segmentation.bin"
     
-    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory='model')
-    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory='model')
+    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory="model")
+    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory="model")
 
 
 
@@ -881,7 +891,7 @@ input shape.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/002-openvino-api/model/segmentation.bin')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvino-api/model/segmentation.bin')
 
 
 
@@ -905,10 +915,7 @@ input shape.
     # help(segmentation_compiled_model)
     print("~~~~ RESHAPED MODEL ~~~~")
     print(f"model input shape: {segmentation_input_layer.shape}")
-    print(
-        f"compiled_model input shape: "
-        f"{segmentation_compiled_model.input(index=0).shape}"
-    )
+    print(f"compiled_model input shape: " f"{segmentation_compiled_model.input(index=0).shape}")
     print(f"compiled_model output shape: {segmentation_output_layer.shape}")
 
 
@@ -1022,12 +1029,12 @@ the cache.
 
 .. code:: ipython3
 
-    ir_model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/'
-    ir_model_name_xml = 'classification.xml'
-    ir_model_name_bin = 'classification.bin'
+    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
+    ir_model_name_xml = "classification.xml"
+    ir_model_name_bin = "classification.bin"
     
-    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory='model')
-    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory='model')
+    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory="model")
+    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory="model")
 
 
 .. parsed-literal::
@@ -1044,7 +1051,7 @@ the cache.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/002-openvino-api/model/classification.bin')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.bin')
 
 
 
@@ -1074,7 +1081,7 @@ the cache.
 
 .. parsed-literal::
 
-    Loading the network to the CPU device took 0.16 seconds.
+    Loading the network to the CPU device took 0.15 seconds.
 
 
 After running the previous cell, we know the model exists in the cache
@@ -1092,5 +1099,5 @@ measure the time it takes now.
 
 .. parsed-literal::
 
-    Loading the network to the CPU device took 0.09 seconds.
+    Loading the network to the CPU device took 0.07 seconds.
 
diff --git a/docs/notebooks/128-opnevino-tokenizers-with-output.rst b/docs/notebooks/openvino-tokenizers-with-output.rst
similarity index 78%
rename from docs/notebooks/128-opnevino-tokenizers-with-output.rst
rename to docs/notebooks/openvino-tokenizers-with-output.rst
index 5cd079c954e..65494af5ccc 100644
--- a/docs/notebooks/128-opnevino-tokenizers-with-output.rst
+++ b/docs/notebooks/openvino-tokenizers-with-output.rst
@@ -96,7 +96,8 @@ use ``pip install openvino-tokenizers[transformers]``.
 
     %pip install -Uq pip
     %pip uninstall -y openvino openvino-nightly openvino-dev
-    %pip install -Uq openvino-tokenizers[transformers]
+    %pip install --pre -Uq openvino-tokenizers[transformers] --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
+    %pip install "torch>=2.1" --extra-index-url https://download.pytorch.org/whl/cpu
 
 
 .. parsed-literal::
@@ -108,18 +109,24 @@ use ``pip install openvino-tokenizers[transformers]``.
 
     Found existing installation: openvino 2024.0.0
     Uninstalling openvino-2024.0.0:
+      Successfully uninstalled openvino-2024.0.0
 
 
 .. parsed-literal::
 
-      Successfully uninstalled openvino-2024.0.0
-    WARNING: Skipping openvino-nightly as it is not installed.
+    Found existing installation: openvino-nightly 2024.2.0.dev20240416
+    Uninstalling openvino-nightly-2024.2.0.dev20240416:
+      Successfully uninstalled openvino-nightly-2024.2.0.dev20240416
     Found existing installation: openvino-dev 2024.0.0
 
 
 .. parsed-literal::
 
     Uninstalling openvino-dev-2024.0.0:
+
+
+.. parsed-literal::
+
       Successfully uninstalled openvino-dev-2024.0.0
 
 
@@ -128,6 +135,36 @@ use ``pip install openvino-tokenizers[transformers]``.
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.2.2+cpu which is incompatible.
+    mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.17.2+cpu which is incompatible.
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cpu
+    Requirement already satisfied: torch>=2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2.2.2+cpu)
+    Requirement already satisfied: filelock in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.13.4)
+    Requirement already satisfied: typing-extensions>=4.8.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (4.11.0)
+    Requirement already satisfied: sympy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (1.12)
+    Requirement already satisfied: networkx in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1)
+    Requirement already satisfied: jinja2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1.3)
+    Requirement already satisfied: fsspec in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (2024.2.0)
+
+
+.. parsed-literal::
+
+    Requirement already satisfied: MarkupSafe>=2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jinja2->torch>=2.1) (2.1.5)
+    Requirement already satisfied: mpmath>=0.19 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from sympy->torch>=2.1) (1.3.0)
+
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -216,7 +253,7 @@ The other method is to pass HuggingFace ``hf_tokenizer`` object to
      ]>,
      
+     
      ]
      outputs[
      
@@ -287,14 +324,14 @@ one:
 
 .. parsed-literal::
 
-    2024-03-12 23:13:39.058345: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 23:13:39.093394: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 00:15:48.664233: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:15:48.699390: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 23:13:39.592323: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:15:49.303976: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -326,17 +363,17 @@ command is commented.
         # converting the original model
         # %pip install -U "git+https://github.com/huggingface/optimum-intel.git" "nncf>=2.8.0" onnx
         # %optimum-cli export openvino -m $model_id --task text-generation-with-past $model_dir
-        
+    
         # load already converted model
         from huggingface_hub import hf_hub_download
     
         hf_hub_download(
-            "chgk13/TinyLlama-1.1B-intermediate-step-1431k-3T", 
+            "chgk13/TinyLlama-1.1B-intermediate-step-1431k-3T",
             filename="openvino_model.xml",
             local_dir=model_dir,
         )
         hf_hub_download(
-            "chgk13/TinyLlama-1.1B-intermediate-step-1431k-3T", 
+            "chgk13/TinyLlama-1.1B-intermediate-step-1431k-3T",
             filename="openvino_model.bin",
             local_dir=model_dir,
         )
@@ -346,15 +383,20 @@ command is commented.
     import numpy as np
     from tqdm.notebook import trange
     from pathlib import Path
-    import openvino_tokenizers  # noqa: F401
-    from openvino import compile_model
+    from openvino_tokenizers import add_greedy_decoding
+    from openvino_tokenizers.constants import EOS_TOKEN_ID_NAME
+    from openvino import Core
     
     
-    compiled_model = compile_model(model_dir / "openvino_model.xml")
+    core = Core()
+    
+    # add the greedy decoding subgraph on top of LLM to get the most probable token as an output
+    ov_model = add_greedy_decoding(core.read_model(model_dir / "openvino_model.xml"))
+    compiled_model = core.compile_model(ov_model)
     infer_request = compiled_model.create_infer_request()
 
-The ``infer_reques``\ t object provides control over the model’s state -
-a Key-Value cache that speeds up inference by reducing computations
+The ``infer_request`` object provides control over the model’s state - a
+Key-Value cache that speeds up inference by reducing computations
 Multiple inference requests can be created, and each request maintains a
 distinct and separate state..
 
@@ -369,9 +411,9 @@ distinct and separate state..
     
     # no beam search, set idx to 0
     model_input["beam_idx"] = np.array([0], dtype=np.int32)
-    # end of sentence token is that model signifies the end of text generation will be available in next version,
-    # for now, can be obtained from the original tokenizer `original_tokenizer.eos_token_id`
-    eos_token = 2
+    # end of sentence token is that model signifies the end of text generation
+    # read EOS token ID from rt_info of tokenizer/detokenizer ov.Model object
+    eos_token = ov_tokenizer.get_rt_info(EOS_TOKEN_ID_NAME).value
     
     tokens_result = np.array([[]], dtype=np.int64)
     
@@ -383,17 +425,20 @@ distinct and separate state..
         infer_request.start_async(model_input)
         infer_request.wait()
     
-        # use greedy decoding to get the most probable token as the model prediction
-        output_token = np.argmax(infer_request.get_output_tensor().data[:, -1, :], axis=-1, keepdims=True)
+        # get a prediction for the last token on the first inference
+        output_token = infer_request.get_output_tensor().data[:, -1:]
         tokens_result = np.hstack((tokens_result, output_token))
-        if output_token[0][0] == eos_token:
+        if output_token[0, 0] == eos_token:
             break
-        
+    
         # prepare input for new inference
         model_input["input_ids"] = output_token
         model_input["attention_mask"] = np.hstack((model_input["attention_mask"].data, [[1]]))
         model_input["position_ids"] = np.hstack(
-            (model_input["position_ids"].data, [[model_input["position_ids"].data.shape[-1]]])
+            (
+                model_input["position_ids"].data,
+                [[model_input["position_ids"].data.shape[-1]]],
+            )
         )
     
     text_result = detokenizer(tokens_result)["string_output"]
@@ -485,12 +530,12 @@ models and tokenizers simplifies memory management.
 
 .. parsed-literal::
 
-    2024-03-12 23:13:56.117320: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:16:04.729219: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
 
-    OpenVINO Tokenizer version is not compatible with OpenVINO version. Installed OpenVINO version: 2024.0.0,OpenVINO Tokenizers requires . OpenVINO Tokenizers models will not be added during export.
+    OpenVINO Tokenizer version is not compatible with OpenVINO version. Installed OpenVINO version: 2024.2.0,OpenVINO Tokenizers requires . OpenVINO Tokenizers models will not be added during export.
 
 
 .. parsed-literal::
@@ -500,7 +545,8 @@ models and tokenizers simplifies memory management.
 
 .. parsed-literal::
 
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
 
 
 .. parsed-literal::
@@ -510,16 +556,10 @@ models and tokenizers simplifies memory management.
 
 .. parsed-literal::
 
-    Using the export variant default. Available variants are:
-        - default: The default ONNX variant.
-
-
-.. parsed-literal::
-
-    Using framework PyTorch: 2.1.0+cpu
+    Using framework PyTorch: 2.2.2+cpu
     Overriding 1 configuration item(s)
     	- use_cache -> False
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
@@ -539,12 +579,6 @@ models and tokenizers simplifies memory management.
 .. parsed-literal::
 
     Converting Huggingface Tokenizer to OpenVINO...
-    RegexNormalization pattern is not supported, operation output might differ from the original tokenizer.
-
-
-.. parsed-literal::
-
-    /tmp/tmpctejxcqg/build/third_party/re2/src/extern_re2/re2/re2.cc:205: Error parsing '((?=[^\n\t\r])\p{Cc})|((?=[^\n\t\r])\p{Cf})': invalid perl operator: (?=
 
 
 .. parsed-literal::
@@ -572,12 +606,6 @@ models and tokenizers simplifies memory management.
     print(f"Logits: {openvino_output['logits']}")
 
 
-.. parsed-literal::
-
-    /tmp/tmpctejxcqg/build/third_party/re2/src/extern_re2/re2/re2.cc:205: Error parsing '((?=[^\n\t\r])\p{Cc})|((?=[^\n\t\r])\p{Cf})': invalid perl operator: (?=
-    /tmp/tmpctejxcqg/build/third_party/re2/src/extern_re2/re2/re2.cc:205: Error parsing '((?=[^\n\t\r])\p{Cc})|((?=[^\n\t\r])\p{Cf})': invalid perl operator: (?=
-
-
 .. parsed-literal::
 
     Logits: [[ 1.2007061 -1.4698029]]
diff --git a/docs/notebooks/openvoice-with-output.rst b/docs/notebooks/openvoice-with-output.rst
new file mode 100644
index 00000000000..a0e386268ec
--- /dev/null
+++ b/docs/notebooks/openvoice-with-output.rst
@@ -0,0 +1,1378 @@
+Voice tone cloning with OpenVoice and OpenVINO
+==============================================
+
+OpenVoice is a versatile instant voice tone transferring and generating
+speech in various languages with just a brief audio snippet from the
+source speaker. OpenVoice has three main features: (i) high quality tone
+color replication with multiple languages and accents; (ii) it provides
+fine-tuned control over voice styles, including emotions, accents, as
+well as other parameters such as rhythm, pauses, and intonation. (iii)
+OpenVoice achieves zero-shot cross-lingual voice cloning, eliminating
+the need for the generated speech and the reference speech to be part of
+a massive-speaker multilingual training dataset.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/5703039/ca7eab80-148d-45b0-84e8-a5a279846b51
+   :alt: image
+
+   image
+
+More details about model can be found in `project web
+page `__,
+`paper `__, and official
+`repository `__
+
+This notebook provides example of converting `PyTorch OpenVoice
+model `__ to OpenVINO IR. In
+this tutorial we will explore how to convert and run OpenVoice using
+OpenVINO.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Clone repository and install
+   requirements <#clone-repository-and-install-requirements>`__
+-  `Download checkpoints and load PyTorch
+   model <#download-checkpoints-and-load-pytorch-model>`__
+-  `Convert models to OpenVINO IR <#convert-models-to-openvino-ir>`__
+-  `Inference <#inference>`__
+
+   -  `Select inference device <#select-inference-device>`__
+   -  `Select reference tone <#select-reference-tone>`__
+   -  `Run inference <#run-inference>`__
+
+-  `Run OpenVoice Gradio interactive
+   demo <#run-openvoice-gradio-interactive-demo>`__
+-  `Cleanup <#cleanup>`__
+
+Clone repository and install requirements
+-----------------------------------------
+
+
+
+.. code:: ipython3
+
+    import sys
+    from pathlib import Path
+    
+    repo_dir = Path("OpenVoice")
+    
+    if not repo_dir.exists():
+        !git clone https://github.com/myshell-ai/OpenVoice
+        orig_english_path = Path("OpenVoice/openvoice/text/_orig_english.py")
+        english_path = Path("OpenVoice/openvoice/text/english.py")
+        
+        english_path.rename(orig_english_path)
+        
+        with orig_english_path.open("r") as f:
+            data = f.read()
+            data = data.replace("unidecode", "anyascii")
+            with english_path.open("w") as out_f:
+                out_f.write(data)
+    # append to sys.path so that modules from the repo could be imported
+    sys.path.append(str(repo_dir))
+    
+    %pip install -q "librosa>=0.8.1" "wavmark>=0.0.3" "faster-whisper>=0.9.0" "pydub>=0.25.1" "whisper-timestamped>=1.14.2" "tqdm" "inflect>=7.0.0" "eng_to_ipa>=0.0.2" "pypinyin>=0.50.0" \
+    "cn2an>=0.5.22" "jieba>=0.42.1" "langid>=1.1.6" "gradio>=4.15" "ipywebrtc" "anyascii" "openvino>=2023.3" "torch>=2.1"
+
+
+.. parsed-literal::
+
+    Cloning into 'OpenVoice'...
+
+
+.. parsed-literal::
+
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+
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+
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+
+
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Download checkpoints and load PyTorch model
+-------------------------------------------
+
+
+
+.. code:: ipython3
+
+    import os
+    import torch
+    import openvino as ov
+    import ipywidgets as widgets
+    from IPython.display import Audio
+    
+    core = ov.Core()
+    
+    from openvoice.api import BaseSpeakerTTS, ToneColorConverter, OpenVoiceBaseClass
+    import openvoice.se_extractor as se_extractor
+
+
+.. parsed-literal::
+
+    Importing the dtw module. When using in academic works please cite:
+      T. Giorgino. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package.
+      J. Stat. Soft., doi:10.18637/jss.v031.i07.
+    
+
+
+.. code:: ipython3
+
+    CKPT_BASE_PATH = "checkpoints"
+    
+    en_suffix = f"{CKPT_BASE_PATH}/base_speakers/EN"
+    zh_suffix = f"{CKPT_BASE_PATH}/base_speakers/ZH"
+    converter_suffix = f"{CKPT_BASE_PATH}/converter"
+
+To make notebook lightweight by default model for Chinese speech is not
+activated, in order turn on please set flag ``enable_chinese_lang`` to
+True
+
+.. code:: ipython3
+
+    enable_chinese_lang = False
+
+.. code:: ipython3
+
+    def download_from_hf_hub(filename, local_dir="./"):
+        from huggingface_hub import hf_hub_download
+    
+        os.makedirs(local_dir, exist_ok=True)
+        hf_hub_download(repo_id="myshell-ai/OpenVoice", filename=filename, local_dir=local_dir)
+    
+    
+    download_from_hf_hub(f"{converter_suffix}/checkpoint.pth")
+    download_from_hf_hub(f"{converter_suffix}/config.json")
+    download_from_hf_hub(f"{en_suffix}/checkpoint.pth")
+    download_from_hf_hub(f"{en_suffix}/config.json")
+    
+    download_from_hf_hub(f"{en_suffix}/en_default_se.pth")
+    download_from_hf_hub(f"{en_suffix}/en_style_se.pth")
+    
+    if enable_chinese_lang:
+        download_from_hf_hub(f"{zh_suffix}/checkpoint.pth")
+        download_from_hf_hub(f"{zh_suffix}/config.json")
+        download_from_hf_hub(f"{zh_suffix}/zh_default_se.pth")
+
+.. code:: ipython3
+
+    pt_device = "cpu"
+    
+    en_base_speaker_tts = BaseSpeakerTTS(f"{en_suffix}/config.json", device=pt_device)
+    en_base_speaker_tts.load_ckpt(f"{en_suffix}/checkpoint.pth")
+    
+    tone_color_converter = ToneColorConverter(f"{converter_suffix}/config.json", device=pt_device)
+    tone_color_converter.load_ckpt(f"{converter_suffix}/checkpoint.pth")
+    
+    if enable_chinese_lang:
+        zh_base_speaker_tts = BaseSpeakerTTS(f"{zh_suffix}/config.json", device=pt_device)
+        zh_base_speaker_tts.load_ckpt(f"{zh_suffix}/checkpoint.pth")
+    else:
+        zh_base_speaker_tts = None
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+      warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+
+
+.. parsed-literal::
+
+    Loaded checkpoint 'checkpoints/base_speakers/EN/checkpoint.pth'
+    missing/unexpected keys: [] []
+
+
+.. parsed-literal::
+
+    Loaded checkpoint 'checkpoints/converter/checkpoint.pth'
+    missing/unexpected keys: [] []
+
+
+Convert models to OpenVINO IR
+-----------------------------
+
+
+
+There are 2 models in OpenVoice: first one is responsible for speech
+generation ``BaseSpeakerTTS`` and the second one ``ToneColorConverter``
+imposes arbitrary voice tone to the original speech. To convert to
+OpenVino IR format first we need to get acceptable ``torch.nn.Module``
+object. Both ToneColorConverter, BaseSpeakerTTS instead of using
+``self.forward`` as the main entry point use custom ``infer`` and
+``convert_voice`` methods respectively, therefore need to wrap them with
+a custom class that is inherited from torch.nn.Module.
+
+.. code:: ipython3
+
+    class OVOpenVoiceBase(torch.nn.Module):
+        """
+        Base class for both TTS and voice tone conversion model: constructor is same for both of them.
+        """
+    
+        def __init__(self, voice_model: OpenVoiceBaseClass):
+            super().__init__()
+            self.voice_model = voice_model
+            for par in voice_model.model.parameters():
+                par.requires_grad = False
+    
+    
+    class OVOpenVoiceTTS(OVOpenVoiceBase):
+        """
+        Constructor of this class accepts BaseSpeakerTTS object for speech generation and wraps it's 'infer' method with forward.
+        """
+    
+        def get_example_input(self):
+            stn_tst = self.voice_model.get_text("this is original text", self.voice_model.hps, False)
+            x_tst = stn_tst.unsqueeze(0)
+            x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
+            speaker_id = torch.LongTensor([1])
+            noise_scale = torch.tensor(0.667)
+            length_scale = torch.tensor(1.0)
+            noise_scale_w = torch.tensor(0.6)
+            return (
+                x_tst,
+                x_tst_lengths,
+                speaker_id,
+                noise_scale,
+                length_scale,
+                noise_scale_w,
+            )
+    
+        def forward(self, x, x_lengths, sid, noise_scale, length_scale, noise_scale_w):
+            return self.voice_model.model.infer(x, x_lengths, sid, noise_scale, length_scale, noise_scale_w)
+    
+    
+    class OVOpenVoiceConverter(OVOpenVoiceBase):
+        """
+        Constructor of this class accepts ToneColorConverter object for voice tone conversion and wraps it's 'voice_conversion' method with forward.
+        """
+    
+        def get_example_input(self):
+            y = torch.randn([1, 513, 238], dtype=torch.float32)
+            y_lengths = torch.LongTensor([y.size(-1)])
+            target_se = torch.randn(*(1, 256, 1))
+            source_se = torch.randn(*(1, 256, 1))
+            tau = torch.tensor(0.3)
+            return (y, y_lengths, source_se, target_se, tau)
+    
+        def forward(self, y, y_lengths, sid_src, sid_tgt, tau):
+            return self.voice_model.model.voice_conversion(y, y_lengths, sid_src, sid_tgt, tau)
+
+Convert to OpenVino IR and save to IRs_path folder for the future use.
+If IRs already exist skip conversion and read them directly
+
+.. code:: ipython3
+
+    IRS_PATH = "openvino_irs/"
+    EN_TTS_IR = f"{IRS_PATH}/openvoice_en_tts.xml"
+    ZH_TTS_IR = f"{IRS_PATH}/openvoice_zh_tts.xml"
+    VOICE_CONVERTER_IR = f"{IRS_PATH}/openvoice_tone_conversion.xml"
+    
+    paths = [EN_TTS_IR, VOICE_CONVERTER_IR]
+    models = [
+        OVOpenVoiceTTS(en_base_speaker_tts),
+        OVOpenVoiceConverter(tone_color_converter),
+    ]
+    if enable_chinese_lang:
+        models.append(OVOpenVoiceTTS(zh_base_speaker_tts))
+        paths.append(ZH_TTS_IR)
+    ov_models = []
+    
+    for model, path in zip(models, paths):
+        if not os.path.exists(path):
+            ov_model = ov.convert_model(model, example_input=model.get_example_input())
+            ov.save_model(ov_model, path)
+        else:
+            ov_model = core.read_model(path)
+        ov_models.append(ov_model)
+    
+    ov_en_tts, ov_voice_conversion = ov_models[:2]
+    if enable_chinese_lang:
+        ov_zh_tts = ov_models[-1]
+
+
+.. parsed-literal::
+
+    this is original text.
+     length:22
+     length:21
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:283: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      assert (
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:346: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      pad_length = max(length - (self.window_size + 1), 0)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:347: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      slice_start_position = max((self.window_size + 1) - length, 0)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:349: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if pad_length > 0:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:114: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if torch.min(inputs) < left or torch.max(inputs) > right:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:119: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if min_bin_width * num_bins > 1.0:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:121: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if min_bin_height * num_bins > 1.0:
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:171: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      assert (discriminant >= 0).all()
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
+    	%3293 : Float(1, 2, 43, strides=[86, 43, 1], requires_grad=0, device=cpu) = aten::randn(%3288, %3289, %3290, %3291, %3292) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/models.py:175:0
+    	%5559 : Float(1, 192, 156, strides=[29952, 1, 192], requires_grad=0, device=cpu) = aten::randn_like(%m_p, %5554, %5555, %5556, %5557, %5558) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/models.py:487:0
+    This may cause errors in trace checking. To disable trace checking, pass check_trace=False to torch.jit.trace()
+      _check_trace(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    The values for attribute 'shape' do not match: torch.Size([1, 1, 39680]) != torch.Size([1, 1, 38656]).
+      _check_trace(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Output nr 2. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    The values for attribute 'shape' do not match: torch.Size([1, 1, 155, 43]) != torch.Size([1, 1, 151, 43]).
+      _check_trace(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Output nr 3. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    The values for attribute 'shape' do not match: torch.Size([1, 1, 155]) != torch.Size([1, 1, 151]).
+      _check_trace(
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
+    	%1596 : Float(1, 192, 238, strides=[91392, 238, 1], requires_grad=0, device=cpu) = aten::randn_like(%m, %1591, %1592, %1593, %1594, %1595) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/models.py:220:0
+    This may cause errors in trace checking. To disable trace checking, pass check_trace=False to torch.jit.trace()
+      _check_trace(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    Tensor-likes are not close!
+    
+    Mismatched elements: 21517 / 60928 (35.3%)
+    Greatest absolute difference: 0.014645109884440899 at index (0, 0, 55667) (up to 1e-05 allowed)
+    Greatest relative difference: 17421.664948453606 at index (0, 0, 4696) (up to 1e-05 allowed)
+      _check_trace(
+
+
+Inference
+---------
+
+
+
+Select inference device
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    core = ov.Core()
+    
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+Select reference tone
+~~~~~~~~~~~~~~~~~~~~~
+
+
+
+First of all, select the reference tone of voice to which the generated
+text will be converted: your can select from existing ones, record your
+own by selecting ``record_manually`` or upload you own file by
+``load_manually``
+
+.. code:: ipython3
+
+    REFERENCE_VOICES_PATH = f"{repo_dir}/resources/"
+    reference_speakers = [
+        *[path for path in os.listdir(REFERENCE_VOICES_PATH) if os.path.splitext(path)[-1] == ".mp3"],
+        "record_manually",
+        "load_manually",
+    ]
+    
+    ref_speaker = widgets.Dropdown(
+        options=reference_speakers,
+        value=reference_speakers[0],
+        description="reference voice from which tone color will be copied",
+        disabled=False,
+    )
+    
+    ref_speaker
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='reference voice from which tone color will be copied', options=('demo_speaker2.mp3', 'de…
+
+
+
+.. code:: ipython3
+
+    OUTPUT_DIR = "outputs/"
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+.. code:: ipython3
+
+    ref_speaker_path = f"{REFERENCE_VOICES_PATH}/{ref_speaker.value}"
+    allowed_audio_types = ".mp4,.mp3,.wav,.wma,.aac,.m4a,.m4b,.webm"
+    
+    if ref_speaker.value == "record_manually":
+        ref_speaker_path = f"{OUTPUT_DIR}/custom_example_sample.webm"
+        from ipywebrtc import AudioRecorder, CameraStream
+    
+        camera = CameraStream(constraints={"audio": True, "video": False})
+        recorder = AudioRecorder(stream=camera, filename=ref_speaker_path, autosave=True)
+        display(recorder)
+    elif ref_speaker.value == "load_manually":
+        upload_ref = widgets.FileUpload(
+            accept=allowed_audio_types,
+            multiple=False,
+            description="Select audio with reference voice",
+        )
+        display(upload_ref)
+
+Play the reference voice sample before cloning it’s tone to another
+speech
+
+.. code:: ipython3
+
+    def save_audio(voice_source: widgets.FileUpload, out_path: str):
+        with open(out_path, "wb") as output_file:
+            assert len(voice_source.value) > 0, "Please select audio file"
+            output_file.write(voice_source.value[0]["content"])
+    
+    
+    if ref_speaker.value == "load_manually":
+        ref_speaker_path = f"{OUTPUT_DIR}/{upload_ref.value[0].name}"
+        save_audio(upload_ref, ref_speaker_path)
+
+.. code:: ipython3
+
+    Audio(ref_speaker_path)
+
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+Load speaker embeddings
+
+.. code:: ipython3
+
+    en_source_default_se = torch.load(f"{en_suffix}/en_default_se.pth")
+    en_source_style_se = torch.load(f"{en_suffix}/en_style_se.pth")
+    zh_source_se = torch.load(f"{zh_suffix}/zh_default_se.pth") if enable_chinese_lang else None
+    
+    target_se, audio_name = se_extractor.get_se(ref_speaker_path, tone_color_converter, target_dir=OUTPUT_DIR, vad=True)
+
+
+.. parsed-literal::
+
+    OpenVoice version: v1
+
+
+.. parsed-literal::
+
+    [(0.0, 8.178), (9.326, 12.914), (13.262, 16.402), (16.654, 29.49225)]
+
+
+.. parsed-literal::
+
+    after vad: dur = 27.743990929705216
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.
+    Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.)
+      return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
+
+
+Replace original infer methods of ``OpenVoiceBaseClass`` with optimized
+OpenVINO inference.
+
+There are pre and post processings that are not traceable and could not
+be offloaded to OpenVINO, instead of writing such processing ourselves
+we will rely on the already existing ones. We just replace infer and
+voice conversion functions of ``OpenVoiceBaseClass`` so that the the
+most computationally expensive part is done in OpenVINO.
+
+.. code:: ipython3
+
+    def get_pathched_infer(ov_model: ov.Model, device: str) -> callable:
+        compiled_model = core.compile_model(ov_model, device)
+    
+        def infer_impl(x, x_lengths, sid, noise_scale, length_scale, noise_scale_w):
+            ov_output = compiled_model((x, x_lengths, sid, noise_scale, length_scale, noise_scale_w))
+            return (torch.tensor(ov_output[0]),)
+    
+        return infer_impl
+    
+    
+    def get_patched_voice_conversion(ov_model: ov.Model, device: str) -> callable:
+        compiled_model = core.compile_model(ov_model, device)
+    
+        def voice_conversion_impl(y, y_lengths, sid_src, sid_tgt, tau):
+            ov_output = compiled_model((y, y_lengths, sid_src, sid_tgt, tau))
+            return (torch.tensor(ov_output[0]),)
+    
+        return voice_conversion_impl
+    
+    
+    en_base_speaker_tts.model.infer = get_pathched_infer(ov_en_tts, device.value)
+    tone_color_converter.model.voice_conversion = get_patched_voice_conversion(ov_voice_conversion, device.value)
+    if enable_chinese_lang:
+        zh_base_speaker_tts.model.infer = get_pathched_infer(ov_zh_tts, device.value)
+
+Run inference
+~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    voice_source = widgets.Dropdown(
+        options=["use TTS", "choose_manually"],
+        value="use TTS",
+        description="Voice source",
+        disabled=False,
+    )
+    
+    voice_source
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Voice source', options=('use TTS', 'choose_manually'), value='use TTS')
+
+
+
+.. code:: ipython3
+
+    if voice_source.value == "choose_manually":
+        upload_orig_voice = widgets.FileUpload(
+            accept=allowed_audio_types,
+            multiple=False,
+            description="audo whose tone will be replaced",
+        )
+        display(upload_orig_voice)
+
+.. code:: ipython3
+
+    if voice_source.value == "choose_manually":
+        orig_voice_path = f"{OUTPUT_DIR}/{upload_orig_voice.value[0].name}"
+        save_audio(upload_orig_voice, orig_voice_path)
+        source_se, _ = se_extractor.get_se(orig_voice_path, tone_color_converter, target_dir=OUTPUT_DIR, vad=True)
+    else:
+        text = """
+        OpenVINO toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve 
+        a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, 
+        recommendation systems, and many others.
+        """
+        source_se = en_source_default_se
+        orig_voice_path = f"{OUTPUT_DIR}/tmp.wav"
+        en_base_speaker_tts.tts(text, orig_voice_path, speaker="default", language="English")
+
+
+.. parsed-literal::
+
+     > Text splitted to sentences.
+    OpenVINO toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision,
+    automatic speech recognition, natural language processing, recommendation systems, and many others.
+     > ===========================
+    ˈoʊpən vino* toolkit* ɪz ə ˌkɑmpɹiˈhɛnsɪv toolkit* fəɹ kˈwɪkli dɪˈvɛləpɪŋ ˌæpləˈkeɪʃənz ənd səˈluʃənz ðət sɑɫv ə vəɹˈaɪəti əv tæsks ˌɪnˈkludɪŋ ˌɛmjəˈleɪʃən əv ˈjumən ˈvɪʒən,
+     length:173
+     length:173
+
+
+.. parsed-literal::
+
+    ˌɔtəˈmætɪk spitʃ ˌɹɛkɪgˈnɪʃən, ˈnætʃəɹəɫ ˈlæŋgwɪdʒ ˈpɹɑsɛsɪŋ, ˌɹɛkəmənˈdeɪʃən ˈsɪstəmz, ənd ˈmɛni ˈəðəɹz.
+     length:105
+     length:105
+
+
+And finally, run voice tone conversion with OpenVINO optimized model
+
+.. code:: ipython3
+
+    tau_slider = widgets.FloatSlider(
+        value=0.3,
+        min=0.01,
+        max=2.0,
+        step=0.01,
+        description="tau",
+        disabled=False,
+        readout_format=".2f",
+    )
+    tau_slider
+
+
+
+
+.. parsed-literal::
+
+    FloatSlider(value=0.3, description='tau', max=2.0, min=0.01, step=0.01)
+
+
+
+.. code:: ipython3
+
+    resulting_voice_path = f"{OUTPUT_DIR}/output_with_cloned_voice_tone.wav"
+    
+    tone_color_converter.convert(
+        audio_src_path=orig_voice_path,
+        src_se=source_se,
+        tgt_se=target_se,
+        output_path=resulting_voice_path,
+        tau=tau_slider.value,
+        message="@MyShell",
+    )
+
+.. code:: ipython3
+
+    Audio(orig_voice_path)
+
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+.. code:: ipython3
+
+    Audio(resulting_voice_path)
+
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+Run OpenVoice Gradio interactive demo
+-------------------------------------
+
+
+
+We can also use `Gradio `__ app to run TTS and
+voice tone conversion online.
+
+.. code:: ipython3
+
+    import gradio as gr
+    import langid
+    
+    supported_languages = ["zh", "en"]
+    
+    
+    def build_predict(
+        output_dir,
+        tone_color_converter,
+        en_tts_model,
+        zh_tts_model,
+        en_source_default_se,
+        en_source_style_se,
+        zh_source_se,
+    ):
+        def predict(prompt, style, audio_file_pth, agree):
+            return predict_impl(
+                prompt,
+                style,
+                audio_file_pth,
+                agree,
+                output_dir,
+                tone_color_converter,
+                en_tts_model,
+                zh_tts_model,
+                en_source_default_se,
+                en_source_style_se,
+                zh_source_se,
+            )
+    
+        return predict
+    
+    
+    def predict_impl(
+        prompt,
+        style,
+        audio_file_pth,
+        agree,
+        output_dir,
+        tone_color_converter,
+        en_tts_model,
+        zh_tts_model,
+        en_source_default_se,
+        en_source_style_se,
+        zh_source_se,
+    ):
+        text_hint = ""
+        if not agree:
+            text_hint += "[ERROR] Please accept the Terms & Condition!\n"
+            gr.Warning("Please accept the Terms & Condition!")
+            return (
+                text_hint,
+                None,
+                None,
+            )
+    
+        language_predicted = langid.classify(prompt)[0].strip()
+        print(f"Detected language:{language_predicted}")
+    
+        if language_predicted not in supported_languages:
+            text_hint += f"[ERROR] The detected language {language_predicted} for your input text is not in our Supported Languages: {supported_languages}\n"
+            gr.Warning(f"The detected language {language_predicted} for your input text is not in our Supported Languages: {supported_languages}")
+    
+            return (
+                text_hint,
+                None,
+            )
+    
+        if language_predicted == "zh":
+            tts_model = zh_tts_model
+            if zh_tts_model is None:
+                gr.Warning("TTS model for Chinece language was not loaded please set 'enable_chinese_lang=True`")
+                return (
+                    text_hint,
+                    None,
+                )
+            source_se = zh_source_se
+            language = "Chinese"
+            if style not in ["default"]:
+                text_hint += f"[ERROR] The style {style} is not supported for Chinese, which should be in ['default']\n"
+                gr.Warning(f"The style {style} is not supported for Chinese, which should be in ['default']")
+                return (
+                    text_hint,
+                    None,
+                )
+        else:
+            tts_model = en_tts_model
+            if style == "default":
+                source_se = en_source_default_se
+            else:
+                source_se = en_source_style_se
+            language = "English"
+            supported_styles = [
+                "default",
+                "whispering",
+                "shouting",
+                "excited",
+                "cheerful",
+                "terrified",
+                "angry",
+                "sad",
+                "friendly",
+            ]
+            if style not in supported_styles:
+                text_hint += f"[ERROR] The style {style} is not supported for English, which should be in {*supported_styles,}\n"
+                gr.Warning(f"The style {style} is not supported for English, which should be in {*supported_styles,}")
+                return (
+                    text_hint,
+                    None,
+                )
+    
+        speaker_wav = audio_file_pth
+    
+        if len(prompt) < 2:
+            text_hint += "[ERROR] Please give a longer prompt text \n"
+            gr.Warning("Please give a longer prompt text")
+            return (
+                text_hint,
+                None,
+            )
+        if len(prompt) > 200:
+            text_hint += (
+                "[ERROR] Text length limited to 200 characters for this demo, please try shorter text. You can clone our open-source repo and try for your usage \n"
+            )
+            gr.Warning("Text length limited to 200 characters for this demo, please try shorter text. You can clone our open-source repo for your usage")
+            return (
+                text_hint,
+                None,
+            )
+    
+        # note diffusion_conditioning not used on hifigan (default mode), it will be empty but need to pass it to model.inference
+        try:
+            target_se, audio_name = se_extractor.get_se(speaker_wav, tone_color_converter, target_dir=OUTPUT_DIR, vad=True)
+        except Exception as e:
+            text_hint += f"[ERROR] Get target tone color error {str(e)} \n"
+            gr.Warning("[ERROR] Get target tone color error {str(e)} \n")
+            return (
+                text_hint,
+                None,
+            )
+    
+        src_path = f"{output_dir}/tmp.wav"
+        tts_model.tts(prompt, src_path, speaker=style, language=language)
+    
+        save_path = f"{output_dir}/output.wav"
+        encode_message = "@MyShell"
+        tone_color_converter.convert(
+            audio_src_path=src_path,
+            src_se=source_se,
+            tgt_se=target_se,
+            output_path=save_path,
+            message=encode_message,
+        )
+    
+        text_hint += "Get response successfully \n"
+    
+        return (
+            text_hint,
+            src_path,
+            save_path,
+        )
+    
+    
+    description = """
+        # OpenVoice accelerated by OpenVINO:
+        
+        a versatile instant voice cloning approach that requires only a short audio clip from the reference speaker to replicate their voice and generate speech in multiple languages. OpenVoice enables granular control over voice styles, including emotion, accent, rhythm, pauses, and intonation, in addition to replicating the tone color of the reference speaker. OpenVoice also achieves zero-shot cross-lingual voice cloning for languages not included in the massive-speaker training set.
+    """
+    
+    content = """
+    

+    If the generated voice does not sound like the reference voice, please refer to this QnA. For multi-lingual & cross-lingual examples, please refer to this jupyter notebook.
+    This online demo mainly supports English. The default style also supports Chinese. But OpenVoice can adapt to any other language as long as a base speaker is provided.
+    

+    """
+    wrapped_markdown_content = f"
{content}
"
+    
+    
+    examples = [
+        [
+            "今天天气真好，我们一起出去吃饭吧。",
+            "default",
+            "OpenVoice/resources/demo_speaker1.mp3",
+            True,
+        ],
+        [
+            "This audio is generated by open voice with a half-performance model.",
+            "whispering",
+            "OpenVoice/resources/demo_speaker2.mp3",
+            True,
+        ],
+        [
+            "He hoped there would be stew for dinner, turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick, peppered, flour-fattened sauce.",
+            "sad",
+            "OpenVoice/resources/demo_speaker0.mp3",
+            True,
+        ],
+    ]
+    
+    
+    def get_demo(
+        output_dir,
+        tone_color_converter,
+        en_tts_model,
+        zh_tts_model,
+        en_source_default_se,
+        en_source_style_se,
+        zh_source_se,
+    ):
+        with gr.Blocks(analytics_enabled=False) as demo:
+            with gr.Row():
+                gr.Markdown(description)
+            with gr.Row():
+                gr.HTML(wrapped_markdown_content)
+    
+            with gr.Row():
+                with gr.Column():
+                    input_text_gr = gr.Textbox(
+                        label="Text Prompt",
+                        info="One or two sentences at a time is better. Up to 200 text characters.",
+                        value="He hoped there would be stew for dinner, turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick, peppered, flour-fattened sauce.",
+                    )
+                    style_gr = gr.Dropdown(
+                        label="Style",
+                        info="Select a style of output audio for the synthesised speech. (Chinese only support 'default' now)",
+                        choices=[
+                            "default",
+                            "whispering",
+                            "cheerful",
+                            "terrified",
+                            "angry",
+                            "sad",
+                            "friendly",
+                        ],
+                        max_choices=1,
+                        value="default",
+                    )
+                    ref_gr = gr.Audio(
+                        label="Reference Audio",
+                        type="filepath",
+                        value="OpenVoice/resources/demo_speaker2.mp3",
+                    )
+                    tos_gr = gr.Checkbox(
+                        label="Agree",
+                        value=False,
+                        info="I agree to the terms of the cc-by-nc-4.0 license-: https://github.com/myshell-ai/OpenVoice/blob/main/LICENSE",
+                    )
+    
+                    tts_button = gr.Button("Send", elem_id="send-btn", visible=True)
+    
+                with gr.Column():
+                    out_text_gr = gr.Text(label="Info")
+                    audio_orig_gr = gr.Audio(label="Synthesised Audio", autoplay=False)
+                    audio_gr = gr.Audio(label="Audio with cloned voice", autoplay=True)
+                    # ref_audio_gr = gr.Audio(label="Reference Audio Used")
+                    predict = build_predict(
+                        output_dir,
+                        tone_color_converter,
+                        en_tts_model,
+                        zh_tts_model,
+                        en_source_default_se,
+                        en_source_style_se,
+                        zh_source_se,
+                    )
+    
+                    gr.Examples(
+                        examples,
+                        label="Examples",
+                        inputs=[input_text_gr, style_gr, ref_gr, tos_gr],
+                        outputs=[out_text_gr, audio_gr],
+                        fn=predict,
+                        cache_examples=False,
+                    )
+                    tts_button.click(
+                        predict,
+                        [input_text_gr, style_gr, ref_gr, tos_gr],
+                        outputs=[out_text_gr, audio_orig_gr, audio_gr],
+                    )
+        return demo
+
+.. code:: ipython3
+
+    demo = get_demo(
+        OUTPUT_DIR,
+        tone_color_converter,
+        en_base_speaker_tts,
+        zh_base_speaker_tts,
+        en_source_default_se,
+        en_source_style_se,
+        zh_source_se,
+    )
+    demo.queue(max_size=2)
+    
+    try:
+        demo.launch(debug=False, height=1000)
+    except Exception:
+        demo.launch(share=True, debug=False, height=1000)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/gradio/components/dropdown.py:93: UserWarning: The `max_choices` parameter is ignored when `multiselect` is False.
+      warnings.warn(
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+    
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
+
+Cleanup
+-------
+
+
+
+.. code:: ipython3
+
+    # import shutil
+    # shutil.rmtree(CKPT_BASE_PATH)
+    # shutil.rmtree(IRS_PATH)
+    # shutil.rmtree(OUTPUT_DIR)
diff --git a/docs/notebooks/optical-character-recognition-with-output.rst b/docs/notebooks/optical-character-recognition-with-output.rst
new file mode 100644
index 00000000000..bb91fd98644
--- /dev/null
+++ b/docs/notebooks/optical-character-recognition-with-output.rst
@@ -0,0 +1,23663 @@
+Optical Character Recognition (OCR) with OpenVINO™
+==================================================
+
+This tutorial demonstrates how to perform optical character recognition
+(OCR) with OpenVINO models. It is a continuation of the
+`hello-detection `__ tutorial,
+which shows only text detection.
+
+The
+`horizontal-text-detection-0001 `__
+and
+`text-recognition-resnet `__
+models are used together for text detection and then text recognition.
+
+In this tutorial, Open Model Zoo tools including Model Downloader, Model
+Converter and Info Dumper are used to download and convert the models
+from `Open Model
+Zoo `__. For more
+information, refer to the
+`model-tools `__ tutorial.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Imports <#imports>`__
+-  `Settings <#settings>`__
+-  `Download Models <#download-models>`__
+-  `Convert Models <#convert-models>`__
+-  `Select inference device <#select-inference-device>`__
+-  `Object Detection <#object-detection>`__
+
+   -  `Load a Detection Model <#load-a-detection-model>`__
+   -  `Load an Image <#load-an-image>`__
+   -  `Do Inference <#do-inference>`__
+   -  `Get Detection Results <#get-detection-results>`__
+
+-  `Text Recognition <#text-recognition>`__
+
+   -  `Load Text Recognition Model <#load-text-recognition-model>`__
+   -  `Do Inference <#do-inference>`__
+
+-  `Show Results <#show-results>`__
+
+   -  `Show Detected Text Boxes and OCR Results for the
+      Image <#show-detected-text-boxes-and-ocr-results-for-the-image>`__
+   -  `Show the OCR Result per Bounding
+      Box <#show-the-ocr-result-per-bounding-box>`__
+   -  `Print Annotations in Plain Text
+      Format <#print-annotations-in-plain-text-format>`__
+
+.. code:: ipython3
+
+    import platform
+
+    # Install openvino-dev package
+    %pip install -q "openvino-dev>=2024.0.0"  onnx torch pillow opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
+
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    openvino-tokenizers 2024.2.0.0.dev20240416 requires openvino~=2024.2.0.0.dev, but you have openvino 2024.0.0 which is incompatible.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Imports
+-------
+
+
+
+.. code:: ipython3
+
+    from pathlib import Path
+
+    import cv2
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import openvino as ov
+    from IPython.display import Markdown, display
+    from PIL import Image
+
+    # Fetch `notebook_utils` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+
+    open("notebook_utils.py", "w").write(r.text)
+    from notebook_utils import load_image
+
+Settings
+--------
+
+
+
+.. code:: ipython3
+
+    core = ov.Core()
+
+    model_dir = Path("model")
+    precision = "FP16"
+    detection_model = "horizontal-text-detection-0001"
+    recognition_model = "text-recognition-resnet-fc"
+
+    model_dir.mkdir(exist_ok=True)
+
+Download Models
+---------------
+
+
+
+The next cells will run Model Downloader to download the detection and
+recognition models. If the models have been downloaded before, they will
+not be downloaded again.
+
+.. code:: ipython3
+
+    download_command = (
+        f"omz_downloader --name {detection_model},{recognition_model} --output_dir {model_dir} --cache_dir {model_dir} --precision {precision}  --num_attempts 5"
+    )
+    display(Markdown(f"Download command: `{download_command}`"))
+    display(Markdown(f"Downloading {detection_model}, {recognition_model}..."))
+    !$download_command
+    display(Markdown(f"Finished downloading {detection_model}, {recognition_model}."))
+
+    detection_model_path = (model_dir / "intel/horizontal-text-detection-0001" / precision / detection_model).with_suffix(".xml")
+    recognition_model_path = (model_dir / "public/text-recognition-resnet-fc" / precision / recognition_model).with_suffix(".xml")
+
+
+
+Download command:
+``omz_downloader --name horizontal-text-detection-0001,text-recognition-resnet-fc --output_dir model --cache_dir model --precision FP16  --num_attempts 5``
+
+
+
+Downloading horizontal-text-detection-0001, text-recognition-resnet-fc…
+
+
+.. parsed-literal::
+
+    ################|| Downloading horizontal-text-detection-0001 ||################
+
+    ========== Downloading model/intel/horizontal-text-detection-0001/FP16/horizontal-text-detection-0001.xml
+
+
+.. parsed-literal::
+
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+    ========== Downloading model/intel/horizontal-text-detection-0001/FP16/horizontal-text-detection-0001.bin
+
+
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+
+    ################|| Downloading text-recognition-resnet-fc ||################
+
+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/models/__init__.py
+
+
+.. parsed-literal::
+
+    ... 100%, 0 KB, 309 KB/s, 0 seconds passed
+
+
+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/models/builder.py
+
+
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+
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+
+
+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/models/model.py
+
+
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+
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+
+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/models/weight_init.py
+
+
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+
+    ... 100%, 3 KB, 13587 KB/s, 0 seconds passed
+
+
+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/models/registry.py
+
+
+.. parsed-literal::
+
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+
+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/models/heads/__init__.py
+
+
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+
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+
+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/models/heads/builder.py
+
+
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+
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+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/models/heads/fc_head.py
+
+
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+
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+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/models/heads/registry.py
+
+
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+
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+
+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/models/bodies/__init__.py
+
+
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+
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
+
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+... 7%, 50688 KB, 58360 KB/s, 0 seconds passed
+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 10%, 76288 KB, 56466 KB/s, 1 seconds passed
+... 10%, 76320 KB, 56479 KB/s, 1 seconds passed
+... 10%, 76352 KB, 56492 KB/s, 1 seconds passed
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+... 11%, 76544 KB, 56581 KB/s, 1 seconds passed
+... 11%, 76576 KB, 56597 KB/s, 1 seconds passed
+... 11%, 76608 KB, 56614 KB/s, 1 seconds passed
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+... 11%, 77600 KB, 56179 KB/s, 1 seconds passed
+... 11%, 77632 KB, 56189 KB/s, 1 seconds passed
+
+.. parsed-literal::
+
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 12%, 89344 KB, 53691 KB/s, 1 seconds passed
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+... 12%, 89408 KB, 53704 KB/s, 1 seconds passed
+... 12%, 89440 KB, 53711 KB/s, 1 seconds passed
+... 12%, 89472 KB, 53718 KB/s, 1 seconds passed
+... 12%, 89504 KB, 53725 KB/s, 1 seconds passed
+... 12%, 89536 KB, 53733 KB/s, 1 seconds passed
+... 12%, 89568 KB, 53739 KB/s, 1 seconds passed
+... 12%, 89600 KB, 53746 KB/s, 1 seconds passed
+... 12%, 89632 KB, 53752 KB/s, 1 seconds passed
+... 12%, 89664 KB, 53758 KB/s, 1 seconds passed
+... 12%, 89696 KB, 53766 KB/s, 1 seconds passed
+... 12%, 89728 KB, 53772 KB/s, 1 seconds passed
+... 12%, 89760 KB, 53779 KB/s, 1 seconds passed
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+... 13%, 91232 KB, 54227 KB/s, 1 seconds passed
+... 13%, 91264 KB, 54240 KB/s, 1 seconds passed
+... 13%, 91296 KB, 54253 KB/s, 1 seconds passed
+
+.. parsed-literal::
+
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+... 13%, 91392 KB, 48181 KB/s, 1 seconds passed
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+... 13%, 91488 KB, 48202 KB/s, 1 seconds passed
+... 13%, 91520 KB, 48209 KB/s, 1 seconds passed
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+... 13%, 92064 KB, 48339 KB/s, 1 seconds passed
+... 13%, 92096 KB, 48349 KB/s, 1 seconds passed
+... 13%, 92128 KB, 48359 KB/s, 1 seconds passed
+
+.. parsed-literal::
+
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+... 13%, 94144 KB, 47178 KB/s, 1 seconds passed
+... 13%, 94176 KB, 47189 KB/s, 1 seconds passed
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+... 13%, 94272 KB, 47219 KB/s, 1 seconds passed
+... 13%, 94304 KB, 47230 KB/s, 1 seconds passed
+... 13%, 94336 KB, 47240 KB/s, 1 seconds passed
+... 13%, 94368 KB, 47251 KB/s, 1 seconds passed
+
+.. parsed-literal::
+
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+... 13%, 95968 KB, 47781 KB/s, 2 seconds passed
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+... 13%, 96832 KB, 48032 KB/s, 2 seconds passed
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+... 13%, 97184 KB, 48133 KB/s, 2 seconds passed
+... 13%, 97216 KB, 48142 KB/s, 2 seconds passed
+... 13%, 97248 KB, 48151 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
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+... 14%, 98272 KB, 46826 KB/s, 2 seconds passed
+... 14%, 98304 KB, 46832 KB/s, 2 seconds passed
+... 14%, 98336 KB, 46838 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
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+... 14%, 100800 KB, 47602 KB/s, 2 seconds passed
+... 14%, 100832 KB, 47611 KB/s, 2 seconds passed
+... 14%, 100864 KB, 47619 KB/s, 2 seconds passed
+... 14%, 100896 KB, 47627 KB/s, 2 seconds passed
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+... 14%, 100992 KB, 47649 KB/s, 2 seconds passed
+... 14%, 101024 KB, 47660 KB/s, 2 seconds passed
+... 14%, 101056 KB, 47671 KB/s, 2 seconds passed
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+... 14%, 101280 KB, 47733 KB/s, 2 seconds passed
+... 14%, 101312 KB, 47742 KB/s, 2 seconds passed
+... 14%, 101344 KB, 47750 KB/s, 2 seconds passed
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+... 14%, 101408 KB, 47769 KB/s, 2 seconds passed
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+... 14%, 101472 KB, 47786 KB/s, 2 seconds passed
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+... 14%, 101568 KB, 47812 KB/s, 2 seconds passed
+... 14%, 101600 KB, 47822 KB/s, 2 seconds passed
+... 14%, 101632 KB, 47831 KB/s, 2 seconds passed
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+... 14%, 101856 KB, 47891 KB/s, 2 seconds passed
+... 14%, 101888 KB, 47900 KB/s, 2 seconds passed
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+... 14%, 101984 KB, 47923 KB/s, 2 seconds passed
+... 14%, 102016 KB, 47932 KB/s, 2 seconds passed
+... 14%, 102048 KB, 47941 KB/s, 2 seconds passed
+... 14%, 102080 KB, 47949 KB/s, 2 seconds passed
+... 14%, 102112 KB, 47960 KB/s, 2 seconds passed
+... 14%, 102144 KB, 47968 KB/s, 2 seconds passed
+... 14%, 102176 KB, 47975 KB/s, 2 seconds passed
+... 14%, 102208 KB, 47980 KB/s, 2 seconds passed
+... 14%, 102240 KB, 47990 KB/s, 2 seconds passed
+... 14%, 102272 KB, 47999 KB/s, 2 seconds passed
+... 14%, 102304 KB, 48008 KB/s, 2 seconds passed
+... 14%, 102336 KB, 48017 KB/s, 2 seconds passed
+... 14%, 102368 KB, 48027 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
+    ... 14%, 102400 KB, 47585 KB/s, 2 seconds passed
+... 14%, 102432 KB, 47592 KB/s, 2 seconds passed
+... 14%, 102464 KB, 47507 KB/s, 2 seconds passed
+... 14%, 102496 KB, 47513 KB/s, 2 seconds passed
+... 14%, 102528 KB, 47519 KB/s, 2 seconds passed
+... 14%, 102560 KB, 47525 KB/s, 2 seconds passed
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 17%, 118336 KB, 49172 KB/s, 2 seconds passed
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+... 17%, 118400 KB, 49187 KB/s, 2 seconds passed
+
+.. parsed-literal::
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+... 17%, 121184 KB, 49372 KB/s, 2 seconds passed
+... 17%, 121216 KB, 49379 KB/s, 2 seconds passed
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+
+.. parsed-literal::
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+... 17%, 124416 KB, 49584 KB/s, 2 seconds passed
+... 17%, 124448 KB, 49592 KB/s, 2 seconds passed
+
+.. parsed-literal::
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+... 18%, 127040 KB, 50154 KB/s, 2 seconds passed
+... 18%, 127072 KB, 50161 KB/s, 2 seconds passed
+... 18%, 127104 KB, 50168 KB/s, 2 seconds passed
+... 18%, 127136 KB, 50176 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
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+... 18%, 127904 KB, 49592 KB/s, 2 seconds passed
+... 18%, 127936 KB, 49598 KB/s, 2 seconds passed
+... 18%, 127968 KB, 49604 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
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+... 18%, 129120 KB, 48729 KB/s, 2 seconds passed
+... 18%, 129152 KB, 48736 KB/s, 2 seconds passed
+... 18%, 129184 KB, 48742 KB/s, 2 seconds passed
+... 18%, 129216 KB, 48748 KB/s, 2 seconds passed
+... 18%, 129248 KB, 48754 KB/s, 2 seconds passed
+... 18%, 129280 KB, 48760 KB/s, 2 seconds passed
+... 18%, 129312 KB, 48767 KB/s, 2 seconds passed
+... 18%, 129344 KB, 48773 KB/s, 2 seconds passed
+... 18%, 129376 KB, 48780 KB/s, 2 seconds passed
+... 18%, 129408 KB, 48786 KB/s, 2 seconds passed
+... 18%, 129440 KB, 48793 KB/s, 2 seconds passed
+... 18%, 129472 KB, 48800 KB/s, 2 seconds passed
+... 18%, 129504 KB, 48806 KB/s, 2 seconds passed
+... 18%, 129536 KB, 48812 KB/s, 2 seconds passed
+... 18%, 129568 KB, 48819 KB/s, 2 seconds passed
+... 18%, 129600 KB, 48826 KB/s, 2 seconds passed
+... 18%, 129632 KB, 48832 KB/s, 2 seconds passed
+... 18%, 129664 KB, 48838 KB/s, 2 seconds passed
+... 18%, 129696 KB, 48844 KB/s, 2 seconds passed
+... 18%, 129728 KB, 48851 KB/s, 2 seconds passed
+... 18%, 129760 KB, 48857 KB/s, 2 seconds passed
+... 18%, 129792 KB, 48864 KB/s, 2 seconds passed
+... 18%, 129824 KB, 48872 KB/s, 2 seconds passed
+... 18%, 129856 KB, 48880 KB/s, 2 seconds passed
+... 18%, 129888 KB, 48888 KB/s, 2 seconds passed
+... 18%, 129920 KB, 48897 KB/s, 2 seconds passed
+... 18%, 129952 KB, 48905 KB/s, 2 seconds passed
+... 18%, 129984 KB, 48914 KB/s, 2 seconds passed
+... 18%, 130016 KB, 48922 KB/s, 2 seconds passed
+... 18%, 130048 KB, 48930 KB/s, 2 seconds passed
+... 18%, 130080 KB, 48938 KB/s, 2 seconds passed
+... 18%, 130112 KB, 48946 KB/s, 2 seconds passed
+... 18%, 130144 KB, 48955 KB/s, 2 seconds passed
+... 18%, 130176 KB, 48963 KB/s, 2 seconds passed
+... 18%, 130208 KB, 48972 KB/s, 2 seconds passed
+... 18%, 130240 KB, 48980 KB/s, 2 seconds passed
+... 18%, 130272 KB, 48988 KB/s, 2 seconds passed
+... 18%, 130304 KB, 48996 KB/s, 2 seconds passed
+... 18%, 130336 KB, 49005 KB/s, 2 seconds passed
+... 18%, 130368 KB, 49013 KB/s, 2 seconds passed
+... 18%, 130400 KB, 49021 KB/s, 2 seconds passed
+... 18%, 130432 KB, 49030 KB/s, 2 seconds passed
+... 18%, 130464 KB, 49039 KB/s, 2 seconds passed
+... 18%, 130496 KB, 49047 KB/s, 2 seconds passed
+... 18%, 130528 KB, 49055 KB/s, 2 seconds passed
+... 18%, 130560 KB, 49064 KB/s, 2 seconds passed
+... 18%, 130592 KB, 49072 KB/s, 2 seconds passed
+... 18%, 130624 KB, 49080 KB/s, 2 seconds passed
+... 18%, 130656 KB, 49089 KB/s, 2 seconds passed
+... 18%, 130688 KB, 49097 KB/s, 2 seconds passed
+... 18%, 130720 KB, 49105 KB/s, 2 seconds passed
+... 18%, 130752 KB, 49112 KB/s, 2 seconds passed
+... 18%, 130784 KB, 49119 KB/s, 2 seconds passed
+... 18%, 130816 KB, 49126 KB/s, 2 seconds passed
+... 18%, 130848 KB, 49132 KB/s, 2 seconds passed
+... 18%, 130880 KB, 49140 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
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+... 18%, 130944 KB, 49154 KB/s, 2 seconds passed
+... 18%, 130976 KB, 49160 KB/s, 2 seconds passed
+... 18%, 131008 KB, 49167 KB/s, 2 seconds passed
+... 18%, 131040 KB, 49174 KB/s, 2 seconds passed
+... 18%, 131072 KB, 49180 KB/s, 2 seconds passed
+... 18%, 131104 KB, 49188 KB/s, 2 seconds passed
+... 18%, 131136 KB, 49195 KB/s, 2 seconds passed
+... 18%, 131168 KB, 49202 KB/s, 2 seconds passed
+... 18%, 131200 KB, 49208 KB/s, 2 seconds passed
+... 18%, 131232 KB, 49217 KB/s, 2 seconds passed
+... 18%, 131264 KB, 49221 KB/s, 2 seconds passed
+... 18%, 131296 KB, 49229 KB/s, 2 seconds passed
+... 18%, 131328 KB, 49236 KB/s, 2 seconds passed
+... 18%, 131360 KB, 49243 KB/s, 2 seconds passed
+... 18%, 131392 KB, 49250 KB/s, 2 seconds passed
+... 18%, 131424 KB, 49257 KB/s, 2 seconds passed
+... 18%, 131456 KB, 49264 KB/s, 2 seconds passed
+... 18%, 131488 KB, 49271 KB/s, 2 seconds passed
+... 18%, 131520 KB, 49277 KB/s, 2 seconds passed
+... 18%, 131552 KB, 49284 KB/s, 2 seconds passed
+... 18%, 131584 KB, 49291 KB/s, 2 seconds passed
+... 18%, 131616 KB, 49298 KB/s, 2 seconds passed
+... 18%, 131648 KB, 49304 KB/s, 2 seconds passed
+... 18%, 131680 KB, 49312 KB/s, 2 seconds passed
+... 18%, 131712 KB, 49319 KB/s, 2 seconds passed
+... 18%, 131744 KB, 49327 KB/s, 2 seconds passed
+... 18%, 131776 KB, 49332 KB/s, 2 seconds passed
+... 18%, 131808 KB, 49336 KB/s, 2 seconds passed
+... 18%, 131840 KB, 49337 KB/s, 2 seconds passed
+... 18%, 131872 KB, 49340 KB/s, 2 seconds passed
+... 18%, 131904 KB, 49349 KB/s, 2 seconds passed
+... 18%, 131936 KB, 49358 KB/s, 2 seconds passed
+... 18%, 131968 KB, 49366 KB/s, 2 seconds passed
+... 18%, 132000 KB, 49375 KB/s, 2 seconds passed
+... 18%, 132032 KB, 49384 KB/s, 2 seconds passed
+... 18%, 132064 KB, 49392 KB/s, 2 seconds passed
+... 18%, 132096 KB, 49400 KB/s, 2 seconds passed
+... 18%, 132128 KB, 49407 KB/s, 2 seconds passed
+... 19%, 132160 KB, 49414 KB/s, 2 seconds passed
+... 19%, 132192 KB, 49421 KB/s, 2 seconds passed
+... 19%, 132224 KB, 49427 KB/s, 2 seconds passed
+... 19%, 132256 KB, 49433 KB/s, 2 seconds passed
+... 19%, 132288 KB, 49100 KB/s, 2 seconds passed
+... 19%, 132320 KB, 49103 KB/s, 2 seconds passed
+... 19%, 132352 KB, 49103 KB/s, 2 seconds passed
+... 19%, 132384 KB, 49108 KB/s, 2 seconds passed
+... 19%, 132416 KB, 49112 KB/s, 2 seconds passed
+... 19%, 132448 KB, 49117 KB/s, 2 seconds passed
+... 19%, 132480 KB, 49123 KB/s, 2 seconds passed
+... 19%, 132512 KB, 49127 KB/s, 2 seconds passed
+... 19%, 132544 KB, 49132 KB/s, 2 seconds passed
+... 19%, 132576 KB, 49137 KB/s, 2 seconds passed
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+... 19%, 132768 KB, 49165 KB/s, 2 seconds passed
+... 19%, 132800 KB, 49169 KB/s, 2 seconds passed
+... 19%, 132832 KB, 49175 KB/s, 2 seconds passed
+... 19%, 132864 KB, 49179 KB/s, 2 seconds passed
+... 19%, 132896 KB, 49185 KB/s, 2 seconds passed
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+... 19%, 132960 KB, 49198 KB/s, 2 seconds passed
+... 19%, 132992 KB, 49205 KB/s, 2 seconds passed
+... 19%, 133024 KB, 49211 KB/s, 2 seconds passed
+... 19%, 133056 KB, 49217 KB/s, 2 seconds passed
+... 19%, 133088 KB, 49224 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
+    ... 19%, 133120 KB, 48429 KB/s, 2 seconds passed
+... 19%, 133152 KB, 48431 KB/s, 2 seconds passed
+... 19%, 133184 KB, 48436 KB/s, 2 seconds passed
+... 19%, 133216 KB, 48442 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
+    ... 19%, 133248 KB, 48166 KB/s, 2 seconds passed
+... 19%, 133280 KB, 48168 KB/s, 2 seconds passed
+... 19%, 133312 KB, 48171 KB/s, 2 seconds passed
+... 19%, 133344 KB, 48175 KB/s, 2 seconds passed
+... 19%, 133376 KB, 48180 KB/s, 2 seconds passed
+... 19%, 133408 KB, 48185 KB/s, 2 seconds passed
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+... 19%, 133664 KB, 48223 KB/s, 2 seconds passed
+... 19%, 133696 KB, 48227 KB/s, 2 seconds passed
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+... 19%, 133952 KB, 48263 KB/s, 2 seconds passed
+... 19%, 133984 KB, 48268 KB/s, 2 seconds passed
+... 19%, 134016 KB, 48272 KB/s, 2 seconds passed
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+... 19%, 134080 KB, 48281 KB/s, 2 seconds passed
+... 19%, 134112 KB, 48286 KB/s, 2 seconds passed
+... 19%, 134144 KB, 48291 KB/s, 2 seconds passed
+... 19%, 134176 KB, 48296 KB/s, 2 seconds passed
+... 19%, 134208 KB, 48303 KB/s, 2 seconds passed
+... 19%, 134240 KB, 48310 KB/s, 2 seconds passed
+... 19%, 134272 KB, 48317 KB/s, 2 seconds passed
+... 19%, 134304 KB, 48324 KB/s, 2 seconds passed
+... 19%, 134336 KB, 48331 KB/s, 2 seconds passed
+... 19%, 134368 KB, 48338 KB/s, 2 seconds passed
+... 19%, 134400 KB, 48345 KB/s, 2 seconds passed
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+... 19%, 134784 KB, 48430 KB/s, 2 seconds passed
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+... 19%, 134880 KB, 48452 KB/s, 2 seconds passed
+... 19%, 134912 KB, 48459 KB/s, 2 seconds passed
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+... 19%, 135104 KB, 48502 KB/s, 2 seconds passed
+... 19%, 135136 KB, 48509 KB/s, 2 seconds passed
+... 19%, 135168 KB, 48516 KB/s, 2 seconds passed
+... 19%, 135200 KB, 48523 KB/s, 2 seconds passed
+... 19%, 135232 KB, 48530 KB/s, 2 seconds passed
+... 19%, 135264 KB, 48537 KB/s, 2 seconds passed
+... 19%, 135296 KB, 48544 KB/s, 2 seconds passed
+... 19%, 135328 KB, 48551 KB/s, 2 seconds passed
+... 19%, 135360 KB, 48558 KB/s, 2 seconds passed
+... 19%, 135392 KB, 48565 KB/s, 2 seconds passed
+... 19%, 135424 KB, 48572 KB/s, 2 seconds passed
+... 19%, 135456 KB, 48579 KB/s, 2 seconds passed
+... 19%, 135488 KB, 48588 KB/s, 2 seconds passed
+... 19%, 135520 KB, 48597 KB/s, 2 seconds passed
+... 19%, 135552 KB, 48605 KB/s, 2 seconds passed
+... 19%, 135584 KB, 48614 KB/s, 2 seconds passed
+... 19%, 135616 KB, 48622 KB/s, 2 seconds passed
+... 19%, 135648 KB, 48630 KB/s, 2 seconds passed
+... 19%, 135680 KB, 48639 KB/s, 2 seconds passed
+... 19%, 135712 KB, 48647 KB/s, 2 seconds passed
+... 19%, 135744 KB, 48656 KB/s, 2 seconds passed
+... 19%, 135776 KB, 48664 KB/s, 2 seconds passed
+... 19%, 135808 KB, 48673 KB/s, 2 seconds passed
+... 19%, 135840 KB, 48681 KB/s, 2 seconds passed
+... 19%, 135872 KB, 48689 KB/s, 2 seconds passed
+... 19%, 135904 KB, 48698 KB/s, 2 seconds passed
+... 19%, 135936 KB, 48706 KB/s, 2 seconds passed
+... 19%, 135968 KB, 48715 KB/s, 2 seconds passed
+... 19%, 136000 KB, 48723 KB/s, 2 seconds passed
+... 19%, 136032 KB, 48732 KB/s, 2 seconds passed
+... 19%, 136064 KB, 48740 KB/s, 2 seconds passed
+... 19%, 136096 KB, 48749 KB/s, 2 seconds passed
+... 19%, 136128 KB, 48757 KB/s, 2 seconds passed
+... 19%, 136160 KB, 48766 KB/s, 2 seconds passed
+... 19%, 136192 KB, 48774 KB/s, 2 seconds passed
+... 19%, 136224 KB, 48783 KB/s, 2 seconds passed
+... 19%, 136256 KB, 48791 KB/s, 2 seconds passed
+... 19%, 136288 KB, 48796 KB/s, 2 seconds passed
+... 19%, 136320 KB, 48803 KB/s, 2 seconds passed
+... 19%, 136352 KB, 48809 KB/s, 2 seconds passed
+... 19%, 136384 KB, 48817 KB/s, 2 seconds passed
+... 19%, 136416 KB, 48823 KB/s, 2 seconds passed
+... 19%, 136448 KB, 48830 KB/s, 2 seconds passed
+... 19%, 136480 KB, 48618 KB/s, 2 seconds passed
+... 19%, 136512 KB, 48621 KB/s, 2 seconds passed
+... 19%, 136544 KB, 48626 KB/s, 2 seconds passed
+... 19%, 136576 KB, 48630 KB/s, 2 seconds passed
+... 19%, 136608 KB, 48635 KB/s, 2 seconds passed
+... 19%, 136640 KB, 48640 KB/s, 2 seconds passed
+... 19%, 136672 KB, 48645 KB/s, 2 seconds passed
+... 19%, 136704 KB, 48649 KB/s, 2 seconds passed
+... 19%, 136736 KB, 48655 KB/s, 2 seconds passed
+... 19%, 136768 KB, 48659 KB/s, 2 seconds passed
+... 19%, 136800 KB, 48663 KB/s, 2 seconds passed
+... 19%, 136832 KB, 48668 KB/s, 2 seconds passed
+... 19%, 136864 KB, 48672 KB/s, 2 seconds passed
+... 19%, 136896 KB, 48677 KB/s, 2 seconds passed
+... 19%, 136928 KB, 48682 KB/s, 2 seconds passed
+... 19%, 136960 KB, 48687 KB/s, 2 seconds passed
+... 19%, 136992 KB, 48692 KB/s, 2 seconds passed
+... 19%, 137024 KB, 48697 KB/s, 2 seconds passed
+... 19%, 137056 KB, 48702 KB/s, 2 seconds passed
+... 19%, 137088 KB, 48707 KB/s, 2 seconds passed
+... 19%, 137120 KB, 48711 KB/s, 2 seconds passed
+... 19%, 137152 KB, 48716 KB/s, 2 seconds passed
+... 19%, 137184 KB, 48722 KB/s, 2 seconds passed
+... 19%, 137216 KB, 48728 KB/s, 2 seconds passed
+... 19%, 137248 KB, 48735 KB/s, 2 seconds passed
+... 19%, 137280 KB, 48741 KB/s, 2 seconds passed
+... 19%, 137312 KB, 48747 KB/s, 2 seconds passed
+... 19%, 137344 KB, 48753 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
+    ... 19%, 137376 KB, 48759 KB/s, 2 seconds passed
+... 19%, 137408 KB, 48714 KB/s, 2 seconds passed
+... 19%, 137440 KB, 48709 KB/s, 2 seconds passed
+... 19%, 137472 KB, 48713 KB/s, 2 seconds passed
+... 19%, 137504 KB, 48718 KB/s, 2 seconds passed
+... 19%, 137536 KB, 48724 KB/s, 2 seconds passed
+... 19%, 137568 KB, 48730 KB/s, 2 seconds passed
+... 19%, 137600 KB, 48736 KB/s, 2 seconds passed
+... 19%, 137632 KB, 48742 KB/s, 2 seconds passed
+... 19%, 137664 KB, 48748 KB/s, 2 seconds passed
+... 19%, 137696 KB, 48754 KB/s, 2 seconds passed
+... 19%, 137728 KB, 48761 KB/s, 2 seconds passed
+... 19%, 137760 KB, 48768 KB/s, 2 seconds passed
+... 19%, 137792 KB, 48774 KB/s, 2 seconds passed
+... 19%, 137824 KB, 48781 KB/s, 2 seconds passed
+... 19%, 137856 KB, 48788 KB/s, 2 seconds passed
+... 19%, 137888 KB, 48794 KB/s, 2 seconds passed
+... 19%, 137920 KB, 48800 KB/s, 2 seconds passed
+... 19%, 137952 KB, 48807 KB/s, 2 seconds passed
+... 19%, 137984 KB, 48813 KB/s, 2 seconds passed
+... 19%, 138016 KB, 48819 KB/s, 2 seconds passed
+... 19%, 138048 KB, 48825 KB/s, 2 seconds passed
+... 19%, 138080 KB, 48832 KB/s, 2 seconds passed
+... 19%, 138112 KB, 48838 KB/s, 2 seconds passed
+... 19%, 138144 KB, 48845 KB/s, 2 seconds passed
+... 19%, 138176 KB, 48851 KB/s, 2 seconds passed
+... 19%, 138208 KB, 48858 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
+    ... 19%, 138240 KB, 47703 KB/s, 2 seconds passed
+... 19%, 138272 KB, 47705 KB/s, 2 seconds passed
+... 19%, 138304 KB, 47709 KB/s, 2 seconds passed
+... 19%, 138336 KB, 47714 KB/s, 2 seconds passed
+... 19%, 138368 KB, 47609 KB/s, 2 seconds passed
+... 19%, 138400 KB, 47611 KB/s, 2 seconds passed
+... 19%, 138432 KB, 47615 KB/s, 2 seconds passed
+... 19%, 138464 KB, 47619 KB/s, 2 seconds passed
+... 19%, 138496 KB, 47623 KB/s, 2 seconds passed
+... 19%, 138528 KB, 47628 KB/s, 2 seconds passed
+... 19%, 138560 KB, 47632 KB/s, 2 seconds passed
+... 19%, 138592 KB, 47637 KB/s, 2 seconds passed
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+... 19%, 138656 KB, 47645 KB/s, 2 seconds passed
+... 19%, 138688 KB, 47650 KB/s, 2 seconds passed
+... 19%, 138720 KB, 47655 KB/s, 2 seconds passed
+... 19%, 138752 KB, 47659 KB/s, 2 seconds passed
+... 19%, 138784 KB, 47664 KB/s, 2 seconds passed
+... 19%, 138816 KB, 47668 KB/s, 2 seconds passed
+... 19%, 138848 KB, 47673 KB/s, 2 seconds passed
+... 19%, 138880 KB, 47678 KB/s, 2 seconds passed
+... 19%, 138912 KB, 47683 KB/s, 2 seconds passed
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+... 19%, 138976 KB, 47691 KB/s, 2 seconds passed
+... 19%, 139008 KB, 47695 KB/s, 2 seconds passed
+... 19%, 139040 KB, 47700 KB/s, 2 seconds passed
+... 19%, 139072 KB, 47704 KB/s, 2 seconds passed
+... 20%, 139104 KB, 47709 KB/s, 2 seconds passed
+... 20%, 139136 KB, 47713 KB/s, 2 seconds passed
+... 20%, 139168 KB, 47718 KB/s, 2 seconds passed
+... 20%, 139200 KB, 47723 KB/s, 2 seconds passed
+... 20%, 139232 KB, 47727 KB/s, 2 seconds passed
+... 20%, 139264 KB, 47732 KB/s, 2 seconds passed
+... 20%, 139296 KB, 47736 KB/s, 2 seconds passed
+... 20%, 139328 KB, 47740 KB/s, 2 seconds passed
+... 20%, 139360 KB, 47745 KB/s, 2 seconds passed
+... 20%, 139392 KB, 47749 KB/s, 2 seconds passed
+... 20%, 139424 KB, 47755 KB/s, 2 seconds passed
+... 20%, 139456 KB, 47762 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
+    ... 20%, 139488 KB, 47769 KB/s, 2 seconds passed
+... 20%, 139520 KB, 47776 KB/s, 2 seconds passed
+... 20%, 139552 KB, 47783 KB/s, 2 seconds passed
+... 20%, 139584 KB, 47790 KB/s, 2 seconds passed
+... 20%, 139616 KB, 47796 KB/s, 2 seconds passed
+... 20%, 139648 KB, 47803 KB/s, 2 seconds passed
+... 20%, 139680 KB, 47810 KB/s, 2 seconds passed
+... 20%, 139712 KB, 47817 KB/s, 2 seconds passed
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+... 20%, 140000 KB, 47878 KB/s, 2 seconds passed
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+... 20%, 140288 KB, 47940 KB/s, 2 seconds passed
+... 20%, 140320 KB, 47947 KB/s, 2 seconds passed
+... 20%, 140352 KB, 47954 KB/s, 2 seconds passed
+... 20%, 140384 KB, 47961 KB/s, 2 seconds passed
+... 20%, 140416 KB, 47967 KB/s, 2 seconds passed
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+... 20%, 140512 KB, 47988 KB/s, 2 seconds passed
+... 20%, 140544 KB, 47995 KB/s, 2 seconds passed
+... 20%, 140576 KB, 48002 KB/s, 2 seconds passed
+... 20%, 140608 KB, 48009 KB/s, 2 seconds passed
+... 20%, 140640 KB, 48015 KB/s, 2 seconds passed
+... 20%, 140672 KB, 48022 KB/s, 2 seconds passed
+... 20%, 140704 KB, 48028 KB/s, 2 seconds passed
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+... 20%, 140800 KB, 48053 KB/s, 2 seconds passed
+... 20%, 140832 KB, 48061 KB/s, 2 seconds passed
+... 20%, 140864 KB, 48069 KB/s, 2 seconds passed
+... 20%, 140896 KB, 48077 KB/s, 2 seconds passed
+... 20%, 140928 KB, 48086 KB/s, 2 seconds passed
+... 20%, 140960 KB, 48094 KB/s, 2 seconds passed
+... 20%, 140992 KB, 48102 KB/s, 2 seconds passed
+... 20%, 141024 KB, 48110 KB/s, 2 seconds passed
+... 20%, 141056 KB, 48118 KB/s, 2 seconds passed
+... 20%, 141088 KB, 48127 KB/s, 2 seconds passed
+... 20%, 141120 KB, 48135 KB/s, 2 seconds passed
+... 20%, 141152 KB, 48143 KB/s, 2 seconds passed
+... 20%, 141184 KB, 48151 KB/s, 2 seconds passed
+... 20%, 141216 KB, 48159 KB/s, 2 seconds passed
+... 20%, 141248 KB, 48168 KB/s, 2 seconds passed
+... 20%, 141280 KB, 48176 KB/s, 2 seconds passed
+... 20%, 141312 KB, 48184 KB/s, 2 seconds passed
+... 20%, 141344 KB, 48192 KB/s, 2 seconds passed
+... 20%, 141376 KB, 48201 KB/s, 2 seconds passed
+... 20%, 141408 KB, 48209 KB/s, 2 seconds passed
+... 20%, 141440 KB, 47801 KB/s, 2 seconds passed
+... 20%, 141472 KB, 47802 KB/s, 2 seconds passed
+... 20%, 141504 KB, 47806 KB/s, 2 seconds passed
+... 20%, 141536 KB, 47810 KB/s, 2 seconds passed
+... 20%, 141568 KB, 47814 KB/s, 2 seconds passed
+... 20%, 141600 KB, 47818 KB/s, 2 seconds passed
+... 20%, 141632 KB, 47823 KB/s, 2 seconds passed
+... 20%, 141664 KB, 47827 KB/s, 2 seconds passed
+... 20%, 141696 KB, 47832 KB/s, 2 seconds passed
+... 20%, 141728 KB, 47837 KB/s, 2 seconds passed
+... 20%, 141760 KB, 47841 KB/s, 2 seconds passed
+... 20%, 141792 KB, 47845 KB/s, 2 seconds passed
+... 20%, 141824 KB, 47849 KB/s, 2 seconds passed
+... 20%, 141856 KB, 47854 KB/s, 2 seconds passed
+... 20%, 141888 KB, 47858 KB/s, 2 seconds passed
+... 20%, 141920 KB, 47863 KB/s, 2 seconds passed
+... 20%, 141952 KB, 47868 KB/s, 2 seconds passed
+... 20%, 141984 KB, 47872 KB/s, 2 seconds passed
+... 20%, 142016 KB, 47876 KB/s, 2 seconds passed
+... 20%, 142048 KB, 47881 KB/s, 2 seconds passed
+... 20%, 142080 KB, 47885 KB/s, 2 seconds passed
+... 20%, 142112 KB, 47890 KB/s, 2 seconds passed
+... 20%, 142144 KB, 47894 KB/s, 2 seconds passed
+... 20%, 142176 KB, 47899 KB/s, 2 seconds passed
+... 20%, 142208 KB, 47904 KB/s, 2 seconds passed
+... 20%, 142240 KB, 47909 KB/s, 2 seconds passed
+... 20%, 142272 KB, 47913 KB/s, 2 seconds passed
+... 20%, 142304 KB, 47918 KB/s, 2 seconds passed
+... 20%, 142336 KB, 47923 KB/s, 2 seconds passed
+... 20%, 142368 KB, 47928 KB/s, 2 seconds passed
+... 20%, 142400 KB, 47934 KB/s, 2 seconds passed
+... 20%, 142432 KB, 47940 KB/s, 2 seconds passed
+
+.. parsed-literal::
+
+    ... 20%, 142464 KB, 47947 KB/s, 2 seconds passed
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+... 20%, 142560 KB, 47968 KB/s, 2 seconds passed
+... 20%, 142592 KB, 47975 KB/s, 2 seconds passed
+... 20%, 142624 KB, 47981 KB/s, 2 seconds passed
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+... 20%, 142720 KB, 48002 KB/s, 2 seconds passed
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+... 20%, 145792 KB, 48244 KB/s, 3 seconds passed
+... 20%, 145824 KB, 48252 KB/s, 3 seconds passed
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+
+.. parsed-literal::
+
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+... 21%, 148384 KB, 48726 KB/s, 3 seconds passed
+... 21%, 148416 KB, 48733 KB/s, 3 seconds passed
+... 21%, 148448 KB, 48738 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
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+... 21%, 149440 KB, 47834 KB/s, 3 seconds passed
+... 21%, 149472 KB, 47839 KB/s, 3 seconds passed
+... 21%, 149504 KB, 47845 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
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+... 21%, 151840 KB, 48324 KB/s, 3 seconds passed
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+... 21%, 151936 KB, 48343 KB/s, 3 seconds passed
+... 21%, 151968 KB, 48348 KB/s, 3 seconds passed
+... 21%, 152000 KB, 48354 KB/s, 3 seconds passed
+... 21%, 152032 KB, 48360 KB/s, 3 seconds passed
+... 21%, 152064 KB, 48366 KB/s, 3 seconds passed
+... 21%, 152096 KB, 48372 KB/s, 3 seconds passed
+... 21%, 152128 KB, 48378 KB/s, 3 seconds passed
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+... 21%, 152320 KB, 48389 KB/s, 3 seconds passed
+... 21%, 152352 KB, 48395 KB/s, 3 seconds passed
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+... 21%, 152512 KB, 48417 KB/s, 3 seconds passed
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+... 22%, 153088 KB, 48521 KB/s, 3 seconds passed
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+... 22%, 153344 KB, 48567 KB/s, 3 seconds passed
+... 22%, 153376 KB, 48572 KB/s, 3 seconds passed
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+... 22%, 153440 KB, 48586 KB/s, 3 seconds passed
+... 22%, 153472 KB, 48593 KB/s, 3 seconds passed
+... 22%, 153504 KB, 48599 KB/s, 3 seconds passed
+... 22%, 153536 KB, 48602 KB/s, 3 seconds passed
+... 22%, 153568 KB, 48606 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
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+... 22%, 153664 KB, 48263 KB/s, 3 seconds passed
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+... 22%, 154112 KB, 48324 KB/s, 3 seconds passed
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+... 22%, 154464 KB, 48371 KB/s, 3 seconds passed
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+... 22%, 154528 KB, 48382 KB/s, 3 seconds passed
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+... 22%, 154592 KB, 48393 KB/s, 3 seconds passed
+... 22%, 154624 KB, 48399 KB/s, 3 seconds passed
+... 22%, 154656 KB, 48404 KB/s, 3 seconds passed
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+... 22%, 154720 KB, 48415 KB/s, 3 seconds passed
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+... 22%, 154784 KB, 48426 KB/s, 3 seconds passed
+... 22%, 154816 KB, 48432 KB/s, 3 seconds passed
+... 22%, 154848 KB, 48438 KB/s, 3 seconds passed
+... 22%, 154880 KB, 48443 KB/s, 3 seconds passed
+... 22%, 154912 KB, 48449 KB/s, 3 seconds passed
+... 22%, 154944 KB, 48455 KB/s, 3 seconds passed
+... 22%, 154976 KB, 48460 KB/s, 3 seconds passed
+... 22%, 155008 KB, 48466 KB/s, 3 seconds passed
+... 22%, 155040 KB, 48471 KB/s, 3 seconds passed
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+... 22%, 155104 KB, 48482 KB/s, 3 seconds passed
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+... 22%, 155168 KB, 48493 KB/s, 3 seconds passed
+... 22%, 155200 KB, 48499 KB/s, 3 seconds passed
+... 22%, 155232 KB, 48504 KB/s, 3 seconds passed
+... 22%, 155264 KB, 48510 KB/s, 3 seconds passed
+... 22%, 155296 KB, 48515 KB/s, 3 seconds passed
+... 22%, 155328 KB, 48521 KB/s, 3 seconds passed
+... 22%, 155360 KB, 48527 KB/s, 3 seconds passed
+... 22%, 155392 KB, 48532 KB/s, 3 seconds passed
+... 22%, 155424 KB, 48538 KB/s, 3 seconds passed
+... 22%, 155456 KB, 48543 KB/s, 3 seconds passed
+... 22%, 155488 KB, 48549 KB/s, 3 seconds passed
+... 22%, 155520 KB, 48554 KB/s, 3 seconds passed
+... 22%, 155552 KB, 48560 KB/s, 3 seconds passed
+... 22%, 155584 KB, 48567 KB/s, 3 seconds passed
+... 22%, 155616 KB, 48574 KB/s, 3 seconds passed
+... 22%, 155648 KB, 48581 KB/s, 3 seconds passed
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 23%, 164448 KB, 47922 KB/s, 3 seconds passed
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+... 23%, 164512 KB, 47931 KB/s, 3 seconds passed
+
+.. parsed-literal::
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+... 24%, 168576 KB, 48576 KB/s, 3 seconds passed
+... 24%, 168608 KB, 48581 KB/s, 3 seconds passed
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+... 24%, 168736 KB, 48602 KB/s, 3 seconds passed
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+... 24%, 168800 KB, 48613 KB/s, 3 seconds passed
+... 24%, 168832 KB, 48619 KB/s, 3 seconds passed
+... 24%, 168864 KB, 48624 KB/s, 3 seconds passed
+... 24%, 168896 KB, 48629 KB/s, 3 seconds passed
+... 24%, 168928 KB, 48634 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
+    ... 24%, 168960 KB, 47972 KB/s, 3 seconds passed
+... 24%, 168992 KB, 47965 KB/s, 3 seconds passed
+... 24%, 169024 KB, 47968 KB/s, 3 seconds passed
+... 24%, 169056 KB, 47972 KB/s, 3 seconds passed
+... 24%, 169088 KB, 47975 KB/s, 3 seconds passed
+... 24%, 169120 KB, 47979 KB/s, 3 seconds passed
+... 24%, 169152 KB, 47983 KB/s, 3 seconds passed
+... 24%, 169184 KB, 47987 KB/s, 3 seconds passed
+... 24%, 169216 KB, 47991 KB/s, 3 seconds passed
+... 24%, 169248 KB, 47994 KB/s, 3 seconds passed
+... 24%, 169280 KB, 47998 KB/s, 3 seconds passed
+... 24%, 169312 KB, 48002 KB/s, 3 seconds passed
+... 24%, 169344 KB, 48005 KB/s, 3 seconds passed
+... 24%, 169376 KB, 48009 KB/s, 3 seconds passed
+... 24%, 169408 KB, 48013 KB/s, 3 seconds passed
+... 24%, 169440 KB, 48017 KB/s, 3 seconds passed
+... 24%, 169472 KB, 48021 KB/s, 3 seconds passed
+... 24%, 169504 KB, 48025 KB/s, 3 seconds passed
+... 24%, 169536 KB, 48028 KB/s, 3 seconds passed
+... 24%, 169568 KB, 48032 KB/s, 3 seconds passed
+... 24%, 169600 KB, 48036 KB/s, 3 seconds passed
+... 24%, 169632 KB, 48040 KB/s, 3 seconds passed
+... 24%, 169664 KB, 48044 KB/s, 3 seconds passed
+... 24%, 169696 KB, 48048 KB/s, 3 seconds passed
+... 24%, 169728 KB, 48052 KB/s, 3 seconds passed
+... 24%, 169760 KB, 48055 KB/s, 3 seconds passed
+... 24%, 169792 KB, 48059 KB/s, 3 seconds passed
+... 24%, 169824 KB, 48063 KB/s, 3 seconds passed
+... 24%, 169856 KB, 48067 KB/s, 3 seconds passed
+... 24%, 169888 KB, 48071 KB/s, 3 seconds passed
+... 24%, 169920 KB, 48075 KB/s, 3 seconds passed
+... 24%, 169952 KB, 48079 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
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+... 24%, 170016 KB, 48086 KB/s, 3 seconds passed
+... 24%, 170048 KB, 48091 KB/s, 3 seconds passed
+... 24%, 170080 KB, 48097 KB/s, 3 seconds passed
+... 24%, 170112 KB, 48102 KB/s, 3 seconds passed
+... 24%, 170144 KB, 48108 KB/s, 3 seconds passed
+... 24%, 170176 KB, 48114 KB/s, 3 seconds passed
+... 24%, 170208 KB, 48119 KB/s, 3 seconds passed
+... 24%, 170240 KB, 48125 KB/s, 3 seconds passed
+... 24%, 170272 KB, 48131 KB/s, 3 seconds passed
+... 24%, 170304 KB, 48137 KB/s, 3 seconds passed
+... 24%, 170336 KB, 48143 KB/s, 3 seconds passed
+... 24%, 170368 KB, 48148 KB/s, 3 seconds passed
+... 24%, 170400 KB, 48154 KB/s, 3 seconds passed
+... 24%, 170432 KB, 48160 KB/s, 3 seconds passed
+... 24%, 170464 KB, 48165 KB/s, 3 seconds passed
+... 24%, 170496 KB, 48171 KB/s, 3 seconds passed
+... 24%, 170528 KB, 48176 KB/s, 3 seconds passed
+... 24%, 170560 KB, 48182 KB/s, 3 seconds passed
+... 24%, 170592 KB, 48188 KB/s, 3 seconds passed
+... 24%, 170624 KB, 48193 KB/s, 3 seconds passed
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+... 24%, 170688 KB, 48205 KB/s, 3 seconds passed
+... 24%, 170720 KB, 48210 KB/s, 3 seconds passed
+... 24%, 170752 KB, 48216 KB/s, 3 seconds passed
+... 24%, 170784 KB, 48222 KB/s, 3 seconds passed
+... 24%, 170816 KB, 48227 KB/s, 3 seconds passed
+... 24%, 170848 KB, 48233 KB/s, 3 seconds passed
+... 24%, 170880 KB, 48239 KB/s, 3 seconds passed
+... 24%, 170912 KB, 48245 KB/s, 3 seconds passed
+... 24%, 170944 KB, 48250 KB/s, 3 seconds passed
+... 24%, 170976 KB, 48256 KB/s, 3 seconds passed
+... 24%, 171008 KB, 48261 KB/s, 3 seconds passed
+... 24%, 171040 KB, 48267 KB/s, 3 seconds passed
+... 24%, 171072 KB, 48273 KB/s, 3 seconds passed
+... 24%, 171104 KB, 48278 KB/s, 3 seconds passed
+... 24%, 171136 KB, 48284 KB/s, 3 seconds passed
+... 24%, 171168 KB, 48290 KB/s, 3 seconds passed
+... 24%, 171200 KB, 48295 KB/s, 3 seconds passed
+... 24%, 171232 KB, 48301 KB/s, 3 seconds passed
+... 24%, 171264 KB, 48307 KB/s, 3 seconds passed
+... 24%, 171296 KB, 48312 KB/s, 3 seconds passed
+... 24%, 171328 KB, 48319 KB/s, 3 seconds passed
+... 24%, 171360 KB, 48325 KB/s, 3 seconds passed
+... 24%, 171392 KB, 48332 KB/s, 3 seconds passed
+... 24%, 171424 KB, 48338 KB/s, 3 seconds passed
+... 24%, 171456 KB, 48345 KB/s, 3 seconds passed
+... 24%, 171488 KB, 48352 KB/s, 3 seconds passed
+... 24%, 171520 KB, 48359 KB/s, 3 seconds passed
+... 24%, 171552 KB, 48366 KB/s, 3 seconds passed
+... 24%, 171584 KB, 48372 KB/s, 3 seconds passed
+... 24%, 171616 KB, 48379 KB/s, 3 seconds passed
+... 24%, 171648 KB, 48386 KB/s, 3 seconds passed
+... 24%, 171680 KB, 48393 KB/s, 3 seconds passed
+... 24%, 171712 KB, 48399 KB/s, 3 seconds passed
+... 24%, 171744 KB, 48406 KB/s, 3 seconds passed
+... 24%, 171776 KB, 48413 KB/s, 3 seconds passed
+... 24%, 171808 KB, 48420 KB/s, 3 seconds passed
+... 24%, 171840 KB, 48427 KB/s, 3 seconds passed
+... 24%, 171872 KB, 48433 KB/s, 3 seconds passed
+... 24%, 171904 KB, 48440 KB/s, 3 seconds passed
+... 24%, 171936 KB, 48447 KB/s, 3 seconds passed
+... 24%, 171968 KB, 48453 KB/s, 3 seconds passed
+... 24%, 172000 KB, 48458 KB/s, 3 seconds passed
+... 24%, 172032 KB, 48463 KB/s, 3 seconds passed
+... 24%, 172064 KB, 48468 KB/s, 3 seconds passed
+... 24%, 172096 KB, 48473 KB/s, 3 seconds passed
+... 24%, 172128 KB, 48479 KB/s, 3 seconds passed
+... 24%, 172160 KB, 48485 KB/s, 3 seconds passed
+... 24%, 172192 KB, 48490 KB/s, 3 seconds passed
+... 24%, 172224 KB, 48495 KB/s, 3 seconds passed
+... 24%, 172256 KB, 48499 KB/s, 3 seconds passed
+... 24%, 172288 KB, 48505 KB/s, 3 seconds passed
+... 24%, 172320 KB, 48510 KB/s, 3 seconds passed
+... 24%, 172352 KB, 48515 KB/s, 3 seconds passed
+... 24%, 172384 KB, 48520 KB/s, 3 seconds passed
+... 24%, 172416 KB, 48526 KB/s, 3 seconds passed
+... 24%, 172448 KB, 48531 KB/s, 3 seconds passed
+... 24%, 172480 KB, 48536 KB/s, 3 seconds passed
+... 24%, 172512 KB, 48541 KB/s, 3 seconds passed
+... 24%, 172544 KB, 48546 KB/s, 3 seconds passed
+... 24%, 172576 KB, 48552 KB/s, 3 seconds passed
+... 24%, 172608 KB, 48557 KB/s, 3 seconds passed
+... 24%, 172640 KB, 48562 KB/s, 3 seconds passed
+... 24%, 172672 KB, 48568 KB/s, 3 seconds passed
+... 24%, 172704 KB, 48573 KB/s, 3 seconds passed
+... 24%, 172736 KB, 48578 KB/s, 3 seconds passed
+... 24%, 172768 KB, 48583 KB/s, 3 seconds passed
+... 24%, 172800 KB, 48589 KB/s, 3 seconds passed
+... 24%, 172832 KB, 48593 KB/s, 3 seconds passed
+... 24%, 172864 KB, 48575 KB/s, 3 seconds passed
+... 24%, 172896 KB, 48566 KB/s, 3 seconds passed
+... 24%, 172928 KB, 48572 KB/s, 3 seconds passed
+... 24%, 172960 KB, 48577 KB/s, 3 seconds passed
+... 24%, 172992 KB, 48582 KB/s, 3 seconds passed
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+... 24%, 173056 KB, 48592 KB/s, 3 seconds passed
+... 24%, 173088 KB, 48598 KB/s, 3 seconds passed
+... 24%, 173120 KB, 48603 KB/s, 3 seconds passed
+... 24%, 173152 KB, 48608 KB/s, 3 seconds passed
+... 24%, 173184 KB, 48613 KB/s, 3 seconds passed
+... 24%, 173216 KB, 48619 KB/s, 3 seconds passed
+... 24%, 173248 KB, 48625 KB/s, 3 seconds passed
+... 24%, 173280 KB, 48630 KB/s, 3 seconds passed
+... 24%, 173312 KB, 48635 KB/s, 3 seconds passed
+... 24%, 173344 KB, 48640 KB/s, 3 seconds passed
+... 24%, 173376 KB, 48645 KB/s, 3 seconds passed
+... 24%, 173408 KB, 48651 KB/s, 3 seconds passed
+... 24%, 173440 KB, 48656 KB/s, 3 seconds passed
+... 24%, 173472 KB, 48661 KB/s, 3 seconds passed
+... 24%, 173504 KB, 48666 KB/s, 3 seconds passed
+... 24%, 173536 KB, 48671 KB/s, 3 seconds passed
+... 24%, 173568 KB, 48676 KB/s, 3 seconds passed
+... 24%, 173600 KB, 48681 KB/s, 3 seconds passed
+... 24%, 173632 KB, 48687 KB/s, 3 seconds passed
+... 24%, 173664 KB, 48692 KB/s, 3 seconds passed
+... 24%, 173696 KB, 48697 KB/s, 3 seconds passed
+... 24%, 173728 KB, 48702 KB/s, 3 seconds passed
+... 24%, 173760 KB, 48707 KB/s, 3 seconds passed
+... 24%, 173792 KB, 48710 KB/s, 3 seconds passed
+... 24%, 173824 KB, 48713 KB/s, 3 seconds passed
+... 24%, 173856 KB, 48717 KB/s, 3 seconds passed
+... 25%, 173888 KB, 48723 KB/s, 3 seconds passed
+... 25%, 173920 KB, 48729 KB/s, 3 seconds passed
+... 25%, 173952 KB, 48736 KB/s, 3 seconds passed
+... 25%, 173984 KB, 48743 KB/s, 3 seconds passed
+... 25%, 174016 KB, 48749 KB/s, 3 seconds passed
+... 25%, 174048 KB, 48754 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
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+... 25%, 174144 KB, 48307 KB/s, 3 seconds passed
+... 25%, 174176 KB, 48311 KB/s, 3 seconds passed
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+... 25%, 174240 KB, 48017 KB/s, 3 seconds passed
+... 25%, 174272 KB, 48019 KB/s, 3 seconds passed
+... 25%, 174304 KB, 48022 KB/s, 3 seconds passed
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+... 25%, 174400 KB, 48033 KB/s, 3 seconds passed
+... 25%, 174432 KB, 48036 KB/s, 3 seconds passed
+... 25%, 174464 KB, 48039 KB/s, 3 seconds passed
+... 25%, 174496 KB, 48043 KB/s, 3 seconds passed
+... 25%, 174528 KB, 48047 KB/s, 3 seconds passed
+... 25%, 174560 KB, 48051 KB/s, 3 seconds passed
+... 25%, 174592 KB, 48054 KB/s, 3 seconds passed
+... 25%, 174624 KB, 48058 KB/s, 3 seconds passed
+... 25%, 174656 KB, 48061 KB/s, 3 seconds passed
+... 25%, 174688 KB, 48065 KB/s, 3 seconds passed
+... 25%, 174720 KB, 48069 KB/s, 3 seconds passed
+... 25%, 174752 KB, 48072 KB/s, 3 seconds passed
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+... 25%, 174816 KB, 48079 KB/s, 3 seconds passed
+... 25%, 174848 KB, 48083 KB/s, 3 seconds passed
+... 25%, 174880 KB, 48086 KB/s, 3 seconds passed
+... 25%, 174912 KB, 48090 KB/s, 3 seconds passed
+... 25%, 174944 KB, 48094 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
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+... 25%, 175040 KB, 48107 KB/s, 3 seconds passed
+... 25%, 175072 KB, 48112 KB/s, 3 seconds passed
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+... 25%, 175232 KB, 48134 KB/s, 3 seconds passed
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+... 25%, 175872 KB, 48221 KB/s, 3 seconds passed
+... 25%, 175904 KB, 48225 KB/s, 3 seconds passed
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+... 25%, 176064 KB, 48254 KB/s, 3 seconds passed
+... 25%, 176096 KB, 48260 KB/s, 3 seconds passed
+... 25%, 176128 KB, 48266 KB/s, 3 seconds passed
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+... 25%, 176224 KB, 48283 KB/s, 3 seconds passed
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+... 25%, 176288 KB, 48295 KB/s, 3 seconds passed
+... 25%, 176320 KB, 48301 KB/s, 3 seconds passed
+... 25%, 176352 KB, 48307 KB/s, 3 seconds passed
+... 25%, 176384 KB, 48313 KB/s, 3 seconds passed
+... 25%, 176416 KB, 48319 KB/s, 3 seconds passed
+... 25%, 176448 KB, 48325 KB/s, 3 seconds passed
+... 25%, 176480 KB, 48330 KB/s, 3 seconds passed
+... 25%, 176512 KB, 48336 KB/s, 3 seconds passed
+... 25%, 176544 KB, 48342 KB/s, 3 seconds passed
+... 25%, 176576 KB, 48348 KB/s, 3 seconds passed
+... 25%, 176608 KB, 48354 KB/s, 3 seconds passed
+... 25%, 176640 KB, 48359 KB/s, 3 seconds passed
+... 25%, 176672 KB, 48365 KB/s, 3 seconds passed
+... 25%, 176704 KB, 48371 KB/s, 3 seconds passed
+... 25%, 176736 KB, 48377 KB/s, 3 seconds passed
+... 25%, 176768 KB, 48383 KB/s, 3 seconds passed
+... 25%, 176800 KB, 48389 KB/s, 3 seconds passed
+... 25%, 176832 KB, 48395 KB/s, 3 seconds passed
+... 25%, 176864 KB, 48401 KB/s, 3 seconds passed
+... 25%, 176896 KB, 48407 KB/s, 3 seconds passed
+... 25%, 176928 KB, 48413 KB/s, 3 seconds passed
+... 25%, 176960 KB, 48419 KB/s, 3 seconds passed
+... 25%, 176992 KB, 48425 KB/s, 3 seconds passed
+... 25%, 177024 KB, 48430 KB/s, 3 seconds passed
+... 25%, 177056 KB, 48436 KB/s, 3 seconds passed
+... 25%, 177088 KB, 48442 KB/s, 3 seconds passed
+... 25%, 177120 KB, 48448 KB/s, 3 seconds passed
+... 25%, 177152 KB, 48453 KB/s, 3 seconds passed
+... 25%, 177184 KB, 48459 KB/s, 3 seconds passed
+... 25%, 177216 KB, 48465 KB/s, 3 seconds passed
+... 25%, 177248 KB, 48471 KB/s, 3 seconds passed
+... 25%, 177280 KB, 48477 KB/s, 3 seconds passed
+... 25%, 177312 KB, 48483 KB/s, 3 seconds passed
+... 25%, 177344 KB, 48489 KB/s, 3 seconds passed
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+... 25%, 177440 KB, 48509 KB/s, 3 seconds passed
+... 25%, 177472 KB, 48516 KB/s, 3 seconds passed
+... 25%, 177504 KB, 48523 KB/s, 3 seconds passed
+... 25%, 177536 KB, 48529 KB/s, 3 seconds passed
+... 25%, 177568 KB, 48536 KB/s, 3 seconds passed
+... 25%, 177600 KB, 48543 KB/s, 3 seconds passed
+... 25%, 177632 KB, 48550 KB/s, 3 seconds passed
+... 25%, 177664 KB, 48556 KB/s, 3 seconds passed
+... 25%, 177696 KB, 48563 KB/s, 3 seconds passed
+... 25%, 177728 KB, 48570 KB/s, 3 seconds passed
+... 25%, 177760 KB, 48576 KB/s, 3 seconds passed
+... 25%, 177792 KB, 48582 KB/s, 3 seconds passed
+... 25%, 177824 KB, 48587 KB/s, 3 seconds passed
+... 25%, 177856 KB, 48592 KB/s, 3 seconds passed
+... 25%, 177888 KB, 48597 KB/s, 3 seconds passed
+... 25%, 177920 KB, 48602 KB/s, 3 seconds passed
+... 25%, 177952 KB, 48607 KB/s, 3 seconds passed
+... 25%, 177984 KB, 48612 KB/s, 3 seconds passed
+... 25%, 178016 KB, 48618 KB/s, 3 seconds passed
+... 25%, 178048 KB, 48622 KB/s, 3 seconds passed
+... 25%, 178080 KB, 48627 KB/s, 3 seconds passed
+... 25%, 178112 KB, 48633 KB/s, 3 seconds passed
+... 25%, 178144 KB, 48638 KB/s, 3 seconds passed
+... 25%, 178176 KB, 48642 KB/s, 3 seconds passed
+... 25%, 178208 KB, 48647 KB/s, 3 seconds passed
+... 25%, 178240 KB, 48652 KB/s, 3 seconds passed
+... 25%, 178272 KB, 48658 KB/s, 3 seconds passed
+... 25%, 178304 KB, 48662 KB/s, 3 seconds passed
+... 25%, 178336 KB, 48667 KB/s, 3 seconds passed
+... 25%, 178368 KB, 48670 KB/s, 3 seconds passed
+... 25%, 178400 KB, 48673 KB/s, 3 seconds passed
+... 25%, 178432 KB, 48679 KB/s, 3 seconds passed
+... 25%, 178464 KB, 48686 KB/s, 3 seconds passed
+... 25%, 178496 KB, 48693 KB/s, 3 seconds passed
+... 25%, 178528 KB, 48698 KB/s, 3 seconds passed
+... 25%, 178560 KB, 48702 KB/s, 3 seconds passed
+... 25%, 178592 KB, 48706 KB/s, 3 seconds passed
+... 25%, 178624 KB, 48712 KB/s, 3 seconds passed
+... 25%, 178656 KB, 48718 KB/s, 3 seconds passed
+... 25%, 178688 KB, 48723 KB/s, 3 seconds passed
+... 25%, 178720 KB, 48728 KB/s, 3 seconds passed
+... 25%, 178752 KB, 48733 KB/s, 3 seconds passed
+... 25%, 178784 KB, 48738 KB/s, 3 seconds passed
+... 25%, 178816 KB, 48743 KB/s, 3 seconds passed
+... 25%, 178848 KB, 48747 KB/s, 3 seconds passed
+... 25%, 178880 KB, 48753 KB/s, 3 seconds passed
+... 25%, 178912 KB, 48758 KB/s, 3 seconds passed
+... 25%, 178944 KB, 48763 KB/s, 3 seconds passed
+... 25%, 178976 KB, 48768 KB/s, 3 seconds passed
+... 25%, 179008 KB, 48773 KB/s, 3 seconds passed
+... 25%, 179040 KB, 48778 KB/s, 3 seconds passed
+... 25%, 179072 KB, 48783 KB/s, 3 seconds passed
+... 25%, 179104 KB, 48788 KB/s, 3 seconds passed
+... 25%, 179136 KB, 48793 KB/s, 3 seconds passed
+... 25%, 179168 KB, 48798 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
+    ... 25%, 179200 KB, 48243 KB/s, 3 seconds passed
+... 25%, 179232 KB, 48245 KB/s, 3 seconds passed
+... 25%, 179264 KB, 48248 KB/s, 3 seconds passed
+... 25%, 179296 KB, 48252 KB/s, 3 seconds passed
+... 25%, 179328 KB, 48256 KB/s, 3 seconds passed
+... 25%, 179360 KB, 48260 KB/s, 3 seconds passed
+... 25%, 179392 KB, 48261 KB/s, 3 seconds passed
+... 25%, 179424 KB, 48246 KB/s, 3 seconds passed
+... 25%, 179456 KB, 48249 KB/s, 3 seconds passed
+... 25%, 179488 KB, 48252 KB/s, 3 seconds passed
+... 25%, 179520 KB, 48255 KB/s, 3 seconds passed
+... 25%, 179552 KB, 48259 KB/s, 3 seconds passed
+... 25%, 179584 KB, 48262 KB/s, 3 seconds passed
+... 25%, 179616 KB, 48266 KB/s, 3 seconds passed
+... 25%, 179648 KB, 48269 KB/s, 3 seconds passed
+... 25%, 179680 KB, 48273 KB/s, 3 seconds passed
+... 25%, 179712 KB, 48278 KB/s, 3 seconds passed
+... 25%, 179744 KB, 48282 KB/s, 3 seconds passed
+... 25%, 179776 KB, 48286 KB/s, 3 seconds passed
+... 25%, 179808 KB, 48289 KB/s, 3 seconds passed
+... 25%, 179840 KB, 48293 KB/s, 3 seconds passed
+... 25%, 179872 KB, 48296 KB/s, 3 seconds passed
+... 25%, 179904 KB, 48300 KB/s, 3 seconds passed
+... 25%, 179936 KB, 48304 KB/s, 3 seconds passed
+... 25%, 179968 KB, 48307 KB/s, 3 seconds passed
+... 25%, 180000 KB, 48311 KB/s, 3 seconds passed
+... 25%, 180032 KB, 48315 KB/s, 3 seconds passed
+... 25%, 180064 KB, 48318 KB/s, 3 seconds passed
+... 25%, 180096 KB, 48322 KB/s, 3 seconds passed
+... 25%, 180128 KB, 48325 KB/s, 3 seconds passed
+... 25%, 180160 KB, 48329 KB/s, 3 seconds passed
+... 25%, 180192 KB, 48333 KB/s, 3 seconds passed
+... 25%, 180224 KB, 48338 KB/s, 3 seconds passed
+... 25%, 180256 KB, 48343 KB/s, 3 seconds passed
+... 25%, 180288 KB, 48348 KB/s, 3 seconds passed
+... 25%, 180320 KB, 48353 KB/s, 3 seconds passed
+... 25%, 180352 KB, 48357 KB/s, 3 seconds passed
+... 25%, 180384 KB, 48362 KB/s, 3 seconds passed
+... 25%, 180416 KB, 48367 KB/s, 3 seconds passed
+... 25%, 180448 KB, 48372 KB/s, 3 seconds passed
+... 25%, 180480 KB, 48377 KB/s, 3 seconds passed
+... 25%, 180512 KB, 48382 KB/s, 3 seconds passed
+... 25%, 180544 KB, 48387 KB/s, 3 seconds passed
+... 25%, 180576 KB, 48391 KB/s, 3 seconds passed
+... 25%, 180608 KB, 48396 KB/s, 3 seconds passed
+... 25%, 180640 KB, 48401 KB/s, 3 seconds passed
+... 25%, 180672 KB, 48406 KB/s, 3 seconds passed
+... 25%, 180704 KB, 48411 KB/s, 3 seconds passed
+... 25%, 180736 KB, 48416 KB/s, 3 seconds passed
+... 25%, 180768 KB, 48421 KB/s, 3 seconds passed
+... 25%, 180800 KB, 48426 KB/s, 3 seconds passed
+... 26%, 180832 KB, 48430 KB/s, 3 seconds passed
+... 26%, 180864 KB, 48435 KB/s, 3 seconds passed
+... 26%, 180896 KB, 48440 KB/s, 3 seconds passed
+... 26%, 180928 KB, 48445 KB/s, 3 seconds passed
+... 26%, 180960 KB, 48450 KB/s, 3 seconds passed
+... 26%, 180992 KB, 48455 KB/s, 3 seconds passed
+... 26%, 181024 KB, 48459 KB/s, 3 seconds passed
+... 26%, 181056 KB, 48464 KB/s, 3 seconds passed
+... 26%, 181088 KB, 48469 KB/s, 3 seconds passed
+... 26%, 181120 KB, 48474 KB/s, 3 seconds passed
+... 26%, 181152 KB, 48479 KB/s, 3 seconds passed
+... 26%, 181184 KB, 48484 KB/s, 3 seconds passed
+... 26%, 181216 KB, 48489 KB/s, 3 seconds passed
+... 26%, 181248 KB, 48494 KB/s, 3 seconds passed
+... 26%, 181280 KB, 48498 KB/s, 3 seconds passed
+... 26%, 181312 KB, 48503 KB/s, 3 seconds passed
+... 26%, 181344 KB, 48508 KB/s, 3 seconds passed
+... 26%, 181376 KB, 48513 KB/s, 3 seconds passed
+... 26%, 181408 KB, 48519 KB/s, 3 seconds passed
+... 26%, 181440 KB, 48525 KB/s, 3 seconds passed
+... 26%, 181472 KB, 48531 KB/s, 3 seconds passed
+... 26%, 181504 KB, 48537 KB/s, 3 seconds passed
+... 26%, 181536 KB, 48543 KB/s, 3 seconds passed
+... 26%, 181568 KB, 48549 KB/s, 3 seconds passed
+... 26%, 181600 KB, 48555 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
+    ... 26%, 181632 KB, 48561 KB/s, 3 seconds passed
+... 26%, 181664 KB, 48567 KB/s, 3 seconds passed
+... 26%, 181696 KB, 48573 KB/s, 3 seconds passed
+... 26%, 181728 KB, 48579 KB/s, 3 seconds passed
+... 26%, 181760 KB, 48585 KB/s, 3 seconds passed
+... 26%, 181792 KB, 48592 KB/s, 3 seconds passed
+... 26%, 181824 KB, 48598 KB/s, 3 seconds passed
+... 26%, 181856 KB, 48604 KB/s, 3 seconds passed
+... 26%, 181888 KB, 48610 KB/s, 3 seconds passed
+... 26%, 181920 KB, 48616 KB/s, 3 seconds passed
+... 26%, 181952 KB, 48622 KB/s, 3 seconds passed
+... 26%, 181984 KB, 48628 KB/s, 3 seconds passed
+... 26%, 182016 KB, 48635 KB/s, 3 seconds passed
+... 26%, 182048 KB, 48641 KB/s, 3 seconds passed
+... 26%, 182080 KB, 48647 KB/s, 3 seconds passed
+... 26%, 182112 KB, 48652 KB/s, 3 seconds passed
+... 26%, 182144 KB, 48659 KB/s, 3 seconds passed
+... 26%, 182176 KB, 48664 KB/s, 3 seconds passed
+... 26%, 182208 KB, 48669 KB/s, 3 seconds passed
+... 26%, 182240 KB, 48674 KB/s, 3 seconds passed
+... 26%, 182272 KB, 48678 KB/s, 3 seconds passed
+... 26%, 182304 KB, 48683 KB/s, 3 seconds passed
+... 26%, 182336 KB, 48688 KB/s, 3 seconds passed
+... 26%, 182368 KB, 48692 KB/s, 3 seconds passed
+... 26%, 182400 KB, 48698 KB/s, 3 seconds passed
+... 26%, 182432 KB, 48703 KB/s, 3 seconds passed
+... 26%, 182464 KB, 48708 KB/s, 3 seconds passed
+... 26%, 182496 KB, 48713 KB/s, 3 seconds passed
+... 26%, 182528 KB, 48718 KB/s, 3 seconds passed
+... 26%, 182560 KB, 48722 KB/s, 3 seconds passed
+... 26%, 182592 KB, 48728 KB/s, 3 seconds passed
+... 26%, 182624 KB, 48732 KB/s, 3 seconds passed
+... 26%, 182656 KB, 48738 KB/s, 3 seconds passed
+... 26%, 182688 KB, 48743 KB/s, 3 seconds passed
+... 26%, 182720 KB, 48748 KB/s, 3 seconds passed
+... 26%, 182752 KB, 48753 KB/s, 3 seconds passed
+... 26%, 182784 KB, 48757 KB/s, 3 seconds passed
+... 26%, 182816 KB, 48762 KB/s, 3 seconds passed
+... 26%, 182848 KB, 48766 KB/s, 3 seconds passed
+... 26%, 182880 KB, 48771 KB/s, 3 seconds passed
+... 26%, 182912 KB, 48776 KB/s, 3 seconds passed
+... 26%, 182944 KB, 48781 KB/s, 3 seconds passed
+... 26%, 182976 KB, 48787 KB/s, 3 seconds passed
+... 26%, 183008 KB, 48792 KB/s, 3 seconds passed
+... 26%, 183040 KB, 48722 KB/s, 3 seconds passed
+... 26%, 183072 KB, 48725 KB/s, 3 seconds passed
+... 26%, 183104 KB, 48730 KB/s, 3 seconds passed
+... 26%, 183136 KB, 48737 KB/s, 3 seconds passed
+... 26%, 183168 KB, 48741 KB/s, 3 seconds passed
+... 26%, 183200 KB, 48746 KB/s, 3 seconds passed
+... 26%, 183232 KB, 48751 KB/s, 3 seconds passed
+... 26%, 183264 KB, 48756 KB/s, 3 seconds passed
+... 26%, 183296 KB, 48761 KB/s, 3 seconds passed
+... 26%, 183328 KB, 48766 KB/s, 3 seconds passed
+... 26%, 183360 KB, 48770 KB/s, 3 seconds passed
+... 26%, 183392 KB, 48776 KB/s, 3 seconds passed
+... 26%, 183424 KB, 48781 KB/s, 3 seconds passed
+... 26%, 183456 KB, 48785 KB/s, 3 seconds passed
+... 26%, 183488 KB, 48790 KB/s, 3 seconds passed
+... 26%, 183520 KB, 48795 KB/s, 3 seconds passed
+... 26%, 183552 KB, 48800 KB/s, 3 seconds passed
+... 26%, 183584 KB, 48805 KB/s, 3 seconds passed
+... 26%, 183616 KB, 48798 KB/s, 3 seconds passed
+... 26%, 183648 KB, 48803 KB/s, 3 seconds passed
+... 26%, 183680 KB, 48808 KB/s, 3 seconds passed
+... 26%, 183712 KB, 48813 KB/s, 3 seconds passed
+... 26%, 183744 KB, 48817 KB/s, 3 seconds passed
+... 26%, 183776 KB, 48822 KB/s, 3 seconds passed
+... 26%, 183808 KB, 48827 KB/s, 3 seconds passed
+... 26%, 183840 KB, 48832 KB/s, 3 seconds passed
+... 26%, 183872 KB, 48837 KB/s, 3 seconds passed
+... 26%, 183904 KB, 48842 KB/s, 3 seconds passed
+... 26%, 183936 KB, 48847 KB/s, 3 seconds passed
+... 26%, 183968 KB, 48852 KB/s, 3 seconds passed
+... 26%, 184000 KB, 48857 KB/s, 3 seconds passed
+... 26%, 184032 KB, 48861 KB/s, 3 seconds passed
+... 26%, 184064 KB, 48866 KB/s, 3 seconds passed
+... 26%, 184096 KB, 48871 KB/s, 3 seconds passed
+... 26%, 184128 KB, 48876 KB/s, 3 seconds passed
+... 26%, 184160 KB, 48881 KB/s, 3 seconds passed
+... 26%, 184192 KB, 48886 KB/s, 3 seconds passed
+... 26%, 184224 KB, 48890 KB/s, 3 seconds passed
+... 26%, 184256 KB, 48895 KB/s, 3 seconds passed
+... 26%, 184288 KB, 48900 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
+    ... 26%, 184320 KB, 48613 KB/s, 3 seconds passed
+... 26%, 184352 KB, 48615 KB/s, 3 seconds passed
+... 26%, 184384 KB, 48618 KB/s, 3 seconds passed
+... 26%, 184416 KB, 48623 KB/s, 3 seconds passed
+... 26%, 184448 KB, 48570 KB/s, 3 seconds passed
+... 26%, 184480 KB, 48573 KB/s, 3 seconds passed
+... 26%, 184512 KB, 48576 KB/s, 3 seconds passed
+... 26%, 184544 KB, 48580 KB/s, 3 seconds passed
+... 26%, 184576 KB, 48583 KB/s, 3 seconds passed
+... 26%, 184608 KB, 48586 KB/s, 3 seconds passed
+... 26%, 184640 KB, 48590 KB/s, 3 seconds passed
+... 26%, 184672 KB, 48593 KB/s, 3 seconds passed
+... 26%, 184704 KB, 48597 KB/s, 3 seconds passed
+... 26%, 184736 KB, 48600 KB/s, 3 seconds passed
+... 26%, 184768 KB, 48604 KB/s, 3 seconds passed
+... 26%, 184800 KB, 48607 KB/s, 3 seconds passed
+... 26%, 184832 KB, 48610 KB/s, 3 seconds passed
+... 26%, 184864 KB, 48614 KB/s, 3 seconds passed
+... 26%, 184896 KB, 48617 KB/s, 3 seconds passed
+... 26%, 184928 KB, 48621 KB/s, 3 seconds passed
+... 26%, 184960 KB, 48624 KB/s, 3 seconds passed
+... 26%, 184992 KB, 48628 KB/s, 3 seconds passed
+... 26%, 185024 KB, 48631 KB/s, 3 seconds passed
+... 26%, 185056 KB, 48634 KB/s, 3 seconds passed
+... 26%, 185088 KB, 48638 KB/s, 3 seconds passed
+... 26%, 185120 KB, 48642 KB/s, 3 seconds passed
+... 26%, 185152 KB, 48645 KB/s, 3 seconds passed
+... 26%, 185184 KB, 48649 KB/s, 3 seconds passed
+... 26%, 185216 KB, 48652 KB/s, 3 seconds passed
+... 26%, 185248 KB, 48655 KB/s, 3 seconds passed
+... 26%, 185280 KB, 48659 KB/s, 3 seconds passed
+... 26%, 185312 KB, 48663 KB/s, 3 seconds passed
+... 26%, 185344 KB, 48666 KB/s, 3 seconds passed
+... 26%, 185376 KB, 48669 KB/s, 3 seconds passed
+... 26%, 185408 KB, 48673 KB/s, 3 seconds passed
+... 26%, 185440 KB, 48678 KB/s, 3 seconds passed
+... 26%, 185472 KB, 48683 KB/s, 3 seconds passed
+... 26%, 185504 KB, 48688 KB/s, 3 seconds passed
+... 26%, 185536 KB, 48694 KB/s, 3 seconds passed
+... 26%, 185568 KB, 48699 KB/s, 3 seconds passed
+... 26%, 185600 KB, 48704 KB/s, 3 seconds passed
+... 26%, 185632 KB, 48709 KB/s, 3 seconds passed
+... 26%, 185664 KB, 48714 KB/s, 3 seconds passed
+... 26%, 185696 KB, 48720 KB/s, 3 seconds passed
+... 26%, 185728 KB, 48725 KB/s, 3 seconds passed
+... 26%, 185760 KB, 48730 KB/s, 3 seconds passed
+... 26%, 185792 KB, 48735 KB/s, 3 seconds passed
+... 26%, 185824 KB, 48740 KB/s, 3 seconds passed
+... 26%, 185856 KB, 48745 KB/s, 3 seconds passed
+... 26%, 185888 KB, 48751 KB/s, 3 seconds passed
+... 26%, 185920 KB, 48756 KB/s, 3 seconds passed
+... 26%, 185952 KB, 48761 KB/s, 3 seconds passed
+... 26%, 185984 KB, 48766 KB/s, 3 seconds passed
+... 26%, 186016 KB, 48771 KB/s, 3 seconds passed
+... 26%, 186048 KB, 48776 KB/s, 3 seconds passed
+... 26%, 186080 KB, 48782 KB/s, 3 seconds passed
+... 26%, 186112 KB, 48787 KB/s, 3 seconds passed
+... 26%, 186144 KB, 48792 KB/s, 3 seconds passed
+... 26%, 186176 KB, 48797 KB/s, 3 seconds passed
+... 26%, 186208 KB, 48802 KB/s, 3 seconds passed
+... 26%, 186240 KB, 48807 KB/s, 3 seconds passed
+... 26%, 186272 KB, 48813 KB/s, 3 seconds passed
+... 26%, 186304 KB, 48818 KB/s, 3 seconds passed
+... 26%, 186336 KB, 48823 KB/s, 3 seconds passed
+... 26%, 186368 KB, 48828 KB/s, 3 seconds passed
+... 26%, 186400 KB, 48834 KB/s, 3 seconds passed
+... 26%, 186432 KB, 48839 KB/s, 3 seconds passed
+... 26%, 186464 KB, 48844 KB/s, 3 seconds passed
+... 26%, 186496 KB, 48849 KB/s, 3 seconds passed
+... 26%, 186528 KB, 48854 KB/s, 3 seconds passed
+... 26%, 186560 KB, 48860 KB/s, 3 seconds passed
+... 26%, 186592 KB, 48865 KB/s, 3 seconds passed
+... 26%, 186624 KB, 48870 KB/s, 3 seconds passed
+... 26%, 186656 KB, 48875 KB/s, 3 seconds passed
+... 26%, 186688 KB, 48880 KB/s, 3 seconds passed
+... 26%, 186720 KB, 48886 KB/s, 3 seconds passed
+... 26%, 186752 KB, 48893 KB/s, 3 seconds passed
+... 26%, 186784 KB, 48899 KB/s, 3 seconds passed
+... 26%, 186816 KB, 48905 KB/s, 3 seconds passed
+... 26%, 186848 KB, 48911 KB/s, 3 seconds passed
+... 26%, 186880 KB, 48918 KB/s, 3 seconds passed
+... 26%, 186912 KB, 48924 KB/s, 3 seconds passed
+... 26%, 186944 KB, 48930 KB/s, 3 seconds passed
+... 26%, 186976 KB, 48936 KB/s, 3 seconds passed
+... 26%, 187008 KB, 48942 KB/s, 3 seconds passed
+... 26%, 187040 KB, 48949 KB/s, 3 seconds passed
+... 26%, 187072 KB, 48955 KB/s, 3 seconds passed
+... 26%, 187104 KB, 48961 KB/s, 3 seconds passed
+... 26%, 187136 KB, 48967 KB/s, 3 seconds passed
+... 26%, 187168 KB, 48973 KB/s, 3 seconds passed
+... 26%, 187200 KB, 48980 KB/s, 3 seconds passed
+... 26%, 187232 KB, 48986 KB/s, 3 seconds passed
+... 26%, 187264 KB, 48992 KB/s, 3 seconds passed
+... 26%, 187296 KB, 48998 KB/s, 3 seconds passed
+... 26%, 187328 KB, 49004 KB/s, 3 seconds passed
+... 26%, 187360 KB, 49009 KB/s, 3 seconds passed
+... 26%, 187392 KB, 49015 KB/s, 3 seconds passed
+... 26%, 187424 KB, 49020 KB/s, 3 seconds passed
+... 26%, 187456 KB, 49025 KB/s, 3 seconds passed
+... 26%, 187488 KB, 49029 KB/s, 3 seconds passed
+... 26%, 187520 KB, 49033 KB/s, 3 seconds passed
+... 26%, 187552 KB, 49039 KB/s, 3 seconds passed
+... 26%, 187584 KB, 49043 KB/s, 3 seconds passed
+... 26%, 187616 KB, 49047 KB/s, 3 seconds passed
+... 26%, 187648 KB, 49052 KB/s, 3 seconds passed
+... 26%, 187680 KB, 49057 KB/s, 3 seconds passed
+... 26%, 187712 KB, 49063 KB/s, 3 seconds passed
+... 26%, 187744 KB, 49068 KB/s, 3 seconds passed
+... 26%, 187776 KB, 49072 KB/s, 3 seconds passed
+... 27%, 187808 KB, 49076 KB/s, 3 seconds passed
+... 27%, 187840 KB, 49080 KB/s, 3 seconds passed
+... 27%, 187872 KB, 49086 KB/s, 3 seconds passed
+... 27%, 187904 KB, 49091 KB/s, 3 seconds passed
+... 27%, 187936 KB, 49096 KB/s, 3 seconds passed
+... 27%, 187968 KB, 49100 KB/s, 3 seconds passed
+... 27%, 188000 KB, 49105 KB/s, 3 seconds passed
+... 27%, 188032 KB, 49111 KB/s, 3 seconds passed
+... 27%, 188064 KB, 49116 KB/s, 3 seconds passed
+... 27%, 188096 KB, 49121 KB/s, 3 seconds passed
+... 27%, 188128 KB, 49125 KB/s, 3 seconds passed
+... 27%, 188160 KB, 49129 KB/s, 3 seconds passed
+... 27%, 188192 KB, 49097 KB/s, 3 seconds passed
+... 27%, 188224 KB, 49099 KB/s, 3 seconds passed
+... 27%, 188256 KB, 49103 KB/s, 3 seconds passed
+... 27%, 188288 KB, 49110 KB/s, 3 seconds passed
+... 27%, 188320 KB, 49116 KB/s, 3 seconds passed
+... 27%, 188352 KB, 49122 KB/s, 3 seconds passed
+... 27%, 188384 KB, 49127 KB/s, 3 seconds passed
+... 27%, 188416 KB, 49132 KB/s, 3 seconds passed
+... 27%, 188448 KB, 49137 KB/s, 3 seconds passed
+... 27%, 188480 KB, 49142 KB/s, 3 seconds passed
+... 27%, 188512 KB, 49147 KB/s, 3 seconds passed
+... 27%, 188544 KB, 49152 KB/s, 3 seconds passed
+... 27%, 188576 KB, 49155 KB/s, 3 seconds passed
+... 27%, 188608 KB, 49157 KB/s, 3 seconds passed
+... 27%, 188640 KB, 49163 KB/s, 3 seconds passed
+... 27%, 188672 KB, 49169 KB/s, 3 seconds passed
+... 27%, 188704 KB, 49175 KB/s, 3 seconds passed
+... 27%, 188736 KB, 49181 KB/s, 3 seconds passed
+... 27%, 188768 KB, 49184 KB/s, 3 seconds passed
+... 27%, 188800 KB, 49190 KB/s, 3 seconds passed
+... 27%, 188832 KB, 49195 KB/s, 3 seconds passed
+... 27%, 188864 KB, 49199 KB/s, 3 seconds passed
+... 27%, 188896 KB, 49204 KB/s, 3 seconds passed
+... 27%, 188928 KB, 49209 KB/s, 3 seconds passed
+... 27%, 188960 KB, 49212 KB/s, 3 seconds passed
+... 27%, 188992 KB, 49218 KB/s, 3 seconds passed
+... 27%, 189024 KB, 49223 KB/s, 3 seconds passed
+... 27%, 189056 KB, 49227 KB/s, 3 seconds passed
+... 27%, 189088 KB, 49231 KB/s, 3 seconds passed
+... 27%, 189120 KB, 49234 KB/s, 3 seconds passed
+... 27%, 189152 KB, 49239 KB/s, 3 seconds passed
+... 27%, 189184 KB, 49244 KB/s, 3 seconds passed
+... 27%, 189216 KB, 49248 KB/s, 3 seconds passed
+... 27%, 189248 KB, 49253 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
+    ... 27%, 189280 KB, 49254 KB/s, 3 seconds passed
+... 27%, 189312 KB, 49256 KB/s, 3 seconds passed
+... 27%, 189344 KB, 49261 KB/s, 3 seconds passed
+... 27%, 189376 KB, 49267 KB/s, 3 seconds passed
+... 27%, 189408 KB, 49273 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
+    ... 27%, 189440 KB, 48408 KB/s, 3 seconds passed
+... 27%, 189472 KB, 48410 KB/s, 3 seconds passed
+... 27%, 189504 KB, 48413 KB/s, 3 seconds passed
+... 27%, 189536 KB, 48417 KB/s, 3 seconds passed
+... 27%, 189568 KB, 48418 KB/s, 3 seconds passed
+... 27%, 189600 KB, 48421 KB/s, 3 seconds passed
+... 27%, 189632 KB, 48424 KB/s, 3 seconds passed
+... 27%, 189664 KB, 48428 KB/s, 3 seconds passed
+... 27%, 189696 KB, 48431 KB/s, 3 seconds passed
+... 27%, 189728 KB, 48435 KB/s, 3 seconds passed
+... 27%, 189760 KB, 48438 KB/s, 3 seconds passed
+... 27%, 189792 KB, 48442 KB/s, 3 seconds passed
+... 27%, 189824 KB, 48445 KB/s, 3 seconds passed
+... 27%, 189856 KB, 48448 KB/s, 3 seconds passed
+... 27%, 189888 KB, 48452 KB/s, 3 seconds passed
+... 27%, 189920 KB, 48455 KB/s, 3 seconds passed
+... 27%, 189952 KB, 48459 KB/s, 3 seconds passed
+... 27%, 189984 KB, 48462 KB/s, 3 seconds passed
+... 27%, 190016 KB, 48465 KB/s, 3 seconds passed
+... 27%, 190048 KB, 48468 KB/s, 3 seconds passed
+... 27%, 190080 KB, 48472 KB/s, 3 seconds passed
+... 27%, 190112 KB, 48475 KB/s, 3 seconds passed
+... 27%, 190144 KB, 48479 KB/s, 3 seconds passed
+... 27%, 190176 KB, 48482 KB/s, 3 seconds passed
+... 27%, 190208 KB, 48485 KB/s, 3 seconds passed
+... 27%, 190240 KB, 48489 KB/s, 3 seconds passed
+... 27%, 190272 KB, 48492 KB/s, 3 seconds passed
+... 27%, 190304 KB, 48495 KB/s, 3 seconds passed
+... 27%, 190336 KB, 48499 KB/s, 3 seconds passed
+... 27%, 190368 KB, 48503 KB/s, 3 seconds passed
+... 27%, 190400 KB, 48506 KB/s, 3 seconds passed
+... 27%, 190432 KB, 48509 KB/s, 3 seconds passed
+... 27%, 190464 KB, 48513 KB/s, 3 seconds passed
+... 27%, 190496 KB, 48516 KB/s, 3 seconds passed
+... 27%, 190528 KB, 48519 KB/s, 3 seconds passed
+... 27%, 190560 KB, 48522 KB/s, 3 seconds passed
+... 27%, 190592 KB, 48526 KB/s, 3 seconds passed
+... 27%, 190624 KB, 48529 KB/s, 3 seconds passed
+... 27%, 190656 KB, 48533 KB/s, 3 seconds passed
+... 27%, 190688 KB, 48537 KB/s, 3 seconds passed
+... 27%, 190720 KB, 48540 KB/s, 3 seconds passed
+... 27%, 190752 KB, 48544 KB/s, 3 seconds passed
+... 27%, 190784 KB, 48547 KB/s, 3 seconds passed
+... 27%, 190816 KB, 48551 KB/s, 3 seconds passed
+... 27%, 190848 KB, 48556 KB/s, 3 seconds passed
+... 27%, 190880 KB, 48561 KB/s, 3 seconds passed
+... 27%, 190912 KB, 48566 KB/s, 3 seconds passed
+... 27%, 190944 KB, 48571 KB/s, 3 seconds passed
+... 27%, 190976 KB, 48576 KB/s, 3 seconds passed
+... 27%, 191008 KB, 48582 KB/s, 3 seconds passed
+... 27%, 191040 KB, 48587 KB/s, 3 seconds passed
+... 27%, 191072 KB, 48592 KB/s, 3 seconds passed
+... 27%, 191104 KB, 48597 KB/s, 3 seconds passed
+... 27%, 191136 KB, 48602 KB/s, 3 seconds passed
+... 27%, 191168 KB, 48607 KB/s, 3 seconds passed
+... 27%, 191200 KB, 48612 KB/s, 3 seconds passed
+... 27%, 191232 KB, 48618 KB/s, 3 seconds passed
+... 27%, 191264 KB, 48623 KB/s, 3 seconds passed
+... 27%, 191296 KB, 48628 KB/s, 3 seconds passed
+... 27%, 191328 KB, 48633 KB/s, 3 seconds passed
+... 27%, 191360 KB, 48638 KB/s, 3 seconds passed
+... 27%, 191392 KB, 48643 KB/s, 3 seconds passed
+... 27%, 191424 KB, 48648 KB/s, 3 seconds passed
+... 27%, 191456 KB, 48653 KB/s, 3 seconds passed
+... 27%, 191488 KB, 48658 KB/s, 3 seconds passed
+... 27%, 191520 KB, 48663 KB/s, 3 seconds passed
+... 27%, 191552 KB, 48668 KB/s, 3 seconds passed
+... 27%, 191584 KB, 48673 KB/s, 3 seconds passed
+... 27%, 191616 KB, 48679 KB/s, 3 seconds passed
+... 27%, 191648 KB, 48684 KB/s, 3 seconds passed
+... 27%, 191680 KB, 48689 KB/s, 3 seconds passed
+... 27%, 191712 KB, 48694 KB/s, 3 seconds passed
+... 27%, 191744 KB, 48699 KB/s, 3 seconds passed
+... 27%, 191776 KB, 48704 KB/s, 3 seconds passed
+... 27%, 191808 KB, 48709 KB/s, 3 seconds passed
+... 27%, 191840 KB, 48714 KB/s, 3 seconds passed
+... 27%, 191872 KB, 48719 KB/s, 3 seconds passed
+... 27%, 191904 KB, 48724 KB/s, 3 seconds passed
+... 27%, 191936 KB, 48730 KB/s, 3 seconds passed
+... 27%, 191968 KB, 48735 KB/s, 3 seconds passed
+... 27%, 192000 KB, 48740 KB/s, 3 seconds passed
+... 27%, 192032 KB, 48745 KB/s, 3 seconds passed
+... 27%, 192064 KB, 48750 KB/s, 3 seconds passed
+... 27%, 192096 KB, 48755 KB/s, 3 seconds passed
+... 27%, 192128 KB, 48760 KB/s, 3 seconds passed
+... 27%, 192160 KB, 48766 KB/s, 3 seconds passed
+... 27%, 192192 KB, 48772 KB/s, 3 seconds passed
+... 27%, 192224 KB, 48778 KB/s, 3 seconds passed
+... 27%, 192256 KB, 48784 KB/s, 3 seconds passed
+... 27%, 192288 KB, 48790 KB/s, 3 seconds passed
+... 27%, 192320 KB, 48796 KB/s, 3 seconds passed
+... 27%, 192352 KB, 48802 KB/s, 3 seconds passed
+... 27%, 192384 KB, 48808 KB/s, 3 seconds passed
+... 27%, 192416 KB, 48814 KB/s, 3 seconds passed
+... 27%, 192448 KB, 48820 KB/s, 3 seconds passed
+... 27%, 192480 KB, 48826 KB/s, 3 seconds passed
+... 27%, 192512 KB, 48832 KB/s, 3 seconds passed
+... 27%, 192544 KB, 48838 KB/s, 3 seconds passed
+... 27%, 192576 KB, 48844 KB/s, 3 seconds passed
+... 27%, 192608 KB, 48850 KB/s, 3 seconds passed
+... 27%, 192640 KB, 48856 KB/s, 3 seconds passed
+... 27%, 192672 KB, 48862 KB/s, 3 seconds passed
+... 27%, 192704 KB, 48868 KB/s, 3 seconds passed
+... 27%, 192736 KB, 48874 KB/s, 3 seconds passed
+... 27%, 192768 KB, 48880 KB/s, 3 seconds passed
+... 27%, 192800 KB, 48886 KB/s, 3 seconds passed
+... 27%, 192832 KB, 48892 KB/s, 3 seconds passed
+... 27%, 192864 KB, 48898 KB/s, 3 seconds passed
+... 27%, 192896 KB, 48905 KB/s, 3 seconds passed
+... 27%, 192928 KB, 48911 KB/s, 3 seconds passed
+... 27%, 192960 KB, 48916 KB/s, 3 seconds passed
+... 27%, 192992 KB, 48920 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
+    ... 27%, 193024 KB, 48925 KB/s, 3 seconds passed
+... 27%, 193056 KB, 48930 KB/s, 3 seconds passed
+... 27%, 193088 KB, 48934 KB/s, 3 seconds passed
+... 27%, 193120 KB, 48939 KB/s, 3 seconds passed
+... 27%, 193152 KB, 48944 KB/s, 3 seconds passed
+... 27%, 193184 KB, 48948 KB/s, 3 seconds passed
+... 27%, 193216 KB, 48953 KB/s, 3 seconds passed
+... 27%, 193248 KB, 48957 KB/s, 3 seconds passed
+... 27%, 193280 KB, 48962 KB/s, 3 seconds passed
+... 27%, 193312 KB, 48919 KB/s, 3 seconds passed
+... 27%, 193344 KB, 48922 KB/s, 3 seconds passed
+... 27%, 193376 KB, 48926 KB/s, 3 seconds passed
+... 27%, 193408 KB, 48930 KB/s, 3 seconds passed
+... 27%, 193440 KB, 48933 KB/s, 3 seconds passed
+... 27%, 193472 KB, 48937 KB/s, 3 seconds passed
+... 27%, 193504 KB, 48941 KB/s, 3 seconds passed
+... 27%, 193536 KB, 48945 KB/s, 3 seconds passed
+... 27%, 193568 KB, 48949 KB/s, 3 seconds passed
+... 27%, 193600 KB, 48952 KB/s, 3 seconds passed
+... 27%, 193632 KB, 48956 KB/s, 3 seconds passed
+... 27%, 193664 KB, 48960 KB/s, 3 seconds passed
+... 27%, 193696 KB, 48963 KB/s, 3 seconds passed
+... 27%, 193728 KB, 48967 KB/s, 3 seconds passed
+... 27%, 193760 KB, 48971 KB/s, 3 seconds passed
+... 27%, 193792 KB, 48975 KB/s, 3 seconds passed
+... 27%, 193824 KB, 48979 KB/s, 3 seconds passed
+... 27%, 193856 KB, 48983 KB/s, 3 seconds passed
+... 27%, 193888 KB, 48987 KB/s, 3 seconds passed
+... 27%, 193920 KB, 48990 KB/s, 3 seconds passed
+... 27%, 193952 KB, 48994 KB/s, 3 seconds passed
+... 27%, 193984 KB, 48998 KB/s, 3 seconds passed
+... 27%, 194016 KB, 49001 KB/s, 3 seconds passed
+... 27%, 194048 KB, 49005 KB/s, 3 seconds passed
+... 27%, 194080 KB, 49009 KB/s, 3 seconds passed
+... 27%, 194112 KB, 49012 KB/s, 3 seconds passed
+... 27%, 194144 KB, 49016 KB/s, 3 seconds passed
+... 27%, 194176 KB, 49021 KB/s, 3 seconds passed
+... 27%, 194208 KB, 49026 KB/s, 3 seconds passed
+... 27%, 194240 KB, 49031 KB/s, 3 seconds passed
+... 27%, 194272 KB, 49035 KB/s, 3 seconds passed
+... 27%, 194304 KB, 49040 KB/s, 3 seconds passed
+... 27%, 194336 KB, 49045 KB/s, 3 seconds passed
+... 27%, 194368 KB, 49050 KB/s, 3 seconds passed
+... 27%, 194400 KB, 49056 KB/s, 3 seconds passed
+... 27%, 194432 KB, 49061 KB/s, 3 seconds passed
+... 27%, 194464 KB, 49066 KB/s, 3 seconds passed
+... 27%, 194496 KB, 49072 KB/s, 3 seconds passed
+... 27%, 194528 KB, 49077 KB/s, 3 seconds passed
+
+.. parsed-literal::
+
+    ... 27%, 194560 KB, 48533 KB/s, 4 seconds passed
+... 27%, 194592 KB, 48534 KB/s, 4 seconds passed
+... 27%, 194624 KB, 48536 KB/s, 4 seconds passed
+... 27%, 194656 KB, 48539 KB/s, 4 seconds passed
+... 27%, 194688 KB, 48542 KB/s, 4 seconds passed
+... 27%, 194720 KB, 48545 KB/s, 4 seconds passed
+... 28%, 194752 KB, 48549 KB/s, 4 seconds passed
+... 28%, 194784 KB, 48553 KB/s, 4 seconds passed
+... 28%, 194816 KB, 48556 KB/s, 4 seconds passed
+... 28%, 194848 KB, 48560 KB/s, 4 seconds passed
+... 28%, 194880 KB, 48563 KB/s, 4 seconds passed
+... 28%, 194912 KB, 48566 KB/s, 4 seconds passed
+... 28%, 194944 KB, 48569 KB/s, 4 seconds passed
+... 28%, 194976 KB, 48573 KB/s, 4 seconds passed
+... 28%, 195008 KB, 48576 KB/s, 4 seconds passed
+... 28%, 195040 KB, 48579 KB/s, 4 seconds passed
+... 28%, 195072 KB, 48582 KB/s, 4 seconds passed
+... 28%, 195104 KB, 48585 KB/s, 4 seconds passed
+... 28%, 195136 KB, 48588 KB/s, 4 seconds passed
+... 28%, 195168 KB, 48591 KB/s, 4 seconds passed
+... 28%, 195200 KB, 48595 KB/s, 4 seconds passed
+... 28%, 195232 KB, 48598 KB/s, 4 seconds passed
+... 28%, 195264 KB, 48602 KB/s, 4 seconds passed
+... 28%, 195296 KB, 48605 KB/s, 4 seconds passed
+... 28%, 195328 KB, 48608 KB/s, 4 seconds passed
+... 28%, 195360 KB, 48611 KB/s, 4 seconds passed
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+.. parsed-literal::
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+
+.. parsed-literal::
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+... 30%, 208736 KB, 49835 KB/s, 4 seconds passed
+... 30%, 208768 KB, 49840 KB/s, 4 seconds passed
+... 30%, 208800 KB, 49844 KB/s, 4 seconds passed
+... 30%, 208832 KB, 49846 KB/s, 4 seconds passed
+... 30%, 208864 KB, 49852 KB/s, 4 seconds passed
+... 30%, 208896 KB, 49857 KB/s, 4 seconds passed
+... 30%, 208928 KB, 49861 KB/s, 4 seconds passed
+... 30%, 208960 KB, 49866 KB/s, 4 seconds passed
+... 30%, 208992 KB, 49851 KB/s, 4 seconds passed
+... 30%, 209024 KB, 49854 KB/s, 4 seconds passed
+... 30%, 209056 KB, 49857 KB/s, 4 seconds passed
+... 30%, 209088 KB, 49860 KB/s, 4 seconds passed
+... 30%, 209120 KB, 49863 KB/s, 4 seconds passed
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+... 30%, 209216 KB, 49873 KB/s, 4 seconds passed
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+... 30%, 209280 KB, 49879 KB/s, 4 seconds passed
+... 30%, 209312 KB, 49882 KB/s, 4 seconds passed
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+... 30%, 209408 KB, 49892 KB/s, 4 seconds passed
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+... 30%, 209472 KB, 49898 KB/s, 4 seconds passed
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+... 30%, 209600 KB, 49910 KB/s, 4 seconds passed
+... 30%, 209632 KB, 49913 KB/s, 4 seconds passed
+... 30%, 209664 KB, 49917 KB/s, 4 seconds passed
+... 30%, 209696 KB, 49921 KB/s, 4 seconds passed
+... 30%, 209728 KB, 49925 KB/s, 4 seconds passed
+... 30%, 209760 KB, 49929 KB/s, 4 seconds passed
+... 30%, 209792 KB, 49933 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
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+... 30%, 209856 KB, 49942 KB/s, 4 seconds passed
+... 30%, 209888 KB, 49946 KB/s, 4 seconds passed
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+... 30%, 209984 KB, 49853 KB/s, 4 seconds passed
+... 30%, 210016 KB, 49858 KB/s, 4 seconds passed
+... 30%, 210048 KB, 49826 KB/s, 4 seconds passed
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+... 30%, 210112 KB, 49832 KB/s, 4 seconds passed
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+... 30%, 210400 KB, 49861 KB/s, 4 seconds passed
+... 30%, 210432 KB, 49865 KB/s, 4 seconds passed
+... 30%, 210464 KB, 49868 KB/s, 4 seconds passed
+... 30%, 210496 KB, 49872 KB/s, 4 seconds passed
+... 30%, 210528 KB, 49876 KB/s, 4 seconds passed
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+... 30%, 210592 KB, 49883 KB/s, 4 seconds passed
+... 30%, 210624 KB, 49887 KB/s, 4 seconds passed
+... 30%, 210656 KB, 49890 KB/s, 4 seconds passed
+... 30%, 210688 KB, 49894 KB/s, 4 seconds passed
+... 30%, 210720 KB, 49898 KB/s, 4 seconds passed
+... 30%, 210752 KB, 49902 KB/s, 4 seconds passed
+... 30%, 210784 KB, 49905 KB/s, 4 seconds passed
+... 30%, 210816 KB, 49909 KB/s, 4 seconds passed
+... 30%, 210848 KB, 49912 KB/s, 4 seconds passed
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+... 30%, 210912 KB, 49920 KB/s, 4 seconds passed
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+... 30%, 212000 KB, 50056 KB/s, 4 seconds passed
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+... 30%, 212064 KB, 50066 KB/s, 4 seconds passed
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+... 30%, 212224 KB, 50091 KB/s, 4 seconds passed
+... 30%, 212256 KB, 50096 KB/s, 4 seconds passed
+... 30%, 212288 KB, 50101 KB/s, 4 seconds passed
+... 30%, 212320 KB, 50106 KB/s, 4 seconds passed
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+... 30%, 212416 KB, 50121 KB/s, 4 seconds passed
+... 30%, 212448 KB, 50126 KB/s, 4 seconds passed
+... 30%, 212480 KB, 50132 KB/s, 4 seconds passed
+... 30%, 212512 KB, 50137 KB/s, 4 seconds passed
+... 30%, 212544 KB, 50142 KB/s, 4 seconds passed
+... 30%, 212576 KB, 50147 KB/s, 4 seconds passed
+... 30%, 212608 KB, 50151 KB/s, 4 seconds passed
+... 30%, 212640 KB, 50156 KB/s, 4 seconds passed
+... 30%, 212672 KB, 50161 KB/s, 4 seconds passed
+... 30%, 212704 KB, 50167 KB/s, 4 seconds passed
+... 30%, 212736 KB, 50172 KB/s, 4 seconds passed
+... 30%, 212768 KB, 50177 KB/s, 4 seconds passed
+... 30%, 212800 KB, 50182 KB/s, 4 seconds passed
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+... 30%, 212864 KB, 50192 KB/s, 4 seconds passed
+... 30%, 212896 KB, 50197 KB/s, 4 seconds passed
+... 30%, 212928 KB, 50202 KB/s, 4 seconds passed
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+... 30%, 213216 KB, 50242 KB/s, 4 seconds passed
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+... 30%, 213344 KB, 50257 KB/s, 4 seconds passed
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+... 30%, 213632 KB, 50296 KB/s, 4 seconds passed
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+... 30%, 213728 KB, 50309 KB/s, 4 seconds passed
+... 30%, 213760 KB, 50314 KB/s, 4 seconds passed
+... 30%, 213792 KB, 50318 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
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+... 30%, 213888 KB, 50280 KB/s, 4 seconds passed
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+... 30%, 214272 KB, 50318 KB/s, 4 seconds passed
+... 30%, 214304 KB, 50321 KB/s, 4 seconds passed
+... 30%, 214336 KB, 50324 KB/s, 4 seconds passed
+... 30%, 214368 KB, 50327 KB/s, 4 seconds passed
+... 30%, 214400 KB, 50330 KB/s, 4 seconds passed
+... 30%, 214432 KB, 50333 KB/s, 4 seconds passed
+... 30%, 214464 KB, 50336 KB/s, 4 seconds passed
+... 30%, 214496 KB, 50340 KB/s, 4 seconds passed
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+... 30%, 214560 KB, 50346 KB/s, 4 seconds passed
+... 30%, 214592 KB, 50349 KB/s, 4 seconds passed
+... 30%, 214624 KB, 50352 KB/s, 4 seconds passed
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+... 30%, 214688 KB, 50358 KB/s, 4 seconds passed
+... 30%, 214720 KB, 50361 KB/s, 4 seconds passed
+... 30%, 214752 KB, 50365 KB/s, 4 seconds passed
+... 30%, 214784 KB, 50369 KB/s, 4 seconds passed
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+... 30%, 214848 KB, 50379 KB/s, 4 seconds passed
+... 30%, 214880 KB, 50384 KB/s, 4 seconds passed
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+... 30%, 214944 KB, 50394 KB/s, 4 seconds passed
+... 30%, 214976 KB, 50399 KB/s, 4 seconds passed
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+... 30%, 215232 KB, 50108 KB/s, 4 seconds passed
+... 30%, 215264 KB, 50111 KB/s, 4 seconds passed
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+... 30%, 215360 KB, 50120 KB/s, 4 seconds passed
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+... 30%, 215552 KB, 50138 KB/s, 4 seconds passed
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+... 31%, 215712 KB, 50153 KB/s, 4 seconds passed
+... 31%, 215744 KB, 50156 KB/s, 4 seconds passed
+... 31%, 215776 KB, 50159 KB/s, 4 seconds passed
+... 31%, 215808 KB, 50162 KB/s, 4 seconds passed
+... 31%, 215840 KB, 50165 KB/s, 4 seconds passed
+... 31%, 215872 KB, 50168 KB/s, 4 seconds passed
+... 31%, 215904 KB, 50170 KB/s, 4 seconds passed
+... 31%, 215936 KB, 50173 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
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+... 31%, 216256 KB, 50214 KB/s, 4 seconds passed
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+... 31%, 216352 KB, 50227 KB/s, 4 seconds passed
+... 31%, 216384 KB, 50231 KB/s, 4 seconds passed
+... 31%, 216416 KB, 50235 KB/s, 4 seconds passed
+... 31%, 216448 KB, 50240 KB/s, 4 seconds passed
+... 31%, 216480 KB, 50245 KB/s, 4 seconds passed
+... 31%, 216512 KB, 50249 KB/s, 4 seconds passed
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+... 31%, 216576 KB, 50258 KB/s, 4 seconds passed
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+... 31%, 216672 KB, 50272 KB/s, 4 seconds passed
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+... 31%, 216800 KB, 50290 KB/s, 4 seconds passed
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+... 31%, 216864 KB, 50299 KB/s, 4 seconds passed
+... 31%, 216896 KB, 50304 KB/s, 4 seconds passed
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+... 31%, 216960 KB, 50313 KB/s, 4 seconds passed
+... 31%, 216992 KB, 50318 KB/s, 4 seconds passed
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+... 31%, 217344 KB, 50371 KB/s, 4 seconds passed
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+... 31%, 217408 KB, 50383 KB/s, 4 seconds passed
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+... 31%, 217568 KB, 50360 KB/s, 4 seconds passed
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+... 31%, 217664 KB, 50375 KB/s, 4 seconds passed
+... 31%, 217696 KB, 50379 KB/s, 4 seconds passed
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+... 31%, 217792 KB, 50391 KB/s, 4 seconds passed
+... 31%, 217824 KB, 50395 KB/s, 4 seconds passed
+... 31%, 217856 KB, 50399 KB/s, 4 seconds passed
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+... 31%, 217952 KB, 50265 KB/s, 4 seconds passed
+... 31%, 217984 KB, 50268 KB/s, 4 seconds passed
+... 31%, 218016 KB, 50271 KB/s, 4 seconds passed
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+... 31%, 218080 KB, 50276 KB/s, 4 seconds passed
+... 31%, 218112 KB, 50280 KB/s, 4 seconds passed
+... 31%, 218144 KB, 50283 KB/s, 4 seconds passed
+... 31%, 218176 KB, 50286 KB/s, 4 seconds passed
+... 31%, 218208 KB, 50288 KB/s, 4 seconds passed
+... 31%, 218240 KB, 50291 KB/s, 4 seconds passed
+... 31%, 218272 KB, 50294 KB/s, 4 seconds passed
+... 31%, 218304 KB, 50297 KB/s, 4 seconds passed
+... 31%, 218336 KB, 50300 KB/s, 4 seconds passed
+... 31%, 218368 KB, 50303 KB/s, 4 seconds passed
+... 31%, 218400 KB, 50306 KB/s, 4 seconds passed
+... 31%, 218432 KB, 50309 KB/s, 4 seconds passed
+... 31%, 218464 KB, 50312 KB/s, 4 seconds passed
+... 31%, 218496 KB, 50315 KB/s, 4 seconds passed
+... 31%, 218528 KB, 50318 KB/s, 4 seconds passed
+... 31%, 218560 KB, 50321 KB/s, 4 seconds passed
+... 31%, 218592 KB, 50323 KB/s, 4 seconds passed
+... 31%, 218624 KB, 50327 KB/s, 4 seconds passed
+... 31%, 218656 KB, 50330 KB/s, 4 seconds passed
+... 31%, 218688 KB, 50333 KB/s, 4 seconds passed
+... 31%, 218720 KB, 50336 KB/s, 4 seconds passed
+... 31%, 218752 KB, 50340 KB/s, 4 seconds passed
+... 31%, 218784 KB, 50343 KB/s, 4 seconds passed
+... 31%, 218816 KB, 50347 KB/s, 4 seconds passed
+... 31%, 218848 KB, 50350 KB/s, 4 seconds passed
+... 31%, 218880 KB, 50354 KB/s, 4 seconds passed
+... 31%, 218912 KB, 50358 KB/s, 4 seconds passed
+... 31%, 218944 KB, 50362 KB/s, 4 seconds passed
+... 31%, 218976 KB, 50366 KB/s, 4 seconds passed
+... 31%, 219008 KB, 50370 KB/s, 4 seconds passed
+... 31%, 219040 KB, 50373 KB/s, 4 seconds passed
+... 31%, 219072 KB, 50377 KB/s, 4 seconds passed
+... 31%, 219104 KB, 50381 KB/s, 4 seconds passed
+... 31%, 219136 KB, 50385 KB/s, 4 seconds passed
+... 31%, 219168 KB, 50389 KB/s, 4 seconds passed
+... 31%, 219200 KB, 50393 KB/s, 4 seconds passed
+... 31%, 219232 KB, 50396 KB/s, 4 seconds passed
+... 31%, 219264 KB, 50400 KB/s, 4 seconds passed
+... 31%, 219296 KB, 50404 KB/s, 4 seconds passed
+... 31%, 219328 KB, 50408 KB/s, 4 seconds passed
+... 31%, 219360 KB, 50412 KB/s, 4 seconds passed
+... 31%, 219392 KB, 50415 KB/s, 4 seconds passed
+... 31%, 219424 KB, 50419 KB/s, 4 seconds passed
+... 31%, 219456 KB, 50423 KB/s, 4 seconds passed
+... 31%, 219488 KB, 50427 KB/s, 4 seconds passed
+... 31%, 219520 KB, 50431 KB/s, 4 seconds passed
+... 31%, 219552 KB, 50434 KB/s, 4 seconds passed
+... 31%, 219584 KB, 50438 KB/s, 4 seconds passed
+... 31%, 219616 KB, 50442 KB/s, 4 seconds passed
+... 31%, 219648 KB, 50446 KB/s, 4 seconds passed
+... 31%, 219680 KB, 50450 KB/s, 4 seconds passed
+... 31%, 219712 KB, 50454 KB/s, 4 seconds passed
+... 31%, 219744 KB, 50458 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 31%, 219776 KB, 50462 KB/s, 4 seconds passed
+... 31%, 219808 KB, 50467 KB/s, 4 seconds passed
+... 31%, 219840 KB, 50473 KB/s, 4 seconds passed
+... 31%, 219872 KB, 50478 KB/s, 4 seconds passed
+... 31%, 219904 KB, 50484 KB/s, 4 seconds passed
+... 31%, 219936 KB, 50489 KB/s, 4 seconds passed
+... 31%, 219968 KB, 50495 KB/s, 4 seconds passed
+... 31%, 220000 KB, 50500 KB/s, 4 seconds passed
+... 31%, 220032 KB, 50505 KB/s, 4 seconds passed
+... 31%, 220064 KB, 50511 KB/s, 4 seconds passed
+... 31%, 220096 KB, 50516 KB/s, 4 seconds passed
+... 31%, 220128 KB, 50522 KB/s, 4 seconds passed
+... 31%, 220160 KB, 50307 KB/s, 4 seconds passed
+... 31%, 220192 KB, 50309 KB/s, 4 seconds passed
+... 31%, 220224 KB, 50312 KB/s, 4 seconds passed
+... 31%, 220256 KB, 50314 KB/s, 4 seconds passed
+... 31%, 220288 KB, 50317 KB/s, 4 seconds passed
+... 31%, 220320 KB, 50320 KB/s, 4 seconds passed
+... 31%, 220352 KB, 50322 KB/s, 4 seconds passed
+... 31%, 220384 KB, 50325 KB/s, 4 seconds passed
+... 31%, 220416 KB, 50328 KB/s, 4 seconds passed
+... 31%, 220448 KB, 50330 KB/s, 4 seconds passed
+... 31%, 220480 KB, 50333 KB/s, 4 seconds passed
+... 31%, 220512 KB, 50336 KB/s, 4 seconds passed
+... 31%, 220544 KB, 50339 KB/s, 4 seconds passed
+... 31%, 220576 KB, 50342 KB/s, 4 seconds passed
+... 31%, 220608 KB, 50345 KB/s, 4 seconds passed
+... 31%, 220640 KB, 50348 KB/s, 4 seconds passed
+... 31%, 220672 KB, 50351 KB/s, 4 seconds passed
+... 31%, 220704 KB, 50353 KB/s, 4 seconds passed
+... 31%, 220736 KB, 50356 KB/s, 4 seconds passed
+... 31%, 220768 KB, 50359 KB/s, 4 seconds passed
+... 31%, 220800 KB, 50362 KB/s, 4 seconds passed
+... 31%, 220832 KB, 50365 KB/s, 4 seconds passed
+... 31%, 220864 KB, 50369 KB/s, 4 seconds passed
+... 31%, 220896 KB, 50371 KB/s, 4 seconds passed
+... 31%, 220928 KB, 50375 KB/s, 4 seconds passed
+... 31%, 220960 KB, 50379 KB/s, 4 seconds passed
+... 31%, 220992 KB, 50382 KB/s, 4 seconds passed
+... 31%, 221024 KB, 50386 KB/s, 4 seconds passed
+... 31%, 221056 KB, 50390 KB/s, 4 seconds passed
+... 31%, 221088 KB, 50394 KB/s, 4 seconds passed
+... 31%, 221120 KB, 50397 KB/s, 4 seconds passed
+... 31%, 221152 KB, 50401 KB/s, 4 seconds passed
+... 31%, 221184 KB, 50405 KB/s, 4 seconds passed
+... 31%, 221216 KB, 50408 KB/s, 4 seconds passed
+... 31%, 221248 KB, 50412 KB/s, 4 seconds passed
+... 31%, 221280 KB, 50415 KB/s, 4 seconds passed
+... 31%, 221312 KB, 50419 KB/s, 4 seconds passed
+... 31%, 221344 KB, 50423 KB/s, 4 seconds passed
+... 31%, 221376 KB, 50427 KB/s, 4 seconds passed
+... 31%, 221408 KB, 50431 KB/s, 4 seconds passed
+... 31%, 221440 KB, 50434 KB/s, 4 seconds passed
+... 31%, 221472 KB, 50438 KB/s, 4 seconds passed
+... 31%, 221504 KB, 50442 KB/s, 4 seconds passed
+... 31%, 221536 KB, 50446 KB/s, 4 seconds passed
+... 31%, 221568 KB, 50449 KB/s, 4 seconds passed
+... 31%, 221600 KB, 50453 KB/s, 4 seconds passed
+... 31%, 221632 KB, 50457 KB/s, 4 seconds passed
+... 31%, 221664 KB, 50461 KB/s, 4 seconds passed
+... 31%, 221696 KB, 50465 KB/s, 4 seconds passed
+... 31%, 221728 KB, 50469 KB/s, 4 seconds passed
+... 31%, 221760 KB, 50473 KB/s, 4 seconds passed
+... 31%, 221792 KB, 50476 KB/s, 4 seconds passed
+... 31%, 221824 KB, 50480 KB/s, 4 seconds passed
+... 31%, 221856 KB, 50484 KB/s, 4 seconds passed
+... 31%, 221888 KB, 50487 KB/s, 4 seconds passed
+... 31%, 221920 KB, 50491 KB/s, 4 seconds passed
+... 31%, 221952 KB, 50495 KB/s, 4 seconds passed
+... 31%, 221984 KB, 50500 KB/s, 4 seconds passed
+... 31%, 222016 KB, 50505 KB/s, 4 seconds passed
+... 31%, 222048 KB, 50510 KB/s, 4 seconds passed
+... 31%, 222080 KB, 50514 KB/s, 4 seconds passed
+... 31%, 222112 KB, 50519 KB/s, 4 seconds passed
+... 31%, 222144 KB, 50524 KB/s, 4 seconds passed
+... 31%, 222176 KB, 50529 KB/s, 4 seconds passed
+... 31%, 222208 KB, 50534 KB/s, 4 seconds passed
+... 31%, 222240 KB, 50539 KB/s, 4 seconds passed
+... 31%, 222272 KB, 50544 KB/s, 4 seconds passed
+... 31%, 222304 KB, 50549 KB/s, 4 seconds passed
+... 31%, 222336 KB, 50554 KB/s, 4 seconds passed
+... 31%, 222368 KB, 50558 KB/s, 4 seconds passed
+... 31%, 222400 KB, 50563 KB/s, 4 seconds passed
+... 31%, 222432 KB, 50568 KB/s, 4 seconds passed
+... 31%, 222464 KB, 50573 KB/s, 4 seconds passed
+... 31%, 222496 KB, 50578 KB/s, 4 seconds passed
+... 31%, 222528 KB, 50583 KB/s, 4 seconds passed
+... 31%, 222560 KB, 50588 KB/s, 4 seconds passed
+... 32%, 222592 KB, 50593 KB/s, 4 seconds passed
+... 32%, 222624 KB, 50598 KB/s, 4 seconds passed
+... 32%, 222656 KB, 50602 KB/s, 4 seconds passed
+... 32%, 222688 KB, 50607 KB/s, 4 seconds passed
+... 32%, 222720 KB, 50612 KB/s, 4 seconds passed
+... 32%, 222752 KB, 50617 KB/s, 4 seconds passed
+... 32%, 222784 KB, 50622 KB/s, 4 seconds passed
+... 32%, 222816 KB, 50627 KB/s, 4 seconds passed
+... 32%, 222848 KB, 50632 KB/s, 4 seconds passed
+... 32%, 222880 KB, 50637 KB/s, 4 seconds passed
+... 32%, 222912 KB, 50642 KB/s, 4 seconds passed
+... 32%, 222944 KB, 50647 KB/s, 4 seconds passed
+... 32%, 222976 KB, 50652 KB/s, 4 seconds passed
+... 32%, 223008 KB, 50657 KB/s, 4 seconds passed
+... 32%, 223040 KB, 50662 KB/s, 4 seconds passed
+... 32%, 223072 KB, 50667 KB/s, 4 seconds passed
+... 32%, 223104 KB, 50672 KB/s, 4 seconds passed
+... 32%, 223136 KB, 50677 KB/s, 4 seconds passed
+... 32%, 223168 KB, 50682 KB/s, 4 seconds passed
+... 32%, 223200 KB, 50686 KB/s, 4 seconds passed
+... 32%, 223232 KB, 50691 KB/s, 4 seconds passed
+... 32%, 223264 KB, 50696 KB/s, 4 seconds passed
+... 32%, 223296 KB, 50701 KB/s, 4 seconds passed
+... 32%, 223328 KB, 50706 KB/s, 4 seconds passed
+... 32%, 223360 KB, 50711 KB/s, 4 seconds passed
+... 32%, 223392 KB, 50716 KB/s, 4 seconds passed
+... 32%, 223424 KB, 50721 KB/s, 4 seconds passed
+... 32%, 223456 KB, 50726 KB/s, 4 seconds passed
+... 32%, 223488 KB, 50731 KB/s, 4 seconds passed
+... 32%, 223520 KB, 50736 KB/s, 4 seconds passed
+... 32%, 223552 KB, 50741 KB/s, 4 seconds passed
+... 32%, 223584 KB, 50744 KB/s, 4 seconds passed
+... 32%, 223616 KB, 50749 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 32%, 223648 KB, 50754 KB/s, 4 seconds passed
+... 32%, 223680 KB, 50757 KB/s, 4 seconds passed
+... 32%, 223712 KB, 50761 KB/s, 4 seconds passed
+... 32%, 223744 KB, 50766 KB/s, 4 seconds passed
+... 32%, 223776 KB, 50769 KB/s, 4 seconds passed
+... 32%, 223808 KB, 50774 KB/s, 4 seconds passed
+... 32%, 223840 KB, 50778 KB/s, 4 seconds passed
+... 32%, 223872 KB, 50782 KB/s, 4 seconds passed
+... 32%, 223904 KB, 50786 KB/s, 4 seconds passed
+... 32%, 223936 KB, 50790 KB/s, 4 seconds passed
+... 32%, 223968 KB, 50794 KB/s, 4 seconds passed
+... 32%, 224000 KB, 50798 KB/s, 4 seconds passed
+... 32%, 224032 KB, 50774 KB/s, 4 seconds passed
+... 32%, 224064 KB, 50755 KB/s, 4 seconds passed
+... 32%, 224096 KB, 50759 KB/s, 4 seconds passed
+... 32%, 224128 KB, 50762 KB/s, 4 seconds passed
+... 32%, 224160 KB, 50767 KB/s, 4 seconds passed
+... 32%, 224192 KB, 50771 KB/s, 4 seconds passed
+... 32%, 224224 KB, 50774 KB/s, 4 seconds passed
+... 32%, 224256 KB, 50779 KB/s, 4 seconds passed
+... 32%, 224288 KB, 50783 KB/s, 4 seconds passed
+... 32%, 224320 KB, 50787 KB/s, 4 seconds passed
+... 32%, 224352 KB, 50791 KB/s, 4 seconds passed
+... 32%, 224384 KB, 50795 KB/s, 4 seconds passed
+... 32%, 224416 KB, 50799 KB/s, 4 seconds passed
+... 32%, 224448 KB, 50803 KB/s, 4 seconds passed
+... 32%, 224480 KB, 50807 KB/s, 4 seconds passed
+... 32%, 224512 KB, 50811 KB/s, 4 seconds passed
+... 32%, 224544 KB, 50815 KB/s, 4 seconds passed
+... 32%, 224576 KB, 50819 KB/s, 4 seconds passed
+... 32%, 224608 KB, 50823 KB/s, 4 seconds passed
+... 32%, 224640 KB, 50827 KB/s, 4 seconds passed
+... 32%, 224672 KB, 50832 KB/s, 4 seconds passed
+... 32%, 224704 KB, 50835 KB/s, 4 seconds passed
+... 32%, 224736 KB, 50839 KB/s, 4 seconds passed
+... 32%, 224768 KB, 50844 KB/s, 4 seconds passed
+... 32%, 224800 KB, 50848 KB/s, 4 seconds passed
+... 32%, 224832 KB, 50851 KB/s, 4 seconds passed
+... 32%, 224864 KB, 50856 KB/s, 4 seconds passed
+... 32%, 224896 KB, 50860 KB/s, 4 seconds passed
+... 32%, 224928 KB, 50864 KB/s, 4 seconds passed
+... 32%, 224960 KB, 50868 KB/s, 4 seconds passed
+... 32%, 224992 KB, 50872 KB/s, 4 seconds passed
+... 32%, 225024 KB, 50876 KB/s, 4 seconds passed
+... 32%, 225056 KB, 50880 KB/s, 4 seconds passed
+... 32%, 225088 KB, 50883 KB/s, 4 seconds passed
+... 32%, 225120 KB, 50888 KB/s, 4 seconds passed
+... 32%, 225152 KB, 50892 KB/s, 4 seconds passed
+... 32%, 225184 KB, 50896 KB/s, 4 seconds passed
+... 32%, 225216 KB, 50900 KB/s, 4 seconds passed
+... 32%, 225248 KB, 50904 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 32%, 225280 KB, 50409 KB/s, 4 seconds passed
+... 32%, 225312 KB, 50397 KB/s, 4 seconds passed
+... 32%, 225344 KB, 50399 KB/s, 4 seconds passed
+... 32%, 225376 KB, 50402 KB/s, 4 seconds passed
+... 32%, 225408 KB, 50243 KB/s, 4 seconds passed
+... 32%, 225440 KB, 50244 KB/s, 4 seconds passed
+... 32%, 225472 KB, 50169 KB/s, 4 seconds passed
+... 32%, 225504 KB, 50172 KB/s, 4 seconds passed
+... 32%, 225536 KB, 50174 KB/s, 4 seconds passed
+... 32%, 225568 KB, 50177 KB/s, 4 seconds passed
+... 32%, 225600 KB, 50179 KB/s, 4 seconds passed
+... 32%, 225632 KB, 50182 KB/s, 4 seconds passed
+... 32%, 225664 KB, 50185 KB/s, 4 seconds passed
+... 32%, 225696 KB, 50188 KB/s, 4 seconds passed
+... 32%, 225728 KB, 50191 KB/s, 4 seconds passed
+... 32%, 225760 KB, 50194 KB/s, 4 seconds passed
+... 32%, 225792 KB, 50197 KB/s, 4 seconds passed
+... 32%, 225824 KB, 50200 KB/s, 4 seconds passed
+... 32%, 225856 KB, 50203 KB/s, 4 seconds passed
+... 32%, 225888 KB, 50205 KB/s, 4 seconds passed
+... 32%, 225920 KB, 50208 KB/s, 4 seconds passed
+... 32%, 225952 KB, 50211 KB/s, 4 seconds passed
+... 32%, 225984 KB, 50214 KB/s, 4 seconds passed
+... 32%, 226016 KB, 50217 KB/s, 4 seconds passed
+... 32%, 226048 KB, 50220 KB/s, 4 seconds passed
+... 32%, 226080 KB, 50223 KB/s, 4 seconds passed
+... 32%, 226112 KB, 50226 KB/s, 4 seconds passed
+... 32%, 226144 KB, 50229 KB/s, 4 seconds passed
+... 32%, 226176 KB, 50231 KB/s, 4 seconds passed
+... 32%, 226208 KB, 50234 KB/s, 4 seconds passed
+... 32%, 226240 KB, 50237 KB/s, 4 seconds passed
+... 32%, 226272 KB, 50240 KB/s, 4 seconds passed
+... 32%, 226304 KB, 50242 KB/s, 4 seconds passed
+... 32%, 226336 KB, 50245 KB/s, 4 seconds passed
+... 32%, 226368 KB, 50248 KB/s, 4 seconds passed
+... 32%, 226400 KB, 50251 KB/s, 4 seconds passed
+... 32%, 226432 KB, 50254 KB/s, 4 seconds passed
+... 32%, 226464 KB, 50257 KB/s, 4 seconds passed
+... 32%, 226496 KB, 50260 KB/s, 4 seconds passed
+... 32%, 226528 KB, 50265 KB/s, 4 seconds passed
+... 32%, 226560 KB, 50269 KB/s, 4 seconds passed
+... 32%, 226592 KB, 50273 KB/s, 4 seconds passed
+... 32%, 226624 KB, 50277 KB/s, 4 seconds passed
+... 32%, 226656 KB, 50282 KB/s, 4 seconds passed
+... 32%, 226688 KB, 50286 KB/s, 4 seconds passed
+... 32%, 226720 KB, 50290 KB/s, 4 seconds passed
+... 32%, 226752 KB, 50295 KB/s, 4 seconds passed
+... 32%, 226784 KB, 50299 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 32%, 226816 KB, 50304 KB/s, 4 seconds passed
+... 32%, 226848 KB, 50308 KB/s, 4 seconds passed
+... 32%, 226880 KB, 50313 KB/s, 4 seconds passed
+... 32%, 226912 KB, 50317 KB/s, 4 seconds passed
+... 32%, 226944 KB, 50321 KB/s, 4 seconds passed
+... 32%, 226976 KB, 50326 KB/s, 4 seconds passed
+... 32%, 227008 KB, 50330 KB/s, 4 seconds passed
+... 32%, 227040 KB, 50334 KB/s, 4 seconds passed
+... 32%, 227072 KB, 50339 KB/s, 4 seconds passed
+... 32%, 227104 KB, 50343 KB/s, 4 seconds passed
+... 32%, 227136 KB, 50347 KB/s, 4 seconds passed
+... 32%, 227168 KB, 50352 KB/s, 4 seconds passed
+... 32%, 227200 KB, 50356 KB/s, 4 seconds passed
+... 32%, 227232 KB, 50360 KB/s, 4 seconds passed
+... 32%, 227264 KB, 50365 KB/s, 4 seconds passed
+... 32%, 227296 KB, 50369 KB/s, 4 seconds passed
+... 32%, 227328 KB, 50373 KB/s, 4 seconds passed
+... 32%, 227360 KB, 50378 KB/s, 4 seconds passed
+... 32%, 227392 KB, 50382 KB/s, 4 seconds passed
+... 32%, 227424 KB, 50387 KB/s, 4 seconds passed
+... 32%, 227456 KB, 50391 KB/s, 4 seconds passed
+... 32%, 227488 KB, 50396 KB/s, 4 seconds passed
+... 32%, 227520 KB, 50400 KB/s, 4 seconds passed
+... 32%, 227552 KB, 50404 KB/s, 4 seconds passed
+... 32%, 227584 KB, 50409 KB/s, 4 seconds passed
+... 32%, 227616 KB, 50413 KB/s, 4 seconds passed
+... 32%, 227648 KB, 50417 KB/s, 4 seconds passed
+... 32%, 227680 KB, 50422 KB/s, 4 seconds passed
+... 32%, 227712 KB, 50426 KB/s, 4 seconds passed
+... 32%, 227744 KB, 50430 KB/s, 4 seconds passed
+... 32%, 227776 KB, 50435 KB/s, 4 seconds passed
+... 32%, 227808 KB, 50440 KB/s, 4 seconds passed
+... 32%, 227840 KB, 50445 KB/s, 4 seconds passed
+... 32%, 227872 KB, 50451 KB/s, 4 seconds passed
+... 32%, 227904 KB, 50456 KB/s, 4 seconds passed
+... 32%, 227936 KB, 50461 KB/s, 4 seconds passed
+... 32%, 227968 KB, 50466 KB/s, 4 seconds passed
+... 32%, 228000 KB, 50471 KB/s, 4 seconds passed
+... 32%, 228032 KB, 50476 KB/s, 4 seconds passed
+... 32%, 228064 KB, 50482 KB/s, 4 seconds passed
+... 32%, 228096 KB, 50487 KB/s, 4 seconds passed
+... 32%, 228128 KB, 50492 KB/s, 4 seconds passed
+... 32%, 228160 KB, 50497 KB/s, 4 seconds passed
+... 32%, 228192 KB, 50503 KB/s, 4 seconds passed
+... 32%, 228224 KB, 50508 KB/s, 4 seconds passed
+... 32%, 228256 KB, 50513 KB/s, 4 seconds passed
+... 32%, 228288 KB, 50518 KB/s, 4 seconds passed
+... 32%, 228320 KB, 50523 KB/s, 4 seconds passed
+... 32%, 228352 KB, 50529 KB/s, 4 seconds passed
+... 32%, 228384 KB, 50534 KB/s, 4 seconds passed
+... 32%, 228416 KB, 50539 KB/s, 4 seconds passed
+... 32%, 228448 KB, 50544 KB/s, 4 seconds passed
+... 32%, 228480 KB, 50550 KB/s, 4 seconds passed
+... 32%, 228512 KB, 50555 KB/s, 4 seconds passed
+... 32%, 228544 KB, 50559 KB/s, 4 seconds passed
+... 32%, 228576 KB, 50563 KB/s, 4 seconds passed
+... 32%, 228608 KB, 50567 KB/s, 4 seconds passed
+... 32%, 228640 KB, 50572 KB/s, 4 seconds passed
+... 32%, 228672 KB, 50576 KB/s, 4 seconds passed
+... 32%, 228704 KB, 50579 KB/s, 4 seconds passed
+... 32%, 228736 KB, 50583 KB/s, 4 seconds passed
+... 32%, 228768 KB, 50586 KB/s, 4 seconds passed
+... 32%, 228800 KB, 50591 KB/s, 4 seconds passed
+... 32%, 228832 KB, 50595 KB/s, 4 seconds passed
+... 32%, 228864 KB, 50599 KB/s, 4 seconds passed
+... 32%, 228896 KB, 50603 KB/s, 4 seconds passed
+... 32%, 228928 KB, 50607 KB/s, 4 seconds passed
+... 32%, 228960 KB, 50611 KB/s, 4 seconds passed
+... 32%, 228992 KB, 50615 KB/s, 4 seconds passed
+... 32%, 229024 KB, 50618 KB/s, 4 seconds passed
+... 32%, 229056 KB, 50622 KB/s, 4 seconds passed
+... 32%, 229088 KB, 50627 KB/s, 4 seconds passed
+... 32%, 229120 KB, 50630 KB/s, 4 seconds passed
+... 32%, 229152 KB, 50634 KB/s, 4 seconds passed
+... 32%, 229184 KB, 50639 KB/s, 4 seconds passed
+... 32%, 229216 KB, 50643 KB/s, 4 seconds passed
+... 32%, 229248 KB, 50647 KB/s, 4 seconds passed
+... 32%, 229280 KB, 50651 KB/s, 4 seconds passed
+... 32%, 229312 KB, 50655 KB/s, 4 seconds passed
+... 32%, 229344 KB, 50659 KB/s, 4 seconds passed
+... 32%, 229376 KB, 50663 KB/s, 4 seconds passed
+... 32%, 229408 KB, 50667 KB/s, 4 seconds passed
+... 32%, 229440 KB, 50671 KB/s, 4 seconds passed
+... 32%, 229472 KB, 50674 KB/s, 4 seconds passed
+... 32%, 229504 KB, 50677 KB/s, 4 seconds passed
+... 33%, 229536 KB, 50682 KB/s, 4 seconds passed
+... 33%, 229568 KB, 50687 KB/s, 4 seconds passed
+... 33%, 229600 KB, 50690 KB/s, 4 seconds passed
+... 33%, 229632 KB, 50694 KB/s, 4 seconds passed
+... 33%, 229664 KB, 50698 KB/s, 4 seconds passed
+... 33%, 229696 KB, 50702 KB/s, 4 seconds passed
+... 33%, 229728 KB, 50706 KB/s, 4 seconds passed
+... 33%, 229760 KB, 50710 KB/s, 4 seconds passed
+... 33%, 229792 KB, 50714 KB/s, 4 seconds passed
+... 33%, 229824 KB, 50718 KB/s, 4 seconds passed
+... 33%, 229856 KB, 50722 KB/s, 4 seconds passed
+... 33%, 229888 KB, 50726 KB/s, 4 seconds passed
+... 33%, 229920 KB, 50730 KB/s, 4 seconds passed
+... 33%, 229952 KB, 50734 KB/s, 4 seconds passed
+... 33%, 229984 KB, 50737 KB/s, 4 seconds passed
+... 33%, 230016 KB, 50742 KB/s, 4 seconds passed
+... 33%, 230048 KB, 50745 KB/s, 4 seconds passed
+... 33%, 230080 KB, 50749 KB/s, 4 seconds passed
+... 33%, 230112 KB, 50753 KB/s, 4 seconds passed
+... 33%, 230144 KB, 50757 KB/s, 4 seconds passed
+... 33%, 230176 KB, 50761 KB/s, 4 seconds passed
+... 33%, 230208 KB, 50765 KB/s, 4 seconds passed
+... 33%, 230240 KB, 50769 KB/s, 4 seconds passed
+... 33%, 230272 KB, 50773 KB/s, 4 seconds passed
+... 33%, 230304 KB, 50777 KB/s, 4 seconds passed
+... 33%, 230336 KB, 50781 KB/s, 4 seconds passed
+... 33%, 230368 KB, 50785 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 33%, 230400 KB, 50489 KB/s, 4 seconds passed
+... 33%, 230432 KB, 50486 KB/s, 4 seconds passed
+... 33%, 230464 KB, 50488 KB/s, 4 seconds passed
+... 33%, 230496 KB, 50490 KB/s, 4 seconds passed
+... 33%, 230528 KB, 50494 KB/s, 4 seconds passed
+... 33%, 230560 KB, 50498 KB/s, 4 seconds passed
+... 33%, 230592 KB, 50501 KB/s, 4 seconds passed
+... 33%, 230624 KB, 50503 KB/s, 4 seconds passed
+... 33%, 230656 KB, 50506 KB/s, 4 seconds passed
+... 33%, 230688 KB, 50507 KB/s, 4 seconds passed
+... 33%, 230720 KB, 50510 KB/s, 4 seconds passed
+... 33%, 230752 KB, 50513 KB/s, 4 seconds passed
+... 33%, 230784 KB, 50516 KB/s, 4 seconds passed
+... 33%, 230816 KB, 50519 KB/s, 4 seconds passed
+... 33%, 230848 KB, 50521 KB/s, 4 seconds passed
+... 33%, 230880 KB, 50524 KB/s, 4 seconds passed
+... 33%, 230912 KB, 50527 KB/s, 4 seconds passed
+... 33%, 230944 KB, 50530 KB/s, 4 seconds passed
+... 33%, 230976 KB, 50533 KB/s, 4 seconds passed
+... 33%, 231008 KB, 50535 KB/s, 4 seconds passed
+... 33%, 231040 KB, 50538 KB/s, 4 seconds passed
+... 33%, 231072 KB, 50541 KB/s, 4 seconds passed
+... 33%, 231104 KB, 50544 KB/s, 4 seconds passed
+... 33%, 231136 KB, 50547 KB/s, 4 seconds passed
+... 33%, 231168 KB, 50550 KB/s, 4 seconds passed
+... 33%, 231200 KB, 50552 KB/s, 4 seconds passed
+... 33%, 231232 KB, 50555 KB/s, 4 seconds passed
+... 33%, 231264 KB, 50558 KB/s, 4 seconds passed
+... 33%, 231296 KB, 50561 KB/s, 4 seconds passed
+... 33%, 231328 KB, 50564 KB/s, 4 seconds passed
+... 33%, 231360 KB, 50567 KB/s, 4 seconds passed
+... 33%, 231392 KB, 50570 KB/s, 4 seconds passed
+... 33%, 231424 KB, 50573 KB/s, 4 seconds passed
+... 33%, 231456 KB, 50576 KB/s, 4 seconds passed
+... 33%, 231488 KB, 50579 KB/s, 4 seconds passed
+... 33%, 231520 KB, 50582 KB/s, 4 seconds passed
+... 33%, 231552 KB, 50586 KB/s, 4 seconds passed
+... 33%, 231584 KB, 50590 KB/s, 4 seconds passed
+... 33%, 231616 KB, 50594 KB/s, 4 seconds passed
+... 33%, 231648 KB, 50599 KB/s, 4 seconds passed
+... 33%, 231680 KB, 50603 KB/s, 4 seconds passed
+... 33%, 231712 KB, 50608 KB/s, 4 seconds passed
+... 33%, 231744 KB, 50612 KB/s, 4 seconds passed
+... 33%, 231776 KB, 50615 KB/s, 4 seconds passed
+... 33%, 231808 KB, 50618 KB/s, 4 seconds passed
+... 33%, 231840 KB, 50620 KB/s, 4 seconds passed
+... 33%, 231872 KB, 50623 KB/s, 4 seconds passed
+... 33%, 231904 KB, 50626 KB/s, 4 seconds passed
+... 33%, 231936 KB, 50628 KB/s, 4 seconds passed
+... 33%, 231968 KB, 50631 KB/s, 4 seconds passed
+... 33%, 232000 KB, 50634 KB/s, 4 seconds passed
+... 33%, 232032 KB, 50637 KB/s, 4 seconds passed
+... 33%, 232064 KB, 50639 KB/s, 4 seconds passed
+... 33%, 232096 KB, 50641 KB/s, 4 seconds passed
+... 33%, 232128 KB, 50643 KB/s, 4 seconds passed
+... 33%, 232160 KB, 50647 KB/s, 4 seconds passed
+... 33%, 232192 KB, 50650 KB/s, 4 seconds passed
+... 33%, 232224 KB, 50654 KB/s, 4 seconds passed
+... 33%, 232256 KB, 50655 KB/s, 4 seconds passed
+... 33%, 232288 KB, 50658 KB/s, 4 seconds passed
+... 33%, 232320 KB, 50661 KB/s, 4 seconds passed
+... 33%, 232352 KB, 50664 KB/s, 4 seconds passed
+... 33%, 232384 KB, 50666 KB/s, 4 seconds passed
+... 33%, 232416 KB, 50446 KB/s, 4 seconds passed
+... 33%, 232448 KB, 50448 KB/s, 4 seconds passed
+... 33%, 232480 KB, 50451 KB/s, 4 seconds passed
+... 33%, 232512 KB, 50454 KB/s, 4 seconds passed
+... 33%, 232544 KB, 50456 KB/s, 4 seconds passed
+... 33%, 232576 KB, 50459 KB/s, 4 seconds passed
+... 33%, 232608 KB, 50462 KB/s, 4 seconds passed
+... 33%, 232640 KB, 50465 KB/s, 4 seconds passed
+... 33%, 232672 KB, 50467 KB/s, 4 seconds passed
+... 33%, 232704 KB, 50470 KB/s, 4 seconds passed
+... 33%, 232736 KB, 50473 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 33%, 232768 KB, 50476 KB/s, 4 seconds passed
+... 33%, 232800 KB, 50479 KB/s, 4 seconds passed
+... 33%, 232832 KB, 50481 KB/s, 4 seconds passed
+... 33%, 232864 KB, 50484 KB/s, 4 seconds passed
+... 33%, 232896 KB, 50487 KB/s, 4 seconds passed
+... 33%, 232928 KB, 50490 KB/s, 4 seconds passed
+... 33%, 232960 KB, 50492 KB/s, 4 seconds passed
+... 33%, 232992 KB, 50495 KB/s, 4 seconds passed
+... 33%, 233024 KB, 50498 KB/s, 4 seconds passed
+... 33%, 233056 KB, 50501 KB/s, 4 seconds passed
+... 33%, 233088 KB, 50504 KB/s, 4 seconds passed
+... 33%, 233120 KB, 50506 KB/s, 4 seconds passed
+... 33%, 233152 KB, 50509 KB/s, 4 seconds passed
+... 33%, 233184 KB, 50512 KB/s, 4 seconds passed
+... 33%, 233216 KB, 50515 KB/s, 4 seconds passed
+... 33%, 233248 KB, 50518 KB/s, 4 seconds passed
+... 33%, 233280 KB, 50520 KB/s, 4 seconds passed
+... 33%, 233312 KB, 50524 KB/s, 4 seconds passed
+... 33%, 233344 KB, 50528 KB/s, 4 seconds passed
+... 33%, 233376 KB, 50533 KB/s, 4 seconds passed
+... 33%, 233408 KB, 50537 KB/s, 4 seconds passed
+... 33%, 233440 KB, 50541 KB/s, 4 seconds passed
+... 33%, 233472 KB, 50545 KB/s, 4 seconds passed
+... 33%, 233504 KB, 50549 KB/s, 4 seconds passed
+... 33%, 233536 KB, 50553 KB/s, 4 seconds passed
+... 33%, 233568 KB, 50558 KB/s, 4 seconds passed
+... 33%, 233600 KB, 50562 KB/s, 4 seconds passed
+... 33%, 233632 KB, 50566 KB/s, 4 seconds passed
+... 33%, 233664 KB, 50570 KB/s, 4 seconds passed
+... 33%, 233696 KB, 50575 KB/s, 4 seconds passed
+... 33%, 233728 KB, 50579 KB/s, 4 seconds passed
+... 33%, 233760 KB, 50583 KB/s, 4 seconds passed
+... 33%, 233792 KB, 50587 KB/s, 4 seconds passed
+... 33%, 233824 KB, 50592 KB/s, 4 seconds passed
+... 33%, 233856 KB, 50596 KB/s, 4 seconds passed
+... 33%, 233888 KB, 50600 KB/s, 4 seconds passed
+... 33%, 233920 KB, 50605 KB/s, 4 seconds passed
+... 33%, 233952 KB, 50609 KB/s, 4 seconds passed
+... 33%, 233984 KB, 50613 KB/s, 4 seconds passed
+... 33%, 234016 KB, 50617 KB/s, 4 seconds passed
+... 33%, 234048 KB, 50621 KB/s, 4 seconds passed
+... 33%, 234080 KB, 50626 KB/s, 4 seconds passed
+... 33%, 234112 KB, 50630 KB/s, 4 seconds passed
+... 33%, 234144 KB, 50634 KB/s, 4 seconds passed
+... 33%, 234176 KB, 50638 KB/s, 4 seconds passed
+... 33%, 234208 KB, 50642 KB/s, 4 seconds passed
+... 33%, 234240 KB, 50647 KB/s, 4 seconds passed
+... 33%, 234272 KB, 50651 KB/s, 4 seconds passed
+... 33%, 234304 KB, 50655 KB/s, 4 seconds passed
+... 33%, 234336 KB, 50660 KB/s, 4 seconds passed
+... 33%, 234368 KB, 50664 KB/s, 4 seconds passed
+... 33%, 234400 KB, 50668 KB/s, 4 seconds passed
+... 33%, 234432 KB, 50672 KB/s, 4 seconds passed
+... 33%, 234464 KB, 50677 KB/s, 4 seconds passed
+... 33%, 234496 KB, 50681 KB/s, 4 seconds passed
+... 33%, 234528 KB, 50685 KB/s, 4 seconds passed
+... 33%, 234560 KB, 50689 KB/s, 4 seconds passed
+... 33%, 234592 KB, 50694 KB/s, 4 seconds passed
+... 33%, 234624 KB, 50698 KB/s, 4 seconds passed
+... 33%, 234656 KB, 50703 KB/s, 4 seconds passed
+... 33%, 234688 KB, 50708 KB/s, 4 seconds passed
+... 33%, 234720 KB, 50713 KB/s, 4 seconds passed
+... 33%, 234752 KB, 50718 KB/s, 4 seconds passed
+... 33%, 234784 KB, 50723 KB/s, 4 seconds passed
+... 33%, 234816 KB, 50729 KB/s, 4 seconds passed
+... 33%, 234848 KB, 50734 KB/s, 4 seconds passed
+... 33%, 234880 KB, 50739 KB/s, 4 seconds passed
+... 33%, 234912 KB, 50744 KB/s, 4 seconds passed
+... 33%, 234944 KB, 50749 KB/s, 4 seconds passed
+... 33%, 234976 KB, 50754 KB/s, 4 seconds passed
+... 33%, 235008 KB, 50759 KB/s, 4 seconds passed
+... 33%, 235040 KB, 50764 KB/s, 4 seconds passed
+... 33%, 235072 KB, 50769 KB/s, 4 seconds passed
+... 33%, 235104 KB, 50773 KB/s, 4 seconds passed
+... 33%, 235136 KB, 50776 KB/s, 4 seconds passed
+... 33%, 235168 KB, 50780 KB/s, 4 seconds passed
+... 33%, 235200 KB, 50784 KB/s, 4 seconds passed
+... 33%, 235232 KB, 50788 KB/s, 4 seconds passed
+... 33%, 235264 KB, 50792 KB/s, 4 seconds passed
+... 33%, 235296 KB, 50796 KB/s, 4 seconds passed
+... 33%, 235328 KB, 50800 KB/s, 4 seconds passed
+... 33%, 235360 KB, 50804 KB/s, 4 seconds passed
+... 33%, 235392 KB, 50808 KB/s, 4 seconds passed
+... 33%, 235424 KB, 50812 KB/s, 4 seconds passed
+... 33%, 235456 KB, 50816 KB/s, 4 seconds passed
+... 33%, 235488 KB, 50819 KB/s, 4 seconds passed
+... 33%, 235520 KB, 50629 KB/s, 4 seconds passed
+... 33%, 235552 KB, 50631 KB/s, 4 seconds passed
+... 33%, 235584 KB, 50633 KB/s, 4 seconds passed
+... 33%, 235616 KB, 50636 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 33%, 235648 KB, 50503 KB/s, 4 seconds passed
+... 33%, 235680 KB, 50497 KB/s, 4 seconds passed
+... 33%, 235712 KB, 50499 KB/s, 4 seconds passed
+... 33%, 235744 KB, 50502 KB/s, 4 seconds passed
+... 33%, 235776 KB, 50504 KB/s, 4 seconds passed
+... 33%, 235808 KB, 50498 KB/s, 4 seconds passed
+... 33%, 235840 KB, 50499 KB/s, 4 seconds passed
+... 33%, 235872 KB, 50502 KB/s, 4 seconds passed
+... 33%, 235904 KB, 50504 KB/s, 4 seconds passed
+... 33%, 235936 KB, 50507 KB/s, 4 seconds passed
+... 33%, 235968 KB, 50509 KB/s, 4 seconds passed
+... 33%, 236000 KB, 50512 KB/s, 4 seconds passed
+... 33%, 236032 KB, 50515 KB/s, 4 seconds passed
+... 33%, 236064 KB, 50517 KB/s, 4 seconds passed
+... 33%, 236096 KB, 50520 KB/s, 4 seconds passed
+... 33%, 236128 KB, 50523 KB/s, 4 seconds passed
+... 33%, 236160 KB, 50526 KB/s, 4 seconds passed
+... 33%, 236192 KB, 50528 KB/s, 4 seconds passed
+... 33%, 236224 KB, 50531 KB/s, 4 seconds passed
+... 33%, 236256 KB, 50533 KB/s, 4 seconds passed
+... 33%, 236288 KB, 50536 KB/s, 4 seconds passed
+... 33%, 236320 KB, 50539 KB/s, 4 seconds passed
+... 33%, 236352 KB, 50541 KB/s, 4 seconds passed
+... 33%, 236384 KB, 50544 KB/s, 4 seconds passed
+... 33%, 236416 KB, 50547 KB/s, 4 seconds passed
+... 33%, 236448 KB, 50550 KB/s, 4 seconds passed
+... 34%, 236480 KB, 50552 KB/s, 4 seconds passed
+... 34%, 236512 KB, 50555 KB/s, 4 seconds passed
+... 34%, 236544 KB, 50558 KB/s, 4 seconds passed
+... 34%, 236576 KB, 50561 KB/s, 4 seconds passed
+... 34%, 236608 KB, 50564 KB/s, 4 seconds passed
+... 34%, 236640 KB, 50568 KB/s, 4 seconds passed
+... 34%, 236672 KB, 50572 KB/s, 4 seconds passed
+... 34%, 236704 KB, 50576 KB/s, 4 seconds passed
+... 34%, 236736 KB, 50579 KB/s, 4 seconds passed
+... 34%, 236768 KB, 50583 KB/s, 4 seconds passed
+... 34%, 236800 KB, 50587 KB/s, 4 seconds passed
+... 34%, 236832 KB, 50590 KB/s, 4 seconds passed
+... 34%, 236864 KB, 50594 KB/s, 4 seconds passed
+... 34%, 236896 KB, 50598 KB/s, 4 seconds passed
+... 34%, 236928 KB, 50601 KB/s, 4 seconds passed
+... 34%, 236960 KB, 50605 KB/s, 4 seconds passed
+... 34%, 236992 KB, 50609 KB/s, 4 seconds passed
+... 34%, 237024 KB, 50612 KB/s, 4 seconds passed
+... 34%, 237056 KB, 50615 KB/s, 4 seconds passed
+... 34%, 237088 KB, 50619 KB/s, 4 seconds passed
+... 34%, 237120 KB, 50623 KB/s, 4 seconds passed
+... 34%, 237152 KB, 50627 KB/s, 4 seconds passed
+... 34%, 237184 KB, 50630 KB/s, 4 seconds passed
+... 34%, 237216 KB, 50634 KB/s, 4 seconds passed
+... 34%, 237248 KB, 50638 KB/s, 4 seconds passed
+... 34%, 237280 KB, 50641 KB/s, 4 seconds passed
+... 34%, 237312 KB, 50645 KB/s, 4 seconds passed
+... 34%, 237344 KB, 50649 KB/s, 4 seconds passed
+... 34%, 237376 KB, 50652 KB/s, 4 seconds passed
+... 34%, 237408 KB, 50656 KB/s, 4 seconds passed
+... 34%, 237440 KB, 50660 KB/s, 4 seconds passed
+... 34%, 237472 KB, 50664 KB/s, 4 seconds passed
+... 34%, 237504 KB, 50667 KB/s, 4 seconds passed
+... 34%, 237536 KB, 50671 KB/s, 4 seconds passed
+... 34%, 237568 KB, 50675 KB/s, 4 seconds passed
+... 34%, 237600 KB, 50678 KB/s, 4 seconds passed
+... 34%, 237632 KB, 50682 KB/s, 4 seconds passed
+... 34%, 237664 KB, 50686 KB/s, 4 seconds passed
+... 34%, 237696 KB, 50691 KB/s, 4 seconds passed
+... 34%, 237728 KB, 50696 KB/s, 4 seconds passed
+... 34%, 237760 KB, 50701 KB/s, 4 seconds passed
+... 34%, 237792 KB, 50705 KB/s, 4 seconds passed
+... 34%, 237824 KB, 50710 KB/s, 4 seconds passed
+... 34%, 237856 KB, 50715 KB/s, 4 seconds passed
+... 34%, 237888 KB, 50720 KB/s, 4 seconds passed
+... 34%, 237920 KB, 50725 KB/s, 4 seconds passed
+... 34%, 237952 KB, 50729 KB/s, 4 seconds passed
+... 34%, 237984 KB, 50734 KB/s, 4 seconds passed
+... 34%, 238016 KB, 50739 KB/s, 4 seconds passed
+... 34%, 238048 KB, 50743 KB/s, 4 seconds passed
+... 34%, 238080 KB, 50748 KB/s, 4 seconds passed
+... 34%, 238112 KB, 50753 KB/s, 4 seconds passed
+... 34%, 238144 KB, 50758 KB/s, 4 seconds passed
+... 34%, 238176 KB, 50762 KB/s, 4 seconds passed
+... 34%, 238208 KB, 50767 KB/s, 4 seconds passed
+... 34%, 238240 KB, 50772 KB/s, 4 seconds passed
+... 34%, 238272 KB, 50776 KB/s, 4 seconds passed
+... 34%, 238304 KB, 50781 KB/s, 4 seconds passed
+... 34%, 238336 KB, 50786 KB/s, 4 seconds passed
+... 34%, 238368 KB, 50790 KB/s, 4 seconds passed
+... 34%, 238400 KB, 50795 KB/s, 4 seconds passed
+... 34%, 238432 KB, 50800 KB/s, 4 seconds passed
+... 34%, 238464 KB, 50804 KB/s, 4 seconds passed
+... 34%, 238496 KB, 50809 KB/s, 4 seconds passed
+... 34%, 238528 KB, 50814 KB/s, 4 seconds passed
+... 34%, 238560 KB, 50819 KB/s, 4 seconds passed
+... 34%, 238592 KB, 50824 KB/s, 4 seconds passed
+... 34%, 238624 KB, 50828 KB/s, 4 seconds passed
+... 34%, 238656 KB, 50833 KB/s, 4 seconds passed
+... 34%, 238688 KB, 50838 KB/s, 4 seconds passed
+... 34%, 238720 KB, 50842 KB/s, 4 seconds passed
+... 34%, 238752 KB, 50847 KB/s, 4 seconds passed
+... 34%, 238784 KB, 50852 KB/s, 4 seconds passed
+... 34%, 238816 KB, 50857 KB/s, 4 seconds passed
+... 34%, 238848 KB, 50861 KB/s, 4 seconds passed
+... 34%, 238880 KB, 50866 KB/s, 4 seconds passed
+... 34%, 238912 KB, 50868 KB/s, 4 seconds passed
+... 34%, 238944 KB, 50873 KB/s, 4 seconds passed
+... 34%, 238976 KB, 50877 KB/s, 4 seconds passed
+... 34%, 239008 KB, 50881 KB/s, 4 seconds passed
+... 34%, 239040 KB, 50885 KB/s, 4 seconds passed
+... 34%, 239072 KB, 50888 KB/s, 4 seconds passed
+... 34%, 239104 KB, 50892 KB/s, 4 seconds passed
+... 34%, 239136 KB, 50896 KB/s, 4 seconds passed
+... 34%, 239168 KB, 50899 KB/s, 4 seconds passed
+... 34%, 239200 KB, 50902 KB/s, 4 seconds passed
+... 34%, 239232 KB, 50907 KB/s, 4 seconds passed
+... 34%, 239264 KB, 50910 KB/s, 4 seconds passed
+... 34%, 239296 KB, 50915 KB/s, 4 seconds passed
+... 34%, 239328 KB, 50919 KB/s, 4 seconds passed
+... 34%, 239360 KB, 50922 KB/s, 4 seconds passed
+... 34%, 239392 KB, 50926 KB/s, 4 seconds passed
+... 34%, 239424 KB, 50929 KB/s, 4 seconds passed
+... 34%, 239456 KB, 50932 KB/s, 4 seconds passed
+... 34%, 239488 KB, 50923 KB/s, 4 seconds passed
+... 34%, 239520 KB, 50927 KB/s, 4 seconds passed
+... 34%, 239552 KB, 50931 KB/s, 4 seconds passed
+... 34%, 239584 KB, 50934 KB/s, 4 seconds passed
+... 34%, 239616 KB, 50938 KB/s, 4 seconds passed
+... 34%, 239648 KB, 50940 KB/s, 4 seconds passed
+... 34%, 239680 KB, 50943 KB/s, 4 seconds passed
+... 34%, 239712 KB, 50945 KB/s, 4 seconds passed
+... 34%, 239744 KB, 50948 KB/s, 4 seconds passed
+... 34%, 239776 KB, 50952 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 34%, 239808 KB, 50815 KB/s, 4 seconds passed
+... 34%, 239840 KB, 50817 KB/s, 4 seconds passed
+... 34%, 239872 KB, 50820 KB/s, 4 seconds passed
+... 34%, 239904 KB, 50822 KB/s, 4 seconds passed
+... 34%, 239936 KB, 50825 KB/s, 4 seconds passed
+... 34%, 239968 KB, 50828 KB/s, 4 seconds passed
+... 34%, 240000 KB, 50830 KB/s, 4 seconds passed
+... 34%, 240032 KB, 50833 KB/s, 4 seconds passed
+... 34%, 240064 KB, 50836 KB/s, 4 seconds passed
+... 34%, 240096 KB, 50838 KB/s, 4 seconds passed
+... 34%, 240128 KB, 50840 KB/s, 4 seconds passed
+... 34%, 240160 KB, 50843 KB/s, 4 seconds passed
+... 34%, 240192 KB, 50845 KB/s, 4 seconds passed
+... 34%, 240224 KB, 50848 KB/s, 4 seconds passed
+... 34%, 240256 KB, 50851 KB/s, 4 seconds passed
+... 34%, 240288 KB, 50853 KB/s, 4 seconds passed
+... 34%, 240320 KB, 50856 KB/s, 4 seconds passed
+... 34%, 240352 KB, 50859 KB/s, 4 seconds passed
+... 34%, 240384 KB, 50862 KB/s, 4 seconds passed
+... 34%, 240416 KB, 50865 KB/s, 4 seconds passed
+... 34%, 240448 KB, 50869 KB/s, 4 seconds passed
+... 34%, 240480 KB, 50872 KB/s, 4 seconds passed
+... 34%, 240512 KB, 50876 KB/s, 4 seconds passed
+... 34%, 240544 KB, 50880 KB/s, 4 seconds passed
+... 34%, 240576 KB, 50883 KB/s, 4 seconds passed
+... 34%, 240608 KB, 50887 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 34%, 240640 KB, 50131 KB/s, 4 seconds passed
+... 34%, 240672 KB, 50132 KB/s, 4 seconds passed
+... 34%, 240704 KB, 50134 KB/s, 4 seconds passed
+... 34%, 240736 KB, 50136 KB/s, 4 seconds passed
+... 34%, 240768 KB, 50139 KB/s, 4 seconds passed
+... 34%, 240800 KB, 50141 KB/s, 4 seconds passed
+... 34%, 240832 KB, 50144 KB/s, 4 seconds passed
+... 34%, 240864 KB, 50146 KB/s, 4 seconds passed
+... 34%, 240896 KB, 50149 KB/s, 4 seconds passed
+... 34%, 240928 KB, 50151 KB/s, 4 seconds passed
+... 34%, 240960 KB, 50154 KB/s, 4 seconds passed
+... 34%, 240992 KB, 50157 KB/s, 4 seconds passed
+... 34%, 241024 KB, 50159 KB/s, 4 seconds passed
+... 34%, 241056 KB, 50162 KB/s, 4 seconds passed
+... 34%, 241088 KB, 50164 KB/s, 4 seconds passed
+... 34%, 241120 KB, 50167 KB/s, 4 seconds passed
+... 34%, 241152 KB, 50170 KB/s, 4 seconds passed
+... 34%, 241184 KB, 50172 KB/s, 4 seconds passed
+... 34%, 241216 KB, 50174 KB/s, 4 seconds passed
+... 34%, 241248 KB, 50177 KB/s, 4 seconds passed
+... 34%, 241280 KB, 50180 KB/s, 4 seconds passed
+... 34%, 241312 KB, 50182 KB/s, 4 seconds passed
+... 34%, 241344 KB, 50185 KB/s, 4 seconds passed
+... 34%, 241376 KB, 50187 KB/s, 4 seconds passed
+... 34%, 241408 KB, 50190 KB/s, 4 seconds passed
+... 34%, 241440 KB, 50195 KB/s, 4 seconds passed
+... 34%, 241472 KB, 50198 KB/s, 4 seconds passed
+... 34%, 241504 KB, 50202 KB/s, 4 seconds passed
+... 34%, 241536 KB, 50206 KB/s, 4 seconds passed
+... 34%, 241568 KB, 50210 KB/s, 4 seconds passed
+... 34%, 241600 KB, 50214 KB/s, 4 seconds passed
+... 34%, 241632 KB, 50218 KB/s, 4 seconds passed
+... 34%, 241664 KB, 50221 KB/s, 4 seconds passed
+... 34%, 241696 KB, 50225 KB/s, 4 seconds passed
+... 34%, 241728 KB, 50229 KB/s, 4 seconds passed
+... 34%, 241760 KB, 50233 KB/s, 4 seconds passed
+... 34%, 241792 KB, 50237 KB/s, 4 seconds passed
+... 34%, 241824 KB, 50241 KB/s, 4 seconds passed
+... 34%, 241856 KB, 50245 KB/s, 4 seconds passed
+... 34%, 241888 KB, 50248 KB/s, 4 seconds passed
+... 34%, 241920 KB, 50252 KB/s, 4 seconds passed
+... 34%, 241952 KB, 50256 KB/s, 4 seconds passed
+... 34%, 241984 KB, 50260 KB/s, 4 seconds passed
+... 34%, 242016 KB, 50264 KB/s, 4 seconds passed
+... 34%, 242048 KB, 50268 KB/s, 4 seconds passed
+... 34%, 242080 KB, 50272 KB/s, 4 seconds passed
+... 34%, 242112 KB, 50276 KB/s, 4 seconds passed
+... 34%, 242144 KB, 50280 KB/s, 4 seconds passed
+... 34%, 242176 KB, 50284 KB/s, 4 seconds passed
+... 34%, 242208 KB, 50288 KB/s, 4 seconds passed
+... 34%, 242240 KB, 50292 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 34%, 242272 KB, 50296 KB/s, 4 seconds passed
+... 34%, 242304 KB, 50300 KB/s, 4 seconds passed
+... 34%, 242336 KB, 50304 KB/s, 4 seconds passed
+... 34%, 242368 KB, 50308 KB/s, 4 seconds passed
+... 34%, 242400 KB, 50312 KB/s, 4 seconds passed
+... 34%, 242432 KB, 50316 KB/s, 4 seconds passed
+... 34%, 242464 KB, 50320 KB/s, 4 seconds passed
+... 34%, 242496 KB, 50324 KB/s, 4 seconds passed
+... 34%, 242528 KB, 50328 KB/s, 4 seconds passed
+... 34%, 242560 KB, 50331 KB/s, 4 seconds passed
+... 34%, 242592 KB, 50335 KB/s, 4 seconds passed
+... 34%, 242624 KB, 50339 KB/s, 4 seconds passed
+... 34%, 242656 KB, 50343 KB/s, 4 seconds passed
+... 34%, 242688 KB, 50347 KB/s, 4 seconds passed
+... 34%, 242720 KB, 50351 KB/s, 4 seconds passed
+... 34%, 242752 KB, 50355 KB/s, 4 seconds passed
+... 34%, 242784 KB, 50360 KB/s, 4 seconds passed
+... 34%, 242816 KB, 50365 KB/s, 4 seconds passed
+... 34%, 242848 KB, 50370 KB/s, 4 seconds passed
+... 34%, 242880 KB, 50375 KB/s, 4 seconds passed
+... 34%, 242912 KB, 50379 KB/s, 4 seconds passed
+... 34%, 242944 KB, 50384 KB/s, 4 seconds passed
+... 34%, 242976 KB, 50389 KB/s, 4 seconds passed
+... 34%, 243008 KB, 50394 KB/s, 4 seconds passed
+... 34%, 243040 KB, 50398 KB/s, 4 seconds passed
+... 34%, 243072 KB, 50403 KB/s, 4 seconds passed
+... 34%, 243104 KB, 50408 KB/s, 4 seconds passed
+... 34%, 243136 KB, 50413 KB/s, 4 seconds passed
+... 34%, 243168 KB, 50281 KB/s, 4 seconds passed
+... 34%, 243200 KB, 50282 KB/s, 4 seconds passed
+... 34%, 243232 KB, 50285 KB/s, 4 seconds passed
+... 34%, 243264 KB, 50287 KB/s, 4 seconds passed
+... 34%, 243296 KB, 50290 KB/s, 4 seconds passed
+... 34%, 243328 KB, 50292 KB/s, 4 seconds passed
+... 34%, 243360 KB, 50295 KB/s, 4 seconds passed
+... 34%, 243392 KB, 50298 KB/s, 4 seconds passed
+... 34%, 243424 KB, 50300 KB/s, 4 seconds passed
+... 35%, 243456 KB, 50303 KB/s, 4 seconds passed
+... 35%, 243488 KB, 50306 KB/s, 4 seconds passed
+... 35%, 243520 KB, 50308 KB/s, 4 seconds passed
+... 35%, 243552 KB, 50311 KB/s, 4 seconds passed
+... 35%, 243584 KB, 50314 KB/s, 4 seconds passed
+... 35%, 243616 KB, 50317 KB/s, 4 seconds passed
+... 35%, 243648 KB, 50319 KB/s, 4 seconds passed
+... 35%, 243680 KB, 50322 KB/s, 4 seconds passed
+... 35%, 243712 KB, 50325 KB/s, 4 seconds passed
+... 35%, 243744 KB, 50327 KB/s, 4 seconds passed
+... 35%, 243776 KB, 50330 KB/s, 4 seconds passed
+... 35%, 243808 KB, 50333 KB/s, 4 seconds passed
+... 35%, 243840 KB, 50336 KB/s, 4 seconds passed
+... 35%, 243872 KB, 50338 KB/s, 4 seconds passed
+... 35%, 243904 KB, 50341 KB/s, 4 seconds passed
+... 35%, 243936 KB, 50343 KB/s, 4 seconds passed
+... 35%, 243968 KB, 50346 KB/s, 4 seconds passed
+... 35%, 244000 KB, 50349 KB/s, 4 seconds passed
+... 35%, 244032 KB, 50351 KB/s, 4 seconds passed
+... 35%, 244064 KB, 50354 KB/s, 4 seconds passed
+... 35%, 244096 KB, 50356 KB/s, 4 seconds passed
+... 35%, 244128 KB, 50359 KB/s, 4 seconds passed
+... 35%, 244160 KB, 50362 KB/s, 4 seconds passed
+... 35%, 244192 KB, 50364 KB/s, 4 seconds passed
+... 35%, 244224 KB, 50367 KB/s, 4 seconds passed
+... 35%, 244256 KB, 50370 KB/s, 4 seconds passed
+... 35%, 244288 KB, 50372 KB/s, 4 seconds passed
+... 35%, 244320 KB, 50375 KB/s, 4 seconds passed
+... 35%, 244352 KB, 50378 KB/s, 4 seconds passed
+... 35%, 244384 KB, 50381 KB/s, 4 seconds passed
+... 35%, 244416 KB, 50385 KB/s, 4 seconds passed
+... 35%, 244448 KB, 50388 KB/s, 4 seconds passed
+... 35%, 244480 KB, 50393 KB/s, 4 seconds passed
+... 35%, 244512 KB, 50397 KB/s, 4 seconds passed
+... 35%, 244544 KB, 50402 KB/s, 4 seconds passed
+... 35%, 244576 KB, 50406 KB/s, 4 seconds passed
+... 35%, 244608 KB, 50411 KB/s, 4 seconds passed
+... 35%, 244640 KB, 50415 KB/s, 4 seconds passed
+... 35%, 244672 KB, 50420 KB/s, 4 seconds passed
+... 35%, 244704 KB, 50424 KB/s, 4 seconds passed
+... 35%, 244736 KB, 50429 KB/s, 4 seconds passed
+... 35%, 244768 KB, 50434 KB/s, 4 seconds passed
+... 35%, 244800 KB, 50438 KB/s, 4 seconds passed
+... 35%, 244832 KB, 50443 KB/s, 4 seconds passed
+... 35%, 244864 KB, 50447 KB/s, 4 seconds passed
+... 35%, 244896 KB, 50452 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 35%, 244928 KB, 50274 KB/s, 4 seconds passed
+... 35%, 244960 KB, 50275 KB/s, 4 seconds passed
+... 35%, 244992 KB, 50278 KB/s, 4 seconds passed
+... 35%, 245024 KB, 50280 KB/s, 4 seconds passed
+... 35%, 245056 KB, 50283 KB/s, 4 seconds passed
+... 35%, 245088 KB, 50285 KB/s, 4 seconds passed
+... 35%, 245120 KB, 50287 KB/s, 4 seconds passed
+... 35%, 245152 KB, 50290 KB/s, 4 seconds passed
+... 35%, 245184 KB, 50292 KB/s, 4 seconds passed
+... 35%, 245216 KB, 50295 KB/s, 4 seconds passed
+... 35%, 245248 KB, 50297 KB/s, 4 seconds passed
+... 35%, 245280 KB, 50300 KB/s, 4 seconds passed
+... 35%, 245312 KB, 50302 KB/s, 4 seconds passed
+... 35%, 245344 KB, 50305 KB/s, 4 seconds passed
+... 35%, 245376 KB, 50308 KB/s, 4 seconds passed
+... 35%, 245408 KB, 50310 KB/s, 4 seconds passed
+... 35%, 245440 KB, 50313 KB/s, 4 seconds passed
+... 35%, 245472 KB, 50316 KB/s, 4 seconds passed
+... 35%, 245504 KB, 50318 KB/s, 4 seconds passed
+... 35%, 245536 KB, 50321 KB/s, 4 seconds passed
+... 35%, 245568 KB, 50323 KB/s, 4 seconds passed
+... 35%, 245600 KB, 50326 KB/s, 4 seconds passed
+... 35%, 245632 KB, 50330 KB/s, 4 seconds passed
+... 35%, 245664 KB, 50333 KB/s, 4 seconds passed
+... 35%, 245696 KB, 50337 KB/s, 4 seconds passed
+... 35%, 245728 KB, 50341 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 35%, 245760 KB, 49536 KB/s, 4 seconds passed
+... 35%, 245792 KB, 49537 KB/s, 4 seconds passed
+... 35%, 245824 KB, 49539 KB/s, 4 seconds passed
+... 35%, 245856 KB, 49541 KB/s, 4 seconds passed
+... 35%, 245888 KB, 49543 KB/s, 4 seconds passed
+... 35%, 245920 KB, 49546 KB/s, 4 seconds passed
+... 35%, 245952 KB, 49548 KB/s, 4 seconds passed
+... 35%, 245984 KB, 49551 KB/s, 4 seconds passed
+... 35%, 246016 KB, 49553 KB/s, 4 seconds passed
+... 35%, 246048 KB, 49556 KB/s, 4 seconds passed
+... 35%, 246080 KB, 49559 KB/s, 4 seconds passed
+... 35%, 246112 KB, 49561 KB/s, 4 seconds passed
+... 35%, 246144 KB, 49564 KB/s, 4 seconds passed
+... 35%, 246176 KB, 49566 KB/s, 4 seconds passed
+... 35%, 246208 KB, 49569 KB/s, 4 seconds passed
+... 35%, 246240 KB, 49571 KB/s, 4 seconds passed
+... 35%, 246272 KB, 49574 KB/s, 4 seconds passed
+... 35%, 246304 KB, 49576 KB/s, 4 seconds passed
+... 35%, 246336 KB, 49579 KB/s, 4 seconds passed
+... 35%, 246368 KB, 49581 KB/s, 4 seconds passed
+... 35%, 246400 KB, 49584 KB/s, 4 seconds passed
+... 35%, 246432 KB, 49587 KB/s, 4 seconds passed
+... 35%, 246464 KB, 49589 KB/s, 4 seconds passed
+... 35%, 246496 KB, 49592 KB/s, 4 seconds passed
+
+.. parsed-literal::
+
+    ... 35%, 246528 KB, 49594 KB/s, 4 seconds passed
+... 35%, 246560 KB, 49597 KB/s, 4 seconds passed
+... 35%, 246592 KB, 49599 KB/s, 4 seconds passed
+... 35%, 246624 KB, 49602 KB/s, 4 seconds passed
+... 35%, 246656 KB, 49606 KB/s, 4 seconds passed
+... 35%, 246688 KB, 49610 KB/s, 4 seconds passed
+... 35%, 246720 KB, 49614 KB/s, 4 seconds passed
+... 35%, 246752 KB, 49618 KB/s, 4 seconds passed
+... 35%, 246784 KB, 49622 KB/s, 4 seconds passed
+... 35%, 246816 KB, 49626 KB/s, 4 seconds passed
+... 35%, 246848 KB, 49630 KB/s, 4 seconds passed
+... 35%, 246880 KB, 49634 KB/s, 4 seconds passed
+... 35%, 246912 KB, 49638 KB/s, 4 seconds passed
+... 35%, 246944 KB, 49641 KB/s, 4 seconds passed
+... 35%, 246976 KB, 49646 KB/s, 4 seconds passed
+... 35%, 247008 KB, 49648 KB/s, 4 seconds passed
+... 35%, 247040 KB, 49651 KB/s, 4 seconds passed
+... 35%, 247072 KB, 49653 KB/s, 4 seconds passed
+... 35%, 247104 KB, 49656 KB/s, 4 seconds passed
+... 35%, 247136 KB, 49660 KB/s, 4 seconds passed
+... 35%, 247168 KB, 49661 KB/s, 4 seconds passed
+... 35%, 247200 KB, 49665 KB/s, 4 seconds passed
+... 35%, 247232 KB, 49666 KB/s, 4 seconds passed
+... 35%, 247264 KB, 49668 KB/s, 4 seconds passed
+... 35%, 247296 KB, 49671 KB/s, 4 seconds passed
+... 35%, 247328 KB, 49675 KB/s, 4 seconds passed
+... 35%, 247360 KB, 49678 KB/s, 4 seconds passed
+... 35%, 247392 KB, 49681 KB/s, 4 seconds passed
+... 35%, 247424 KB, 49528 KB/s, 4 seconds passed
+... 35%, 247456 KB, 49530 KB/s, 4 seconds passed
+... 35%, 247488 KB, 49533 KB/s, 4 seconds passed
+... 35%, 247520 KB, 49536 KB/s, 4 seconds passed
+... 35%, 247552 KB, 49539 KB/s, 4 seconds passed
+... 35%, 247584 KB, 49478 KB/s, 5 seconds passed
+... 35%, 247616 KB, 49480 KB/s, 5 seconds passed
+... 35%, 247648 KB, 49483 KB/s, 5 seconds passed
+... 35%, 247680 KB, 49486 KB/s, 5 seconds passed
+... 35%, 247712 KB, 49488 KB/s, 5 seconds passed
+... 35%, 247744 KB, 49491 KB/s, 5 seconds passed
+... 35%, 247776 KB, 49493 KB/s, 5 seconds passed
+... 35%, 247808 KB, 49497 KB/s, 5 seconds passed
+... 35%, 247840 KB, 49500 KB/s, 5 seconds passed
+... 35%, 247872 KB, 49479 KB/s, 5 seconds passed
+... 35%, 247904 KB, 49481 KB/s, 5 seconds passed
+... 35%, 247936 KB, 49484 KB/s, 5 seconds passed
+... 35%, 247968 KB, 49486 KB/s, 5 seconds passed
+... 35%, 248000 KB, 49489 KB/s, 5 seconds passed
+... 35%, 248032 KB, 49491 KB/s, 5 seconds passed
+... 35%, 248064 KB, 49494 KB/s, 5 seconds passed
+... 35%, 248096 KB, 49497 KB/s, 5 seconds passed
+... 35%, 248128 KB, 49499 KB/s, 5 seconds passed
+... 35%, 248160 KB, 49502 KB/s, 5 seconds passed
+... 35%, 248192 KB, 49504 KB/s, 5 seconds passed
+... 35%, 248224 KB, 49507 KB/s, 5 seconds passed
+... 35%, 248256 KB, 49510 KB/s, 5 seconds passed
+... 35%, 248288 KB, 49513 KB/s, 5 seconds passed
+... 35%, 248320 KB, 49517 KB/s, 5 seconds passed
+... 35%, 248352 KB, 49520 KB/s, 5 seconds passed
+... 35%, 248384 KB, 49506 KB/s, 5 seconds passed
+... 35%, 248416 KB, 49509 KB/s, 5 seconds passed
+... 35%, 248448 KB, 49511 KB/s, 5 seconds passed
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+... 35%, 248672 KB, 49531 KB/s, 5 seconds passed
+... 35%, 248704 KB, 49530 KB/s, 5 seconds passed
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 37%, 259840 KB, 50391 KB/s, 5 seconds passed
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+... 37%, 259968 KB, 50331 KB/s, 5 seconds passed
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+... 37%, 260544 KB, 50378 KB/s, 5 seconds passed
+... 37%, 260576 KB, 50381 KB/s, 5 seconds passed
+... 37%, 260608 KB, 50383 KB/s, 5 seconds passed
+... 37%, 260640 KB, 50386 KB/s, 5 seconds passed
+... 37%, 260672 KB, 50389 KB/s, 5 seconds passed
+... 37%, 260704 KB, 50392 KB/s, 5 seconds passed
+... 37%, 260736 KB, 50395 KB/s, 5 seconds passed
+... 37%, 260768 KB, 50399 KB/s, 5 seconds passed
+... 37%, 260800 KB, 50402 KB/s, 5 seconds passed
+... 37%, 260832 KB, 50406 KB/s, 5 seconds passed
+... 37%, 260864 KB, 50410 KB/s, 5 seconds passed
+... 37%, 260896 KB, 50414 KB/s, 5 seconds passed
+... 37%, 260928 KB, 50418 KB/s, 5 seconds passed
+
+.. parsed-literal::
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+... 37%, 261088 KB, 50438 KB/s, 5 seconds passed
+... 37%, 261120 KB, 50175 KB/s, 5 seconds passed
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+... 37%, 261216 KB, 50181 KB/s, 5 seconds passed
+... 37%, 261248 KB, 50155 KB/s, 5 seconds passed
+... 37%, 261280 KB, 50156 KB/s, 5 seconds passed
+... 37%, 261312 KB, 50159 KB/s, 5 seconds passed
+... 37%, 261344 KB, 50148 KB/s, 5 seconds passed
+... 37%, 261376 KB, 50151 KB/s, 5 seconds passed
+... 37%, 261408 KB, 50153 KB/s, 5 seconds passed
+... 37%, 261440 KB, 50155 KB/s, 5 seconds passed
+... 37%, 261472 KB, 50138 KB/s, 5 seconds passed
+... 37%, 261504 KB, 50140 KB/s, 5 seconds passed
+... 37%, 261536 KB, 50142 KB/s, 5 seconds passed
+... 37%, 261568 KB, 50144 KB/s, 5 seconds passed
+... 37%, 261600 KB, 50147 KB/s, 5 seconds passed
+... 37%, 261632 KB, 50150 KB/s, 5 seconds passed
+... 37%, 261664 KB, 50152 KB/s, 5 seconds passed
+... 37%, 261696 KB, 50155 KB/s, 5 seconds passed
+... 37%, 261728 KB, 50157 KB/s, 5 seconds passed
+... 37%, 261760 KB, 50159 KB/s, 5 seconds passed
+... 37%, 261792 KB, 50121 KB/s, 5 seconds passed
+... 37%, 261824 KB, 50124 KB/s, 5 seconds passed
+... 37%, 261856 KB, 50125 KB/s, 5 seconds passed
+... 37%, 261888 KB, 50127 KB/s, 5 seconds passed
+... 37%, 261920 KB, 50130 KB/s, 5 seconds passed
+... 37%, 261952 KB, 50132 KB/s, 5 seconds passed
+... 37%, 261984 KB, 50134 KB/s, 5 seconds passed
+... 37%, 262016 KB, 50137 KB/s, 5 seconds passed
+... 37%, 262048 KB, 50139 KB/s, 5 seconds passed
+
+.. parsed-literal::
+
+    ... 37%, 262080 KB, 50142 KB/s, 5 seconds passed
+... 37%, 262112 KB, 50144 KB/s, 5 seconds passed
+... 37%, 262144 KB, 50147 KB/s, 5 seconds passed
+... 37%, 262176 KB, 50149 KB/s, 5 seconds passed
+... 37%, 262208 KB, 50152 KB/s, 5 seconds passed
+... 37%, 262240 KB, 50154 KB/s, 5 seconds passed
+... 37%, 262272 KB, 50156 KB/s, 5 seconds passed
+... 37%, 262304 KB, 50158 KB/s, 5 seconds passed
+... 37%, 262336 KB, 50161 KB/s, 5 seconds passed
+... 37%, 262368 KB, 50163 KB/s, 5 seconds passed
+... 37%, 262400 KB, 50165 KB/s, 5 seconds passed
+... 37%, 262432 KB, 50168 KB/s, 5 seconds passed
+... 37%, 262464 KB, 50170 KB/s, 5 seconds passed
+... 37%, 262496 KB, 50173 KB/s, 5 seconds passed
+... 37%, 262528 KB, 50175 KB/s, 5 seconds passed
+... 37%, 262560 KB, 50177 KB/s, 5 seconds passed
+... 37%, 262592 KB, 50180 KB/s, 5 seconds passed
+... 37%, 262624 KB, 50182 KB/s, 5 seconds passed
+... 37%, 262656 KB, 50186 KB/s, 5 seconds passed
+... 37%, 262688 KB, 50190 KB/s, 5 seconds passed
+... 37%, 262720 KB, 50193 KB/s, 5 seconds passed
+... 37%, 262752 KB, 50197 KB/s, 5 seconds passed
+... 37%, 262784 KB, 50201 KB/s, 5 seconds passed
+... 37%, 262816 KB, 50204 KB/s, 5 seconds passed
+... 37%, 262848 KB, 50208 KB/s, 5 seconds passed
+... 37%, 262880 KB, 50212 KB/s, 5 seconds passed
+... 37%, 262912 KB, 50215 KB/s, 5 seconds passed
+... 37%, 262944 KB, 50219 KB/s, 5 seconds passed
+... 37%, 262976 KB, 50222 KB/s, 5 seconds passed
+... 37%, 263008 KB, 50226 KB/s, 5 seconds passed
+... 37%, 263040 KB, 50230 KB/s, 5 seconds passed
+... 37%, 263072 KB, 50233 KB/s, 5 seconds passed
+... 37%, 263104 KB, 50237 KB/s, 5 seconds passed
+... 37%, 263136 KB, 50241 KB/s, 5 seconds passed
+... 37%, 263168 KB, 50245 KB/s, 5 seconds passed
+... 37%, 263200 KB, 50248 KB/s, 5 seconds passed
+... 37%, 263232 KB, 50252 KB/s, 5 seconds passed
+... 37%, 263264 KB, 50256 KB/s, 5 seconds passed
+... 37%, 263296 KB, 50260 KB/s, 5 seconds passed
+... 37%, 263328 KB, 50263 KB/s, 5 seconds passed
+... 37%, 263360 KB, 50267 KB/s, 5 seconds passed
+... 37%, 263392 KB, 50271 KB/s, 5 seconds passed
+... 37%, 263424 KB, 50274 KB/s, 5 seconds passed
+... 37%, 263456 KB, 50278 KB/s, 5 seconds passed
+... 37%, 263488 KB, 50282 KB/s, 5 seconds passed
+... 37%, 263520 KB, 50285 KB/s, 5 seconds passed
+... 37%, 263552 KB, 50289 KB/s, 5 seconds passed
+... 37%, 263584 KB, 50293 KB/s, 5 seconds passed
+... 37%, 263616 KB, 50297 KB/s, 5 seconds passed
+... 37%, 263648 KB, 50301 KB/s, 5 seconds passed
+... 37%, 263680 KB, 50304 KB/s, 5 seconds passed
+... 37%, 263712 KB, 50308 KB/s, 5 seconds passed
+... 37%, 263744 KB, 50312 KB/s, 5 seconds passed
+... 37%, 263776 KB, 50316 KB/s, 5 seconds passed
+... 37%, 263808 KB, 50319 KB/s, 5 seconds passed
+... 37%, 263840 KB, 50323 KB/s, 5 seconds passed
+... 37%, 263872 KB, 50327 KB/s, 5 seconds passed
+... 37%, 263904 KB, 50330 KB/s, 5 seconds passed
+... 37%, 263936 KB, 50334 KB/s, 5 seconds passed
+... 37%, 263968 KB, 50339 KB/s, 5 seconds passed
+... 37%, 264000 KB, 50343 KB/s, 5 seconds passed
+... 37%, 264032 KB, 50347 KB/s, 5 seconds passed
+... 37%, 264064 KB, 50352 KB/s, 5 seconds passed
+... 37%, 264096 KB, 50356 KB/s, 5 seconds passed
+... 37%, 264128 KB, 50361 KB/s, 5 seconds passed
+... 37%, 264160 KB, 50366 KB/s, 5 seconds passed
+... 37%, 264192 KB, 50370 KB/s, 5 seconds passed
+... 37%, 264224 KB, 50375 KB/s, 5 seconds passed
+... 37%, 264256 KB, 50379 KB/s, 5 seconds passed
+... 37%, 264288 KB, 50384 KB/s, 5 seconds passed
+... 38%, 264320 KB, 50388 KB/s, 5 seconds passed
+... 38%, 264352 KB, 50393 KB/s, 5 seconds passed
+... 38%, 264384 KB, 50396 KB/s, 5 seconds passed
+... 38%, 264416 KB, 50399 KB/s, 5 seconds passed
+... 38%, 264448 KB, 50404 KB/s, 5 seconds passed
+... 38%, 264480 KB, 50407 KB/s, 5 seconds passed
+... 38%, 264512 KB, 50410 KB/s, 5 seconds passed
+... 38%, 264544 KB, 50413 KB/s, 5 seconds passed
+... 38%, 264576 KB, 50417 KB/s, 5 seconds passed
+... 38%, 264608 KB, 50421 KB/s, 5 seconds passed
+... 38%, 264640 KB, 50423 KB/s, 5 seconds passed
+... 38%, 264672 KB, 50426 KB/s, 5 seconds passed
+... 38%, 264704 KB, 50430 KB/s, 5 seconds passed
+... 38%, 264736 KB, 50434 KB/s, 5 seconds passed
+... 38%, 264768 KB, 50438 KB/s, 5 seconds passed
+... 38%, 264800 KB, 50441 KB/s, 5 seconds passed
+... 38%, 264832 KB, 50444 KB/s, 5 seconds passed
+... 38%, 264864 KB, 50447 KB/s, 5 seconds passed
+... 38%, 264896 KB, 50451 KB/s, 5 seconds passed
+... 38%, 264928 KB, 50454 KB/s, 5 seconds passed
+... 38%, 264960 KB, 50458 KB/s, 5 seconds passed
+... 38%, 264992 KB, 50461 KB/s, 5 seconds passed
+... 38%, 265024 KB, 50465 KB/s, 5 seconds passed
+... 38%, 265056 KB, 50468 KB/s, 5 seconds passed
+... 38%, 265088 KB, 50472 KB/s, 5 seconds passed
+... 38%, 265120 KB, 50475 KB/s, 5 seconds passed
+... 38%, 265152 KB, 50478 KB/s, 5 seconds passed
+... 38%, 265184 KB, 50482 KB/s, 5 seconds passed
+... 38%, 265216 KB, 50486 KB/s, 5 seconds passed
+... 38%, 265248 KB, 50489 KB/s, 5 seconds passed
+... 38%, 265280 KB, 50492 KB/s, 5 seconds passed
+... 38%, 265312 KB, 50496 KB/s, 5 seconds passed
+... 38%, 265344 KB, 50499 KB/s, 5 seconds passed
+... 38%, 265376 KB, 50502 KB/s, 5 seconds passed
+
+.. parsed-literal::
+
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+... 38%, 265440 KB, 50157 KB/s, 5 seconds passed
+... 38%, 265472 KB, 50159 KB/s, 5 seconds passed
+... 38%, 265504 KB, 50161 KB/s, 5 seconds passed
+... 38%, 265536 KB, 50164 KB/s, 5 seconds passed
+... 38%, 265568 KB, 50166 KB/s, 5 seconds passed
+... 38%, 265600 KB, 50168 KB/s, 5 seconds passed
+... 38%, 265632 KB, 50171 KB/s, 5 seconds passed
+... 38%, 265664 KB, 50174 KB/s, 5 seconds passed
+... 38%, 265696 KB, 50176 KB/s, 5 seconds passed
+... 38%, 265728 KB, 50179 KB/s, 5 seconds passed
+... 38%, 265760 KB, 50181 KB/s, 5 seconds passed
+... 38%, 265792 KB, 50183 KB/s, 5 seconds passed
+... 38%, 265824 KB, 50186 KB/s, 5 seconds passed
+... 38%, 265856 KB, 50188 KB/s, 5 seconds passed
+... 38%, 265888 KB, 50191 KB/s, 5 seconds passed
+... 38%, 265920 KB, 50193 KB/s, 5 seconds passed
+... 38%, 265952 KB, 50196 KB/s, 5 seconds passed
+... 38%, 265984 KB, 50198 KB/s, 5 seconds passed
+... 38%, 266016 KB, 50201 KB/s, 5 seconds passed
+... 38%, 266048 KB, 50204 KB/s, 5 seconds passed
+... 38%, 266080 KB, 50207 KB/s, 5 seconds passed
+... 38%, 266112 KB, 50211 KB/s, 5 seconds passed
+... 38%, 266144 KB, 50214 KB/s, 5 seconds passed
+... 38%, 266176 KB, 50217 KB/s, 5 seconds passed
+... 38%, 266208 KB, 50220 KB/s, 5 seconds passed
+
+.. parsed-literal::
+
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+... 38%, 266272 KB, 49646 KB/s, 5 seconds passed
+... 38%, 266304 KB, 49648 KB/s, 5 seconds passed
+... 38%, 266336 KB, 49651 KB/s, 5 seconds passed
+... 38%, 266368 KB, 49607 KB/s, 5 seconds passed
+... 38%, 266400 KB, 49598 KB/s, 5 seconds passed
+... 38%, 266432 KB, 49600 KB/s, 5 seconds passed
+... 38%, 266464 KB, 49602 KB/s, 5 seconds passed
+... 38%, 266496 KB, 49604 KB/s, 5 seconds passed
+... 38%, 266528 KB, 49607 KB/s, 5 seconds passed
+... 38%, 266560 KB, 49609 KB/s, 5 seconds passed
+... 38%, 266592 KB, 49612 KB/s, 5 seconds passed
+... 38%, 266624 KB, 49614 KB/s, 5 seconds passed
+... 38%, 266656 KB, 49616 KB/s, 5 seconds passed
+... 38%, 266688 KB, 49619 KB/s, 5 seconds passed
+... 38%, 266720 KB, 49621 KB/s, 5 seconds passed
+... 38%, 266752 KB, 49624 KB/s, 5 seconds passed
+... 38%, 266784 KB, 49626 KB/s, 5 seconds passed
+... 38%, 266816 KB, 49628 KB/s, 5 seconds passed
+... 38%, 266848 KB, 49630 KB/s, 5 seconds passed
+... 38%, 266880 KB, 49633 KB/s, 5 seconds passed
+... 38%, 266912 KB, 49635 KB/s, 5 seconds passed
+... 38%, 266944 KB, 49638 KB/s, 5 seconds passed
+... 38%, 266976 KB, 49640 KB/s, 5 seconds passed
+... 38%, 267008 KB, 49642 KB/s, 5 seconds passed
+... 38%, 267040 KB, 49645 KB/s, 5 seconds passed
+... 38%, 267072 KB, 49647 KB/s, 5 seconds passed
+... 38%, 267104 KB, 49649 KB/s, 5 seconds passed
+... 38%, 267136 KB, 49652 KB/s, 5 seconds passed
+... 38%, 267168 KB, 49654 KB/s, 5 seconds passed
+
+.. parsed-literal::
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+... 38%, 267232 KB, 49659 KB/s, 5 seconds passed
+... 38%, 267264 KB, 49662 KB/s, 5 seconds passed
+... 38%, 267296 KB, 49664 KB/s, 5 seconds passed
+... 38%, 267328 KB, 49666 KB/s, 5 seconds passed
+... 38%, 267360 KB, 49669 KB/s, 5 seconds passed
+... 38%, 267392 KB, 49671 KB/s, 5 seconds passed
+... 38%, 267424 KB, 49673 KB/s, 5 seconds passed
+... 38%, 267456 KB, 49676 KB/s, 5 seconds passed
+... 38%, 267488 KB, 49678 KB/s, 5 seconds passed
+... 38%, 267520 KB, 49680 KB/s, 5 seconds passed
+... 38%, 267552 KB, 49683 KB/s, 5 seconds passed
+... 38%, 267584 KB, 49685 KB/s, 5 seconds passed
+... 38%, 267616 KB, 49688 KB/s, 5 seconds passed
+... 38%, 267648 KB, 49692 KB/s, 5 seconds passed
+... 38%, 267680 KB, 49696 KB/s, 5 seconds passed
+... 38%, 267712 KB, 49700 KB/s, 5 seconds passed
+... 38%, 267744 KB, 49703 KB/s, 5 seconds passed
+... 38%, 267776 KB, 49707 KB/s, 5 seconds passed
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+... 38%, 267872 KB, 49718 KB/s, 5 seconds passed
+... 38%, 267904 KB, 49721 KB/s, 5 seconds passed
+... 38%, 267936 KB, 49725 KB/s, 5 seconds passed
+... 38%, 267968 KB, 49729 KB/s, 5 seconds passed
+... 38%, 268000 KB, 49732 KB/s, 5 seconds passed
+... 38%, 268032 KB, 49736 KB/s, 5 seconds passed
+... 38%, 268064 KB, 49739 KB/s, 5 seconds passed
+... 38%, 268096 KB, 49743 KB/s, 5 seconds passed
+... 38%, 268128 KB, 49747 KB/s, 5 seconds passed
+... 38%, 268160 KB, 49751 KB/s, 5 seconds passed
+... 38%, 268192 KB, 49754 KB/s, 5 seconds passed
+... 38%, 268224 KB, 49758 KB/s, 5 seconds passed
+... 38%, 268256 KB, 49761 KB/s, 5 seconds passed
+... 38%, 268288 KB, 49765 KB/s, 5 seconds passed
+... 38%, 268320 KB, 49769 KB/s, 5 seconds passed
+... 38%, 268352 KB, 49772 KB/s, 5 seconds passed
+... 38%, 268384 KB, 49776 KB/s, 5 seconds passed
+... 38%, 268416 KB, 49780 KB/s, 5 seconds passed
+... 38%, 268448 KB, 49783 KB/s, 5 seconds passed
+... 38%, 268480 KB, 49787 KB/s, 5 seconds passed
+... 38%, 268512 KB, 49791 KB/s, 5 seconds passed
+... 38%, 268544 KB, 49794 KB/s, 5 seconds passed
+... 38%, 268576 KB, 49798 KB/s, 5 seconds passed
+... 38%, 268608 KB, 49802 KB/s, 5 seconds passed
+... 38%, 268640 KB, 49805 KB/s, 5 seconds passed
+... 38%, 268672 KB, 49809 KB/s, 5 seconds passed
+... 38%, 268704 KB, 49813 KB/s, 5 seconds passed
+... 38%, 268736 KB, 49816 KB/s, 5 seconds passed
+... 38%, 268768 KB, 49820 KB/s, 5 seconds passed
+... 38%, 268800 KB, 49824 KB/s, 5 seconds passed
+... 38%, 268832 KB, 49827 KB/s, 5 seconds passed
+... 38%, 268864 KB, 49831 KB/s, 5 seconds passed
+... 38%, 268896 KB, 49834 KB/s, 5 seconds passed
+... 38%, 268928 KB, 49838 KB/s, 5 seconds passed
+... 38%, 268960 KB, 49842 KB/s, 5 seconds passed
+... 38%, 268992 KB, 49846 KB/s, 5 seconds passed
+... 38%, 269024 KB, 49851 KB/s, 5 seconds passed
+... 38%, 269056 KB, 49855 KB/s, 5 seconds passed
+... 38%, 269088 KB, 49859 KB/s, 5 seconds passed
+... 38%, 269120 KB, 49864 KB/s, 5 seconds passed
+... 38%, 269152 KB, 49868 KB/s, 5 seconds passed
+... 38%, 269184 KB, 49872 KB/s, 5 seconds passed
+... 38%, 269216 KB, 49877 KB/s, 5 seconds passed
+... 38%, 269248 KB, 49881 KB/s, 5 seconds passed
+... 38%, 269280 KB, 49886 KB/s, 5 seconds passed
+... 38%, 269312 KB, 49890 KB/s, 5 seconds passed
+... 38%, 269344 KB, 49894 KB/s, 5 seconds passed
+... 38%, 269376 KB, 49899 KB/s, 5 seconds passed
+... 38%, 269408 KB, 49903 KB/s, 5 seconds passed
+... 38%, 269440 KB, 49908 KB/s, 5 seconds passed
+... 38%, 269472 KB, 49912 KB/s, 5 seconds passed
+... 38%, 269504 KB, 49917 KB/s, 5 seconds passed
+... 38%, 269536 KB, 49921 KB/s, 5 seconds passed
+... 38%, 269568 KB, 49925 KB/s, 5 seconds passed
+... 38%, 269600 KB, 49930 KB/s, 5 seconds passed
+... 38%, 269632 KB, 49934 KB/s, 5 seconds passed
+... 38%, 269664 KB, 49939 KB/s, 5 seconds passed
+... 38%, 269696 KB, 49943 KB/s, 5 seconds passed
+... 38%, 269728 KB, 49947 KB/s, 5 seconds passed
+... 38%, 269760 KB, 49951 KB/s, 5 seconds passed
+... 38%, 269792 KB, 49955 KB/s, 5 seconds passed
+... 38%, 269824 KB, 49958 KB/s, 5 seconds passed
+... 38%, 269856 KB, 49962 KB/s, 5 seconds passed
+... 38%, 269888 KB, 49964 KB/s, 5 seconds passed
+... 38%, 269920 KB, 49968 KB/s, 5 seconds passed
+... 38%, 269952 KB, 49971 KB/s, 5 seconds passed
+... 38%, 269984 KB, 49975 KB/s, 5 seconds passed
+... 38%, 270016 KB, 49978 KB/s, 5 seconds passed
+... 38%, 270048 KB, 49981 KB/s, 5 seconds passed
+... 38%, 270080 KB, 49970 KB/s, 5 seconds passed
+... 38%, 270112 KB, 49963 KB/s, 5 seconds passed
+... 38%, 270144 KB, 49965 KB/s, 5 seconds passed
+... 38%, 270176 KB, 49968 KB/s, 5 seconds passed
+... 38%, 270208 KB, 49971 KB/s, 5 seconds passed
+... 38%, 270240 KB, 49974 KB/s, 5 seconds passed
+... 38%, 270272 KB, 49976 KB/s, 5 seconds passed
+... 38%, 270304 KB, 49979 KB/s, 5 seconds passed
+... 38%, 270336 KB, 49982 KB/s, 5 seconds passed
+... 38%, 270368 KB, 49985 KB/s, 5 seconds passed
+... 38%, 270400 KB, 49988 KB/s, 5 seconds passed
+... 38%, 270432 KB, 49991 KB/s, 5 seconds passed
+... 38%, 270464 KB, 49993 KB/s, 5 seconds passed
+... 38%, 270496 KB, 49996 KB/s, 5 seconds passed
+... 38%, 270528 KB, 49999 KB/s, 5 seconds passed
+... 38%, 270560 KB, 50001 KB/s, 5 seconds passed
+... 38%, 270592 KB, 50004 KB/s, 5 seconds passed
+... 38%, 270624 KB, 50007 KB/s, 5 seconds passed
+... 38%, 270656 KB, 50010 KB/s, 5 seconds passed
+... 38%, 270688 KB, 50013 KB/s, 5 seconds passed
+... 38%, 270720 KB, 50015 KB/s, 5 seconds passed
+... 38%, 270752 KB, 50018 KB/s, 5 seconds passed
+... 38%, 270784 KB, 50021 KB/s, 5 seconds passed
+... 38%, 270816 KB, 50024 KB/s, 5 seconds passed
+... 38%, 270848 KB, 50027 KB/s, 5 seconds passed
+... 38%, 270880 KB, 50029 KB/s, 5 seconds passed
+... 38%, 270912 KB, 50032 KB/s, 5 seconds passed
+... 38%, 270944 KB, 50035 KB/s, 5 seconds passed
+... 38%, 270976 KB, 50037 KB/s, 5 seconds passed
+... 38%, 271008 KB, 50040 KB/s, 5 seconds passed
+... 38%, 271040 KB, 50043 KB/s, 5 seconds passed
+... 38%, 271072 KB, 50046 KB/s, 5 seconds passed
+... 38%, 271104 KB, 50049 KB/s, 5 seconds passed
+... 38%, 271136 KB, 50053 KB/s, 5 seconds passed
+... 38%, 271168 KB, 50057 KB/s, 5 seconds passed
+... 38%, 271200 KB, 50060 KB/s, 5 seconds passed
+... 38%, 271232 KB, 50065 KB/s, 5 seconds passed
+... 39%, 271264 KB, 50069 KB/s, 5 seconds passed
+... 39%, 271296 KB, 50073 KB/s, 5 seconds passed
+... 39%, 271328 KB, 50077 KB/s, 5 seconds passed
+
+.. parsed-literal::
+
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+... 39%, 271392 KB, 49893 KB/s, 5 seconds passed
+... 39%, 271424 KB, 49895 KB/s, 5 seconds passed
+... 39%, 271456 KB, 49898 KB/s, 5 seconds passed
+... 39%, 271488 KB, 49900 KB/s, 5 seconds passed
+... 39%, 271520 KB, 49902 KB/s, 5 seconds passed
+... 39%, 271552 KB, 49905 KB/s, 5 seconds passed
+... 39%, 271584 KB, 49907 KB/s, 5 seconds passed
+... 39%, 271616 KB, 49909 KB/s, 5 seconds passed
+... 39%, 271648 KB, 49912 KB/s, 5 seconds passed
+... 39%, 271680 KB, 49914 KB/s, 5 seconds passed
+... 39%, 271712 KB, 49916 KB/s, 5 seconds passed
+... 39%, 271744 KB, 49919 KB/s, 5 seconds passed
+... 39%, 271776 KB, 49921 KB/s, 5 seconds passed
+... 39%, 271808 KB, 49923 KB/s, 5 seconds passed
+... 39%, 271840 KB, 49925 KB/s, 5 seconds passed
+... 39%, 271872 KB, 49928 KB/s, 5 seconds passed
+... 39%, 271904 KB, 49930 KB/s, 5 seconds passed
+... 39%, 271936 KB, 49932 KB/s, 5 seconds passed
+... 39%, 271968 KB, 49935 KB/s, 5 seconds passed
+... 39%, 272000 KB, 49937 KB/s, 5 seconds passed
+... 39%, 272032 KB, 49939 KB/s, 5 seconds passed
+... 39%, 272064 KB, 49942 KB/s, 5 seconds passed
+... 39%, 272096 KB, 49945 KB/s, 5 seconds passed
+... 39%, 272128 KB, 49947 KB/s, 5 seconds passed
+... 39%, 272160 KB, 49950 KB/s, 5 seconds passed
+... 39%, 272192 KB, 49953 KB/s, 5 seconds passed
+... 39%, 272224 KB, 49955 KB/s, 5 seconds passed
+... 39%, 272256 KB, 49958 KB/s, 5 seconds passed
+... 39%, 272288 KB, 49961 KB/s, 5 seconds passed
+... 39%, 272320 KB, 49963 KB/s, 5 seconds passed
+... 39%, 272352 KB, 49966 KB/s, 5 seconds passed
+... 39%, 272384 KB, 49968 KB/s, 5 seconds passed
+... 39%, 272416 KB, 49971 KB/s, 5 seconds passed
+... 39%, 272448 KB, 49973 KB/s, 5 seconds passed
+... 39%, 272480 KB, 49976 KB/s, 5 seconds passed
+... 39%, 272512 KB, 49979 KB/s, 5 seconds passed
+... 39%, 272544 KB, 49982 KB/s, 5 seconds passed
+... 39%, 272576 KB, 49984 KB/s, 5 seconds passed
+... 39%, 272608 KB, 49987 KB/s, 5 seconds passed
+... 39%, 272640 KB, 49990 KB/s, 5 seconds passed
+... 39%, 272672 KB, 49992 KB/s, 5 seconds passed
+... 39%, 272704 KB, 49995 KB/s, 5 seconds passed
+... 39%, 272736 KB, 49998 KB/s, 5 seconds passed
+... 39%, 272768 KB, 50000 KB/s, 5 seconds passed
+... 39%, 272800 KB, 50003 KB/s, 5 seconds passed
+... 39%, 272832 KB, 50005 KB/s, 5 seconds passed
+... 39%, 272864 KB, 50008 KB/s, 5 seconds passed
+... 39%, 272896 KB, 50011 KB/s, 5 seconds passed
+... 39%, 272928 KB, 50013 KB/s, 5 seconds passed
+... 39%, 272960 KB, 50017 KB/s, 5 seconds passed
+... 39%, 272992 KB, 50021 KB/s, 5 seconds passed
+... 39%, 273024 KB, 50024 KB/s, 5 seconds passed
+... 39%, 273056 KB, 50028 KB/s, 5 seconds passed
+... 39%, 273088 KB, 50032 KB/s, 5 seconds passed
+... 39%, 273120 KB, 50036 KB/s, 5 seconds passed
+... 39%, 273152 KB, 50039 KB/s, 5 seconds passed
+... 39%, 273184 KB, 50043 KB/s, 5 seconds passed
+... 39%, 273216 KB, 50046 KB/s, 5 seconds passed
+... 39%, 273248 KB, 50050 KB/s, 5 seconds passed
+... 39%, 273280 KB, 50054 KB/s, 5 seconds passed
+... 39%, 273312 KB, 50057 KB/s, 5 seconds passed
+... 39%, 273344 KB, 50061 KB/s, 5 seconds passed
+... 39%, 273376 KB, 50065 KB/s, 5 seconds passed
+... 39%, 273408 KB, 50069 KB/s, 5 seconds passed
+... 39%, 273440 KB, 50072 KB/s, 5 seconds passed
+... 39%, 273472 KB, 50076 KB/s, 5 seconds passed
+... 39%, 273504 KB, 50080 KB/s, 5 seconds passed
+... 39%, 273536 KB, 50084 KB/s, 5 seconds passed
+... 39%, 273568 KB, 50087 KB/s, 5 seconds passed
+... 39%, 273600 KB, 50091 KB/s, 5 seconds passed
+... 39%, 273632 KB, 50095 KB/s, 5 seconds passed
+... 39%, 273664 KB, 50099 KB/s, 5 seconds passed
+... 39%, 273696 KB, 50102 KB/s, 5 seconds passed
+... 39%, 273728 KB, 50106 KB/s, 5 seconds passed
+... 39%, 273760 KB, 50110 KB/s, 5 seconds passed
+... 39%, 273792 KB, 50114 KB/s, 5 seconds passed
+... 39%, 273824 KB, 50117 KB/s, 5 seconds passed
+... 39%, 273856 KB, 50121 KB/s, 5 seconds passed
+... 39%, 273888 KB, 50125 KB/s, 5 seconds passed
+... 39%, 273920 KB, 50129 KB/s, 5 seconds passed
+... 39%, 273952 KB, 50132 KB/s, 5 seconds passed
+... 39%, 273984 KB, 50136 KB/s, 5 seconds passed
+... 39%, 274016 KB, 50140 KB/s, 5 seconds passed
+... 39%, 274048 KB, 50143 KB/s, 5 seconds passed
+... 39%, 274080 KB, 50147 KB/s, 5 seconds passed
+... 39%, 274112 KB, 50151 KB/s, 5 seconds passed
+... 39%, 274144 KB, 50155 KB/s, 5 seconds passed
+... 39%, 274176 KB, 50158 KB/s, 5 seconds passed
+... 39%, 274208 KB, 50162 KB/s, 5 seconds passed
+... 39%, 274240 KB, 50166 KB/s, 5 seconds passed
+... 39%, 274272 KB, 50170 KB/s, 5 seconds passed
+... 39%, 274304 KB, 50173 KB/s, 5 seconds passed
+... 39%, 274336 KB, 50178 KB/s, 5 seconds passed
+... 39%, 274368 KB, 50182 KB/s, 5 seconds passed
+... 39%, 274400 KB, 50186 KB/s, 5 seconds passed
+... 39%, 274432 KB, 50191 KB/s, 5 seconds passed
+... 39%, 274464 KB, 50195 KB/s, 5 seconds passed
+... 39%, 274496 KB, 50199 KB/s, 5 seconds passed
+... 39%, 274528 KB, 50204 KB/s, 5 seconds passed
+... 39%, 274560 KB, 50208 KB/s, 5 seconds passed
+... 39%, 274592 KB, 50213 KB/s, 5 seconds passed
+... 39%, 274624 KB, 50217 KB/s, 5 seconds passed
+... 39%, 274656 KB, 50221 KB/s, 5 seconds passed
+... 39%, 274688 KB, 50226 KB/s, 5 seconds passed
+... 39%, 274720 KB, 50230 KB/s, 5 seconds passed
+... 39%, 274752 KB, 50235 KB/s, 5 seconds passed
+... 39%, 274784 KB, 50239 KB/s, 5 seconds passed
+... 39%, 274816 KB, 50243 KB/s, 5 seconds passed
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
+
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+... 41%, 288032 KB, 50435 KB/s, 5 seconds passed
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+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 43%, 301216 KB, 50172 KB/s, 6 seconds passed
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+... 43%, 301280 KB, 50179 KB/s, 6 seconds passed
+... 43%, 301312 KB, 50182 KB/s, 6 seconds passed
+... 43%, 301344 KB, 50186 KB/s, 6 seconds passed
+... 43%, 301376 KB, 50189 KB/s, 6 seconds passed
+... 43%, 301408 KB, 50192 KB/s, 6 seconds passed
+... 43%, 301440 KB, 50195 KB/s, 6 seconds passed
+... 43%, 301472 KB, 50199 KB/s, 6 seconds passed
+... 43%, 301504 KB, 50202 KB/s, 6 seconds passed
+... 43%, 301536 KB, 50205 KB/s, 6 seconds passed
+... 43%, 301568 KB, 50209 KB/s, 6 seconds passed
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+... 43%, 301664 KB, 50220 KB/s, 6 seconds passed
+... 43%, 301696 KB, 50223 KB/s, 6 seconds passed
+... 43%, 301728 KB, 50227 KB/s, 6 seconds passed
+... 43%, 301760 KB, 50231 KB/s, 6 seconds passed
+... 43%, 301792 KB, 50235 KB/s, 6 seconds passed
+... 43%, 301824 KB, 50238 KB/s, 6 seconds passed
+... 43%, 301856 KB, 50242 KB/s, 6 seconds passed
+... 43%, 301888 KB, 50246 KB/s, 6 seconds passed
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+... 43%, 301952 KB, 50253 KB/s, 6 seconds passed
+... 43%, 301984 KB, 50257 KB/s, 6 seconds passed
+... 43%, 302016 KB, 50261 KB/s, 6 seconds passed
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+... 43%, 302080 KB, 49993 KB/s, 6 seconds passed
+... 43%, 302112 KB, 49994 KB/s, 6 seconds passed
+... 43%, 302144 KB, 49996 KB/s, 6 seconds passed
+... 43%, 302176 KB, 49998 KB/s, 6 seconds passed
+
+.. parsed-literal::
+
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+... 43%, 302240 KB, 49964 KB/s, 6 seconds passed
+... 43%, 302272 KB, 49965 KB/s, 6 seconds passed
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+... 43%, 302336 KB, 49967 KB/s, 6 seconds passed
+... 43%, 302368 KB, 49964 KB/s, 6 seconds passed
+... 43%, 302400 KB, 49964 KB/s, 6 seconds passed
+... 43%, 302432 KB, 49963 KB/s, 6 seconds passed
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+... 43%, 302624 KB, 49967 KB/s, 6 seconds passed
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+... 43%, 302752 KB, 49965 KB/s, 6 seconds passed
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+... 43%, 303232 KB, 49811 KB/s, 6 seconds passed
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+... 43%, 303424 KB, 49823 KB/s, 6 seconds passed
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+... 43%, 303488 KB, 49828 KB/s, 6 seconds passed
+... 43%, 303520 KB, 49830 KB/s, 6 seconds passed
+... 43%, 303552 KB, 49832 KB/s, 6 seconds passed
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+... 43%, 303616 KB, 49836 KB/s, 6 seconds passed
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+... 43%, 303744 KB, 49847 KB/s, 6 seconds passed
+... 43%, 303776 KB, 49850 KB/s, 6 seconds passed
+... 43%, 303808 KB, 49853 KB/s, 6 seconds passed
+
+.. parsed-literal::
+
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+... 43%, 303872 KB, 49767 KB/s, 6 seconds passed
+... 43%, 303904 KB, 49769 KB/s, 6 seconds passed
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+... 43%, 304352 KB, 49798 KB/s, 6 seconds passed
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+... 43%, 304448 KB, 49804 KB/s, 6 seconds passed
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+... 43%, 304704 KB, 49822 KB/s, 6 seconds passed
+... 43%, 304736 KB, 49825 KB/s, 6 seconds passed
+... 43%, 304768 KB, 49827 KB/s, 6 seconds passed
+... 43%, 304800 KB, 49829 KB/s, 6 seconds passed
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+... 43%, 304864 KB, 49834 KB/s, 6 seconds passed
+... 43%, 304896 KB, 49836 KB/s, 6 seconds passed
+... 43%, 304928 KB, 49839 KB/s, 6 seconds passed
+... 43%, 304960 KB, 49841 KB/s, 6 seconds passed
+... 43%, 304992 KB, 49843 KB/s, 6 seconds passed
+... 43%, 305024 KB, 49845 KB/s, 6 seconds passed
+... 43%, 305056 KB, 49847 KB/s, 6 seconds passed
+... 43%, 305088 KB, 49849 KB/s, 6 seconds passed
+... 43%, 305120 KB, 49852 KB/s, 6 seconds passed
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+... 43%, 305280 KB, 49864 KB/s, 6 seconds passed
+... 43%, 305312 KB, 49866 KB/s, 6 seconds passed
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+... 43%, 305504 KB, 49884 KB/s, 6 seconds passed
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+... 43%, 305568 KB, 49891 KB/s, 6 seconds passed
+... 43%, 305600 KB, 49894 KB/s, 6 seconds passed
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+... 43%, 305696 KB, 49904 KB/s, 6 seconds passed
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+... 43%, 305888 KB, 49924 KB/s, 6 seconds passed
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+... 43%, 306016 KB, 49937 KB/s, 6 seconds passed
+... 44%, 306048 KB, 49940 KB/s, 6 seconds passed
+... 44%, 306080 KB, 49943 KB/s, 6 seconds passed
+... 44%, 306112 KB, 49946 KB/s, 6 seconds passed
+... 44%, 306144 KB, 49950 KB/s, 6 seconds passed
+... 44%, 306176 KB, 49953 KB/s, 6 seconds passed
+... 44%, 306208 KB, 49956 KB/s, 6 seconds passed
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+... 44%, 306304 KB, 49966 KB/s, 6 seconds passed
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+... 44%, 306432 KB, 49979 KB/s, 6 seconds passed
+... 44%, 306464 KB, 49983 KB/s, 6 seconds passed
+... 44%, 306496 KB, 49986 KB/s, 6 seconds passed
+... 44%, 306528 KB, 49989 KB/s, 6 seconds passed
+... 44%, 306560 KB, 49993 KB/s, 6 seconds passed
+... 44%, 306592 KB, 49996 KB/s, 6 seconds passed
+... 44%, 306624 KB, 49999 KB/s, 6 seconds passed
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+... 44%, 306720 KB, 50009 KB/s, 6 seconds passed
+... 44%, 306752 KB, 50013 KB/s, 6 seconds passed
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+... 44%, 306816 KB, 50021 KB/s, 6 seconds passed
+... 44%, 306848 KB, 50025 KB/s, 6 seconds passed
+... 44%, 306880 KB, 50029 KB/s, 6 seconds passed
+... 44%, 306912 KB, 50033 KB/s, 6 seconds passed
+... 44%, 306944 KB, 50037 KB/s, 6 seconds passed
+... 44%, 306976 KB, 50041 KB/s, 6 seconds passed
+... 44%, 307008 KB, 50045 KB/s, 6 seconds passed
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+... 44%, 307072 KB, 50053 KB/s, 6 seconds passed
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+... 44%, 307264 KB, 50076 KB/s, 6 seconds passed
+... 44%, 307296 KB, 50080 KB/s, 6 seconds passed
+... 44%, 307328 KB, 50084 KB/s, 6 seconds passed
+... 44%, 307360 KB, 50088 KB/s, 6 seconds passed
+... 44%, 307392 KB, 50092 KB/s, 6 seconds passed
+... 44%, 307424 KB, 50096 KB/s, 6 seconds passed
+... 44%, 307456 KB, 50100 KB/s, 6 seconds passed
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+... 44%, 307552 KB, 50110 KB/s, 6 seconds passed
+... 44%, 307584 KB, 50112 KB/s, 6 seconds passed
+... 44%, 307616 KB, 50115 KB/s, 6 seconds passed
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+... 44%, 307680 KB, 50122 KB/s, 6 seconds passed
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+... 44%, 307744 KB, 50126 KB/s, 6 seconds passed
+... 44%, 307776 KB, 50129 KB/s, 6 seconds passed
+... 44%, 307808 KB, 50132 KB/s, 6 seconds passed
+... 44%, 307840 KB, 50135 KB/s, 6 seconds passed
+... 44%, 307872 KB, 50138 KB/s, 6 seconds passed
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+... 44%, 308000 KB, 50149 KB/s, 6 seconds passed
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+... 44%, 308096 KB, 50159 KB/s, 6 seconds passed
+... 44%, 308128 KB, 50161 KB/s, 6 seconds passed
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+... 44%, 308192 KB, 50164 KB/s, 6 seconds passed
+... 44%, 308224 KB, 50168 KB/s, 6 seconds passed
+... 44%, 308256 KB, 50170 KB/s, 6 seconds passed
+... 44%, 308288 KB, 50174 KB/s, 6 seconds passed
+... 44%, 308320 KB, 50177 KB/s, 6 seconds passed
+... 44%, 308352 KB, 50179 KB/s, 6 seconds passed
+... 44%, 308384 KB, 50182 KB/s, 6 seconds passed
+... 44%, 308416 KB, 50185 KB/s, 6 seconds passed
+... 44%, 308448 KB, 50188 KB/s, 6 seconds passed
+... 44%, 308480 KB, 50191 KB/s, 6 seconds passed
+... 44%, 308512 KB, 50194 KB/s, 6 seconds passed
+... 44%, 308544 KB, 50197 KB/s, 6 seconds passed
+... 44%, 308576 KB, 50200 KB/s, 6 seconds passed
+... 44%, 308608 KB, 50203 KB/s, 6 seconds passed
+... 44%, 308640 KB, 50206 KB/s, 6 seconds passed
+... 44%, 308672 KB, 50209 KB/s, 6 seconds passed
+... 44%, 308704 KB, 50212 KB/s, 6 seconds passed
+... 44%, 308736 KB, 50215 KB/s, 6 seconds passed
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+... 44%, 308800 KB, 50221 KB/s, 6 seconds passed
+... 44%, 308832 KB, 50223 KB/s, 6 seconds passed
+... 44%, 308864 KB, 50226 KB/s, 6 seconds passed
+... 44%, 308896 KB, 50229 KB/s, 6 seconds passed
+
+.. parsed-literal::
+
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+... 44%, 308992 KB, 50238 KB/s, 6 seconds passed
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+... 44%, 309056 KB, 50245 KB/s, 6 seconds passed
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+... 44%, 309120 KB, 50250 KB/s, 6 seconds passed
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+... 44%, 309792 KB, 50312 KB/s, 6 seconds passed
+... 44%, 309824 KB, 50316 KB/s, 6 seconds passed
+... 44%, 309856 KB, 50319 KB/s, 6 seconds passed
+... 44%, 309888 KB, 50321 KB/s, 6 seconds passed
+... 44%, 309920 KB, 50323 KB/s, 6 seconds passed
+... 44%, 309952 KB, 50326 KB/s, 6 seconds passed
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+... 44%, 310048 KB, 50336 KB/s, 6 seconds passed
+... 44%, 310080 KB, 50339 KB/s, 6 seconds passed
+... 44%, 310112 KB, 50342 KB/s, 6 seconds passed
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+... 44%, 310208 KB, 50351 KB/s, 6 seconds passed
+... 44%, 310240 KB, 50354 KB/s, 6 seconds passed
+... 44%, 310272 KB, 50358 KB/s, 6 seconds passed
+... 44%, 310304 KB, 50358 KB/s, 6 seconds passed
+... 44%, 310336 KB, 50361 KB/s, 6 seconds passed
+... 44%, 310368 KB, 50363 KB/s, 6 seconds passed
+... 44%, 310400 KB, 50247 KB/s, 6 seconds passed
+... 44%, 310432 KB, 50249 KB/s, 6 seconds passed
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+... 44%, 310528 KB, 50255 KB/s, 6 seconds passed
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+... 44%, 310656 KB, 50263 KB/s, 6 seconds passed
+... 44%, 310688 KB, 50265 KB/s, 6 seconds passed
+... 44%, 310720 KB, 50267 KB/s, 6 seconds passed
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+... 44%, 310784 KB, 50272 KB/s, 6 seconds passed
+... 44%, 310816 KB, 50274 KB/s, 6 seconds passed
+... 44%, 310848 KB, 50276 KB/s, 6 seconds passed
+... 44%, 310880 KB, 50278 KB/s, 6 seconds passed
+... 44%, 310912 KB, 50280 KB/s, 6 seconds passed
+... 44%, 310944 KB, 50282 KB/s, 6 seconds passed
+... 44%, 310976 KB, 50284 KB/s, 6 seconds passed
+... 44%, 311008 KB, 50286 KB/s, 6 seconds passed
+... 44%, 311040 KB, 50289 KB/s, 6 seconds passed
+... 44%, 311072 KB, 50291 KB/s, 6 seconds passed
+... 44%, 311104 KB, 50292 KB/s, 6 seconds passed
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+... 44%, 311200 KB, 50299 KB/s, 6 seconds passed
+... 44%, 311232 KB, 50301 KB/s, 6 seconds passed
+... 44%, 311264 KB, 50303 KB/s, 6 seconds passed
+... 44%, 311296 KB, 50305 KB/s, 6 seconds passed
+... 44%, 311328 KB, 50307 KB/s, 6 seconds passed
+... 44%, 311360 KB, 50309 KB/s, 6 seconds passed
+... 44%, 311392 KB, 50311 KB/s, 6 seconds passed
+... 44%, 311424 KB, 50313 KB/s, 6 seconds passed
+... 44%, 311456 KB, 50315 KB/s, 6 seconds passed
+... 44%, 311488 KB, 50317 KB/s, 6 seconds passed
+... 44%, 311520 KB, 50319 KB/s, 6 seconds passed
+... 44%, 311552 KB, 50321 KB/s, 6 seconds passed
+... 44%, 311584 KB, 50323 KB/s, 6 seconds passed
+... 44%, 311616 KB, 50325 KB/s, 6 seconds passed
+... 44%, 311648 KB, 50328 KB/s, 6 seconds passed
+... 44%, 311680 KB, 50330 KB/s, 6 seconds passed
+... 44%, 311712 KB, 50332 KB/s, 6 seconds passed
+... 44%, 311744 KB, 50334 KB/s, 6 seconds passed
+... 44%, 311776 KB, 50336 KB/s, 6 seconds passed
+... 44%, 311808 KB, 50339 KB/s, 6 seconds passed
+... 44%, 311840 KB, 50342 KB/s, 6 seconds passed
+... 44%, 311872 KB, 50346 KB/s, 6 seconds passed
+... 44%, 311904 KB, 50350 KB/s, 6 seconds passed
+... 44%, 311936 KB, 50353 KB/s, 6 seconds passed
+... 44%, 311968 KB, 50357 KB/s, 6 seconds passed
+... 44%, 312000 KB, 50360 KB/s, 6 seconds passed
+... 44%, 312032 KB, 50364 KB/s, 6 seconds passed
+... 44%, 312064 KB, 50367 KB/s, 6 seconds passed
+... 44%, 312096 KB, 50371 KB/s, 6 seconds passed
+... 44%, 312128 KB, 50375 KB/s, 6 seconds passed
+... 44%, 312160 KB, 50378 KB/s, 6 seconds passed
+... 44%, 312192 KB, 50382 KB/s, 6 seconds passed
+... 44%, 312224 KB, 50385 KB/s, 6 seconds passed
+... 44%, 312256 KB, 50389 KB/s, 6 seconds passed
+... 44%, 312288 KB, 50393 KB/s, 6 seconds passed
+
+.. parsed-literal::
+
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+... 44%, 312352 KB, 50259 KB/s, 6 seconds passed
+... 44%, 312384 KB, 50260 KB/s, 6 seconds passed
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+... 44%, 312480 KB, 50266 KB/s, 6 seconds passed
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+... 44%, 312544 KB, 50270 KB/s, 6 seconds passed
+... 44%, 312576 KB, 50272 KB/s, 6 seconds passed
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+... 44%, 312864 KB, 50291 KB/s, 6 seconds passed
+... 44%, 312896 KB, 50293 KB/s, 6 seconds passed
+... 44%, 312928 KB, 50295 KB/s, 6 seconds passed
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+... 45%, 312992 KB, 50299 KB/s, 6 seconds passed
+... 45%, 313024 KB, 50301 KB/s, 6 seconds passed
+... 45%, 313056 KB, 50303 KB/s, 6 seconds passed
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+... 45%, 313120 KB, 50307 KB/s, 6 seconds passed
+... 45%, 313152 KB, 50310 KB/s, 6 seconds passed
+... 45%, 313184 KB, 50313 KB/s, 6 seconds passed
+... 45%, 313216 KB, 50316 KB/s, 6 seconds passed
+... 45%, 313248 KB, 50319 KB/s, 6 seconds passed
+... 45%, 313280 KB, 50322 KB/s, 6 seconds passed
+... 45%, 313312 KB, 50325 KB/s, 6 seconds passed
+... 45%, 313344 KB, 50328 KB/s, 6 seconds passed
+... 45%, 313376 KB, 50331 KB/s, 6 seconds passed
+... 45%, 313408 KB, 50334 KB/s, 6 seconds passed
+... 45%, 313440 KB, 50337 KB/s, 6 seconds passed
+... 45%, 313472 KB, 50340 KB/s, 6 seconds passed
+... 45%, 313504 KB, 50343 KB/s, 6 seconds passed
+... 45%, 313536 KB, 50345 KB/s, 6 seconds passed
+... 45%, 313568 KB, 50348 KB/s, 6 seconds passed
+... 45%, 313600 KB, 50351 KB/s, 6 seconds passed
+... 45%, 313632 KB, 50354 KB/s, 6 seconds passed
+... 45%, 313664 KB, 50357 KB/s, 6 seconds passed
+... 45%, 313696 KB, 50360 KB/s, 6 seconds passed
+... 45%, 313728 KB, 50363 KB/s, 6 seconds passed
+... 45%, 313760 KB, 50366 KB/s, 6 seconds passed
+... 45%, 313792 KB, 50369 KB/s, 6 seconds passed
+... 45%, 313824 KB, 50372 KB/s, 6 seconds passed
+... 45%, 313856 KB, 50375 KB/s, 6 seconds passed
+... 45%, 313888 KB, 50378 KB/s, 6 seconds passed
+... 45%, 313920 KB, 50381 KB/s, 6 seconds passed
+... 45%, 313952 KB, 50384 KB/s, 6 seconds passed
+... 45%, 313984 KB, 50387 KB/s, 6 seconds passed
+... 45%, 314016 KB, 50389 KB/s, 6 seconds passed
+... 45%, 314048 KB, 50392 KB/s, 6 seconds passed
+... 45%, 314080 KB, 50395 KB/s, 6 seconds passed
+... 45%, 314112 KB, 50398 KB/s, 6 seconds passed
+... 45%, 314144 KB, 50401 KB/s, 6 seconds passed
+... 45%, 314176 KB, 50404 KB/s, 6 seconds passed
+... 45%, 314208 KB, 50407 KB/s, 6 seconds passed
+... 45%, 314240 KB, 50410 KB/s, 6 seconds passed
+... 45%, 314272 KB, 50413 KB/s, 6 seconds passed
+... 45%, 314304 KB, 50416 KB/s, 6 seconds passed
+... 45%, 314336 KB, 50420 KB/s, 6 seconds passed
+... 45%, 314368 KB, 50423 KB/s, 6 seconds passed
+... 45%, 314400 KB, 50427 KB/s, 6 seconds passed
+... 45%, 314432 KB, 50430 KB/s, 6 seconds passed
+... 45%, 314464 KB, 50434 KB/s, 6 seconds passed
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+... 45%, 314528 KB, 50441 KB/s, 6 seconds passed
+... 45%, 314560 KB, 50445 KB/s, 6 seconds passed
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+... 45%, 314720 KB, 50463 KB/s, 6 seconds passed
+... 45%, 314752 KB, 50467 KB/s, 6 seconds passed
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+... 45%, 314816 KB, 50474 KB/s, 6 seconds passed
+... 45%, 314848 KB, 50478 KB/s, 6 seconds passed
+... 45%, 314880 KB, 50481 KB/s, 6 seconds passed
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+... 45%, 315360 KB, 50533 KB/s, 6 seconds passed
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+... 45%, 316320 KB, 50596 KB/s, 6 seconds passed
+... 45%, 316352 KB, 50599 KB/s, 6 seconds passed
+
+.. parsed-literal::
+
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+... 45%, 317472 KB, 50378 KB/s, 6 seconds passed
+... 45%, 317504 KB, 50379 KB/s, 6 seconds passed
+... 45%, 317536 KB, 50382 KB/s, 6 seconds passed
+
+.. parsed-literal::
+
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+... 45%, 318976 KB, 50482 KB/s, 6 seconds passed
+... 45%, 319008 KB, 50486 KB/s, 6 seconds passed
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+... 45%, 319264 KB, 50510 KB/s, 6 seconds passed
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+... 46%, 321088 KB, 50690 KB/s, 6 seconds passed
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+... 46%, 321664 KB, 50712 KB/s, 6 seconds passed
+... 46%, 321696 KB, 50715 KB/s, 6 seconds passed
+
+.. parsed-literal::
+
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+... 46%, 322528 KB, 50506 KB/s, 6 seconds passed
+
+.. parsed-literal::
+
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+... 46%, 325568 KB, 47773 KB/s, 6 seconds passed
+... 46%, 325600 KB, 47774 KB/s, 6 seconds passed
+
+.. parsed-literal::
+
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+... 47%, 327264 KB, 47901 KB/s, 6 seconds passed
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+... 47%, 327552 KB, 47930 KB/s, 6 seconds passed
+... 47%, 327584 KB, 47933 KB/s, 6 seconds passed
+... 47%, 327616 KB, 47936 KB/s, 6 seconds passed
+... 47%, 327648 KB, 47940 KB/s, 6 seconds passed
+
+.. parsed-literal::
+
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+... 47%, 329728 KB, 47738 KB/s, 6 seconds passed
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+... 47%, 330880 KB, 47851 KB/s, 6 seconds passed
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+... 47%, 331200 KB, 47877 KB/s, 6 seconds passed
+... 47%, 331232 KB, 47879 KB/s, 6 seconds passed
+... 47%, 331264 KB, 47883 KB/s, 6 seconds passed
+
+.. parsed-literal::
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+... 47%, 332512 KB, 47931 KB/s, 6 seconds passed
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+... 47%, 332576 KB, 47936 KB/s, 6 seconds passed
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+... 47%, 332672 KB, 47946 KB/s, 6 seconds passed
+... 47%, 332704 KB, 47949 KB/s, 6 seconds passed
+... 47%, 332736 KB, 47950 KB/s, 6 seconds passed
+... 47%, 332768 KB, 47952 KB/s, 6 seconds passed
+
+.. parsed-literal::
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+... 48%, 334048 KB, 47586 KB/s, 7 seconds passed
+... 48%, 334080 KB, 47588 KB/s, 7 seconds passed
+... 48%, 334112 KB, 47590 KB/s, 7 seconds passed
+
+.. parsed-literal::
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+... 48%, 337824 KB, 47883 KB/s, 7 seconds passed
+... 48%, 337856 KB, 47886 KB/s, 7 seconds passed
+... 48%, 337888 KB, 47889 KB/s, 7 seconds passed
+
+.. parsed-literal::
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+... 48%, 338816 KB, 47792 KB/s, 7 seconds passed
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+... 48%, 340416 KB, 47930 KB/s, 7 seconds passed
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+... 48%, 340512 KB, 47940 KB/s, 7 seconds passed
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+... 48%, 340608 KB, 47950 KB/s, 7 seconds passed
+... 48%, 340640 KB, 47954 KB/s, 7 seconds passed
+... 48%, 340672 KB, 47957 KB/s, 7 seconds passed
+... 48%, 340704 KB, 47960 KB/s, 7 seconds passed
+... 48%, 340736 KB, 47964 KB/s, 7 seconds passed
+... 48%, 340768 KB, 47967 KB/s, 7 seconds passed
+... 49%, 340800 KB, 47970 KB/s, 7 seconds passed
+... 49%, 340832 KB, 47974 KB/s, 7 seconds passed
+... 49%, 340864 KB, 47977 KB/s, 7 seconds passed
+... 49%, 340896 KB, 47980 KB/s, 7 seconds passed
+... 49%, 340928 KB, 47983 KB/s, 7 seconds passed
+... 49%, 340960 KB, 47985 KB/s, 7 seconds passed
+... 49%, 340992 KB, 47988 KB/s, 7 seconds passed
+... 49%, 341024 KB, 47991 KB/s, 7 seconds passed
+... 49%, 341056 KB, 47993 KB/s, 7 seconds passed
+... 49%, 341088 KB, 47996 KB/s, 7 seconds passed
+... 49%, 341120 KB, 47999 KB/s, 7 seconds passed
+... 49%, 341152 KB, 48002 KB/s, 7 seconds passed
+... 49%, 341184 KB, 48003 KB/s, 7 seconds passed
+... 49%, 341216 KB, 48006 KB/s, 7 seconds passed
+... 49%, 341248 KB, 48009 KB/s, 7 seconds passed
+... 49%, 341280 KB, 48012 KB/s, 7 seconds passed
+... 49%, 341312 KB, 48015 KB/s, 7 seconds passed
+... 49%, 341344 KB, 48017 KB/s, 7 seconds passed
+... 49%, 341376 KB, 48019 KB/s, 7 seconds passed
+... 49%, 341408 KB, 48022 KB/s, 7 seconds passed
+... 49%, 341440 KB, 48025 KB/s, 7 seconds passed
+... 49%, 341472 KB, 48027 KB/s, 7 seconds passed
+... 49%, 341504 KB, 48030 KB/s, 7 seconds passed
+... 49%, 341536 KB, 48033 KB/s, 7 seconds passed
+... 49%, 341568 KB, 48036 KB/s, 7 seconds passed
+... 49%, 341600 KB, 48037 KB/s, 7 seconds passed
+... 49%, 341632 KB, 48040 KB/s, 7 seconds passed
+... 49%, 341664 KB, 48043 KB/s, 7 seconds passed
+... 49%, 341696 KB, 48046 KB/s, 7 seconds passed
+... 49%, 341728 KB, 48048 KB/s, 7 seconds passed
+... 49%, 341760 KB, 48051 KB/s, 7 seconds passed
+... 49%, 341792 KB, 48045 KB/s, 7 seconds passed
+... 49%, 341824 KB, 48047 KB/s, 7 seconds passed
+... 49%, 341856 KB, 48049 KB/s, 7 seconds passed
+... 49%, 341888 KB, 48052 KB/s, 7 seconds passed
+... 49%, 341920 KB, 48056 KB/s, 7 seconds passed
+... 49%, 341952 KB, 48059 KB/s, 7 seconds passed
+... 49%, 341984 KB, 48061 KB/s, 7 seconds passed
+... 49%, 342016 KB, 48064 KB/s, 7 seconds passed
+... 49%, 342048 KB, 48067 KB/s, 7 seconds passed
+... 49%, 342080 KB, 48069 KB/s, 7 seconds passed
+... 49%, 342112 KB, 48072 KB/s, 7 seconds passed
+... 49%, 342144 KB, 48075 KB/s, 7 seconds passed
+... 49%, 342176 KB, 48077 KB/s, 7 seconds passed
+... 49%, 342208 KB, 48080 KB/s, 7 seconds passed
+... 49%, 342240 KB, 48083 KB/s, 7 seconds passed
+... 49%, 342272 KB, 48085 KB/s, 7 seconds passed
+... 49%, 342304 KB, 48088 KB/s, 7 seconds passed
+... 49%, 342336 KB, 48090 KB/s, 7 seconds passed
+... 49%, 342368 KB, 48093 KB/s, 7 seconds passed
+... 49%, 342400 KB, 48095 KB/s, 7 seconds passed
+... 49%, 342432 KB, 48098 KB/s, 7 seconds passed
+... 49%, 342464 KB, 48100 KB/s, 7 seconds passed
+... 49%, 342496 KB, 48102 KB/s, 7 seconds passed
+... 49%, 342528 KB, 48105 KB/s, 7 seconds passed
+... 49%, 342560 KB, 48108 KB/s, 7 seconds passed
+... 49%, 342592 KB, 48111 KB/s, 7 seconds passed
+... 49%, 342624 KB, 48114 KB/s, 7 seconds passed
+... 49%, 342656 KB, 48116 KB/s, 7 seconds passed
+... 49%, 342688 KB, 48119 KB/s, 7 seconds passed
+... 49%, 342720 KB, 48122 KB/s, 7 seconds passed
+... 49%, 342752 KB, 48124 KB/s, 7 seconds passed
+... 49%, 342784 KB, 48127 KB/s, 7 seconds passed
+... 49%, 342816 KB, 48129 KB/s, 7 seconds passed
+... 49%, 342848 KB, 48132 KB/s, 7 seconds passed
+... 49%, 342880 KB, 48134 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
+    ... 49%, 342912 KB, 48137 KB/s, 7 seconds passed
+... 49%, 342944 KB, 48140 KB/s, 7 seconds passed
+... 49%, 342976 KB, 48142 KB/s, 7 seconds passed
+... 49%, 343008 KB, 48145 KB/s, 7 seconds passed
+... 49%, 343040 KB, 47979 KB/s, 7 seconds passed
+... 49%, 343072 KB, 47980 KB/s, 7 seconds passed
+... 49%, 343104 KB, 47982 KB/s, 7 seconds passed
+... 49%, 343136 KB, 47973 KB/s, 7 seconds passed
+... 49%, 343168 KB, 47974 KB/s, 7 seconds passed
+... 49%, 343200 KB, 47976 KB/s, 7 seconds passed
+... 49%, 343232 KB, 47978 KB/s, 7 seconds passed
+... 49%, 343264 KB, 47980 KB/s, 7 seconds passed
+... 49%, 343296 KB, 47981 KB/s, 7 seconds passed
+... 49%, 343328 KB, 47983 KB/s, 7 seconds passed
+... 49%, 343360 KB, 47985 KB/s, 7 seconds passed
+... 49%, 343392 KB, 47987 KB/s, 7 seconds passed
+... 49%, 343424 KB, 47989 KB/s, 7 seconds passed
+... 49%, 343456 KB, 47991 KB/s, 7 seconds passed
+... 49%, 343488 KB, 47993 KB/s, 7 seconds passed
+... 49%, 343520 KB, 47995 KB/s, 7 seconds passed
+... 49%, 343552 KB, 47997 KB/s, 7 seconds passed
+... 49%, 343584 KB, 47999 KB/s, 7 seconds passed
+... 49%, 343616 KB, 48001 KB/s, 7 seconds passed
+... 49%, 343648 KB, 48003 KB/s, 7 seconds passed
+... 49%, 343680 KB, 48004 KB/s, 7 seconds passed
+... 49%, 343712 KB, 48006 KB/s, 7 seconds passed
+... 49%, 343744 KB, 48008 KB/s, 7 seconds passed
+... 49%, 343776 KB, 48010 KB/s, 7 seconds passed
+... 49%, 343808 KB, 48012 KB/s, 7 seconds passed
+... 49%, 343840 KB, 48014 KB/s, 7 seconds passed
+... 49%, 343872 KB, 48015 KB/s, 7 seconds passed
+... 49%, 343904 KB, 48018 KB/s, 7 seconds passed
+... 49%, 343936 KB, 48020 KB/s, 7 seconds passed
+... 49%, 343968 KB, 48022 KB/s, 7 seconds passed
+... 49%, 344000 KB, 48025 KB/s, 7 seconds passed
+... 49%, 344032 KB, 48027 KB/s, 7 seconds passed
+... 49%, 344064 KB, 48030 KB/s, 7 seconds passed
+... 49%, 344096 KB, 48032 KB/s, 7 seconds passed
+... 49%, 344128 KB, 48035 KB/s, 7 seconds passed
+... 49%, 344160 KB, 48037 KB/s, 7 seconds passed
+... 49%, 344192 KB, 48040 KB/s, 7 seconds passed
+... 49%, 344224 KB, 48042 KB/s, 7 seconds passed
+... 49%, 344256 KB, 48045 KB/s, 7 seconds passed
+... 49%, 344288 KB, 48047 KB/s, 7 seconds passed
+... 49%, 344320 KB, 48050 KB/s, 7 seconds passed
+... 49%, 344352 KB, 48052 KB/s, 7 seconds passed
+... 49%, 344384 KB, 48055 KB/s, 7 seconds passed
+... 49%, 344416 KB, 48057 KB/s, 7 seconds passed
+... 49%, 344448 KB, 48059 KB/s, 7 seconds passed
+... 49%, 344480 KB, 48062 KB/s, 7 seconds passed
+... 49%, 344512 KB, 48064 KB/s, 7 seconds passed
+... 49%, 344544 KB, 48067 KB/s, 7 seconds passed
+... 49%, 344576 KB, 48069 KB/s, 7 seconds passed
+... 49%, 344608 KB, 48072 KB/s, 7 seconds passed
+... 49%, 344640 KB, 48074 KB/s, 7 seconds passed
+... 49%, 344672 KB, 48077 KB/s, 7 seconds passed
+... 49%, 344704 KB, 48079 KB/s, 7 seconds passed
+... 49%, 344736 KB, 48082 KB/s, 7 seconds passed
+... 49%, 344768 KB, 48084 KB/s, 7 seconds passed
+... 49%, 344800 KB, 48086 KB/s, 7 seconds passed
+... 49%, 344832 KB, 48089 KB/s, 7 seconds passed
+... 49%, 344864 KB, 48091 KB/s, 7 seconds passed
+... 49%, 344896 KB, 48093 KB/s, 7 seconds passed
+... 49%, 344928 KB, 48096 KB/s, 7 seconds passed
+... 49%, 344960 KB, 48099 KB/s, 7 seconds passed
+... 49%, 344992 KB, 48102 KB/s, 7 seconds passed
+... 49%, 345024 KB, 48105 KB/s, 7 seconds passed
+... 49%, 345056 KB, 48108 KB/s, 7 seconds passed
+... 49%, 345088 KB, 48111 KB/s, 7 seconds passed
+... 49%, 345120 KB, 48114 KB/s, 7 seconds passed
+... 49%, 345152 KB, 48117 KB/s, 7 seconds passed
+... 49%, 345184 KB, 48120 KB/s, 7 seconds passed
+... 49%, 345216 KB, 48123 KB/s, 7 seconds passed
+... 49%, 345248 KB, 48127 KB/s, 7 seconds passed
+... 49%, 345280 KB, 48130 KB/s, 7 seconds passed
+... 49%, 345312 KB, 48133 KB/s, 7 seconds passed
+... 49%, 345344 KB, 48136 KB/s, 7 seconds passed
+... 49%, 345376 KB, 48139 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
+    ... 49%, 345408 KB, 48142 KB/s, 7 seconds passed
+... 49%, 345440 KB, 48145 KB/s, 7 seconds passed
+... 49%, 345472 KB, 48148 KB/s, 7 seconds passed
+... 49%, 345504 KB, 48151 KB/s, 7 seconds passed
+... 49%, 345536 KB, 48154 KB/s, 7 seconds passed
+... 49%, 345568 KB, 48157 KB/s, 7 seconds passed
+... 49%, 345600 KB, 48160 KB/s, 7 seconds passed
+... 49%, 345632 KB, 48163 KB/s, 7 seconds passed
+... 49%, 345664 KB, 48166 KB/s, 7 seconds passed
+... 49%, 345696 KB, 48169 KB/s, 7 seconds passed
+... 49%, 345728 KB, 48172 KB/s, 7 seconds passed
+... 49%, 345760 KB, 48176 KB/s, 7 seconds passed
+... 49%, 345792 KB, 48179 KB/s, 7 seconds passed
+... 49%, 345824 KB, 48182 KB/s, 7 seconds passed
+... 49%, 345856 KB, 48185 KB/s, 7 seconds passed
+... 49%, 345888 KB, 48188 KB/s, 7 seconds passed
+... 49%, 345920 KB, 48191 KB/s, 7 seconds passed
+... 49%, 345952 KB, 48194 KB/s, 7 seconds passed
+... 49%, 345984 KB, 48197 KB/s, 7 seconds passed
+... 49%, 346016 KB, 48200 KB/s, 7 seconds passed
+... 49%, 346048 KB, 48204 KB/s, 7 seconds passed
+... 49%, 346080 KB, 48207 KB/s, 7 seconds passed
+... 49%, 346112 KB, 48210 KB/s, 7 seconds passed
+... 49%, 346144 KB, 48213 KB/s, 7 seconds passed
+... 49%, 346176 KB, 48216 KB/s, 7 seconds passed
+... 49%, 346208 KB, 48219 KB/s, 7 seconds passed
+... 49%, 346240 KB, 48222 KB/s, 7 seconds passed
+... 49%, 346272 KB, 48225 KB/s, 7 seconds passed
+... 49%, 346304 KB, 48228 KB/s, 7 seconds passed
+... 49%, 346336 KB, 48231 KB/s, 7 seconds passed
+... 49%, 346368 KB, 48235 KB/s, 7 seconds passed
+... 49%, 346400 KB, 48237 KB/s, 7 seconds passed
+... 49%, 346432 KB, 48240 KB/s, 7 seconds passed
+... 49%, 346464 KB, 48243 KB/s, 7 seconds passed
+... 49%, 346496 KB, 48246 KB/s, 7 seconds passed
+... 49%, 346528 KB, 48248 KB/s, 7 seconds passed
+... 49%, 346560 KB, 48251 KB/s, 7 seconds passed
+... 49%, 346592 KB, 48253 KB/s, 7 seconds passed
+... 49%, 346624 KB, 48255 KB/s, 7 seconds passed
+... 49%, 346656 KB, 48258 KB/s, 7 seconds passed
+... 49%, 346688 KB, 48259 KB/s, 7 seconds passed
+... 49%, 346720 KB, 48261 KB/s, 7 seconds passed
+... 49%, 346752 KB, 48263 KB/s, 7 seconds passed
+... 49%, 346784 KB, 48267 KB/s, 7 seconds passed
+... 49%, 346816 KB, 48270 KB/s, 7 seconds passed
+... 49%, 346848 KB, 48273 KB/s, 7 seconds passed
+... 49%, 346880 KB, 48277 KB/s, 7 seconds passed
+... 49%, 346912 KB, 48279 KB/s, 7 seconds passed
+... 49%, 346944 KB, 48279 KB/s, 7 seconds passed
+... 49%, 346976 KB, 48282 KB/s, 7 seconds passed
+... 49%, 347008 KB, 48284 KB/s, 7 seconds passed
+... 49%, 347040 KB, 48287 KB/s, 7 seconds passed
+... 49%, 347072 KB, 48289 KB/s, 7 seconds passed
+... 49%, 347104 KB, 48288 KB/s, 7 seconds passed
+... 49%, 347136 KB, 48290 KB/s, 7 seconds passed
+... 49%, 347168 KB, 48293 KB/s, 7 seconds passed
+... 49%, 347200 KB, 48296 KB/s, 7 seconds passed
+... 49%, 347232 KB, 48298 KB/s, 7 seconds passed
+... 49%, 347264 KB, 48301 KB/s, 7 seconds passed
+... 49%, 347296 KB, 48303 KB/s, 7 seconds passed
+... 49%, 347328 KB, 48196 KB/s, 7 seconds passed
+... 49%, 347360 KB, 48197 KB/s, 7 seconds passed
+... 49%, 347392 KB, 48199 KB/s, 7 seconds passed
+... 49%, 347424 KB, 48200 KB/s, 7 seconds passed
+... 49%, 347456 KB, 48202 KB/s, 7 seconds passed
+... 49%, 347488 KB, 48204 KB/s, 7 seconds passed
+... 49%, 347520 KB, 48206 KB/s, 7 seconds passed
+... 49%, 347552 KB, 48208 KB/s, 7 seconds passed
+... 49%, 347584 KB, 48210 KB/s, 7 seconds passed
+... 49%, 347616 KB, 48212 KB/s, 7 seconds passed
+... 49%, 347648 KB, 48214 KB/s, 7 seconds passed
+... 49%, 347680 KB, 48216 KB/s, 7 seconds passed
+... 49%, 347712 KB, 48217 KB/s, 7 seconds passed
+... 49%, 347744 KB, 48219 KB/s, 7 seconds passed
+... 50%, 347776 KB, 48221 KB/s, 7 seconds passed
+... 50%, 347808 KB, 48223 KB/s, 7 seconds passed
+... 50%, 347840 KB, 48224 KB/s, 7 seconds passed
+... 50%, 347872 KB, 48226 KB/s, 7 seconds passed
+... 50%, 347904 KB, 48228 KB/s, 7 seconds passed
+... 50%, 347936 KB, 48230 KB/s, 7 seconds passed
+... 50%, 347968 KB, 48232 KB/s, 7 seconds passed
+... 50%, 348000 KB, 48235 KB/s, 7 seconds passed
+... 50%, 348032 KB, 48237 KB/s, 7 seconds passed
+... 50%, 348064 KB, 48239 KB/s, 7 seconds passed
+... 50%, 348096 KB, 48242 KB/s, 7 seconds passed
+... 50%, 348128 KB, 48245 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
+    ... 50%, 348160 KB, 47941 KB/s, 7 seconds passed
+... 50%, 348192 KB, 47942 KB/s, 7 seconds passed
+... 50%, 348224 KB, 47943 KB/s, 7 seconds passed
+... 50%, 348256 KB, 47945 KB/s, 7 seconds passed
+... 50%, 348288 KB, 47947 KB/s, 7 seconds passed
+... 50%, 348320 KB, 47948 KB/s, 7 seconds passed
+... 50%, 348352 KB, 47950 KB/s, 7 seconds passed
+... 50%, 348384 KB, 47952 KB/s, 7 seconds passed
+... 50%, 348416 KB, 47954 KB/s, 7 seconds passed
+... 50%, 348448 KB, 47956 KB/s, 7 seconds passed
+... 50%, 348480 KB, 47958 KB/s, 7 seconds passed
+... 50%, 348512 KB, 47960 KB/s, 7 seconds passed
+... 50%, 348544 KB, 47962 KB/s, 7 seconds passed
+... 50%, 348576 KB, 47963 KB/s, 7 seconds passed
+... 50%, 348608 KB, 47965 KB/s, 7 seconds passed
+... 50%, 348640 KB, 47967 KB/s, 7 seconds passed
+... 50%, 348672 KB, 47969 KB/s, 7 seconds passed
+... 50%, 348704 KB, 47971 KB/s, 7 seconds passed
+... 50%, 348736 KB, 47973 KB/s, 7 seconds passed
+... 50%, 348768 KB, 47975 KB/s, 7 seconds passed
+... 50%, 348800 KB, 47977 KB/s, 7 seconds passed
+... 50%, 348832 KB, 47979 KB/s, 7 seconds passed
+... 50%, 348864 KB, 47980 KB/s, 7 seconds passed
+... 50%, 348896 KB, 47982 KB/s, 7 seconds passed
+... 50%, 348928 KB, 47984 KB/s, 7 seconds passed
+... 50%, 348960 KB, 47986 KB/s, 7 seconds passed
+... 50%, 348992 KB, 47988 KB/s, 7 seconds passed
+... 50%, 349024 KB, 47990 KB/s, 7 seconds passed
+... 50%, 349056 KB, 47992 KB/s, 7 seconds passed
+... 50%, 349088 KB, 47995 KB/s, 7 seconds passed
+... 50%, 349120 KB, 47998 KB/s, 7 seconds passed
+... 50%, 349152 KB, 48001 KB/s, 7 seconds passed
+... 50%, 349184 KB, 48003 KB/s, 7 seconds passed
+... 50%, 349216 KB, 48006 KB/s, 7 seconds passed
+... 50%, 349248 KB, 48009 KB/s, 7 seconds passed
+... 50%, 349280 KB, 48012 KB/s, 7 seconds passed
+... 50%, 349312 KB, 48015 KB/s, 7 seconds passed
+... 50%, 349344 KB, 48017 KB/s, 7 seconds passed
+... 50%, 349376 KB, 48020 KB/s, 7 seconds passed
+... 50%, 349408 KB, 48022 KB/s, 7 seconds passed
+... 50%, 349440 KB, 48025 KB/s, 7 seconds passed
+... 50%, 349472 KB, 48026 KB/s, 7 seconds passed
+... 50%, 349504 KB, 48028 KB/s, 7 seconds passed
+... 50%, 349536 KB, 48031 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
+    ... 50%, 349568 KB, 48032 KB/s, 7 seconds passed
+... 50%, 349600 KB, 48034 KB/s, 7 seconds passed
+... 50%, 349632 KB, 48036 KB/s, 7 seconds passed
+... 50%, 349664 KB, 48038 KB/s, 7 seconds passed
+... 50%, 349696 KB, 48041 KB/s, 7 seconds passed
+... 50%, 349728 KB, 48042 KB/s, 7 seconds passed
+... 50%, 349760 KB, 48044 KB/s, 7 seconds passed
+... 50%, 349792 KB, 48047 KB/s, 7 seconds passed
+... 50%, 349824 KB, 48049 KB/s, 7 seconds passed
+... 50%, 349856 KB, 48051 KB/s, 7 seconds passed
+... 50%, 349888 KB, 48053 KB/s, 7 seconds passed
+... 50%, 349920 KB, 48055 KB/s, 7 seconds passed
+... 50%, 349952 KB, 48057 KB/s, 7 seconds passed
+... 50%, 349984 KB, 48060 KB/s, 7 seconds passed
+... 50%, 350016 KB, 48061 KB/s, 7 seconds passed
+... 50%, 350048 KB, 48063 KB/s, 7 seconds passed
+... 50%, 350080 KB, 48064 KB/s, 7 seconds passed
+... 50%, 350112 KB, 48067 KB/s, 7 seconds passed
+... 50%, 350144 KB, 48070 KB/s, 7 seconds passed
+... 50%, 350176 KB, 48071 KB/s, 7 seconds passed
+... 50%, 350208 KB, 48073 KB/s, 7 seconds passed
+... 50%, 350240 KB, 48074 KB/s, 7 seconds passed
+... 50%, 350272 KB, 48077 KB/s, 7 seconds passed
+... 50%, 350304 KB, 48079 KB/s, 7 seconds passed
+... 50%, 350336 KB, 48081 KB/s, 7 seconds passed
+... 50%, 350368 KB, 48083 KB/s, 7 seconds passed
+... 50%, 350400 KB, 48085 KB/s, 7 seconds passed
+... 50%, 350432 KB, 48087 KB/s, 7 seconds passed
+... 50%, 350464 KB, 48088 KB/s, 7 seconds passed
+... 50%, 350496 KB, 48090 KB/s, 7 seconds passed
+... 50%, 350528 KB, 48093 KB/s, 7 seconds passed
+... 50%, 350560 KB, 48096 KB/s, 7 seconds passed
+... 50%, 350592 KB, 48099 KB/s, 7 seconds passed
+... 50%, 350624 KB, 48102 KB/s, 7 seconds passed
+... 50%, 350656 KB, 48104 KB/s, 7 seconds passed
+... 50%, 350688 KB, 48107 KB/s, 7 seconds passed
+... 50%, 350720 KB, 48017 KB/s, 7 seconds passed
+... 50%, 350752 KB, 48018 KB/s, 7 seconds passed
+... 50%, 350784 KB, 48020 KB/s, 7 seconds passed
+... 50%, 350816 KB, 48022 KB/s, 7 seconds passed
+... 50%, 350848 KB, 48023 KB/s, 7 seconds passed
+... 50%, 350880 KB, 48025 KB/s, 7 seconds passed
+... 50%, 350912 KB, 48027 KB/s, 7 seconds passed
+... 50%, 350944 KB, 48029 KB/s, 7 seconds passed
+... 50%, 350976 KB, 48030 KB/s, 7 seconds passed
+... 50%, 351008 KB, 48032 KB/s, 7 seconds passed
+... 50%, 351040 KB, 48034 KB/s, 7 seconds passed
+... 50%, 351072 KB, 48036 KB/s, 7 seconds passed
+... 50%, 351104 KB, 48038 KB/s, 7 seconds passed
+... 50%, 351136 KB, 48039 KB/s, 7 seconds passed
+... 50%, 351168 KB, 48041 KB/s, 7 seconds passed
+... 50%, 351200 KB, 48043 KB/s, 7 seconds passed
+... 50%, 351232 KB, 48045 KB/s, 7 seconds passed
+... 50%, 351264 KB, 48047 KB/s, 7 seconds passed
+... 50%, 351296 KB, 48049 KB/s, 7 seconds passed
+... 50%, 351328 KB, 48050 KB/s, 7 seconds passed
+... 50%, 351360 KB, 48052 KB/s, 7 seconds passed
+... 50%, 351392 KB, 48054 KB/s, 7 seconds passed
+... 50%, 351424 KB, 48057 KB/s, 7 seconds passed
+... 50%, 351456 KB, 48059 KB/s, 7 seconds passed
+... 50%, 351488 KB, 48062 KB/s, 7 seconds passed
+... 50%, 351520 KB, 48065 KB/s, 7 seconds passed
+... 50%, 351552 KB, 48067 KB/s, 7 seconds passed
+... 50%, 351584 KB, 48070 KB/s, 7 seconds passed
+... 50%, 351616 KB, 48073 KB/s, 7 seconds passed
+... 50%, 351648 KB, 48074 KB/s, 7 seconds passed
+... 50%, 351680 KB, 48076 KB/s, 7 seconds passed
+... 50%, 351712 KB, 48078 KB/s, 7 seconds passed
+... 50%, 351744 KB, 48081 KB/s, 7 seconds passed
+... 50%, 351776 KB, 48083 KB/s, 7 seconds passed
+... 50%, 351808 KB, 48085 KB/s, 7 seconds passed
+... 50%, 351840 KB, 48088 KB/s, 7 seconds passed
+... 50%, 351872 KB, 48090 KB/s, 7 seconds passed
+... 50%, 351904 KB, 48093 KB/s, 7 seconds passed
+... 50%, 351936 KB, 48095 KB/s, 7 seconds passed
+... 50%, 351968 KB, 48098 KB/s, 7 seconds passed
+... 50%, 352000 KB, 48100 KB/s, 7 seconds passed
+... 50%, 352032 KB, 48102 KB/s, 7 seconds passed
+... 50%, 352064 KB, 48105 KB/s, 7 seconds passed
+... 50%, 352096 KB, 48107 KB/s, 7 seconds passed
+... 50%, 352128 KB, 48110 KB/s, 7 seconds passed
+... 50%, 352160 KB, 48112 KB/s, 7 seconds passed
+... 50%, 352192 KB, 48115 KB/s, 7 seconds passed
+... 50%, 352224 KB, 48117 KB/s, 7 seconds passed
+... 50%, 352256 KB, 48120 KB/s, 7 seconds passed
+... 50%, 352288 KB, 48122 KB/s, 7 seconds passed
+... 50%, 352320 KB, 48125 KB/s, 7 seconds passed
+... 50%, 352352 KB, 48127 KB/s, 7 seconds passed
+... 50%, 352384 KB, 48130 KB/s, 7 seconds passed
+... 50%, 352416 KB, 48132 KB/s, 7 seconds passed
+... 50%, 352448 KB, 48135 KB/s, 7 seconds passed
+... 50%, 352480 KB, 48137 KB/s, 7 seconds passed
+... 50%, 352512 KB, 48140 KB/s, 7 seconds passed
+... 50%, 352544 KB, 48142 KB/s, 7 seconds passed
+... 50%, 352576 KB, 48145 KB/s, 7 seconds passed
+... 50%, 352608 KB, 48148 KB/s, 7 seconds passed
+... 50%, 352640 KB, 48151 KB/s, 7 seconds passed
+... 50%, 352672 KB, 48154 KB/s, 7 seconds passed
+... 50%, 352704 KB, 48157 KB/s, 7 seconds passed
+... 50%, 352736 KB, 48160 KB/s, 7 seconds passed
+... 50%, 352768 KB, 48163 KB/s, 7 seconds passed
+... 50%, 352800 KB, 48165 KB/s, 7 seconds passed
+... 50%, 352832 KB, 48168 KB/s, 7 seconds passed
+... 50%, 352864 KB, 48171 KB/s, 7 seconds passed
+... 50%, 352896 KB, 48173 KB/s, 7 seconds passed
+... 50%, 352928 KB, 48175 KB/s, 7 seconds passed
+... 50%, 352960 KB, 48178 KB/s, 7 seconds passed
+... 50%, 352992 KB, 48181 KB/s, 7 seconds passed
+... 50%, 353024 KB, 48183 KB/s, 7 seconds passed
+... 50%, 353056 KB, 48186 KB/s, 7 seconds passed
+... 50%, 353088 KB, 48188 KB/s, 7 seconds passed
+... 50%, 353120 KB, 48191 KB/s, 7 seconds passed
+... 50%, 353152 KB, 48193 KB/s, 7 seconds passed
+... 50%, 353184 KB, 48196 KB/s, 7 seconds passed
+... 50%, 353216 KB, 48198 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
+    ... 50%, 353248 KB, 48201 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
+    ... 50%, 353280 KB, 47827 KB/s, 7 seconds passed
+... 50%, 353312 KB, 47826 KB/s, 7 seconds passed
+... 50%, 353344 KB, 47827 KB/s, 7 seconds passed
+... 50%, 353376 KB, 47829 KB/s, 7 seconds passed
+... 50%, 353408 KB, 47831 KB/s, 7 seconds passed
+... 50%, 353440 KB, 47833 KB/s, 7 seconds passed
+... 50%, 353472 KB, 47835 KB/s, 7 seconds passed
+... 50%, 353504 KB, 47837 KB/s, 7 seconds passed
+... 50%, 353536 KB, 47840 KB/s, 7 seconds passed
+... 50%, 353568 KB, 47842 KB/s, 7 seconds passed
+... 50%, 353600 KB, 47844 KB/s, 7 seconds passed
+... 50%, 353632 KB, 47846 KB/s, 7 seconds passed
+... 50%, 353664 KB, 47848 KB/s, 7 seconds passed
+... 50%, 353696 KB, 47850 KB/s, 7 seconds passed
+... 50%, 353728 KB, 47852 KB/s, 7 seconds passed
+... 50%, 353760 KB, 47854 KB/s, 7 seconds passed
+... 50%, 353792 KB, 47857 KB/s, 7 seconds passed
+... 50%, 353824 KB, 47859 KB/s, 7 seconds passed
+... 50%, 353856 KB, 47861 KB/s, 7 seconds passed
+... 50%, 353888 KB, 47863 KB/s, 7 seconds passed
+... 50%, 353920 KB, 47865 KB/s, 7 seconds passed
+... 50%, 353952 KB, 47867 KB/s, 7 seconds passed
+... 50%, 353984 KB, 47869 KB/s, 7 seconds passed
+... 50%, 354016 KB, 47872 KB/s, 7 seconds passed
+... 50%, 354048 KB, 47874 KB/s, 7 seconds passed
+... 50%, 354080 KB, 47876 KB/s, 7 seconds passed
+... 50%, 354112 KB, 47878 KB/s, 7 seconds passed
+... 50%, 354144 KB, 47880 KB/s, 7 seconds passed
+... 50%, 354176 KB, 47882 KB/s, 7 seconds passed
+... 50%, 354208 KB, 47884 KB/s, 7 seconds passed
+... 50%, 354240 KB, 47886 KB/s, 7 seconds passed
+... 50%, 354272 KB, 47888 KB/s, 7 seconds passed
+... 50%, 354304 KB, 47890 KB/s, 7 seconds passed
+... 50%, 354336 KB, 47892 KB/s, 7 seconds passed
+... 50%, 354368 KB, 47894 KB/s, 7 seconds passed
+... 50%, 354400 KB, 47897 KB/s, 7 seconds passed
+... 50%, 354432 KB, 47899 KB/s, 7 seconds passed
+... 50%, 354464 KB, 47901 KB/s, 7 seconds passed
+... 50%, 354496 KB, 47903 KB/s, 7 seconds passed
+... 50%, 354528 KB, 47905 KB/s, 7 seconds passed
+... 50%, 354560 KB, 47907 KB/s, 7 seconds passed
+... 50%, 354592 KB, 47910 KB/s, 7 seconds passed
+... 50%, 354624 KB, 47912 KB/s, 7 seconds passed
+... 50%, 354656 KB, 47914 KB/s, 7 seconds passed
+... 50%, 354688 KB, 47916 KB/s, 7 seconds passed
+... 51%, 354720 KB, 47914 KB/s, 7 seconds passed
+... 51%, 354752 KB, 47916 KB/s, 7 seconds passed
+... 51%, 354784 KB, 47918 KB/s, 7 seconds passed
+... 51%, 354816 KB, 47920 KB/s, 7 seconds passed
+... 51%, 354848 KB, 47923 KB/s, 7 seconds passed
+... 51%, 354880 KB, 47926 KB/s, 7 seconds passed
+... 51%, 354912 KB, 47929 KB/s, 7 seconds passed
+... 51%, 354944 KB, 47931 KB/s, 7 seconds passed
+... 51%, 354976 KB, 47934 KB/s, 7 seconds passed
+... 51%, 355008 KB, 47937 KB/s, 7 seconds passed
+... 51%, 355040 KB, 47939 KB/s, 7 seconds passed
+... 51%, 355072 KB, 47942 KB/s, 7 seconds passed
+... 51%, 355104 KB, 47944 KB/s, 7 seconds passed
+... 51%, 355136 KB, 47947 KB/s, 7 seconds passed
+... 51%, 355168 KB, 47949 KB/s, 7 seconds passed
+... 51%, 355200 KB, 47952 KB/s, 7 seconds passed
+... 51%, 355232 KB, 47954 KB/s, 7 seconds passed
+... 51%, 355264 KB, 47957 KB/s, 7 seconds passed
+... 51%, 355296 KB, 47959 KB/s, 7 seconds passed
+... 51%, 355328 KB, 47962 KB/s, 7 seconds passed
+... 51%, 355360 KB, 47965 KB/s, 7 seconds passed
+... 51%, 355392 KB, 47967 KB/s, 7 seconds passed
+... 51%, 355424 KB, 47969 KB/s, 7 seconds passed
+... 51%, 355456 KB, 47972 KB/s, 7 seconds passed
+... 51%, 355488 KB, 47974 KB/s, 7 seconds passed
+... 51%, 355520 KB, 47977 KB/s, 7 seconds passed
+... 51%, 355552 KB, 47979 KB/s, 7 seconds passed
+... 51%, 355584 KB, 47982 KB/s, 7 seconds passed
+... 51%, 355616 KB, 47985 KB/s, 7 seconds passed
+... 51%, 355648 KB, 47987 KB/s, 7 seconds passed
+... 51%, 355680 KB, 47990 KB/s, 7 seconds passed
+... 51%, 355712 KB, 47992 KB/s, 7 seconds passed
+... 51%, 355744 KB, 47995 KB/s, 7 seconds passed
+... 51%, 355776 KB, 47998 KB/s, 7 seconds passed
+... 51%, 355808 KB, 48000 KB/s, 7 seconds passed
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+... 51%, 355904 KB, 48007 KB/s, 7 seconds passed
+... 51%, 355936 KB, 48009 KB/s, 7 seconds passed
+... 51%, 355968 KB, 48012 KB/s, 7 seconds passed
+... 51%, 356000 KB, 48015 KB/s, 7 seconds passed
+... 51%, 356032 KB, 48018 KB/s, 7 seconds passed
+... 51%, 356064 KB, 48020 KB/s, 7 seconds passed
+... 51%, 356096 KB, 48022 KB/s, 7 seconds passed
+... 51%, 356128 KB, 48025 KB/s, 7 seconds passed
+... 51%, 356160 KB, 48027 KB/s, 7 seconds passed
+... 51%, 356192 KB, 48030 KB/s, 7 seconds passed
+... 51%, 356224 KB, 48033 KB/s, 7 seconds passed
+... 51%, 356256 KB, 48035 KB/s, 7 seconds passed
+... 51%, 356288 KB, 48037 KB/s, 7 seconds passed
+... 51%, 356320 KB, 48040 KB/s, 7 seconds passed
+... 51%, 356352 KB, 48042 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
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+... 51%, 356448 KB, 47961 KB/s, 7 seconds passed
+... 51%, 356480 KB, 47963 KB/s, 7 seconds passed
+... 51%, 356512 KB, 47965 KB/s, 7 seconds passed
+... 51%, 356544 KB, 47967 KB/s, 7 seconds passed
+... 51%, 356576 KB, 47969 KB/s, 7 seconds passed
+... 51%, 356608 KB, 47971 KB/s, 7 seconds passed
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+... 51%, 356832 KB, 47984 KB/s, 7 seconds passed
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+... 51%, 356896 KB, 47988 KB/s, 7 seconds passed
+... 51%, 356928 KB, 47990 KB/s, 7 seconds passed
+... 51%, 356960 KB, 47993 KB/s, 7 seconds passed
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+... 51%, 357024 KB, 47997 KB/s, 7 seconds passed
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+... 51%, 357120 KB, 48004 KB/s, 7 seconds passed
+... 51%, 357152 KB, 48006 KB/s, 7 seconds passed
+... 51%, 357184 KB, 48008 KB/s, 7 seconds passed
+... 51%, 357216 KB, 48011 KB/s, 7 seconds passed
+... 51%, 357248 KB, 48013 KB/s, 7 seconds passed
+... 51%, 357280 KB, 48015 KB/s, 7 seconds passed
+... 51%, 357312 KB, 48017 KB/s, 7 seconds passed
+... 51%, 357344 KB, 48020 KB/s, 7 seconds passed
+... 51%, 357376 KB, 48022 KB/s, 7 seconds passed
+... 51%, 357408 KB, 48024 KB/s, 7 seconds passed
+... 51%, 357440 KB, 48026 KB/s, 7 seconds passed
+... 51%, 357472 KB, 48029 KB/s, 7 seconds passed
+... 51%, 357504 KB, 48031 KB/s, 7 seconds passed
+... 51%, 357536 KB, 48033 KB/s, 7 seconds passed
+... 51%, 357568 KB, 48035 KB/s, 7 seconds passed
+... 51%, 357600 KB, 48038 KB/s, 7 seconds passed
+... 51%, 357632 KB, 48040 KB/s, 7 seconds passed
+... 51%, 357664 KB, 48043 KB/s, 7 seconds passed
+... 51%, 357696 KB, 48046 KB/s, 7 seconds passed
+... 51%, 357728 KB, 48048 KB/s, 7 seconds passed
+... 51%, 357760 KB, 48051 KB/s, 7 seconds passed
+... 51%, 357792 KB, 48053 KB/s, 7 seconds passed
+... 51%, 357824 KB, 48056 KB/s, 7 seconds passed
+... 51%, 357856 KB, 48059 KB/s, 7 seconds passed
+... 51%, 357888 KB, 48061 KB/s, 7 seconds passed
+... 51%, 357920 KB, 48064 KB/s, 7 seconds passed
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+... 51%, 357984 KB, 48069 KB/s, 7 seconds passed
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+... 51%, 358048 KB, 48075 KB/s, 7 seconds passed
+... 51%, 358080 KB, 48077 KB/s, 7 seconds passed
+... 51%, 358112 KB, 48080 KB/s, 7 seconds passed
+... 51%, 358144 KB, 48083 KB/s, 7 seconds passed
+... 51%, 358176 KB, 48085 KB/s, 7 seconds passed
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+... 51%, 358272 KB, 48094 KB/s, 7 seconds passed
+... 51%, 358304 KB, 48096 KB/s, 7 seconds passed
+... 51%, 358336 KB, 48099 KB/s, 7 seconds passed
+... 51%, 358368 KB, 48102 KB/s, 7 seconds passed
+... 51%, 358400 KB, 47989 KB/s, 7 seconds passed
+... 51%, 358432 KB, 47988 KB/s, 7 seconds passed
+... 51%, 358464 KB, 47990 KB/s, 7 seconds passed
+... 51%, 358496 KB, 47992 KB/s, 7 seconds passed
+
+.. parsed-literal::
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+... 51%, 358592 KB, 47881 KB/s, 7 seconds passed
+... 51%, 358624 KB, 47883 KB/s, 7 seconds passed
+... 51%, 358656 KB, 47885 KB/s, 7 seconds passed
+... 51%, 358688 KB, 47887 KB/s, 7 seconds passed
+... 51%, 358720 KB, 47889 KB/s, 7 seconds passed
+... 51%, 358752 KB, 47892 KB/s, 7 seconds passed
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+... 51%, 358848 KB, 47899 KB/s, 7 seconds passed
+... 51%, 358880 KB, 47901 KB/s, 7 seconds passed
+... 51%, 358912 KB, 47903 KB/s, 7 seconds passed
+... 51%, 358944 KB, 47905 KB/s, 7 seconds passed
+... 51%, 358976 KB, 47908 KB/s, 7 seconds passed
+... 51%, 359008 KB, 47910 KB/s, 7 seconds passed
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+... 51%, 359200 KB, 47924 KB/s, 7 seconds passed
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+... 51%, 359264 KB, 47928 KB/s, 7 seconds passed
+... 51%, 359296 KB, 47930 KB/s, 7 seconds passed
+... 51%, 359328 KB, 47933 KB/s, 7 seconds passed
+... 51%, 359360 KB, 47935 KB/s, 7 seconds passed
+... 51%, 359392 KB, 47937 KB/s, 7 seconds passed
+... 51%, 359424 KB, 47940 KB/s, 7 seconds passed
+... 51%, 359456 KB, 47942 KB/s, 7 seconds passed
+... 51%, 359488 KB, 47944 KB/s, 7 seconds passed
+... 51%, 359520 KB, 47946 KB/s, 7 seconds passed
+... 51%, 359552 KB, 47949 KB/s, 7 seconds passed
+... 51%, 359584 KB, 47951 KB/s, 7 seconds passed
+... 51%, 359616 KB, 47953 KB/s, 7 seconds passed
+... 51%, 359648 KB, 47955 KB/s, 7 seconds passed
+... 51%, 359680 KB, 47958 KB/s, 7 seconds passed
+... 51%, 359712 KB, 47960 KB/s, 7 seconds passed
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+... 51%, 359776 KB, 47964 KB/s, 7 seconds passed
+... 51%, 359808 KB, 47966 KB/s, 7 seconds passed
+... 51%, 359840 KB, 47969 KB/s, 7 seconds passed
+... 51%, 359872 KB, 47971 KB/s, 7 seconds passed
+... 51%, 359904 KB, 47974 KB/s, 7 seconds passed
+... 51%, 359936 KB, 47976 KB/s, 7 seconds passed
+... 51%, 359968 KB, 47978 KB/s, 7 seconds passed
+... 51%, 360000 KB, 47980 KB/s, 7 seconds passed
+... 51%, 360032 KB, 47983 KB/s, 7 seconds passed
+... 51%, 360064 KB, 47981 KB/s, 7 seconds passed
+... 51%, 360096 KB, 47983 KB/s, 7 seconds passed
+... 51%, 360128 KB, 47986 KB/s, 7 seconds passed
+... 51%, 360160 KB, 47987 KB/s, 7 seconds passed
+... 51%, 360192 KB, 47989 KB/s, 7 seconds passed
+... 51%, 360224 KB, 47991 KB/s, 7 seconds passed
+... 51%, 360256 KB, 47991 KB/s, 7 seconds passed
+... 51%, 360288 KB, 47994 KB/s, 7 seconds passed
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+... 51%, 360352 KB, 47999 KB/s, 7 seconds passed
+... 51%, 360384 KB, 48001 KB/s, 7 seconds passed
+... 51%, 360416 KB, 48004 KB/s, 7 seconds passed
+... 51%, 360448 KB, 48007 KB/s, 7 seconds passed
+... 51%, 360480 KB, 48009 KB/s, 7 seconds passed
+... 51%, 360512 KB, 48011 KB/s, 7 seconds passed
+... 51%, 360544 KB, 48013 KB/s, 7 seconds passed
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+... 51%, 360672 KB, 48023 KB/s, 7 seconds passed
+... 51%, 360704 KB, 48026 KB/s, 7 seconds passed
+... 51%, 360736 KB, 48029 KB/s, 7 seconds passed
+... 51%, 360768 KB, 48031 KB/s, 7 seconds passed
+... 51%, 360800 KB, 48033 KB/s, 7 seconds passed
+... 51%, 360832 KB, 48036 KB/s, 7 seconds passed
+... 51%, 360864 KB, 48039 KB/s, 7 seconds passed
+... 51%, 360896 KB, 48041 KB/s, 7 seconds passed
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+... 51%, 360992 KB, 48048 KB/s, 7 seconds passed
+... 51%, 361024 KB, 48050 KB/s, 7 seconds passed
+... 51%, 361056 KB, 48053 KB/s, 7 seconds passed
+... 51%, 361088 KB, 48056 KB/s, 7 seconds passed
+... 51%, 361120 KB, 48058 KB/s, 7 seconds passed
+... 51%, 361152 KB, 48060 KB/s, 7 seconds passed
+... 51%, 361184 KB, 48063 KB/s, 7 seconds passed
+... 51%, 361216 KB, 48066 KB/s, 7 seconds passed
+... 51%, 361248 KB, 48068 KB/s, 7 seconds passed
+... 51%, 361280 KB, 48071 KB/s, 7 seconds passed
+... 51%, 361312 KB, 48073 KB/s, 7 seconds passed
+... 51%, 361344 KB, 48076 KB/s, 7 seconds passed
+... 51%, 361376 KB, 48078 KB/s, 7 seconds passed
+... 51%, 361408 KB, 48080 KB/s, 7 seconds passed
+... 51%, 361440 KB, 48083 KB/s, 7 seconds passed
+... 51%, 361472 KB, 48085 KB/s, 7 seconds passed
+... 51%, 361504 KB, 48088 KB/s, 7 seconds passed
+... 51%, 361536 KB, 48090 KB/s, 7 seconds passed
+... 51%, 361568 KB, 48093 KB/s, 7 seconds passed
+... 51%, 361600 KB, 48095 KB/s, 7 seconds passed
+... 51%, 361632 KB, 48097 KB/s, 7 seconds passed
+... 52%, 361664 KB, 48100 KB/s, 7 seconds passed
+... 52%, 361696 KB, 48102 KB/s, 7 seconds passed
+... 52%, 361728 KB, 48103 KB/s, 7 seconds passed
+... 52%, 361760 KB, 48104 KB/s, 7 seconds passed
+... 52%, 361792 KB, 48106 KB/s, 7 seconds passed
+... 52%, 361824 KB, 48108 KB/s, 7 seconds passed
+... 52%, 361856 KB, 48110 KB/s, 7 seconds passed
+... 52%, 361888 KB, 48112 KB/s, 7 seconds passed
+... 52%, 361920 KB, 48113 KB/s, 7 seconds passed
+... 52%, 361952 KB, 48115 KB/s, 7 seconds passed
+... 52%, 361984 KB, 48117 KB/s, 7 seconds passed
+... 52%, 362016 KB, 48119 KB/s, 7 seconds passed
+... 52%, 362048 KB, 48120 KB/s, 7 seconds passed
+... 52%, 362080 KB, 48122 KB/s, 7 seconds passed
+... 52%, 362112 KB, 48124 KB/s, 7 seconds passed
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+... 52%, 362272 KB, 48133 KB/s, 7 seconds passed
+... 52%, 362304 KB, 48135 KB/s, 7 seconds passed
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+... 52%, 362368 KB, 48138 KB/s, 7 seconds passed
+... 52%, 362400 KB, 48140 KB/s, 7 seconds passed
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+... 52%, 362464 KB, 48144 KB/s, 7 seconds passed
+... 52%, 362496 KB, 48146 KB/s, 7 seconds passed
+... 52%, 362528 KB, 48147 KB/s, 7 seconds passed
+... 52%, 362560 KB, 48149 KB/s, 7 seconds passed
+... 52%, 362592 KB, 48151 KB/s, 7 seconds passed
+... 52%, 362624 KB, 48153 KB/s, 7 seconds passed
+... 52%, 362656 KB, 48155 KB/s, 7 seconds passed
+... 52%, 362688 KB, 48156 KB/s, 7 seconds passed
+... 52%, 362720 KB, 48158 KB/s, 7 seconds passed
+... 52%, 362752 KB, 48160 KB/s, 7 seconds passed
+... 52%, 362784 KB, 48162 KB/s, 7 seconds passed
+... 52%, 362816 KB, 48164 KB/s, 7 seconds passed
+... 52%, 362848 KB, 48165 KB/s, 7 seconds passed
+... 52%, 362880 KB, 48167 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
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+... 52%, 362976 KB, 48173 KB/s, 7 seconds passed
+... 52%, 363008 KB, 48175 KB/s, 7 seconds passed
+... 52%, 363040 KB, 48178 KB/s, 7 seconds passed
+... 52%, 363072 KB, 48181 KB/s, 7 seconds passed
+... 52%, 363104 KB, 48184 KB/s, 7 seconds passed
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+... 52%, 363168 KB, 48190 KB/s, 7 seconds passed
+... 52%, 363200 KB, 48193 KB/s, 7 seconds passed
+... 52%, 363232 KB, 48196 KB/s, 7 seconds passed
+... 52%, 363264 KB, 48199 KB/s, 7 seconds passed
+... 52%, 363296 KB, 48202 KB/s, 7 seconds passed
+... 52%, 363328 KB, 48205 KB/s, 7 seconds passed
+... 52%, 363360 KB, 48208 KB/s, 7 seconds passed
+... 52%, 363392 KB, 48211 KB/s, 7 seconds passed
+... 52%, 363424 KB, 48214 KB/s, 7 seconds passed
+... 52%, 363456 KB, 48217 KB/s, 7 seconds passed
+... 52%, 363488 KB, 48220 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
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+... 52%, 363584 KB, 47715 KB/s, 7 seconds passed
+... 52%, 363616 KB, 47716 KB/s, 7 seconds passed
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+... 52%, 363680 KB, 47720 KB/s, 7 seconds passed
+... 52%, 363712 KB, 47722 KB/s, 7 seconds passed
+... 52%, 363744 KB, 47723 KB/s, 7 seconds passed
+... 52%, 363776 KB, 47725 KB/s, 7 seconds passed
+... 52%, 363808 KB, 47727 KB/s, 7 seconds passed
+... 52%, 363840 KB, 47728 KB/s, 7 seconds passed
+... 52%, 363872 KB, 47730 KB/s, 7 seconds passed
+... 52%, 363904 KB, 47732 KB/s, 7 seconds passed
+... 52%, 363936 KB, 47734 KB/s, 7 seconds passed
+... 52%, 363968 KB, 47735 KB/s, 7 seconds passed
+... 52%, 364000 KB, 47737 KB/s, 7 seconds passed
+... 52%, 364032 KB, 47739 KB/s, 7 seconds passed
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+... 52%, 364256 KB, 47751 KB/s, 7 seconds passed
+... 52%, 364288 KB, 47753 KB/s, 7 seconds passed
+... 52%, 364320 KB, 47755 KB/s, 7 seconds passed
+... 52%, 364352 KB, 47757 KB/s, 7 seconds passed
+... 52%, 364384 KB, 47759 KB/s, 7 seconds passed
+... 52%, 364416 KB, 47761 KB/s, 7 seconds passed
+... 52%, 364448 KB, 47763 KB/s, 7 seconds passed
+... 52%, 364480 KB, 47765 KB/s, 7 seconds passed
+... 52%, 364512 KB, 47766 KB/s, 7 seconds passed
+... 52%, 364544 KB, 47768 KB/s, 7 seconds passed
+... 52%, 364576 KB, 47770 KB/s, 7 seconds passed
+... 52%, 364608 KB, 47772 KB/s, 7 seconds passed
+... 52%, 364640 KB, 47774 KB/s, 7 seconds passed
+... 52%, 364672 KB, 47776 KB/s, 7 seconds passed
+... 52%, 364704 KB, 47777 KB/s, 7 seconds passed
+... 52%, 364736 KB, 47779 KB/s, 7 seconds passed
+... 52%, 364768 KB, 47781 KB/s, 7 seconds passed
+... 52%, 364800 KB, 47783 KB/s, 7 seconds passed
+... 52%, 364832 KB, 47785 KB/s, 7 seconds passed
+... 52%, 364864 KB, 47787 KB/s, 7 seconds passed
+... 52%, 364896 KB, 47789 KB/s, 7 seconds passed
+... 52%, 364928 KB, 47791 KB/s, 7 seconds passed
+... 52%, 364960 KB, 47793 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
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+... 52%, 365056 KB, 47800 KB/s, 7 seconds passed
+... 52%, 365088 KB, 47803 KB/s, 7 seconds passed
+... 52%, 365120 KB, 47806 KB/s, 7 seconds passed
+... 52%, 365152 KB, 47808 KB/s, 7 seconds passed
+... 52%, 365184 KB, 47811 KB/s, 7 seconds passed
+... 52%, 365216 KB, 47814 KB/s, 7 seconds passed
+... 52%, 365248 KB, 47816 KB/s, 7 seconds passed
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+... 52%, 365312 KB, 47822 KB/s, 7 seconds passed
+... 52%, 365344 KB, 47824 KB/s, 7 seconds passed
+... 52%, 365376 KB, 47827 KB/s, 7 seconds passed
+... 52%, 365408 KB, 47830 KB/s, 7 seconds passed
+... 52%, 365440 KB, 47833 KB/s, 7 seconds passed
+... 52%, 365472 KB, 47835 KB/s, 7 seconds passed
+... 52%, 365504 KB, 47838 KB/s, 7 seconds passed
+... 52%, 365536 KB, 47838 KB/s, 7 seconds passed
+... 52%, 365568 KB, 47841 KB/s, 7 seconds passed
+... 52%, 365600 KB, 47843 KB/s, 7 seconds passed
+... 52%, 365632 KB, 47843 KB/s, 7 seconds passed
+... 52%, 365664 KB, 47844 KB/s, 7 seconds passed
+... 52%, 365696 KB, 47845 KB/s, 7 seconds passed
+... 52%, 365728 KB, 47847 KB/s, 7 seconds passed
+... 52%, 365760 KB, 47849 KB/s, 7 seconds passed
+... 52%, 365792 KB, 47851 KB/s, 7 seconds passed
+... 52%, 365824 KB, 47853 KB/s, 7 seconds passed
+... 52%, 365856 KB, 47855 KB/s, 7 seconds passed
+... 52%, 365888 KB, 47856 KB/s, 7 seconds passed
+... 52%, 365920 KB, 47858 KB/s, 7 seconds passed
+... 52%, 365952 KB, 47861 KB/s, 7 seconds passed
+... 52%, 365984 KB, 47863 KB/s, 7 seconds passed
+... 52%, 366016 KB, 47864 KB/s, 7 seconds passed
+... 52%, 366048 KB, 47865 KB/s, 7 seconds passed
+... 52%, 366080 KB, 47867 KB/s, 7 seconds passed
+... 52%, 366112 KB, 47870 KB/s, 7 seconds passed
+... 52%, 366144 KB, 47871 KB/s, 7 seconds passed
+... 52%, 366176 KB, 47872 KB/s, 7 seconds passed
+... 52%, 366208 KB, 47874 KB/s, 7 seconds passed
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+... 52%, 366272 KB, 47878 KB/s, 7 seconds passed
+... 52%, 366304 KB, 47880 KB/s, 7 seconds passed
+... 52%, 366336 KB, 47883 KB/s, 7 seconds passed
+... 52%, 366368 KB, 47883 KB/s, 7 seconds passed
+... 52%, 366400 KB, 47884 KB/s, 7 seconds passed
+... 52%, 366432 KB, 47886 KB/s, 7 seconds passed
+... 52%, 366464 KB, 47889 KB/s, 7 seconds passed
+... 52%, 366496 KB, 47891 KB/s, 7 seconds passed
+... 52%, 366528 KB, 47892 KB/s, 7 seconds passed
+... 52%, 366560 KB, 47894 KB/s, 7 seconds passed
+... 52%, 366592 KB, 47896 KB/s, 7 seconds passed
+... 52%, 366624 KB, 47898 KB/s, 7 seconds passed
+... 52%, 366656 KB, 47787 KB/s, 7 seconds passed
+... 52%, 366688 KB, 47788 KB/s, 7 seconds passed
+... 52%, 366720 KB, 47789 KB/s, 7 seconds passed
+... 52%, 366752 KB, 47790 KB/s, 7 seconds passed
+... 52%, 366784 KB, 47791 KB/s, 7 seconds passed
+... 52%, 366816 KB, 47792 KB/s, 7 seconds passed
+... 52%, 366848 KB, 47792 KB/s, 7 seconds passed
+... 52%, 366880 KB, 47794 KB/s, 7 seconds passed
+... 52%, 366912 KB, 47794 KB/s, 7 seconds passed
+... 52%, 366944 KB, 47795 KB/s, 7 seconds passed
+... 52%, 366976 KB, 47796 KB/s, 7 seconds passed
+... 52%, 367008 KB, 47797 KB/s, 7 seconds passed
+... 52%, 367040 KB, 47798 KB/s, 7 seconds passed
+... 52%, 367072 KB, 47799 KB/s, 7 seconds passed
+... 52%, 367104 KB, 47800 KB/s, 7 seconds passed
+... 52%, 367136 KB, 47802 KB/s, 7 seconds passed
+... 52%, 367168 KB, 47804 KB/s, 7 seconds passed
+... 52%, 367200 KB, 47806 KB/s, 7 seconds passed
+... 52%, 367232 KB, 47807 KB/s, 7 seconds passed
+... 52%, 367264 KB, 47809 KB/s, 7 seconds passed
+... 52%, 367296 KB, 47811 KB/s, 7 seconds passed
+... 52%, 367328 KB, 47813 KB/s, 7 seconds passed
+... 52%, 367360 KB, 47815 KB/s, 7 seconds passed
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+... 52%, 367456 KB, 47820 KB/s, 7 seconds passed
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+... 52%, 367552 KB, 47825 KB/s, 7 seconds passed
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+... 52%, 367712 KB, 47834 KB/s, 7 seconds passed
+... 52%, 367744 KB, 47837 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 54%, 377792 KB, 48362 KB/s, 7 seconds passed
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+... 54%, 378816 KB, 48437 KB/s, 7 seconds passed
+... 54%, 378848 KB, 48440 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
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+... 54%, 380416 KB, 48259 KB/s, 7 seconds passed
+... 54%, 380448 KB, 48262 KB/s, 7 seconds passed
+... 54%, 380480 KB, 48264 KB/s, 7 seconds passed
+... 54%, 380512 KB, 48267 KB/s, 7 seconds passed
+... 54%, 380544 KB, 48269 KB/s, 7 seconds passed
+... 54%, 380576 KB, 48272 KB/s, 7 seconds passed
+... 54%, 380608 KB, 48274 KB/s, 7 seconds passed
+... 54%, 380640 KB, 48277 KB/s, 7 seconds passed
+... 54%, 380672 KB, 48280 KB/s, 7 seconds passed
+... 54%, 380704 KB, 48282 KB/s, 7 seconds passed
+... 54%, 380736 KB, 48285 KB/s, 7 seconds passed
+... 54%, 380768 KB, 48287 KB/s, 7 seconds passed
+... 54%, 380800 KB, 48290 KB/s, 7 seconds passed
+... 54%, 380832 KB, 48293 KB/s, 7 seconds passed
+... 54%, 380864 KB, 48296 KB/s, 7 seconds passed
+... 54%, 380896 KB, 48299 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
+    ... 54%, 380928 KB, 48217 KB/s, 7 seconds passed
+... 54%, 380960 KB, 48218 KB/s, 7 seconds passed
+... 54%, 380992 KB, 48220 KB/s, 7 seconds passed
+... 54%, 381024 KB, 48222 KB/s, 7 seconds passed
+... 54%, 381056 KB, 48224 KB/s, 7 seconds passed
+... 54%, 381088 KB, 48225 KB/s, 7 seconds passed
+... 54%, 381120 KB, 48227 KB/s, 7 seconds passed
+... 54%, 381152 KB, 48229 KB/s, 7 seconds passed
+... 54%, 381184 KB, 48230 KB/s, 7 seconds passed
+... 54%, 381216 KB, 48232 KB/s, 7 seconds passed
+... 54%, 381248 KB, 48234 KB/s, 7 seconds passed
+... 54%, 381280 KB, 48236 KB/s, 7 seconds passed
+... 54%, 381312 KB, 48237 KB/s, 7 seconds passed
+... 54%, 381344 KB, 48239 KB/s, 7 seconds passed
+... 54%, 381376 KB, 48241 KB/s, 7 seconds passed
+... 54%, 381408 KB, 48243 KB/s, 7 seconds passed
+... 54%, 381440 KB, 48245 KB/s, 7 seconds passed
+... 54%, 381472 KB, 48246 KB/s, 7 seconds passed
+... 54%, 381504 KB, 48248 KB/s, 7 seconds passed
+... 54%, 381536 KB, 48249 KB/s, 7 seconds passed
+... 54%, 381568 KB, 48251 KB/s, 7 seconds passed
+... 54%, 381600 KB, 48253 KB/s, 7 seconds passed
+... 54%, 381632 KB, 48255 KB/s, 7 seconds passed
+... 54%, 381664 KB, 48256 KB/s, 7 seconds passed
+... 54%, 381696 KB, 48258 KB/s, 7 seconds passed
+... 54%, 381728 KB, 48260 KB/s, 7 seconds passed
+... 54%, 381760 KB, 48262 KB/s, 7 seconds passed
+... 54%, 381792 KB, 48264 KB/s, 7 seconds passed
+... 54%, 381824 KB, 48267 KB/s, 7 seconds passed
+... 54%, 381856 KB, 48269 KB/s, 7 seconds passed
+... 54%, 381888 KB, 48271 KB/s, 7 seconds passed
+... 54%, 381920 KB, 48273 KB/s, 7 seconds passed
+... 54%, 381952 KB, 48275 KB/s, 7 seconds passed
+... 54%, 381984 KB, 48277 KB/s, 7 seconds passed
+... 54%, 382016 KB, 48279 KB/s, 7 seconds passed
+... 54%, 382048 KB, 48281 KB/s, 7 seconds passed
+... 54%, 382080 KB, 48283 KB/s, 7 seconds passed
+... 54%, 382112 KB, 48285 KB/s, 7 seconds passed
+... 54%, 382144 KB, 48287 KB/s, 7 seconds passed
+... 54%, 382176 KB, 48289 KB/s, 7 seconds passed
+... 54%, 382208 KB, 48291 KB/s, 7 seconds passed
+... 54%, 382240 KB, 48293 KB/s, 7 seconds passed
+... 54%, 382272 KB, 48295 KB/s, 7 seconds passed
+... 54%, 382304 KB, 48297 KB/s, 7 seconds passed
+... 54%, 382336 KB, 48299 KB/s, 7 seconds passed
+... 54%, 382368 KB, 48301 KB/s, 7 seconds passed
+... 54%, 382400 KB, 48303 KB/s, 7 seconds passed
+... 54%, 382432 KB, 48306 KB/s, 7 seconds passed
+... 54%, 382464 KB, 48307 KB/s, 7 seconds passed
+... 54%, 382496 KB, 48309 KB/s, 7 seconds passed
+... 54%, 382528 KB, 48312 KB/s, 7 seconds passed
+... 55%, 382560 KB, 48314 KB/s, 7 seconds passed
+... 55%, 382592 KB, 48316 KB/s, 7 seconds passed
+... 55%, 382624 KB, 48318 KB/s, 7 seconds passed
+... 55%, 382656 KB, 48320 KB/s, 7 seconds passed
+... 55%, 382688 KB, 48322 KB/s, 7 seconds passed
+... 55%, 382720 KB, 48325 KB/s, 7 seconds passed
+... 55%, 382752 KB, 48328 KB/s, 7 seconds passed
+... 55%, 382784 KB, 48331 KB/s, 7 seconds passed
+... 55%, 382816 KB, 48333 KB/s, 7 seconds passed
+... 55%, 382848 KB, 48336 KB/s, 7 seconds passed
+... 55%, 382880 KB, 48339 KB/s, 7 seconds passed
+... 55%, 382912 KB, 48341 KB/s, 7 seconds passed
+... 55%, 382944 KB, 48344 KB/s, 7 seconds passed
+... 55%, 382976 KB, 48347 KB/s, 7 seconds passed
+... 55%, 383008 KB, 48350 KB/s, 7 seconds passed
+... 55%, 383040 KB, 48352 KB/s, 7 seconds passed
+... 55%, 383072 KB, 48355 KB/s, 7 seconds passed
+... 55%, 383104 KB, 48358 KB/s, 7 seconds passed
+... 55%, 383136 KB, 48361 KB/s, 7 seconds passed
+... 55%, 383168 KB, 48363 KB/s, 7 seconds passed
+... 55%, 383200 KB, 48366 KB/s, 7 seconds passed
+... 55%, 383232 KB, 48369 KB/s, 7 seconds passed
+... 55%, 383264 KB, 48371 KB/s, 7 seconds passed
+... 55%, 383296 KB, 48374 KB/s, 7 seconds passed
+... 55%, 383328 KB, 48377 KB/s, 7 seconds passed
+... 55%, 383360 KB, 48380 KB/s, 7 seconds passed
+... 55%, 383392 KB, 48382 KB/s, 7 seconds passed
+... 55%, 383424 KB, 48385 KB/s, 7 seconds passed
+... 55%, 383456 KB, 48388 KB/s, 7 seconds passed
+... 55%, 383488 KB, 48391 KB/s, 7 seconds passed
+... 55%, 383520 KB, 48393 KB/s, 7 seconds passed
+... 55%, 383552 KB, 48396 KB/s, 7 seconds passed
+... 55%, 383584 KB, 48399 KB/s, 7 seconds passed
+... 55%, 383616 KB, 48402 KB/s, 7 seconds passed
+... 55%, 383648 KB, 48404 KB/s, 7 seconds passed
+... 55%, 383680 KB, 48407 KB/s, 7 seconds passed
+... 55%, 383712 KB, 48410 KB/s, 7 seconds passed
+... 55%, 383744 KB, 48413 KB/s, 7 seconds passed
+... 55%, 383776 KB, 48415 KB/s, 7 seconds passed
+... 55%, 383808 KB, 48418 KB/s, 7 seconds passed
+... 55%, 383840 KB, 48421 KB/s, 7 seconds passed
+... 55%, 383872 KB, 48424 KB/s, 7 seconds passed
+... 55%, 383904 KB, 48426 KB/s, 7 seconds passed
+... 55%, 383936 KB, 48429 KB/s, 7 seconds passed
+... 55%, 383968 KB, 48432 KB/s, 7 seconds passed
+... 55%, 384000 KB, 48360 KB/s, 7 seconds passed
+... 55%, 384032 KB, 48362 KB/s, 7 seconds passed
+... 55%, 384064 KB, 48363 KB/s, 7 seconds passed
+... 55%, 384096 KB, 48365 KB/s, 7 seconds passed
+... 55%, 384128 KB, 48366 KB/s, 7 seconds passed
+... 55%, 384160 KB, 48368 KB/s, 7 seconds passed
+... 55%, 384192 KB, 48370 KB/s, 7 seconds passed
+... 55%, 384224 KB, 48368 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
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+... 55%, 384288 KB, 48369 KB/s, 7 seconds passed
+... 55%, 384320 KB, 48371 KB/s, 7 seconds passed
+... 55%, 384352 KB, 48371 KB/s, 7 seconds passed
+... 55%, 384384 KB, 48371 KB/s, 7 seconds passed
+... 55%, 384416 KB, 48371 KB/s, 7 seconds passed
+... 55%, 384448 KB, 48372 KB/s, 7 seconds passed
+... 55%, 384480 KB, 48374 KB/s, 7 seconds passed
+... 55%, 384512 KB, 48375 KB/s, 7 seconds passed
+... 55%, 384544 KB, 48377 KB/s, 7 seconds passed
+... 55%, 384576 KB, 48379 KB/s, 7 seconds passed
+... 55%, 384608 KB, 48381 KB/s, 7 seconds passed
+... 55%, 384640 KB, 48382 KB/s, 7 seconds passed
+... 55%, 384672 KB, 48384 KB/s, 7 seconds passed
+... 55%, 384704 KB, 48386 KB/s, 7 seconds passed
+... 55%, 384736 KB, 48387 KB/s, 7 seconds passed
+... 55%, 384768 KB, 48389 KB/s, 7 seconds passed
+... 55%, 384800 KB, 48391 KB/s, 7 seconds passed
+... 55%, 384832 KB, 48393 KB/s, 7 seconds passed
+... 55%, 384864 KB, 48395 KB/s, 7 seconds passed
+... 55%, 384896 KB, 48396 KB/s, 7 seconds passed
+... 55%, 384928 KB, 48398 KB/s, 7 seconds passed
+... 55%, 384960 KB, 48400 KB/s, 7 seconds passed
+... 55%, 384992 KB, 48402 KB/s, 7 seconds passed
+... 55%, 385024 KB, 48404 KB/s, 7 seconds passed
+... 55%, 385056 KB, 48406 KB/s, 7 seconds passed
+... 55%, 385088 KB, 48408 KB/s, 7 seconds passed
+... 55%, 385120 KB, 48410 KB/s, 7 seconds passed
+... 55%, 385152 KB, 48411 KB/s, 7 seconds passed
+... 55%, 385184 KB, 48413 KB/s, 7 seconds passed
+... 55%, 385216 KB, 48415 KB/s, 7 seconds passed
+... 55%, 385248 KB, 48417 KB/s, 7 seconds passed
+... 55%, 385280 KB, 48419 KB/s, 7 seconds passed
+... 55%, 385312 KB, 48421 KB/s, 7 seconds passed
+... 55%, 385344 KB, 48423 KB/s, 7 seconds passed
+... 55%, 385376 KB, 48425 KB/s, 7 seconds passed
+... 55%, 385408 KB, 48426 KB/s, 7 seconds passed
+... 55%, 385440 KB, 48428 KB/s, 7 seconds passed
+... 55%, 385472 KB, 48430 KB/s, 7 seconds passed
+... 55%, 385504 KB, 48433 KB/s, 7 seconds passed
+... 55%, 385536 KB, 48436 KB/s, 7 seconds passed
+... 55%, 385568 KB, 48438 KB/s, 7 seconds passed
+... 55%, 385600 KB, 48441 KB/s, 7 seconds passed
+... 55%, 385632 KB, 48443 KB/s, 7 seconds passed
+... 55%, 385664 KB, 48446 KB/s, 7 seconds passed
+... 55%, 385696 KB, 48449 KB/s, 7 seconds passed
+... 55%, 385728 KB, 48451 KB/s, 7 seconds passed
+... 55%, 385760 KB, 48454 KB/s, 7 seconds passed
+... 55%, 385792 KB, 48456 KB/s, 7 seconds passed
+... 55%, 385824 KB, 48459 KB/s, 7 seconds passed
+... 55%, 385856 KB, 48461 KB/s, 7 seconds passed
+... 55%, 385888 KB, 48464 KB/s, 7 seconds passed
+... 55%, 385920 KB, 48467 KB/s, 7 seconds passed
+... 55%, 385952 KB, 48469 KB/s, 7 seconds passed
+... 55%, 385984 KB, 48472 KB/s, 7 seconds passed
+... 55%, 386016 KB, 48474 KB/s, 7 seconds passed
+... 55%, 386048 KB, 48477 KB/s, 7 seconds passed
+... 55%, 386080 KB, 48480 KB/s, 7 seconds passed
+... 55%, 386112 KB, 48482 KB/s, 7 seconds passed
+... 55%, 386144 KB, 48485 KB/s, 7 seconds passed
+... 55%, 386176 KB, 48487 KB/s, 7 seconds passed
+... 55%, 386208 KB, 48490 KB/s, 7 seconds passed
+... 55%, 386240 KB, 48492 KB/s, 7 seconds passed
+... 55%, 386272 KB, 48495 KB/s, 7 seconds passed
+... 55%, 386304 KB, 48498 KB/s, 7 seconds passed
+... 55%, 386336 KB, 48500 KB/s, 7 seconds passed
+... 55%, 386368 KB, 48503 KB/s, 7 seconds passed
+... 55%, 386400 KB, 48506 KB/s, 7 seconds passed
+... 55%, 386432 KB, 48508 KB/s, 7 seconds passed
+... 55%, 386464 KB, 48511 KB/s, 7 seconds passed
+... 55%, 386496 KB, 48513 KB/s, 7 seconds passed
+... 55%, 386528 KB, 48516 KB/s, 7 seconds passed
+... 55%, 386560 KB, 48519 KB/s, 7 seconds passed
+... 55%, 386592 KB, 48521 KB/s, 7 seconds passed
+... 55%, 386624 KB, 48524 KB/s, 7 seconds passed
+... 55%, 386656 KB, 48526 KB/s, 7 seconds passed
+... 55%, 386688 KB, 48529 KB/s, 7 seconds passed
+... 55%, 386720 KB, 48532 KB/s, 7 seconds passed
+... 55%, 386752 KB, 48534 KB/s, 7 seconds passed
+... 55%, 386784 KB, 48537 KB/s, 7 seconds passed
+... 55%, 386816 KB, 48540 KB/s, 7 seconds passed
+... 55%, 386848 KB, 48542 KB/s, 7 seconds passed
+... 55%, 386880 KB, 48545 KB/s, 7 seconds passed
+... 55%, 386912 KB, 48548 KB/s, 7 seconds passed
+... 55%, 386944 KB, 48551 KB/s, 7 seconds passed
+... 55%, 386976 KB, 48554 KB/s, 7 seconds passed
+... 55%, 387008 KB, 48557 KB/s, 7 seconds passed
+... 55%, 387040 KB, 48560 KB/s, 7 seconds passed
+... 55%, 387072 KB, 48563 KB/s, 7 seconds passed
+... 55%, 387104 KB, 48566 KB/s, 7 seconds passed
+... 55%, 387136 KB, 48569 KB/s, 7 seconds passed
+... 55%, 387168 KB, 48572 KB/s, 7 seconds passed
+... 55%, 387200 KB, 48575 KB/s, 7 seconds passed
+... 55%, 387232 KB, 48578 KB/s, 7 seconds passed
+... 55%, 387264 KB, 48581 KB/s, 7 seconds passed
+... 55%, 387296 KB, 48584 KB/s, 7 seconds passed
+... 55%, 387328 KB, 48588 KB/s, 7 seconds passed
+... 55%, 387360 KB, 48591 KB/s, 7 seconds passed
+... 55%, 387392 KB, 48594 KB/s, 7 seconds passed
+... 55%, 387424 KB, 48597 KB/s, 7 seconds passed
+... 55%, 387456 KB, 48600 KB/s, 7 seconds passed
+... 55%, 387488 KB, 48603 KB/s, 7 seconds passed
+... 55%, 387520 KB, 48606 KB/s, 7 seconds passed
+... 55%, 387552 KB, 48608 KB/s, 7 seconds passed
+... 55%, 387584 KB, 48611 KB/s, 7 seconds passed
+... 55%, 387616 KB, 48613 KB/s, 7 seconds passed
+... 55%, 387648 KB, 48615 KB/s, 7 seconds passed
+... 55%, 387680 KB, 48617 KB/s, 7 seconds passed
+... 55%, 387712 KB, 48619 KB/s, 7 seconds passed
+... 55%, 387744 KB, 48621 KB/s, 7 seconds passed
+... 55%, 387776 KB, 48624 KB/s, 7 seconds passed
+... 55%, 387808 KB, 48627 KB/s, 7 seconds passed
+... 55%, 387840 KB, 48630 KB/s, 7 seconds passed
+... 55%, 387872 KB, 48631 KB/s, 7 seconds passed
+... 55%, 387904 KB, 48634 KB/s, 7 seconds passed
+... 55%, 387936 KB, 48634 KB/s, 7 seconds passed
+... 55%, 387968 KB, 48636 KB/s, 7 seconds passed
+... 55%, 388000 KB, 48639 KB/s, 7 seconds passed
+... 55%, 388032 KB, 48640 KB/s, 7 seconds passed
+... 55%, 388064 KB, 48641 KB/s, 7 seconds passed
+... 55%, 388096 KB, 48643 KB/s, 7 seconds passed
+... 55%, 388128 KB, 48645 KB/s, 7 seconds passed
+... 55%, 388160 KB, 48648 KB/s, 7 seconds passed
+... 55%, 388192 KB, 48651 KB/s, 7 seconds passed
+... 55%, 388224 KB, 48654 KB/s, 7 seconds passed
+... 55%, 388256 KB, 48657 KB/s, 7 seconds passed
+... 55%, 388288 KB, 48659 KB/s, 7 seconds passed
+... 55%, 388320 KB, 48661 KB/s, 7 seconds passed
+... 55%, 388352 KB, 48663 KB/s, 7 seconds passed
+... 55%, 388384 KB, 48666 KB/s, 7 seconds passed
+... 55%, 388416 KB, 48668 KB/s, 7 seconds passed
+... 55%, 388448 KB, 48670 KB/s, 7 seconds passed
+... 55%, 388480 KB, 48673 KB/s, 7 seconds passed
+... 55%, 388512 KB, 48675 KB/s, 7 seconds passed
+... 55%, 388544 KB, 48678 KB/s, 7 seconds passed
+... 55%, 388576 KB, 48680 KB/s, 7 seconds passed
+... 55%, 388608 KB, 48683 KB/s, 7 seconds passed
+... 55%, 388640 KB, 48685 KB/s, 7 seconds passed
+... 55%, 388672 KB, 48687 KB/s, 7 seconds passed
+... 55%, 388704 KB, 48690 KB/s, 7 seconds passed
+... 55%, 388736 KB, 48691 KB/s, 7 seconds passed
+... 55%, 388768 KB, 48692 KB/s, 7 seconds passed
+... 55%, 388800 KB, 48694 KB/s, 7 seconds passed
+... 55%, 388832 KB, 48697 KB/s, 7 seconds passed
+... 55%, 388864 KB, 48700 KB/s, 7 seconds passed
+... 55%, 388896 KB, 48703 KB/s, 7 seconds passed
+... 55%, 388928 KB, 48706 KB/s, 7 seconds passed
+... 55%, 388960 KB, 48708 KB/s, 7 seconds passed
+... 55%, 388992 KB, 48709 KB/s, 7 seconds passed
+... 55%, 389024 KB, 48711 KB/s, 7 seconds passed
+... 55%, 389056 KB, 48713 KB/s, 7 seconds passed
+... 55%, 389088 KB, 48716 KB/s, 7 seconds passed
+
+.. parsed-literal::
+
+    ... 55%, 389120 KB, 48212 KB/s, 8 seconds passed
+... 55%, 389152 KB, 48213 KB/s, 8 seconds passed
+... 55%, 389184 KB, 48215 KB/s, 8 seconds passed
+... 55%, 389216 KB, 48217 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 55%, 389248 KB, 48057 KB/s, 8 seconds passed
+... 55%, 389280 KB, 48058 KB/s, 8 seconds passed
+... 55%, 389312 KB, 48060 KB/s, 8 seconds passed
+... 55%, 389344 KB, 48061 KB/s, 8 seconds passed
+... 55%, 389376 KB, 48063 KB/s, 8 seconds passed
+... 55%, 389408 KB, 48064 KB/s, 8 seconds passed
+... 55%, 389440 KB, 48066 KB/s, 8 seconds passed
+... 55%, 389472 KB, 48067 KB/s, 8 seconds passed
+... 56%, 389504 KB, 48069 KB/s, 8 seconds passed
+... 56%, 389536 KB, 48070 KB/s, 8 seconds passed
+... 56%, 389568 KB, 48072 KB/s, 8 seconds passed
+... 56%, 389600 KB, 48074 KB/s, 8 seconds passed
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+... 56%, 389696 KB, 48079 KB/s, 8 seconds passed
+... 56%, 389728 KB, 48080 KB/s, 8 seconds passed
+... 56%, 389760 KB, 48082 KB/s, 8 seconds passed
+... 56%, 389792 KB, 48084 KB/s, 8 seconds passed
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+... 56%, 389920 KB, 48091 KB/s, 8 seconds passed
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+... 56%, 389984 KB, 48094 KB/s, 8 seconds passed
+... 56%, 390016 KB, 48095 KB/s, 8 seconds passed
+... 56%, 390048 KB, 48097 KB/s, 8 seconds passed
+... 56%, 390080 KB, 48099 KB/s, 8 seconds passed
+... 56%, 390112 KB, 48101 KB/s, 8 seconds passed
+... 56%, 390144 KB, 48103 KB/s, 8 seconds passed
+... 56%, 390176 KB, 48106 KB/s, 8 seconds passed
+... 56%, 390208 KB, 48109 KB/s, 8 seconds passed
+... 56%, 390240 KB, 48111 KB/s, 8 seconds passed
+... 56%, 390272 KB, 48114 KB/s, 8 seconds passed
+... 56%, 390304 KB, 48117 KB/s, 8 seconds passed
+... 56%, 390336 KB, 48085 KB/s, 8 seconds passed
+... 56%, 390368 KB, 48087 KB/s, 8 seconds passed
+... 56%, 390400 KB, 48089 KB/s, 8 seconds passed
+... 56%, 390432 KB, 48092 KB/s, 8 seconds passed
+... 56%, 390464 KB, 48094 KB/s, 8 seconds passed
+... 56%, 390496 KB, 48097 KB/s, 8 seconds passed
+... 56%, 390528 KB, 48099 KB/s, 8 seconds passed
+... 56%, 390560 KB, 48102 KB/s, 8 seconds passed
+... 56%, 390592 KB, 48104 KB/s, 8 seconds passed
+... 56%, 390624 KB, 48107 KB/s, 8 seconds passed
+... 56%, 390656 KB, 48109 KB/s, 8 seconds passed
+... 56%, 390688 KB, 48111 KB/s, 8 seconds passed
+... 56%, 390720 KB, 48113 KB/s, 8 seconds passed
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+... 56%, 390784 KB, 48116 KB/s, 8 seconds passed
+... 56%, 390816 KB, 48118 KB/s, 8 seconds passed
+... 56%, 390848 KB, 48120 KB/s, 8 seconds passed
+... 56%, 390880 KB, 48123 KB/s, 8 seconds passed
+... 56%, 390912 KB, 48125 KB/s, 8 seconds passed
+... 56%, 390944 KB, 48126 KB/s, 8 seconds passed
+... 56%, 390976 KB, 48128 KB/s, 8 seconds passed
+... 56%, 391008 KB, 48130 KB/s, 8 seconds passed
+... 56%, 391040 KB, 48132 KB/s, 8 seconds passed
+... 56%, 391072 KB, 48134 KB/s, 8 seconds passed
+... 56%, 391104 KB, 48137 KB/s, 8 seconds passed
+... 56%, 391136 KB, 48139 KB/s, 8 seconds passed
+... 56%, 391168 KB, 48141 KB/s, 8 seconds passed
+... 56%, 391200 KB, 48143 KB/s, 8 seconds passed
+... 56%, 391232 KB, 48145 KB/s, 8 seconds passed
+... 56%, 391264 KB, 48147 KB/s, 8 seconds passed
+... 56%, 391296 KB, 48149 KB/s, 8 seconds passed
+... 56%, 391328 KB, 48151 KB/s, 8 seconds passed
+... 56%, 391360 KB, 48153 KB/s, 8 seconds passed
+... 56%, 391392 KB, 48155 KB/s, 8 seconds passed
+... 56%, 391424 KB, 48157 KB/s, 8 seconds passed
+... 56%, 391456 KB, 48159 KB/s, 8 seconds passed
+... 56%, 391488 KB, 48161 KB/s, 8 seconds passed
+... 56%, 391520 KB, 48163 KB/s, 8 seconds passed
+... 56%, 391552 KB, 48165 KB/s, 8 seconds passed
+... 56%, 391584 KB, 48167 KB/s, 8 seconds passed
+... 56%, 391616 KB, 48169 KB/s, 8 seconds passed
+... 56%, 391648 KB, 48171 KB/s, 8 seconds passed
+... 56%, 391680 KB, 48173 KB/s, 8 seconds passed
+... 56%, 391712 KB, 48175 KB/s, 8 seconds passed
+... 56%, 391744 KB, 48177 KB/s, 8 seconds passed
+... 56%, 391776 KB, 48180 KB/s, 8 seconds passed
+... 56%, 391808 KB, 48182 KB/s, 8 seconds passed
+... 56%, 391840 KB, 48185 KB/s, 8 seconds passed
+... 56%, 391872 KB, 48188 KB/s, 8 seconds passed
+... 56%, 391904 KB, 48190 KB/s, 8 seconds passed
+... 56%, 391936 KB, 48193 KB/s, 8 seconds passed
+... 56%, 391968 KB, 48196 KB/s, 8 seconds passed
+... 56%, 392000 KB, 48198 KB/s, 8 seconds passed
+... 56%, 392032 KB, 48201 KB/s, 8 seconds passed
+... 56%, 392064 KB, 48204 KB/s, 8 seconds passed
+... 56%, 392096 KB, 48206 KB/s, 8 seconds passed
+... 56%, 392128 KB, 48209 KB/s, 8 seconds passed
+... 56%, 392160 KB, 48212 KB/s, 8 seconds passed
+... 56%, 392192 KB, 48214 KB/s, 8 seconds passed
+... 56%, 392224 KB, 48217 KB/s, 8 seconds passed
+... 56%, 392256 KB, 48220 KB/s, 8 seconds passed
+... 56%, 392288 KB, 48222 KB/s, 8 seconds passed
+... 56%, 392320 KB, 48225 KB/s, 8 seconds passed
+... 56%, 392352 KB, 48228 KB/s, 8 seconds passed
+... 56%, 392384 KB, 48230 KB/s, 8 seconds passed
+... 56%, 392416 KB, 48233 KB/s, 8 seconds passed
+... 56%, 392448 KB, 48236 KB/s, 8 seconds passed
+... 56%, 392480 KB, 48239 KB/s, 8 seconds passed
+... 56%, 392512 KB, 48241 KB/s, 8 seconds passed
+... 56%, 392544 KB, 48243 KB/s, 8 seconds passed
+... 56%, 392576 KB, 48246 KB/s, 8 seconds passed
+... 56%, 392608 KB, 48248 KB/s, 8 seconds passed
+... 56%, 392640 KB, 48251 KB/s, 8 seconds passed
+... 56%, 392672 KB, 48253 KB/s, 8 seconds passed
+... 56%, 392704 KB, 48255 KB/s, 8 seconds passed
+... 56%, 392736 KB, 48257 KB/s, 8 seconds passed
+... 56%, 392768 KB, 48259 KB/s, 8 seconds passed
+... 56%, 392800 KB, 48262 KB/s, 8 seconds passed
+... 56%, 392832 KB, 48264 KB/s, 8 seconds passed
+... 56%, 392864 KB, 48266 KB/s, 8 seconds passed
+... 56%, 392896 KB, 48269 KB/s, 8 seconds passed
+... 56%, 392928 KB, 48271 KB/s, 8 seconds passed
+... 56%, 392960 KB, 48273 KB/s, 8 seconds passed
+... 56%, 392992 KB, 48276 KB/s, 8 seconds passed
+... 56%, 393024 KB, 48278 KB/s, 8 seconds passed
+... 56%, 393056 KB, 48280 KB/s, 8 seconds passed
+... 56%, 393088 KB, 48283 KB/s, 8 seconds passed
+... 56%, 393120 KB, 48285 KB/s, 8 seconds passed
+... 56%, 393152 KB, 48287 KB/s, 8 seconds passed
+... 56%, 393184 KB, 48290 KB/s, 8 seconds passed
+... 56%, 393216 KB, 48292 KB/s, 8 seconds passed
+... 56%, 393248 KB, 48294 KB/s, 8 seconds passed
+... 56%, 393280 KB, 48297 KB/s, 8 seconds passed
+... 56%, 393312 KB, 48299 KB/s, 8 seconds passed
+... 56%, 393344 KB, 48301 KB/s, 8 seconds passed
+... 56%, 393376 KB, 48303 KB/s, 8 seconds passed
+... 56%, 393408 KB, 48305 KB/s, 8 seconds passed
+... 56%, 393440 KB, 48308 KB/s, 8 seconds passed
+... 56%, 393472 KB, 48310 KB/s, 8 seconds passed
+... 56%, 393504 KB, 48313 KB/s, 8 seconds passed
+... 56%, 393536 KB, 48315 KB/s, 8 seconds passed
+... 56%, 393568 KB, 48317 KB/s, 8 seconds passed
+... 56%, 393600 KB, 48319 KB/s, 8 seconds passed
+... 56%, 393632 KB, 48322 KB/s, 8 seconds passed
+... 56%, 393664 KB, 48324 KB/s, 8 seconds passed
+... 56%, 393696 KB, 48326 KB/s, 8 seconds passed
+... 56%, 393728 KB, 48327 KB/s, 8 seconds passed
+... 56%, 393760 KB, 48330 KB/s, 8 seconds passed
+... 56%, 393792 KB, 48333 KB/s, 8 seconds passed
+... 56%, 393824 KB, 48335 KB/s, 8 seconds passed
+... 56%, 393856 KB, 48338 KB/s, 8 seconds passed
+... 56%, 393888 KB, 48340 KB/s, 8 seconds passed
+... 56%, 393920 KB, 48342 KB/s, 8 seconds passed
+... 56%, 393952 KB, 48344 KB/s, 8 seconds passed
+... 56%, 393984 KB, 48346 KB/s, 8 seconds passed
+... 56%, 394016 KB, 48349 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 56%, 394048 KB, 48351 KB/s, 8 seconds passed
+... 56%, 394080 KB, 48353 KB/s, 8 seconds passed
+... 56%, 394112 KB, 48356 KB/s, 8 seconds passed
+... 56%, 394144 KB, 48358 KB/s, 8 seconds passed
+... 56%, 394176 KB, 48360 KB/s, 8 seconds passed
+... 56%, 394208 KB, 48362 KB/s, 8 seconds passed
+... 56%, 394240 KB, 48176 KB/s, 8 seconds passed
+... 56%, 394272 KB, 48177 KB/s, 8 seconds passed
+... 56%, 394304 KB, 48179 KB/s, 8 seconds passed
+... 56%, 394336 KB, 48180 KB/s, 8 seconds passed
+... 56%, 394368 KB, 48182 KB/s, 8 seconds passed
+... 56%, 394400 KB, 48184 KB/s, 8 seconds passed
+... 56%, 394432 KB, 48185 KB/s, 8 seconds passed
+... 56%, 394464 KB, 48187 KB/s, 8 seconds passed
+... 56%, 394496 KB, 48189 KB/s, 8 seconds passed
+... 56%, 394528 KB, 48190 KB/s, 8 seconds passed
+... 56%, 394560 KB, 48192 KB/s, 8 seconds passed
+... 56%, 394592 KB, 48194 KB/s, 8 seconds passed
+... 56%, 394624 KB, 48195 KB/s, 8 seconds passed
+... 56%, 394656 KB, 48197 KB/s, 8 seconds passed
+... 56%, 394688 KB, 48199 KB/s, 8 seconds passed
+... 56%, 394720 KB, 48201 KB/s, 8 seconds passed
+... 56%, 394752 KB, 48202 KB/s, 8 seconds passed
+... 56%, 394784 KB, 48204 KB/s, 8 seconds passed
+... 56%, 394816 KB, 48206 KB/s, 8 seconds passed
+... 56%, 394848 KB, 48208 KB/s, 8 seconds passed
+... 56%, 394880 KB, 48209 KB/s, 8 seconds passed
+... 56%, 394912 KB, 48211 KB/s, 8 seconds passed
+... 56%, 394944 KB, 48213 KB/s, 8 seconds passed
+... 56%, 394976 KB, 48215 KB/s, 8 seconds passed
+... 56%, 395008 KB, 48217 KB/s, 8 seconds passed
+... 56%, 395040 KB, 48219 KB/s, 8 seconds passed
+... 56%, 395072 KB, 48221 KB/s, 8 seconds passed
+... 56%, 395104 KB, 48223 KB/s, 8 seconds passed
+... 56%, 395136 KB, 48225 KB/s, 8 seconds passed
+... 56%, 395168 KB, 48227 KB/s, 8 seconds passed
+... 56%, 395200 KB, 48229 KB/s, 8 seconds passed
+... 56%, 395232 KB, 48231 KB/s, 8 seconds passed
+... 56%, 395264 KB, 48233 KB/s, 8 seconds passed
+... 56%, 395296 KB, 48235 KB/s, 8 seconds passed
+... 56%, 395328 KB, 48237 KB/s, 8 seconds passed
+... 56%, 395360 KB, 48239 KB/s, 8 seconds passed
+... 56%, 395392 KB, 48241 KB/s, 8 seconds passed
+... 56%, 395424 KB, 48243 KB/s, 8 seconds passed
+... 56%, 395456 KB, 48245 KB/s, 8 seconds passed
+... 56%, 395488 KB, 48247 KB/s, 8 seconds passed
+... 56%, 395520 KB, 48249 KB/s, 8 seconds passed
+... 56%, 395552 KB, 48251 KB/s, 8 seconds passed
+... 56%, 395584 KB, 48253 KB/s, 8 seconds passed
+... 56%, 395616 KB, 48255 KB/s, 8 seconds passed
+... 56%, 395648 KB, 48257 KB/s, 8 seconds passed
+... 56%, 395680 KB, 48259 KB/s, 8 seconds passed
+... 56%, 395712 KB, 48261 KB/s, 8 seconds passed
+... 56%, 395744 KB, 48263 KB/s, 8 seconds passed
+... 56%, 395776 KB, 48265 KB/s, 8 seconds passed
+... 56%, 395808 KB, 48267 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 56%, 395840 KB, 48269 KB/s, 8 seconds passed
+... 56%, 395872 KB, 48271 KB/s, 8 seconds passed
+... 56%, 395904 KB, 48273 KB/s, 8 seconds passed
+... 56%, 395936 KB, 48275 KB/s, 8 seconds passed
+... 56%, 395968 KB, 48278 KB/s, 8 seconds passed
+... 56%, 396000 KB, 48280 KB/s, 8 seconds passed
+... 56%, 396032 KB, 48283 KB/s, 8 seconds passed
+... 56%, 396064 KB, 48286 KB/s, 8 seconds passed
+... 56%, 396096 KB, 48288 KB/s, 8 seconds passed
+... 56%, 396128 KB, 48291 KB/s, 8 seconds passed
+... 56%, 396160 KB, 48293 KB/s, 8 seconds passed
+... 56%, 396192 KB, 48296 KB/s, 8 seconds passed
+... 56%, 396224 KB, 48299 KB/s, 8 seconds passed
+... 56%, 396256 KB, 48301 KB/s, 8 seconds passed
+... 56%, 396288 KB, 48304 KB/s, 8 seconds passed
+... 56%, 396320 KB, 48307 KB/s, 8 seconds passed
+... 56%, 396352 KB, 48309 KB/s, 8 seconds passed
+... 56%, 396384 KB, 48312 KB/s, 8 seconds passed
+... 56%, 396416 KB, 48314 KB/s, 8 seconds passed
+... 57%, 396448 KB, 48317 KB/s, 8 seconds passed
+... 57%, 396480 KB, 48320 KB/s, 8 seconds passed
+... 57%, 396512 KB, 48322 KB/s, 8 seconds passed
+... 57%, 396544 KB, 48325 KB/s, 8 seconds passed
+... 57%, 396576 KB, 48328 KB/s, 8 seconds passed
+... 57%, 396608 KB, 48330 KB/s, 8 seconds passed
+... 57%, 396640 KB, 48333 KB/s, 8 seconds passed
+... 57%, 396672 KB, 48336 KB/s, 8 seconds passed
+... 57%, 396704 KB, 48338 KB/s, 8 seconds passed
+... 57%, 396736 KB, 48341 KB/s, 8 seconds passed
+... 57%, 396768 KB, 48344 KB/s, 8 seconds passed
+... 57%, 396800 KB, 48346 KB/s, 8 seconds passed
+... 57%, 396832 KB, 48349 KB/s, 8 seconds passed
+... 57%, 396864 KB, 48352 KB/s, 8 seconds passed
+... 57%, 396896 KB, 48354 KB/s, 8 seconds passed
+... 57%, 396928 KB, 48357 KB/s, 8 seconds passed
+... 57%, 396960 KB, 48360 KB/s, 8 seconds passed
+... 57%, 396992 KB, 48363 KB/s, 8 seconds passed
+... 57%, 397024 KB, 48365 KB/s, 8 seconds passed
+... 57%, 397056 KB, 48368 KB/s, 8 seconds passed
+... 57%, 397088 KB, 48371 KB/s, 8 seconds passed
+... 57%, 397120 KB, 48374 KB/s, 8 seconds passed
+... 57%, 397152 KB, 48376 KB/s, 8 seconds passed
+... 57%, 397184 KB, 48378 KB/s, 8 seconds passed
+... 57%, 397216 KB, 48380 KB/s, 8 seconds passed
+... 57%, 397248 KB, 48382 KB/s, 8 seconds passed
+... 57%, 397280 KB, 48384 KB/s, 8 seconds passed
+... 57%, 397312 KB, 48387 KB/s, 8 seconds passed
+... 57%, 397344 KB, 48389 KB/s, 8 seconds passed
+... 57%, 397376 KB, 48391 KB/s, 8 seconds passed
+... 57%, 397408 KB, 48394 KB/s, 8 seconds passed
+... 57%, 397440 KB, 48396 KB/s, 8 seconds passed
+... 57%, 397472 KB, 48398 KB/s, 8 seconds passed
+... 57%, 397504 KB, 48400 KB/s, 8 seconds passed
+... 57%, 397536 KB, 48402 KB/s, 8 seconds passed
+... 57%, 397568 KB, 48405 KB/s, 8 seconds passed
+... 57%, 397600 KB, 48407 KB/s, 8 seconds passed
+... 57%, 397632 KB, 48409 KB/s, 8 seconds passed
+... 57%, 397664 KB, 48412 KB/s, 8 seconds passed
+... 57%, 397696 KB, 48414 KB/s, 8 seconds passed
+... 57%, 397728 KB, 48416 KB/s, 8 seconds passed
+... 57%, 397760 KB, 48418 KB/s, 8 seconds passed
+... 57%, 397792 KB, 48420 KB/s, 8 seconds passed
+... 57%, 397824 KB, 48421 KB/s, 8 seconds passed
+... 57%, 397856 KB, 48423 KB/s, 8 seconds passed
+... 57%, 397888 KB, 48425 KB/s, 8 seconds passed
+... 57%, 397920 KB, 48428 KB/s, 8 seconds passed
+... 57%, 397952 KB, 48431 KB/s, 8 seconds passed
+... 57%, 397984 KB, 48434 KB/s, 8 seconds passed
+... 57%, 398016 KB, 48437 KB/s, 8 seconds passed
+... 57%, 398048 KB, 48439 KB/s, 8 seconds passed
+... 57%, 398080 KB, 48440 KB/s, 8 seconds passed
+... 57%, 398112 KB, 48442 KB/s, 8 seconds passed
+... 57%, 398144 KB, 48445 KB/s, 8 seconds passed
+... 57%, 398176 KB, 48448 KB/s, 8 seconds passed
+... 57%, 398208 KB, 48450 KB/s, 8 seconds passed
+... 57%, 398240 KB, 48452 KB/s, 8 seconds passed
+... 57%, 398272 KB, 48454 KB/s, 8 seconds passed
+... 57%, 398304 KB, 48457 KB/s, 8 seconds passed
+... 57%, 398336 KB, 48459 KB/s, 8 seconds passed
+... 57%, 398368 KB, 48461 KB/s, 8 seconds passed
+... 57%, 398400 KB, 48463 KB/s, 8 seconds passed
+... 57%, 398432 KB, 48466 KB/s, 8 seconds passed
+... 57%, 398464 KB, 48468 KB/s, 8 seconds passed
+... 57%, 398496 KB, 48470 KB/s, 8 seconds passed
+... 57%, 398528 KB, 48472 KB/s, 8 seconds passed
+... 57%, 398560 KB, 48475 KB/s, 8 seconds passed
+... 57%, 398592 KB, 48477 KB/s, 8 seconds passed
+... 57%, 398624 KB, 48479 KB/s, 8 seconds passed
+... 57%, 398656 KB, 48481 KB/s, 8 seconds passed
+... 57%, 398688 KB, 48483 KB/s, 8 seconds passed
+... 57%, 398720 KB, 48486 KB/s, 8 seconds passed
+... 57%, 398752 KB, 48487 KB/s, 8 seconds passed
+... 57%, 398784 KB, 48488 KB/s, 8 seconds passed
+... 57%, 398816 KB, 48491 KB/s, 8 seconds passed
+... 57%, 398848 KB, 48494 KB/s, 8 seconds passed
+... 57%, 398880 KB, 48496 KB/s, 8 seconds passed
+... 57%, 398912 KB, 48499 KB/s, 8 seconds passed
+... 57%, 398944 KB, 48502 KB/s, 8 seconds passed
+... 57%, 398976 KB, 48504 KB/s, 8 seconds passed
+... 57%, 399008 KB, 48506 KB/s, 8 seconds passed
+... 57%, 399040 KB, 48508 KB/s, 8 seconds passed
+... 57%, 399072 KB, 48510 KB/s, 8 seconds passed
+... 57%, 399104 KB, 48513 KB/s, 8 seconds passed
+... 57%, 399136 KB, 48515 KB/s, 8 seconds passed
+... 57%, 399168 KB, 48517 KB/s, 8 seconds passed
+... 57%, 399200 KB, 48519 KB/s, 8 seconds passed
+... 57%, 399232 KB, 48522 KB/s, 8 seconds passed
+... 57%, 399264 KB, 48524 KB/s, 8 seconds passed
+... 57%, 399296 KB, 48526 KB/s, 8 seconds passed
+... 57%, 399328 KB, 48528 KB/s, 8 seconds passed
+... 57%, 399360 KB, 48412 KB/s, 8 seconds passed
+... 57%, 399392 KB, 48414 KB/s, 8 seconds passed
+... 57%, 399424 KB, 48415 KB/s, 8 seconds passed
+... 57%, 399456 KB, 48417 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 57%, 399488 KB, 48409 KB/s, 8 seconds passed
+... 57%, 399520 KB, 48410 KB/s, 8 seconds passed
+... 57%, 399552 KB, 48411 KB/s, 8 seconds passed
+... 57%, 399584 KB, 48413 KB/s, 8 seconds passed
+... 57%, 399616 KB, 48414 KB/s, 8 seconds passed
+... 57%, 399648 KB, 48416 KB/s, 8 seconds passed
+... 57%, 399680 KB, 48418 KB/s, 8 seconds passed
+... 57%, 399712 KB, 48419 KB/s, 8 seconds passed
+... 57%, 399744 KB, 48421 KB/s, 8 seconds passed
+... 57%, 399776 KB, 48422 KB/s, 8 seconds passed
+... 57%, 399808 KB, 48424 KB/s, 8 seconds passed
+... 57%, 399840 KB, 48426 KB/s, 8 seconds passed
+... 57%, 399872 KB, 48427 KB/s, 8 seconds passed
+... 57%, 399904 KB, 48429 KB/s, 8 seconds passed
+... 57%, 399936 KB, 48431 KB/s, 8 seconds passed
+... 57%, 399968 KB, 48433 KB/s, 8 seconds passed
+... 57%, 400000 KB, 48434 KB/s, 8 seconds passed
+... 57%, 400032 KB, 48436 KB/s, 8 seconds passed
+... 57%, 400064 KB, 48437 KB/s, 8 seconds passed
+... 57%, 400096 KB, 48439 KB/s, 8 seconds passed
+... 57%, 400128 KB, 48441 KB/s, 8 seconds passed
+... 57%, 400160 KB, 48443 KB/s, 8 seconds passed
+... 57%, 400192 KB, 48444 KB/s, 8 seconds passed
+... 57%, 400224 KB, 48446 KB/s, 8 seconds passed
+... 57%, 400256 KB, 48448 KB/s, 8 seconds passed
+... 57%, 400288 KB, 48449 KB/s, 8 seconds passed
+... 57%, 400320 KB, 48451 KB/s, 8 seconds passed
+... 57%, 400352 KB, 48453 KB/s, 8 seconds passed
+... 57%, 400384 KB, 48455 KB/s, 8 seconds passed
+... 57%, 400416 KB, 48457 KB/s, 8 seconds passed
+... 57%, 400448 KB, 48460 KB/s, 8 seconds passed
+... 57%, 400480 KB, 48462 KB/s, 8 seconds passed
+... 57%, 400512 KB, 48464 KB/s, 8 seconds passed
+... 57%, 400544 KB, 48466 KB/s, 8 seconds passed
+... 57%, 400576 KB, 48469 KB/s, 8 seconds passed
+... 57%, 400608 KB, 48471 KB/s, 8 seconds passed
+... 57%, 400640 KB, 48474 KB/s, 8 seconds passed
+... 57%, 400672 KB, 48476 KB/s, 8 seconds passed
+... 57%, 400704 KB, 48478 KB/s, 8 seconds passed
+... 57%, 400736 KB, 48480 KB/s, 8 seconds passed
+... 57%, 400768 KB, 48483 KB/s, 8 seconds passed
+... 57%, 400800 KB, 48485 KB/s, 8 seconds passed
+... 57%, 400832 KB, 48487 KB/s, 8 seconds passed
+... 57%, 400864 KB, 48490 KB/s, 8 seconds passed
+... 57%, 400896 KB, 48492 KB/s, 8 seconds passed
+... 57%, 400928 KB, 48494 KB/s, 8 seconds passed
+... 57%, 400960 KB, 48497 KB/s, 8 seconds passed
+... 57%, 400992 KB, 48499 KB/s, 8 seconds passed
+... 57%, 401024 KB, 48501 KB/s, 8 seconds passed
+... 57%, 401056 KB, 48504 KB/s, 8 seconds passed
+... 57%, 401088 KB, 48506 KB/s, 8 seconds passed
+... 57%, 401120 KB, 48508 KB/s, 8 seconds passed
+... 57%, 401152 KB, 48511 KB/s, 8 seconds passed
+... 57%, 401184 KB, 48513 KB/s, 8 seconds passed
+... 57%, 401216 KB, 48515 KB/s, 8 seconds passed
+... 57%, 401248 KB, 48518 KB/s, 8 seconds passed
+... 57%, 401280 KB, 48521 KB/s, 8 seconds passed
+... 57%, 401312 KB, 48523 KB/s, 8 seconds passed
+... 57%, 401344 KB, 48415 KB/s, 8 seconds passed
+... 57%, 401376 KB, 48417 KB/s, 8 seconds passed
+... 57%, 401408 KB, 48418 KB/s, 8 seconds passed
+... 57%, 401440 KB, 48420 KB/s, 8 seconds passed
+... 57%, 401472 KB, 48421 KB/s, 8 seconds passed
+... 57%, 401504 KB, 48423 KB/s, 8 seconds passed
+... 57%, 401536 KB, 48424 KB/s, 8 seconds passed
+... 57%, 401568 KB, 48426 KB/s, 8 seconds passed
+... 57%, 401600 KB, 48428 KB/s, 8 seconds passed
+... 57%, 401632 KB, 48429 KB/s, 8 seconds passed
+... 57%, 401664 KB, 48431 KB/s, 8 seconds passed
+... 57%, 401696 KB, 48433 KB/s, 8 seconds passed
+... 57%, 401728 KB, 48435 KB/s, 8 seconds passed
+... 57%, 401760 KB, 48436 KB/s, 8 seconds passed
+... 57%, 401792 KB, 48438 KB/s, 8 seconds passed
+... 57%, 401824 KB, 48440 KB/s, 8 seconds passed
+... 57%, 401856 KB, 48441 KB/s, 8 seconds passed
+... 57%, 401888 KB, 48443 KB/s, 8 seconds passed
+... 57%, 401920 KB, 48444 KB/s, 8 seconds passed
+... 57%, 401952 KB, 48446 KB/s, 8 seconds passed
+... 57%, 401984 KB, 48448 KB/s, 8 seconds passed
+... 57%, 402016 KB, 48449 KB/s, 8 seconds passed
+... 57%, 402048 KB, 48451 KB/s, 8 seconds passed
+... 57%, 402080 KB, 48453 KB/s, 8 seconds passed
+... 57%, 402112 KB, 48454 KB/s, 8 seconds passed
+... 57%, 402144 KB, 48456 KB/s, 8 seconds passed
+... 57%, 402176 KB, 48457 KB/s, 8 seconds passed
+... 57%, 402208 KB, 48459 KB/s, 8 seconds passed
+... 57%, 402240 KB, 48461 KB/s, 8 seconds passed
+... 57%, 402272 KB, 48462 KB/s, 8 seconds passed
+... 57%, 402304 KB, 48464 KB/s, 8 seconds passed
+... 57%, 402336 KB, 48467 KB/s, 8 seconds passed
+... 57%, 402368 KB, 48469 KB/s, 8 seconds passed
+... 57%, 402400 KB, 48471 KB/s, 8 seconds passed
+... 57%, 402432 KB, 48473 KB/s, 8 seconds passed
+... 57%, 402464 KB, 48475 KB/s, 8 seconds passed
+... 57%, 402496 KB, 48477 KB/s, 8 seconds passed
+... 57%, 402528 KB, 48480 KB/s, 8 seconds passed
+... 57%, 402560 KB, 48483 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 57%, 402592 KB, 48485 KB/s, 8 seconds passed
+... 57%, 402624 KB, 48488 KB/s, 8 seconds passed
+... 57%, 402656 KB, 48490 KB/s, 8 seconds passed
+... 57%, 402688 KB, 48493 KB/s, 8 seconds passed
+... 57%, 402720 KB, 48495 KB/s, 8 seconds passed
+... 57%, 402752 KB, 48497 KB/s, 8 seconds passed
+... 57%, 402784 KB, 48500 KB/s, 8 seconds passed
+... 57%, 402816 KB, 48502 KB/s, 8 seconds passed
+... 57%, 402848 KB, 48505 KB/s, 8 seconds passed
+... 57%, 402880 KB, 48507 KB/s, 8 seconds passed
+... 57%, 402912 KB, 48510 KB/s, 8 seconds passed
+... 57%, 402944 KB, 48512 KB/s, 8 seconds passed
+... 57%, 402976 KB, 48514 KB/s, 8 seconds passed
+... 57%, 403008 KB, 48516 KB/s, 8 seconds passed
+... 57%, 403040 KB, 48518 KB/s, 8 seconds passed
+... 57%, 403072 KB, 48521 KB/s, 8 seconds passed
+... 57%, 403104 KB, 48523 KB/s, 8 seconds passed
+... 57%, 403136 KB, 48526 KB/s, 8 seconds passed
+... 57%, 403168 KB, 48528 KB/s, 8 seconds passed
+... 57%, 403200 KB, 48530 KB/s, 8 seconds passed
+... 57%, 403232 KB, 48532 KB/s, 8 seconds passed
+... 57%, 403264 KB, 48534 KB/s, 8 seconds passed
+... 57%, 403296 KB, 48536 KB/s, 8 seconds passed
+... 57%, 403328 KB, 48539 KB/s, 8 seconds passed
+... 57%, 403360 KB, 48541 KB/s, 8 seconds passed
+... 57%, 403392 KB, 48543 KB/s, 8 seconds passed
+... 58%, 403424 KB, 48545 KB/s, 8 seconds passed
+... 58%, 403456 KB, 48547 KB/s, 8 seconds passed
+... 58%, 403488 KB, 48550 KB/s, 8 seconds passed
+... 58%, 403520 KB, 48552 KB/s, 8 seconds passed
+... 58%, 403552 KB, 48554 KB/s, 8 seconds passed
+... 58%, 403584 KB, 48557 KB/s, 8 seconds passed
+... 58%, 403616 KB, 48559 KB/s, 8 seconds passed
+... 58%, 403648 KB, 48561 KB/s, 8 seconds passed
+... 58%, 403680 KB, 48564 KB/s, 8 seconds passed
+... 58%, 403712 KB, 48565 KB/s, 8 seconds passed
+... 58%, 403744 KB, 48568 KB/s, 8 seconds passed
+... 58%, 403776 KB, 48570 KB/s, 8 seconds passed
+... 58%, 403808 KB, 48573 KB/s, 8 seconds passed
+... 58%, 403840 KB, 48575 KB/s, 8 seconds passed
+... 58%, 403872 KB, 48577 KB/s, 8 seconds passed
+... 58%, 403904 KB, 48579 KB/s, 8 seconds passed
+... 58%, 403936 KB, 48581 KB/s, 8 seconds passed
+... 58%, 403968 KB, 48583 KB/s, 8 seconds passed
+... 58%, 404000 KB, 48586 KB/s, 8 seconds passed
+... 58%, 404032 KB, 48588 KB/s, 8 seconds passed
+... 58%, 404064 KB, 48590 KB/s, 8 seconds passed
+... 58%, 404096 KB, 48592 KB/s, 8 seconds passed
+... 58%, 404128 KB, 48595 KB/s, 8 seconds passed
+... 58%, 404160 KB, 48597 KB/s, 8 seconds passed
+... 58%, 404192 KB, 48599 KB/s, 8 seconds passed
+... 58%, 404224 KB, 48601 KB/s, 8 seconds passed
+... 58%, 404256 KB, 48604 KB/s, 8 seconds passed
+... 58%, 404288 KB, 48606 KB/s, 8 seconds passed
+... 58%, 404320 KB, 48608 KB/s, 8 seconds passed
+... 58%, 404352 KB, 48610 KB/s, 8 seconds passed
+... 58%, 404384 KB, 48613 KB/s, 8 seconds passed
+... 58%, 404416 KB, 48615 KB/s, 8 seconds passed
+... 58%, 404448 KB, 48617 KB/s, 8 seconds passed
+... 58%, 404480 KB, 48488 KB/s, 8 seconds passed
+... 58%, 404512 KB, 48489 KB/s, 8 seconds passed
+... 58%, 404544 KB, 48490 KB/s, 8 seconds passed
+... 58%, 404576 KB, 48492 KB/s, 8 seconds passed
+... 58%, 404608 KB, 48494 KB/s, 8 seconds passed
+... 58%, 404640 KB, 48491 KB/s, 8 seconds passed
+... 58%, 404672 KB, 48493 KB/s, 8 seconds passed
+... 58%, 404704 KB, 48494 KB/s, 8 seconds passed
+... 58%, 404736 KB, 48496 KB/s, 8 seconds passed
+... 58%, 404768 KB, 48497 KB/s, 8 seconds passed
+... 58%, 404800 KB, 48499 KB/s, 8 seconds passed
+... 58%, 404832 KB, 48500 KB/s, 8 seconds passed
+... 58%, 404864 KB, 48502 KB/s, 8 seconds passed
+... 58%, 404896 KB, 48504 KB/s, 8 seconds passed
+... 58%, 404928 KB, 48505 KB/s, 8 seconds passed
+... 58%, 404960 KB, 48507 KB/s, 8 seconds passed
+... 58%, 404992 KB, 48509 KB/s, 8 seconds passed
+... 58%, 405024 KB, 48510 KB/s, 8 seconds passed
+... 58%, 405056 KB, 48512 KB/s, 8 seconds passed
+... 58%, 405088 KB, 48513 KB/s, 8 seconds passed
+... 58%, 405120 KB, 48515 KB/s, 8 seconds passed
+... 58%, 405152 KB, 48516 KB/s, 8 seconds passed
+... 58%, 405184 KB, 48518 KB/s, 8 seconds passed
+... 58%, 405216 KB, 48519 KB/s, 8 seconds passed
+... 58%, 405248 KB, 48521 KB/s, 8 seconds passed
+... 58%, 405280 KB, 48522 KB/s, 8 seconds passed
+... 58%, 405312 KB, 48524 KB/s, 8 seconds passed
+... 58%, 405344 KB, 48526 KB/s, 8 seconds passed
+... 58%, 405376 KB, 48528 KB/s, 8 seconds passed
+... 58%, 405408 KB, 48530 KB/s, 8 seconds passed
+... 58%, 405440 KB, 48532 KB/s, 8 seconds passed
+... 58%, 405472 KB, 48535 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 58%, 405504 KB, 48537 KB/s, 8 seconds passed
+... 58%, 405536 KB, 48539 KB/s, 8 seconds passed
+... 58%, 405568 KB, 48541 KB/s, 8 seconds passed
+... 58%, 405600 KB, 48543 KB/s, 8 seconds passed
+... 58%, 405632 KB, 48545 KB/s, 8 seconds passed
+... 58%, 405664 KB, 48548 KB/s, 8 seconds passed
+... 58%, 405696 KB, 48550 KB/s, 8 seconds passed
+... 58%, 405728 KB, 48552 KB/s, 8 seconds passed
+... 58%, 405760 KB, 48554 KB/s, 8 seconds passed
+... 58%, 405792 KB, 48556 KB/s, 8 seconds passed
+... 58%, 405824 KB, 48559 KB/s, 8 seconds passed
+... 58%, 405856 KB, 48561 KB/s, 8 seconds passed
+... 58%, 405888 KB, 48563 KB/s, 8 seconds passed
+... 58%, 405920 KB, 48566 KB/s, 8 seconds passed
+... 58%, 405952 KB, 48568 KB/s, 8 seconds passed
+... 58%, 405984 KB, 48570 KB/s, 8 seconds passed
+... 58%, 406016 KB, 48572 KB/s, 8 seconds passed
+... 58%, 406048 KB, 48575 KB/s, 8 seconds passed
+... 58%, 406080 KB, 48577 KB/s, 8 seconds passed
+... 58%, 406112 KB, 48579 KB/s, 8 seconds passed
+... 58%, 406144 KB, 48581 KB/s, 8 seconds passed
+... 58%, 406176 KB, 48584 KB/s, 8 seconds passed
+... 58%, 406208 KB, 48586 KB/s, 8 seconds passed
+... 58%, 406240 KB, 48588 KB/s, 8 seconds passed
+... 58%, 406272 KB, 48590 KB/s, 8 seconds passed
+... 58%, 406304 KB, 48593 KB/s, 8 seconds passed
+... 58%, 406336 KB, 48595 KB/s, 8 seconds passed
+... 58%, 406368 KB, 48597 KB/s, 8 seconds passed
+... 58%, 406400 KB, 48599 KB/s, 8 seconds passed
+... 58%, 406432 KB, 48602 KB/s, 8 seconds passed
+... 58%, 406464 KB, 48604 KB/s, 8 seconds passed
+... 58%, 406496 KB, 48606 KB/s, 8 seconds passed
+... 58%, 406528 KB, 48609 KB/s, 8 seconds passed
+... 58%, 406560 KB, 48611 KB/s, 8 seconds passed
+... 58%, 406592 KB, 48613 KB/s, 8 seconds passed
+... 58%, 406624 KB, 48616 KB/s, 8 seconds passed
+... 58%, 406656 KB, 48619 KB/s, 8 seconds passed
+... 58%, 406688 KB, 48622 KB/s, 8 seconds passed
+... 58%, 406720 KB, 48625 KB/s, 8 seconds passed
+... 58%, 406752 KB, 48627 KB/s, 8 seconds passed
+... 58%, 406784 KB, 48630 KB/s, 8 seconds passed
+... 58%, 406816 KB, 48633 KB/s, 8 seconds passed
+... 58%, 406848 KB, 48636 KB/s, 8 seconds passed
+... 58%, 406880 KB, 48639 KB/s, 8 seconds passed
+... 58%, 406912 KB, 48641 KB/s, 8 seconds passed
+... 58%, 406944 KB, 48644 KB/s, 8 seconds passed
+... 58%, 406976 KB, 48647 KB/s, 8 seconds passed
+... 58%, 407008 KB, 48650 KB/s, 8 seconds passed
+... 58%, 407040 KB, 48653 KB/s, 8 seconds passed
+... 58%, 407072 KB, 48656 KB/s, 8 seconds passed
+... 58%, 407104 KB, 48658 KB/s, 8 seconds passed
+... 58%, 407136 KB, 48661 KB/s, 8 seconds passed
+... 58%, 407168 KB, 48664 KB/s, 8 seconds passed
+... 58%, 407200 KB, 48667 KB/s, 8 seconds passed
+... 58%, 407232 KB, 48669 KB/s, 8 seconds passed
+... 58%, 407264 KB, 48669 KB/s, 8 seconds passed
+... 58%, 407296 KB, 48671 KB/s, 8 seconds passed
+... 58%, 407328 KB, 48672 KB/s, 8 seconds passed
+... 58%, 407360 KB, 48672 KB/s, 8 seconds passed
+... 58%, 407392 KB, 48673 KB/s, 8 seconds passed
+... 58%, 407424 KB, 48672 KB/s, 8 seconds passed
+... 58%, 407456 KB, 48673 KB/s, 8 seconds passed
+... 58%, 407488 KB, 48675 KB/s, 8 seconds passed
+... 58%, 407520 KB, 48674 KB/s, 8 seconds passed
+... 58%, 407552 KB, 48593 KB/s, 8 seconds passed
+... 58%, 407584 KB, 48594 KB/s, 8 seconds passed
+... 58%, 407616 KB, 48596 KB/s, 8 seconds passed
+... 58%, 407648 KB, 48597 KB/s, 8 seconds passed
+... 58%, 407680 KB, 48599 KB/s, 8 seconds passed
+... 58%, 407712 KB, 48601 KB/s, 8 seconds passed
+... 58%, 407744 KB, 48602 KB/s, 8 seconds passed
+... 58%, 407776 KB, 48604 KB/s, 8 seconds passed
+... 58%, 407808 KB, 48605 KB/s, 8 seconds passed
+... 58%, 407840 KB, 48607 KB/s, 8 seconds passed
+... 58%, 407872 KB, 48608 KB/s, 8 seconds passed
+... 58%, 407904 KB, 48610 KB/s, 8 seconds passed
+... 58%, 407936 KB, 48611 KB/s, 8 seconds passed
+... 58%, 407968 KB, 48613 KB/s, 8 seconds passed
+... 58%, 408000 KB, 48615 KB/s, 8 seconds passed
+... 58%, 408032 KB, 48616 KB/s, 8 seconds passed
+... 58%, 408064 KB, 48618 KB/s, 8 seconds passed
+... 58%, 408096 KB, 48619 KB/s, 8 seconds passed
+... 58%, 408128 KB, 48621 KB/s, 8 seconds passed
+... 58%, 408160 KB, 48623 KB/s, 8 seconds passed
+... 58%, 408192 KB, 48624 KB/s, 8 seconds passed
+... 58%, 408224 KB, 48626 KB/s, 8 seconds passed
+... 58%, 408256 KB, 48627 KB/s, 8 seconds passed
+... 58%, 408288 KB, 48629 KB/s, 8 seconds passed
+... 58%, 408320 KB, 48631 KB/s, 8 seconds passed
+... 58%, 408352 KB, 48632 KB/s, 8 seconds passed
+... 58%, 408384 KB, 48634 KB/s, 8 seconds passed
+... 58%, 408416 KB, 48635 KB/s, 8 seconds passed
+... 58%, 408448 KB, 48637 KB/s, 8 seconds passed
+... 58%, 408480 KB, 48639 KB/s, 8 seconds passed
+... 58%, 408512 KB, 48640 KB/s, 8 seconds passed
+... 58%, 408544 KB, 48642 KB/s, 8 seconds passed
+... 58%, 408576 KB, 48643 KB/s, 8 seconds passed
+... 58%, 408608 KB, 48645 KB/s, 8 seconds passed
+... 58%, 408640 KB, 48646 KB/s, 8 seconds passed
+... 58%, 408672 KB, 48648 KB/s, 8 seconds passed
+... 58%, 408704 KB, 48649 KB/s, 8 seconds passed
+... 58%, 408736 KB, 48651 KB/s, 8 seconds passed
+... 58%, 408768 KB, 48653 KB/s, 8 seconds passed
+... 58%, 408800 KB, 48654 KB/s, 8 seconds passed
+... 58%, 408832 KB, 48656 KB/s, 8 seconds passed
+... 58%, 408864 KB, 48658 KB/s, 8 seconds passed
+... 58%, 408896 KB, 48659 KB/s, 8 seconds passed
+... 58%, 408928 KB, 48661 KB/s, 8 seconds passed
+... 58%, 408960 KB, 48662 KB/s, 8 seconds passed
+... 58%, 408992 KB, 48664 KB/s, 8 seconds passed
+... 58%, 409024 KB, 48666 KB/s, 8 seconds passed
+... 58%, 409056 KB, 48669 KB/s, 8 seconds passed
+... 58%, 409088 KB, 48671 KB/s, 8 seconds passed
+... 58%, 409120 KB, 48674 KB/s, 8 seconds passed
+... 58%, 409152 KB, 48677 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
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+... 58%, 409216 KB, 48682 KB/s, 8 seconds passed
+... 58%, 409248 KB, 48685 KB/s, 8 seconds passed
+... 58%, 409280 KB, 48687 KB/s, 8 seconds passed
+... 58%, 409312 KB, 48690 KB/s, 8 seconds passed
+... 58%, 409344 KB, 48693 KB/s, 8 seconds passed
+... 58%, 409376 KB, 48696 KB/s, 8 seconds passed
+... 58%, 409408 KB, 48698 KB/s, 8 seconds passed
+... 58%, 409440 KB, 48701 KB/s, 8 seconds passed
+... 58%, 409472 KB, 48704 KB/s, 8 seconds passed
+... 58%, 409504 KB, 48706 KB/s, 8 seconds passed
+... 58%, 409536 KB, 48709 KB/s, 8 seconds passed
+... 58%, 409568 KB, 48712 KB/s, 8 seconds passed
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+... 58%, 409696 KB, 48578 KB/s, 8 seconds passed
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+... 58%, 409760 KB, 48581 KB/s, 8 seconds passed
+... 58%, 409792 KB, 48583 KB/s, 8 seconds passed
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+... 58%, 409856 KB, 48586 KB/s, 8 seconds passed
+... 58%, 409888 KB, 48587 KB/s, 8 seconds passed
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+... 58%, 409952 KB, 48590 KB/s, 8 seconds passed
+... 58%, 409984 KB, 48592 KB/s, 8 seconds passed
+... 58%, 410016 KB, 48593 KB/s, 8 seconds passed
+... 58%, 410048 KB, 48595 KB/s, 8 seconds passed
+... 58%, 410080 KB, 48597 KB/s, 8 seconds passed
+... 58%, 410112 KB, 48598 KB/s, 8 seconds passed
+... 58%, 410144 KB, 48600 KB/s, 8 seconds passed
+... 58%, 410176 KB, 48601 KB/s, 8 seconds passed
+... 58%, 410208 KB, 48603 KB/s, 8 seconds passed
+... 58%, 410240 KB, 48604 KB/s, 8 seconds passed
+... 58%, 410272 KB, 48606 KB/s, 8 seconds passed
+... 58%, 410304 KB, 48607 KB/s, 8 seconds passed
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+... 59%, 410368 KB, 48610 KB/s, 8 seconds passed
+... 59%, 410400 KB, 48612 KB/s, 8 seconds passed
+... 59%, 410432 KB, 48614 KB/s, 8 seconds passed
+... 59%, 410464 KB, 48615 KB/s, 8 seconds passed
+... 59%, 410496 KB, 48617 KB/s, 8 seconds passed
+... 59%, 410528 KB, 48618 KB/s, 8 seconds passed
+... 59%, 410560 KB, 48620 KB/s, 8 seconds passed
+... 59%, 410592 KB, 48621 KB/s, 8 seconds passed
+... 59%, 410624 KB, 48623 KB/s, 8 seconds passed
+... 59%, 410656 KB, 48625 KB/s, 8 seconds passed
+... 59%, 410688 KB, 48627 KB/s, 8 seconds passed
+... 59%, 410720 KB, 48629 KB/s, 8 seconds passed
+... 59%, 410752 KB, 48632 KB/s, 8 seconds passed
+... 59%, 410784 KB, 48634 KB/s, 8 seconds passed
+... 59%, 410816 KB, 48636 KB/s, 8 seconds passed
+... 59%, 410848 KB, 48639 KB/s, 8 seconds passed
+... 59%, 410880 KB, 48641 KB/s, 8 seconds passed
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+... 59%, 410944 KB, 48646 KB/s, 8 seconds passed
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+... 59%, 411104 KB, 48657 KB/s, 8 seconds passed
+... 59%, 411136 KB, 48660 KB/s, 8 seconds passed
+... 59%, 411168 KB, 48662 KB/s, 8 seconds passed
+... 59%, 411200 KB, 48665 KB/s, 8 seconds passed
+... 59%, 411232 KB, 48667 KB/s, 8 seconds passed
+... 59%, 411264 KB, 48669 KB/s, 8 seconds passed
+... 59%, 411296 KB, 48672 KB/s, 8 seconds passed
+... 59%, 411328 KB, 48674 KB/s, 8 seconds passed
+... 59%, 411360 KB, 48676 KB/s, 8 seconds passed
+... 59%, 411392 KB, 48679 KB/s, 8 seconds passed
+... 59%, 411424 KB, 48681 KB/s, 8 seconds passed
+... 59%, 411456 KB, 48683 KB/s, 8 seconds passed
+... 59%, 411488 KB, 48686 KB/s, 8 seconds passed
+... 59%, 411520 KB, 48688 KB/s, 8 seconds passed
+... 59%, 411552 KB, 48691 KB/s, 8 seconds passed
+... 59%, 411584 KB, 48693 KB/s, 8 seconds passed
+... 59%, 411616 KB, 48695 KB/s, 8 seconds passed
+... 59%, 411648 KB, 48698 KB/s, 8 seconds passed
+... 59%, 411680 KB, 48700 KB/s, 8 seconds passed
+... 59%, 411712 KB, 48703 KB/s, 8 seconds passed
+... 59%, 411744 KB, 48705 KB/s, 8 seconds passed
+... 59%, 411776 KB, 48707 KB/s, 8 seconds passed
+... 59%, 411808 KB, 48710 KB/s, 8 seconds passed
+... 59%, 411840 KB, 48712 KB/s, 8 seconds passed
+... 59%, 411872 KB, 48715 KB/s, 8 seconds passed
+... 59%, 411904 KB, 48717 KB/s, 8 seconds passed
+... 59%, 411936 KB, 48719 KB/s, 8 seconds passed
+... 59%, 411968 KB, 48722 KB/s, 8 seconds passed
+... 59%, 412000 KB, 48725 KB/s, 8 seconds passed
+... 59%, 412032 KB, 48728 KB/s, 8 seconds passed
+... 59%, 412064 KB, 48730 KB/s, 8 seconds passed
+... 59%, 412096 KB, 48733 KB/s, 8 seconds passed
+... 59%, 412128 KB, 48736 KB/s, 8 seconds passed
+... 59%, 412160 KB, 48739 KB/s, 8 seconds passed
+... 59%, 412192 KB, 48742 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
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+... 59%, 412384 KB, 48759 KB/s, 8 seconds passed
+... 59%, 412416 KB, 48761 KB/s, 8 seconds passed
+... 59%, 412448 KB, 48764 KB/s, 8 seconds passed
+... 59%, 412480 KB, 48767 KB/s, 8 seconds passed
+... 59%, 412512 KB, 48769 KB/s, 8 seconds passed
+... 59%, 412544 KB, 48771 KB/s, 8 seconds passed
+... 59%, 412576 KB, 48773 KB/s, 8 seconds passed
+... 59%, 412608 KB, 48776 KB/s, 8 seconds passed
+... 59%, 412640 KB, 48778 KB/s, 8 seconds passed
+... 59%, 412672 KB, 48780 KB/s, 8 seconds passed
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+... 59%, 412992 KB, 48692 KB/s, 8 seconds passed
+... 59%, 413024 KB, 48694 KB/s, 8 seconds passed
+... 59%, 413056 KB, 48695 KB/s, 8 seconds passed
+... 59%, 413088 KB, 48697 KB/s, 8 seconds passed
+... 59%, 413120 KB, 48698 KB/s, 8 seconds passed
+... 59%, 413152 KB, 48700 KB/s, 8 seconds passed
+... 59%, 413184 KB, 48701 KB/s, 8 seconds passed
+... 59%, 413216 KB, 48703 KB/s, 8 seconds passed
+... 59%, 413248 KB, 48705 KB/s, 8 seconds passed
+... 59%, 413280 KB, 48706 KB/s, 8 seconds passed
+... 59%, 413312 KB, 48708 KB/s, 8 seconds passed
+... 59%, 413344 KB, 48709 KB/s, 8 seconds passed
+... 59%, 413376 KB, 48711 KB/s, 8 seconds passed
+... 59%, 413408 KB, 48713 KB/s, 8 seconds passed
+... 59%, 413440 KB, 48714 KB/s, 8 seconds passed
+... 59%, 413472 KB, 48715 KB/s, 8 seconds passed
+... 59%, 413504 KB, 48717 KB/s, 8 seconds passed
+... 59%, 413536 KB, 48719 KB/s, 8 seconds passed
+... 59%, 413568 KB, 48720 KB/s, 8 seconds passed
+... 59%, 413600 KB, 48722 KB/s, 8 seconds passed
+... 59%, 413632 KB, 48723 KB/s, 8 seconds passed
+... 59%, 413664 KB, 48725 KB/s, 8 seconds passed
+... 59%, 413696 KB, 48727 KB/s, 8 seconds passed
+... 59%, 413728 KB, 48728 KB/s, 8 seconds passed
+... 59%, 413760 KB, 48730 KB/s, 8 seconds passed
+... 59%, 413792 KB, 48731 KB/s, 8 seconds passed
+... 59%, 413824 KB, 48733 KB/s, 8 seconds passed
+... 59%, 413856 KB, 48734 KB/s, 8 seconds passed
+... 59%, 413888 KB, 48736 KB/s, 8 seconds passed
+... 59%, 413920 KB, 48737 KB/s, 8 seconds passed
+... 59%, 413952 KB, 48739 KB/s, 8 seconds passed
+... 59%, 413984 KB, 48740 KB/s, 8 seconds passed
+... 59%, 414016 KB, 48742 KB/s, 8 seconds passed
+... 59%, 414048 KB, 48744 KB/s, 8 seconds passed
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+... 59%, 414112 KB, 48747 KB/s, 8 seconds passed
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+... 59%, 414176 KB, 48751 KB/s, 8 seconds passed
+... 59%, 414208 KB, 48754 KB/s, 8 seconds passed
+... 59%, 414240 KB, 48756 KB/s, 8 seconds passed
+... 59%, 414272 KB, 48759 KB/s, 8 seconds passed
+... 59%, 414304 KB, 48762 KB/s, 8 seconds passed
+... 59%, 414336 KB, 48764 KB/s, 8 seconds passed
+... 59%, 414368 KB, 48767 KB/s, 8 seconds passed
+... 59%, 414400 KB, 48770 KB/s, 8 seconds passed
+... 59%, 414432 KB, 48772 KB/s, 8 seconds passed
+... 59%, 414464 KB, 48775 KB/s, 8 seconds passed
+... 59%, 414496 KB, 48778 KB/s, 8 seconds passed
+... 59%, 414528 KB, 48780 KB/s, 8 seconds passed
+... 59%, 414560 KB, 48783 KB/s, 8 seconds passed
+... 59%, 414592 KB, 48786 KB/s, 8 seconds passed
+... 59%, 414624 KB, 48788 KB/s, 8 seconds passed
+... 59%, 414656 KB, 48791 KB/s, 8 seconds passed
+... 59%, 414688 KB, 48794 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
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+... 59%, 414784 KB, 48608 KB/s, 8 seconds passed
+... 59%, 414816 KB, 48609 KB/s, 8 seconds passed
+... 59%, 414848 KB, 48611 KB/s, 8 seconds passed
+... 59%, 414880 KB, 48612 KB/s, 8 seconds passed
+... 59%, 414912 KB, 48614 KB/s, 8 seconds passed
+... 59%, 414944 KB, 48615 KB/s, 8 seconds passed
+... 59%, 414976 KB, 48617 KB/s, 8 seconds passed
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+... 59%, 415040 KB, 48620 KB/s, 8 seconds passed
+... 59%, 415072 KB, 48622 KB/s, 8 seconds passed
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+... 59%, 415200 KB, 48628 KB/s, 8 seconds passed
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+... 59%, 415552 KB, 48645 KB/s, 8 seconds passed
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+... 59%, 415616 KB, 48648 KB/s, 8 seconds passed
+... 59%, 415648 KB, 48650 KB/s, 8 seconds passed
+... 59%, 415680 KB, 48652 KB/s, 8 seconds passed
+... 59%, 415712 KB, 48654 KB/s, 8 seconds passed
+... 59%, 415744 KB, 48655 KB/s, 8 seconds passed
+... 59%, 415776 KB, 48657 KB/s, 8 seconds passed
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+... 59%, 415968 KB, 48669 KB/s, 8 seconds passed
+... 59%, 416000 KB, 48671 KB/s, 8 seconds passed
+... 59%, 416032 KB, 48674 KB/s, 8 seconds passed
+... 59%, 416064 KB, 48676 KB/s, 8 seconds passed
+... 59%, 416096 KB, 48678 KB/s, 8 seconds passed
+... 59%, 416128 KB, 48680 KB/s, 8 seconds passed
+... 59%, 416160 KB, 48683 KB/s, 8 seconds passed
+... 59%, 416192 KB, 48685 KB/s, 8 seconds passed
+... 59%, 416224 KB, 48687 KB/s, 8 seconds passed
+... 59%, 416256 KB, 48690 KB/s, 8 seconds passed
+... 59%, 416288 KB, 48692 KB/s, 8 seconds passed
+... 59%, 416320 KB, 48694 KB/s, 8 seconds passed
+... 59%, 416352 KB, 48697 KB/s, 8 seconds passed
+... 59%, 416384 KB, 48699 KB/s, 8 seconds passed
+... 59%, 416416 KB, 48702 KB/s, 8 seconds passed
+... 59%, 416448 KB, 48704 KB/s, 8 seconds passed
+... 59%, 416480 KB, 48706 KB/s, 8 seconds passed
+... 59%, 416512 KB, 48708 KB/s, 8 seconds passed
+... 59%, 416544 KB, 48711 KB/s, 8 seconds passed
+... 59%, 416576 KB, 48713 KB/s, 8 seconds passed
+... 59%, 416608 KB, 48715 KB/s, 8 seconds passed
+... 59%, 416640 KB, 48718 KB/s, 8 seconds passed
+... 59%, 416672 KB, 48720 KB/s, 8 seconds passed
+... 59%, 416704 KB, 48722 KB/s, 8 seconds passed
+... 59%, 416736 KB, 48725 KB/s, 8 seconds passed
+... 59%, 416768 KB, 48727 KB/s, 8 seconds passed
+... 59%, 416800 KB, 48729 KB/s, 8 seconds passed
+... 59%, 416832 KB, 48732 KB/s, 8 seconds passed
+... 59%, 416864 KB, 48734 KB/s, 8 seconds passed
+... 59%, 416896 KB, 48736 KB/s, 8 seconds passed
+... 59%, 416928 KB, 48739 KB/s, 8 seconds passed
+... 59%, 416960 KB, 48741 KB/s, 8 seconds passed
+... 59%, 416992 KB, 48743 KB/s, 8 seconds passed
+... 59%, 417024 KB, 48746 KB/s, 8 seconds passed
+... 59%, 417056 KB, 48748 KB/s, 8 seconds passed
+... 59%, 417088 KB, 48750 KB/s, 8 seconds passed
+... 59%, 417120 KB, 48753 KB/s, 8 seconds passed
+... 59%, 417152 KB, 48755 KB/s, 8 seconds passed
+... 59%, 417184 KB, 48757 KB/s, 8 seconds passed
+... 59%, 417216 KB, 48760 KB/s, 8 seconds passed
+... 59%, 417248 KB, 48763 KB/s, 8 seconds passed
+... 59%, 417280 KB, 48766 KB/s, 8 seconds passed
+... 60%, 417312 KB, 48769 KB/s, 8 seconds passed
+... 60%, 417344 KB, 48771 KB/s, 8 seconds passed
+... 60%, 417376 KB, 48774 KB/s, 8 seconds passed
+... 60%, 417408 KB, 48777 KB/s, 8 seconds passed
+... 60%, 417440 KB, 48780 KB/s, 8 seconds passed
+... 60%, 417472 KB, 48782 KB/s, 8 seconds passed
+... 60%, 417504 KB, 48784 KB/s, 8 seconds passed
+... 60%, 417536 KB, 48786 KB/s, 8 seconds passed
+... 60%, 417568 KB, 48789 KB/s, 8 seconds passed
+... 60%, 417600 KB, 48791 KB/s, 8 seconds passed
+... 60%, 417632 KB, 48793 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 60%, 417664 KB, 48796 KB/s, 8 seconds passed
+... 60%, 417696 KB, 48798 KB/s, 8 seconds passed
+... 60%, 417728 KB, 48799 KB/s, 8 seconds passed
+... 60%, 417760 KB, 48801 KB/s, 8 seconds passed
+... 60%, 417792 KB, 48804 KB/s, 8 seconds passed
+... 60%, 417824 KB, 48806 KB/s, 8 seconds passed
+... 60%, 417856 KB, 48808 KB/s, 8 seconds passed
+... 60%, 417888 KB, 48810 KB/s, 8 seconds passed
+... 60%, 417920 KB, 48812 KB/s, 8 seconds passed
+... 60%, 417952 KB, 48815 KB/s, 8 seconds passed
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+... 60%, 418016 KB, 48819 KB/s, 8 seconds passed
+... 60%, 418048 KB, 48821 KB/s, 8 seconds passed
+... 60%, 418080 KB, 48823 KB/s, 8 seconds passed
+... 60%, 418112 KB, 48825 KB/s, 8 seconds passed
+... 60%, 418144 KB, 48828 KB/s, 8 seconds passed
+... 60%, 418176 KB, 48829 KB/s, 8 seconds passed
+... 60%, 418208 KB, 48831 KB/s, 8 seconds passed
+... 60%, 418240 KB, 48834 KB/s, 8 seconds passed
+... 60%, 418272 KB, 48836 KB/s, 8 seconds passed
+... 60%, 418304 KB, 48838 KB/s, 8 seconds passed
+... 60%, 418336 KB, 48840 KB/s, 8 seconds passed
+... 60%, 418368 KB, 48843 KB/s, 8 seconds passed
+... 60%, 418400 KB, 48845 KB/s, 8 seconds passed
+... 60%, 418432 KB, 48846 KB/s, 8 seconds passed
+... 60%, 418464 KB, 48849 KB/s, 8 seconds passed
+... 60%, 418496 KB, 48851 KB/s, 8 seconds passed
+... 60%, 418528 KB, 48853 KB/s, 8 seconds passed
+... 60%, 418560 KB, 48855 KB/s, 8 seconds passed
+... 60%, 418592 KB, 48857 KB/s, 8 seconds passed
+... 60%, 418624 KB, 48859 KB/s, 8 seconds passed
+... 60%, 418656 KB, 48846 KB/s, 8 seconds passed
+... 60%, 418688 KB, 48848 KB/s, 8 seconds passed
+... 60%, 418720 KB, 48849 KB/s, 8 seconds passed
+... 60%, 418752 KB, 48852 KB/s, 8 seconds passed
+... 60%, 418784 KB, 48855 KB/s, 8 seconds passed
+... 60%, 418816 KB, 48857 KB/s, 8 seconds passed
+... 60%, 418848 KB, 48860 KB/s, 8 seconds passed
+... 60%, 418880 KB, 48862 KB/s, 8 seconds passed
+... 60%, 418912 KB, 48864 KB/s, 8 seconds passed
+... 60%, 418944 KB, 48866 KB/s, 8 seconds passed
+... 60%, 418976 KB, 48868 KB/s, 8 seconds passed
+... 60%, 419008 KB, 48870 KB/s, 8 seconds passed
+... 60%, 419040 KB, 48872 KB/s, 8 seconds passed
+... 60%, 419072 KB, 48874 KB/s, 8 seconds passed
+... 60%, 419104 KB, 48877 KB/s, 8 seconds passed
+... 60%, 419136 KB, 48878 KB/s, 8 seconds passed
+... 60%, 419168 KB, 48879 KB/s, 8 seconds passed
+... 60%, 419200 KB, 48881 KB/s, 8 seconds passed
+... 60%, 419232 KB, 48884 KB/s, 8 seconds passed
+... 60%, 419264 KB, 48887 KB/s, 8 seconds passed
+... 60%, 419296 KB, 48890 KB/s, 8 seconds passed
+... 60%, 419328 KB, 48891 KB/s, 8 seconds passed
+... 60%, 419360 KB, 48893 KB/s, 8 seconds passed
+... 60%, 419392 KB, 48895 KB/s, 8 seconds passed
+... 60%, 419424 KB, 48896 KB/s, 8 seconds passed
+... 60%, 419456 KB, 48898 KB/s, 8 seconds passed
+... 60%, 419488 KB, 48900 KB/s, 8 seconds passed
+... 60%, 419520 KB, 48901 KB/s, 8 seconds passed
+... 60%, 419552 KB, 48903 KB/s, 8 seconds passed
+... 60%, 419584 KB, 48904 KB/s, 8 seconds passed
+... 60%, 419616 KB, 48906 KB/s, 8 seconds passed
+... 60%, 419648 KB, 48908 KB/s, 8 seconds passed
+... 60%, 419680 KB, 48909 KB/s, 8 seconds passed
+... 60%, 419712 KB, 48911 KB/s, 8 seconds passed
+... 60%, 419744 KB, 48913 KB/s, 8 seconds passed
+... 60%, 419776 KB, 48915 KB/s, 8 seconds passed
+... 60%, 419808 KB, 48917 KB/s, 8 seconds passed
+... 60%, 419840 KB, 48834 KB/s, 8 seconds passed
+... 60%, 419872 KB, 48835 KB/s, 8 seconds passed
+... 60%, 419904 KB, 48836 KB/s, 8 seconds passed
+... 60%, 419936 KB, 48838 KB/s, 8 seconds passed
+... 60%, 419968 KB, 48839 KB/s, 8 seconds passed
+... 60%, 420000 KB, 48841 KB/s, 8 seconds passed
+... 60%, 420032 KB, 48842 KB/s, 8 seconds passed
+... 60%, 420064 KB, 48844 KB/s, 8 seconds passed
+... 60%, 420096 KB, 48845 KB/s, 8 seconds passed
+... 60%, 420128 KB, 48847 KB/s, 8 seconds passed
+... 60%, 420160 KB, 48848 KB/s, 8 seconds passed
+... 60%, 420192 KB, 48850 KB/s, 8 seconds passed
+... 60%, 420224 KB, 48852 KB/s, 8 seconds passed
+... 60%, 420256 KB, 48853 KB/s, 8 seconds passed
+... 60%, 420288 KB, 48855 KB/s, 8 seconds passed
+... 60%, 420320 KB, 48856 KB/s, 8 seconds passed
+... 60%, 420352 KB, 48858 KB/s, 8 seconds passed
+... 60%, 420384 KB, 48859 KB/s, 8 seconds passed
+... 60%, 420416 KB, 48861 KB/s, 8 seconds passed
+... 60%, 420448 KB, 48862 KB/s, 8 seconds passed
+... 60%, 420480 KB, 48864 KB/s, 8 seconds passed
+... 60%, 420512 KB, 48865 KB/s, 8 seconds passed
+... 60%, 420544 KB, 48867 KB/s, 8 seconds passed
+... 60%, 420576 KB, 48869 KB/s, 8 seconds passed
+... 60%, 420608 KB, 48871 KB/s, 8 seconds passed
+... 60%, 420640 KB, 48873 KB/s, 8 seconds passed
+... 60%, 420672 KB, 48874 KB/s, 8 seconds passed
+... 60%, 420704 KB, 48876 KB/s, 8 seconds passed
+... 60%, 420736 KB, 48878 KB/s, 8 seconds passed
+... 60%, 420768 KB, 48880 KB/s, 8 seconds passed
+... 60%, 420800 KB, 48882 KB/s, 8 seconds passed
+... 60%, 420832 KB, 48884 KB/s, 8 seconds passed
+... 60%, 420864 KB, 48886 KB/s, 8 seconds passed
+... 60%, 420896 KB, 48888 KB/s, 8 seconds passed
+... 60%, 420928 KB, 48890 KB/s, 8 seconds passed
+... 60%, 420960 KB, 48892 KB/s, 8 seconds passed
+... 60%, 420992 KB, 48894 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
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+... 60%, 421056 KB, 48898 KB/s, 8 seconds passed
+... 60%, 421088 KB, 48900 KB/s, 8 seconds passed
+... 60%, 421120 KB, 48902 KB/s, 8 seconds passed
+... 60%, 421152 KB, 48904 KB/s, 8 seconds passed
+... 60%, 421184 KB, 48906 KB/s, 8 seconds passed
+... 60%, 421216 KB, 48908 KB/s, 8 seconds passed
+... 60%, 421248 KB, 48910 KB/s, 8 seconds passed
+... 60%, 421280 KB, 48912 KB/s, 8 seconds passed
+... 60%, 421312 KB, 48914 KB/s, 8 seconds passed
+... 60%, 421344 KB, 48916 KB/s, 8 seconds passed
+... 60%, 421376 KB, 48918 KB/s, 8 seconds passed
+... 60%, 421408 KB, 48920 KB/s, 8 seconds passed
+... 60%, 421440 KB, 48922 KB/s, 8 seconds passed
+... 60%, 421472 KB, 48924 KB/s, 8 seconds passed
+... 60%, 421504 KB, 48925 KB/s, 8 seconds passed
+... 60%, 421536 KB, 48927 KB/s, 8 seconds passed
+... 60%, 421568 KB, 48929 KB/s, 8 seconds passed
+... 60%, 421600 KB, 48931 KB/s, 8 seconds passed
+... 60%, 421632 KB, 48934 KB/s, 8 seconds passed
+... 60%, 421664 KB, 48936 KB/s, 8 seconds passed
+... 60%, 421696 KB, 48939 KB/s, 8 seconds passed
+... 60%, 421728 KB, 48941 KB/s, 8 seconds passed
+... 60%, 421760 KB, 48944 KB/s, 8 seconds passed
+... 60%, 421792 KB, 48946 KB/s, 8 seconds passed
+... 60%, 421824 KB, 48949 KB/s, 8 seconds passed
+... 60%, 421856 KB, 48952 KB/s, 8 seconds passed
+... 60%, 421888 KB, 48954 KB/s, 8 seconds passed
+... 60%, 421920 KB, 48957 KB/s, 8 seconds passed
+... 60%, 421952 KB, 48959 KB/s, 8 seconds passed
+... 60%, 421984 KB, 48962 KB/s, 8 seconds passed
+... 60%, 422016 KB, 48964 KB/s, 8 seconds passed
+... 60%, 422048 KB, 48966 KB/s, 8 seconds passed
+... 60%, 422080 KB, 48969 KB/s, 8 seconds passed
+... 60%, 422112 KB, 48971 KB/s, 8 seconds passed
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+... 60%, 422176 KB, 48976 KB/s, 8 seconds passed
+... 60%, 422208 KB, 48979 KB/s, 8 seconds passed
+... 60%, 422240 KB, 48981 KB/s, 8 seconds passed
+... 60%, 422272 KB, 48984 KB/s, 8 seconds passed
+... 60%, 422304 KB, 48987 KB/s, 8 seconds passed
+... 60%, 422336 KB, 48989 KB/s, 8 seconds passed
+... 60%, 422368 KB, 48992 KB/s, 8 seconds passed
+... 60%, 422400 KB, 48994 KB/s, 8 seconds passed
+... 60%, 422432 KB, 48997 KB/s, 8 seconds passed
+... 60%, 422464 KB, 48999 KB/s, 8 seconds passed
+... 60%, 422496 KB, 49002 KB/s, 8 seconds passed
+... 60%, 422528 KB, 49004 KB/s, 8 seconds passed
+... 60%, 422560 KB, 49007 KB/s, 8 seconds passed
+... 60%, 422592 KB, 49010 KB/s, 8 seconds passed
+... 60%, 422624 KB, 49012 KB/s, 8 seconds passed
+... 60%, 422656 KB, 49015 KB/s, 8 seconds passed
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+... 60%, 422720 KB, 49020 KB/s, 8 seconds passed
+... 60%, 422752 KB, 49022 KB/s, 8 seconds passed
+... 60%, 422784 KB, 49025 KB/s, 8 seconds passed
+... 60%, 422816 KB, 49027 KB/s, 8 seconds passed
+... 60%, 422848 KB, 49030 KB/s, 8 seconds passed
+... 60%, 422880 KB, 49032 KB/s, 8 seconds passed
+... 60%, 422912 KB, 49035 KB/s, 8 seconds passed
+... 60%, 422944 KB, 49037 KB/s, 8 seconds passed
+... 60%, 422976 KB, 49040 KB/s, 8 seconds passed
+... 60%, 423008 KB, 49043 KB/s, 8 seconds passed
+... 60%, 423040 KB, 49045 KB/s, 8 seconds passed
+... 60%, 423072 KB, 49048 KB/s, 8 seconds passed
+... 60%, 423104 KB, 49050 KB/s, 8 seconds passed
+... 60%, 423136 KB, 49053 KB/s, 8 seconds passed
+... 60%, 423168 KB, 49056 KB/s, 8 seconds passed
+... 60%, 423200 KB, 49058 KB/s, 8 seconds passed
+... 60%, 423232 KB, 49060 KB/s, 8 seconds passed
+... 60%, 423264 KB, 49062 KB/s, 8 seconds passed
+... 60%, 423296 KB, 49064 KB/s, 8 seconds passed
+... 60%, 423328 KB, 49066 KB/s, 8 seconds passed
+... 60%, 423360 KB, 49068 KB/s, 8 seconds passed
+... 60%, 423392 KB, 49071 KB/s, 8 seconds passed
+... 60%, 423424 KB, 49073 KB/s, 8 seconds passed
+... 60%, 423456 KB, 49075 KB/s, 8 seconds passed
+... 60%, 423488 KB, 49076 KB/s, 8 seconds passed
+... 60%, 423520 KB, 49079 KB/s, 8 seconds passed
+... 60%, 423552 KB, 49081 KB/s, 8 seconds passed
+... 60%, 423584 KB, 49084 KB/s, 8 seconds passed
+... 60%, 423616 KB, 49086 KB/s, 8 seconds passed
+... 60%, 423648 KB, 49088 KB/s, 8 seconds passed
+... 60%, 423680 KB, 49077 KB/s, 8 seconds passed
+... 60%, 423712 KB, 49079 KB/s, 8 seconds passed
+... 60%, 423744 KB, 49081 KB/s, 8 seconds passed
+... 60%, 423776 KB, 49083 KB/s, 8 seconds passed
+... 60%, 423808 KB, 49085 KB/s, 8 seconds passed
+... 60%, 423840 KB, 49087 KB/s, 8 seconds passed
+... 60%, 423872 KB, 49089 KB/s, 8 seconds passed
+... 60%, 423904 KB, 49091 KB/s, 8 seconds passed
+... 60%, 423936 KB, 49093 KB/s, 8 seconds passed
+... 60%, 423968 KB, 49096 KB/s, 8 seconds passed
+... 60%, 424000 KB, 49098 KB/s, 8 seconds passed
+... 60%, 424032 KB, 49100 KB/s, 8 seconds passed
+... 60%, 424064 KB, 49102 KB/s, 8 seconds passed
+... 60%, 424096 KB, 49104 KB/s, 8 seconds passed
+... 60%, 424128 KB, 49106 KB/s, 8 seconds passed
+... 60%, 424160 KB, 49108 KB/s, 8 seconds passed
+... 60%, 424192 KB, 49110 KB/s, 8 seconds passed
+... 60%, 424224 KB, 49105 KB/s, 8 seconds passed
+... 60%, 424256 KB, 49105 KB/s, 8 seconds passed
+... 61%, 424288 KB, 49108 KB/s, 8 seconds passed
+... 61%, 424320 KB, 49110 KB/s, 8 seconds passed
+... 61%, 424352 KB, 49112 KB/s, 8 seconds passed
+... 61%, 424384 KB, 49114 KB/s, 8 seconds passed
+... 61%, 424416 KB, 49116 KB/s, 8 seconds passed
+... 61%, 424448 KB, 49118 KB/s, 8 seconds passed
+... 61%, 424480 KB, 49121 KB/s, 8 seconds passed
+... 61%, 424512 KB, 49123 KB/s, 8 seconds passed
+... 61%, 424544 KB, 49125 KB/s, 8 seconds passed
+... 61%, 424576 KB, 49127 KB/s, 8 seconds passed
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+... 61%, 424640 KB, 49131 KB/s, 8 seconds passed
+... 61%, 424672 KB, 49133 KB/s, 8 seconds passed
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+... 61%, 424736 KB, 49138 KB/s, 8 seconds passed
+... 61%, 424768 KB, 49140 KB/s, 8 seconds passed
+... 61%, 424800 KB, 49142 KB/s, 8 seconds passed
+... 61%, 424832 KB, 49144 KB/s, 8 seconds passed
+... 61%, 424864 KB, 49146 KB/s, 8 seconds passed
+... 61%, 424896 KB, 49148 KB/s, 8 seconds passed
+... 61%, 424928 KB, 49150 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
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+... 61%, 424992 KB, 48993 KB/s, 8 seconds passed
+... 61%, 425024 KB, 48995 KB/s, 8 seconds passed
+... 61%, 425056 KB, 48996 KB/s, 8 seconds passed
+... 61%, 425088 KB, 48997 KB/s, 8 seconds passed
+... 61%, 425120 KB, 48998 KB/s, 8 seconds passed
+... 61%, 425152 KB, 49000 KB/s, 8 seconds passed
+... 61%, 425184 KB, 49002 KB/s, 8 seconds passed
+... 61%, 425216 KB, 49003 KB/s, 8 seconds passed
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+... 61%, 425280 KB, 48998 KB/s, 8 seconds passed
+... 61%, 425312 KB, 49000 KB/s, 8 seconds passed
+... 61%, 425344 KB, 49001 KB/s, 8 seconds passed
+... 61%, 425376 KB, 49003 KB/s, 8 seconds passed
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+... 61%, 425440 KB, 49006 KB/s, 8 seconds passed
+... 61%, 425472 KB, 49008 KB/s, 8 seconds passed
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+... 61%, 425536 KB, 49011 KB/s, 8 seconds passed
+... 61%, 425568 KB, 49012 KB/s, 8 seconds passed
+... 61%, 425600 KB, 49014 KB/s, 8 seconds passed
+... 61%, 425632 KB, 49015 KB/s, 8 seconds passed
+... 61%, 425664 KB, 49017 KB/s, 8 seconds passed
+... 61%, 425696 KB, 49019 KB/s, 8 seconds passed
+... 61%, 425728 KB, 49020 KB/s, 8 seconds passed
+... 61%, 425760 KB, 49022 KB/s, 8 seconds passed
+... 61%, 425792 KB, 49023 KB/s, 8 seconds passed
+... 61%, 425824 KB, 49025 KB/s, 8 seconds passed
+... 61%, 425856 KB, 49026 KB/s, 8 seconds passed
+... 61%, 425888 KB, 49028 KB/s, 8 seconds passed
+... 61%, 425920 KB, 49030 KB/s, 8 seconds passed
+... 61%, 425952 KB, 49031 KB/s, 8 seconds passed
+... 61%, 425984 KB, 49033 KB/s, 8 seconds passed
+... 61%, 426016 KB, 49035 KB/s, 8 seconds passed
+... 61%, 426048 KB, 49037 KB/s, 8 seconds passed
+... 61%, 426080 KB, 49039 KB/s, 8 seconds passed
+... 61%, 426112 KB, 49041 KB/s, 8 seconds passed
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+... 61%, 426176 KB, 49045 KB/s, 8 seconds passed
+... 61%, 426208 KB, 49047 KB/s, 8 seconds passed
+... 61%, 426240 KB, 49049 KB/s, 8 seconds passed
+... 61%, 426272 KB, 49051 KB/s, 8 seconds passed
+... 61%, 426304 KB, 49053 KB/s, 8 seconds passed
+... 61%, 426336 KB, 49055 KB/s, 8 seconds passed
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+... 61%, 426400 KB, 49059 KB/s, 8 seconds passed
+... 61%, 426432 KB, 49061 KB/s, 8 seconds passed
+... 61%, 426464 KB, 49063 KB/s, 8 seconds passed
+... 61%, 426496 KB, 49064 KB/s, 8 seconds passed
+... 61%, 426528 KB, 49067 KB/s, 8 seconds passed
+... 61%, 426560 KB, 49068 KB/s, 8 seconds passed
+... 61%, 426592 KB, 49070 KB/s, 8 seconds passed
+... 61%, 426624 KB, 49072 KB/s, 8 seconds passed
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+... 61%, 426688 KB, 49076 KB/s, 8 seconds passed
+... 61%, 426720 KB, 49078 KB/s, 8 seconds passed
+... 61%, 426752 KB, 49080 KB/s, 8 seconds passed
+... 61%, 426784 KB, 49082 KB/s, 8 seconds passed
+... 61%, 426816 KB, 49084 KB/s, 8 seconds passed
+... 61%, 426848 KB, 49086 KB/s, 8 seconds passed
+... 61%, 426880 KB, 49088 KB/s, 8 seconds passed
+... 61%, 426912 KB, 49090 KB/s, 8 seconds passed
+... 61%, 426944 KB, 49092 KB/s, 8 seconds passed
+... 61%, 426976 KB, 49095 KB/s, 8 seconds passed
+... 61%, 427008 KB, 49097 KB/s, 8 seconds passed
+... 61%, 427040 KB, 49100 KB/s, 8 seconds passed
+... 61%, 427072 KB, 49102 KB/s, 8 seconds passed
+... 61%, 427104 KB, 49105 KB/s, 8 seconds passed
+... 61%, 427136 KB, 49107 KB/s, 8 seconds passed
+... 61%, 427168 KB, 49110 KB/s, 8 seconds passed
+... 61%, 427200 KB, 49112 KB/s, 8 seconds passed
+... 61%, 427232 KB, 49115 KB/s, 8 seconds passed
+... 61%, 427264 KB, 49117 KB/s, 8 seconds passed
+... 61%, 427296 KB, 49120 KB/s, 8 seconds passed
+... 61%, 427328 KB, 49122 KB/s, 8 seconds passed
+... 61%, 427360 KB, 49125 KB/s, 8 seconds passed
+... 61%, 427392 KB, 49127 KB/s, 8 seconds passed
+... 61%, 427424 KB, 49130 KB/s, 8 seconds passed
+... 61%, 427456 KB, 49132 KB/s, 8 seconds passed
+... 61%, 427488 KB, 49135 KB/s, 8 seconds passed
+... 61%, 427520 KB, 49137 KB/s, 8 seconds passed
+... 61%, 427552 KB, 49140 KB/s, 8 seconds passed
+... 61%, 427584 KB, 49142 KB/s, 8 seconds passed
+... 61%, 427616 KB, 49145 KB/s, 8 seconds passed
+... 61%, 427648 KB, 49147 KB/s, 8 seconds passed
+... 61%, 427680 KB, 49150 KB/s, 8 seconds passed
+... 61%, 427712 KB, 49152 KB/s, 8 seconds passed
+... 61%, 427744 KB, 49155 KB/s, 8 seconds passed
+... 61%, 427776 KB, 49157 KB/s, 8 seconds passed
+... 61%, 427808 KB, 49160 KB/s, 8 seconds passed
+... 61%, 427840 KB, 49162 KB/s, 8 seconds passed
+... 61%, 427872 KB, 49165 KB/s, 8 seconds passed
+... 61%, 427904 KB, 49168 KB/s, 8 seconds passed
+... 61%, 427936 KB, 49170 KB/s, 8 seconds passed
+... 61%, 427968 KB, 49172 KB/s, 8 seconds passed
+... 61%, 428000 KB, 49175 KB/s, 8 seconds passed
+... 61%, 428032 KB, 49177 KB/s, 8 seconds passed
+... 61%, 428064 KB, 49180 KB/s, 8 seconds passed
+... 61%, 428096 KB, 49183 KB/s, 8 seconds passed
+... 61%, 428128 KB, 49185 KB/s, 8 seconds passed
+... 61%, 428160 KB, 49188 KB/s, 8 seconds passed
+... 61%, 428192 KB, 49190 KB/s, 8 seconds passed
+... 61%, 428224 KB, 49192 KB/s, 8 seconds passed
+... 61%, 428256 KB, 49194 KB/s, 8 seconds passed
+... 61%, 428288 KB, 49197 KB/s, 8 seconds passed
+... 61%, 428320 KB, 49199 KB/s, 8 seconds passed
+... 61%, 428352 KB, 49201 KB/s, 8 seconds passed
+... 61%, 428384 KB, 49202 KB/s, 8 seconds passed
+... 61%, 428416 KB, 49205 KB/s, 8 seconds passed
+... 61%, 428448 KB, 49207 KB/s, 8 seconds passed
+... 61%, 428480 KB, 49209 KB/s, 8 seconds passed
+... 61%, 428512 KB, 49211 KB/s, 8 seconds passed
+... 61%, 428544 KB, 49213 KB/s, 8 seconds passed
+... 61%, 428576 KB, 49215 KB/s, 8 seconds passed
+... 61%, 428608 KB, 49218 KB/s, 8 seconds passed
+... 61%, 428640 KB, 49219 KB/s, 8 seconds passed
+... 61%, 428672 KB, 49222 KB/s, 8 seconds passed
+... 61%, 428704 KB, 49224 KB/s, 8 seconds passed
+... 61%, 428736 KB, 49226 KB/s, 8 seconds passed
+... 61%, 428768 KB, 49227 KB/s, 8 seconds passed
+... 61%, 428800 KB, 49228 KB/s, 8 seconds passed
+... 61%, 428832 KB, 49230 KB/s, 8 seconds passed
+... 61%, 428864 KB, 49226 KB/s, 8 seconds passed
+... 61%, 428896 KB, 49228 KB/s, 8 seconds passed
+... 61%, 428928 KB, 49230 KB/s, 8 seconds passed
+... 61%, 428960 KB, 49233 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 61%, 428992 KB, 49235 KB/s, 8 seconds passed
+... 61%, 429024 KB, 49237 KB/s, 8 seconds passed
+... 61%, 429056 KB, 49239 KB/s, 8 seconds passed
+... 61%, 429088 KB, 49241 KB/s, 8 seconds passed
+... 61%, 429120 KB, 49243 KB/s, 8 seconds passed
+... 61%, 429152 KB, 49245 KB/s, 8 seconds passed
+... 61%, 429184 KB, 49247 KB/s, 8 seconds passed
+... 61%, 429216 KB, 49249 KB/s, 8 seconds passed
+... 61%, 429248 KB, 49251 KB/s, 8 seconds passed
+... 61%, 429280 KB, 49253 KB/s, 8 seconds passed
+... 61%, 429312 KB, 49255 KB/s, 8 seconds passed
+... 61%, 429344 KB, 49258 KB/s, 8 seconds passed
+... 61%, 429376 KB, 49260 KB/s, 8 seconds passed
+... 61%, 429408 KB, 49262 KB/s, 8 seconds passed
+... 61%, 429440 KB, 49263 KB/s, 8 seconds passed
+... 61%, 429472 KB, 49266 KB/s, 8 seconds passed
+... 61%, 429504 KB, 49268 KB/s, 8 seconds passed
+... 61%, 429536 KB, 49270 KB/s, 8 seconds passed
+... 61%, 429568 KB, 49272 KB/s, 8 seconds passed
+... 61%, 429600 KB, 49274 KB/s, 8 seconds passed
+... 61%, 429632 KB, 49277 KB/s, 8 seconds passed
+... 61%, 429664 KB, 49278 KB/s, 8 seconds passed
+... 61%, 429696 KB, 49280 KB/s, 8 seconds passed
+... 61%, 429728 KB, 49283 KB/s, 8 seconds passed
+... 61%, 429760 KB, 49285 KB/s, 8 seconds passed
+... 61%, 429792 KB, 49287 KB/s, 8 seconds passed
+... 61%, 429824 KB, 49289 KB/s, 8 seconds passed
+... 61%, 429856 KB, 49291 KB/s, 8 seconds passed
+... 61%, 429888 KB, 49293 KB/s, 8 seconds passed
+... 61%, 429920 KB, 49296 KB/s, 8 seconds passed
+... 61%, 429952 KB, 49298 KB/s, 8 seconds passed
+... 61%, 429984 KB, 49300 KB/s, 8 seconds passed
+... 61%, 430016 KB, 49302 KB/s, 8 seconds passed
+... 61%, 430048 KB, 49303 KB/s, 8 seconds passed
+... 61%, 430080 KB, 49133 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 61%, 430112 KB, 49076 KB/s, 8 seconds passed
+... 61%, 430144 KB, 49076 KB/s, 8 seconds passed
+... 61%, 430176 KB, 49078 KB/s, 8 seconds passed
+... 61%, 430208 KB, 49079 KB/s, 8 seconds passed
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+... 61%, 430272 KB, 49082 KB/s, 8 seconds passed
+... 61%, 430304 KB, 49084 KB/s, 8 seconds passed
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+... 61%, 430528 KB, 49094 KB/s, 8 seconds passed
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+... 61%, 430592 KB, 49097 KB/s, 8 seconds passed
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+... 61%, 430720 KB, 49103 KB/s, 8 seconds passed
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+... 61%, 431008 KB, 49117 KB/s, 8 seconds passed
+... 61%, 431040 KB, 49118 KB/s, 8 seconds passed
+... 61%, 431072 KB, 49120 KB/s, 8 seconds passed
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+... 61%, 431136 KB, 49123 KB/s, 8 seconds passed
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+... 61%, 431200 KB, 49126 KB/s, 8 seconds passed
+... 62%, 431232 KB, 49128 KB/s, 8 seconds passed
+... 62%, 431264 KB, 49130 KB/s, 8 seconds passed
+... 62%, 431296 KB, 49133 KB/s, 8 seconds passed
+... 62%, 431328 KB, 49135 KB/s, 8 seconds passed
+... 62%, 431360 KB, 49137 KB/s, 8 seconds passed
+... 62%, 431392 KB, 49140 KB/s, 8 seconds passed
+... 62%, 431424 KB, 49142 KB/s, 8 seconds passed
+... 62%, 431456 KB, 49144 KB/s, 8 seconds passed
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+... 62%, 431520 KB, 49148 KB/s, 8 seconds passed
+... 62%, 431552 KB, 49151 KB/s, 8 seconds passed
+... 62%, 431584 KB, 49153 KB/s, 8 seconds passed
+... 62%, 431616 KB, 49155 KB/s, 8 seconds passed
+... 62%, 431648 KB, 49158 KB/s, 8 seconds passed
+... 62%, 431680 KB, 49160 KB/s, 8 seconds passed
+... 62%, 431712 KB, 49162 KB/s, 8 seconds passed
+... 62%, 431744 KB, 49164 KB/s, 8 seconds passed
+... 62%, 431776 KB, 49167 KB/s, 8 seconds passed
+... 62%, 431808 KB, 49169 KB/s, 8 seconds passed
+... 62%, 431840 KB, 49171 KB/s, 8 seconds passed
+... 62%, 431872 KB, 49173 KB/s, 8 seconds passed
+... 62%, 431904 KB, 49176 KB/s, 8 seconds passed
+... 62%, 431936 KB, 49178 KB/s, 8 seconds passed
+... 62%, 431968 KB, 49180 KB/s, 8 seconds passed
+... 62%, 432000 KB, 49182 KB/s, 8 seconds passed
+... 62%, 432032 KB, 49185 KB/s, 8 seconds passed
+... 62%, 432064 KB, 49187 KB/s, 8 seconds passed
+... 62%, 432096 KB, 49189 KB/s, 8 seconds passed
+... 62%, 432128 KB, 49192 KB/s, 8 seconds passed
+... 62%, 432160 KB, 49194 KB/s, 8 seconds passed
+... 62%, 432192 KB, 49196 KB/s, 8 seconds passed
+... 62%, 432224 KB, 49199 KB/s, 8 seconds passed
+... 62%, 432256 KB, 49201 KB/s, 8 seconds passed
+... 62%, 432288 KB, 49203 KB/s, 8 seconds passed
+... 62%, 432320 KB, 49205 KB/s, 8 seconds passed
+... 62%, 432352 KB, 49208 KB/s, 8 seconds passed
+... 62%, 432384 KB, 49210 KB/s, 8 seconds passed
+... 62%, 432416 KB, 49212 KB/s, 8 seconds passed
+... 62%, 432448 KB, 49215 KB/s, 8 seconds passed
+... 62%, 432480 KB, 49217 KB/s, 8 seconds passed
+... 62%, 432512 KB, 49219 KB/s, 8 seconds passed
+... 62%, 432544 KB, 49222 KB/s, 8 seconds passed
+... 62%, 432576 KB, 49224 KB/s, 8 seconds passed
+... 62%, 432608 KB, 49227 KB/s, 8 seconds passed
+... 62%, 432640 KB, 49230 KB/s, 8 seconds passed
+... 62%, 432672 KB, 49232 KB/s, 8 seconds passed
+... 62%, 432704 KB, 49235 KB/s, 8 seconds passed
+... 62%, 432736 KB, 49238 KB/s, 8 seconds passed
+... 62%, 432768 KB, 49240 KB/s, 8 seconds passed
+... 62%, 432800 KB, 49243 KB/s, 8 seconds passed
+... 62%, 432832 KB, 49246 KB/s, 8 seconds passed
+... 62%, 432864 KB, 49249 KB/s, 8 seconds passed
+... 62%, 432896 KB, 49251 KB/s, 8 seconds passed
+... 62%, 432928 KB, 49254 KB/s, 8 seconds passed
+... 62%, 432960 KB, 49257 KB/s, 8 seconds passed
+... 62%, 432992 KB, 49259 KB/s, 8 seconds passed
+... 62%, 433024 KB, 49262 KB/s, 8 seconds passed
+... 62%, 433056 KB, 49265 KB/s, 8 seconds passed
+... 62%, 433088 KB, 49268 KB/s, 8 seconds passed
+... 62%, 433120 KB, 49270 KB/s, 8 seconds passed
+... 62%, 433152 KB, 49273 KB/s, 8 seconds passed
+... 62%, 433184 KB, 49276 KB/s, 8 seconds passed
+... 62%, 433216 KB, 49278 KB/s, 8 seconds passed
+... 62%, 433248 KB, 49280 KB/s, 8 seconds passed
+... 62%, 433280 KB, 49282 KB/s, 8 seconds passed
+... 62%, 433312 KB, 49284 KB/s, 8 seconds passed
+... 62%, 433344 KB, 49285 KB/s, 8 seconds passed
+... 62%, 433376 KB, 49288 KB/s, 8 seconds passed
+... 62%, 433408 KB, 49291 KB/s, 8 seconds passed
+... 62%, 433440 KB, 49292 KB/s, 8 seconds passed
+... 62%, 433472 KB, 49295 KB/s, 8 seconds passed
+... 62%, 433504 KB, 49297 KB/s, 8 seconds passed
+... 62%, 433536 KB, 49298 KB/s, 8 seconds passed
+... 62%, 433568 KB, 49300 KB/s, 8 seconds passed
+... 62%, 433600 KB, 49303 KB/s, 8 seconds passed
+... 62%, 433632 KB, 49305 KB/s, 8 seconds passed
+... 62%, 433664 KB, 49307 KB/s, 8 seconds passed
+... 62%, 433696 KB, 49309 KB/s, 8 seconds passed
+... 62%, 433728 KB, 49311 KB/s, 8 seconds passed
+... 62%, 433760 KB, 49313 KB/s, 8 seconds passed
+... 62%, 433792 KB, 49315 KB/s, 8 seconds passed
+... 62%, 433824 KB, 49317 KB/s, 8 seconds passed
+... 62%, 433856 KB, 49319 KB/s, 8 seconds passed
+... 62%, 433888 KB, 49322 KB/s, 8 seconds passed
+... 62%, 433920 KB, 49324 KB/s, 8 seconds passed
+... 62%, 433952 KB, 49326 KB/s, 8 seconds passed
+... 62%, 433984 KB, 49328 KB/s, 8 seconds passed
+... 62%, 434016 KB, 49330 KB/s, 8 seconds passed
+... 62%, 434048 KB, 49332 KB/s, 8 seconds passed
+... 62%, 434080 KB, 49335 KB/s, 8 seconds passed
+... 62%, 434112 KB, 49336 KB/s, 8 seconds passed
+... 62%, 434144 KB, 49338 KB/s, 8 seconds passed
+... 62%, 434176 KB, 49339 KB/s, 8 seconds passed
+... 62%, 434208 KB, 49340 KB/s, 8 seconds passed
+... 62%, 434240 KB, 49342 KB/s, 8 seconds passed
+... 62%, 434272 KB, 49345 KB/s, 8 seconds passed
+... 62%, 434304 KB, 49347 KB/s, 8 seconds passed
+... 62%, 434336 KB, 49350 KB/s, 8 seconds passed
+... 62%, 434368 KB, 49353 KB/s, 8 seconds passed
+... 62%, 434400 KB, 49355 KB/s, 8 seconds passed
+... 62%, 434432 KB, 49357 KB/s, 8 seconds passed
+... 62%, 434464 KB, 49359 KB/s, 8 seconds passed
+... 62%, 434496 KB, 49361 KB/s, 8 seconds passed
+... 62%, 434528 KB, 49363 KB/s, 8 seconds passed
+... 62%, 434560 KB, 49364 KB/s, 8 seconds passed
+... 62%, 434592 KB, 49366 KB/s, 8 seconds passed
+... 62%, 434624 KB, 49369 KB/s, 8 seconds passed
+... 62%, 434656 KB, 49371 KB/s, 8 seconds passed
+... 62%, 434688 KB, 49372 KB/s, 8 seconds passed
+... 62%, 434720 KB, 49373 KB/s, 8 seconds passed
+... 62%, 434752 KB, 49375 KB/s, 8 seconds passed
+... 62%, 434784 KB, 49377 KB/s, 8 seconds passed
+... 62%, 434816 KB, 49380 KB/s, 8 seconds passed
+... 62%, 434848 KB, 49383 KB/s, 8 seconds passed
+... 62%, 434880 KB, 49385 KB/s, 8 seconds passed
+... 62%, 434912 KB, 49387 KB/s, 8 seconds passed
+... 62%, 434944 KB, 49390 KB/s, 8 seconds passed
+... 62%, 434976 KB, 49392 KB/s, 8 seconds passed
+... 62%, 435008 KB, 49392 KB/s, 8 seconds passed
+... 62%, 435040 KB, 49394 KB/s, 8 seconds passed
+... 62%, 435072 KB, 49396 KB/s, 8 seconds passed
+... 62%, 435104 KB, 49398 KB/s, 8 seconds passed
+... 62%, 435136 KB, 49400 KB/s, 8 seconds passed
+... 62%, 435168 KB, 49402 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 62%, 435200 KB, 49203 KB/s, 8 seconds passed
+... 62%, 435232 KB, 49203 KB/s, 8 seconds passed
+... 62%, 435264 KB, 49205 KB/s, 8 seconds passed
+... 62%, 435296 KB, 49206 KB/s, 8 seconds passed
+... 62%, 435328 KB, 49207 KB/s, 8 seconds passed
+... 62%, 435360 KB, 49206 KB/s, 8 seconds passed
+... 62%, 435392 KB, 49208 KB/s, 8 seconds passed
+... 62%, 435424 KB, 49209 KB/s, 8 seconds passed
+... 62%, 435456 KB, 49210 KB/s, 8 seconds passed
+... 62%, 435488 KB, 49212 KB/s, 8 seconds passed
+... 62%, 435520 KB, 49214 KB/s, 8 seconds passed
+... 62%, 435552 KB, 49215 KB/s, 8 seconds passed
+... 62%, 435584 KB, 49217 KB/s, 8 seconds passed
+... 62%, 435616 KB, 49219 KB/s, 8 seconds passed
+... 62%, 435648 KB, 49220 KB/s, 8 seconds passed
+... 62%, 435680 KB, 49222 KB/s, 8 seconds passed
+... 62%, 435712 KB, 49224 KB/s, 8 seconds passed
+... 62%, 435744 KB, 49225 KB/s, 8 seconds passed
+... 62%, 435776 KB, 49227 KB/s, 8 seconds passed
+... 62%, 435808 KB, 49229 KB/s, 8 seconds passed
+... 62%, 435840 KB, 49230 KB/s, 8 seconds passed
+... 62%, 435872 KB, 49232 KB/s, 8 seconds passed
+... 62%, 435904 KB, 49234 KB/s, 8 seconds passed
+... 62%, 435936 KB, 49235 KB/s, 8 seconds passed
+... 62%, 435968 KB, 49237 KB/s, 8 seconds passed
+... 62%, 436000 KB, 49238 KB/s, 8 seconds passed
+... 62%, 436032 KB, 49240 KB/s, 8 seconds passed
+... 62%, 436064 KB, 49242 KB/s, 8 seconds passed
+... 62%, 436096 KB, 49243 KB/s, 8 seconds passed
+... 62%, 436128 KB, 49245 KB/s, 8 seconds passed
+... 62%, 436160 KB, 49246 KB/s, 8 seconds passed
+... 62%, 436192 KB, 49248 KB/s, 8 seconds passed
+... 62%, 436224 KB, 49250 KB/s, 8 seconds passed
+... 62%, 436256 KB, 49251 KB/s, 8 seconds passed
+... 62%, 436288 KB, 49253 KB/s, 8 seconds passed
+... 62%, 436320 KB, 49255 KB/s, 8 seconds passed
+... 62%, 436352 KB, 49256 KB/s, 8 seconds passed
+... 62%, 436384 KB, 49259 KB/s, 8 seconds passed
+... 62%, 436416 KB, 49261 KB/s, 8 seconds passed
+... 62%, 436448 KB, 49263 KB/s, 8 seconds passed
+... 62%, 436480 KB, 49266 KB/s, 8 seconds passed
+... 62%, 436512 KB, 49268 KB/s, 8 seconds passed
+... 62%, 436544 KB, 49270 KB/s, 8 seconds passed
+... 62%, 436576 KB, 49273 KB/s, 8 seconds passed
+... 62%, 436608 KB, 49275 KB/s, 8 seconds passed
+... 62%, 436640 KB, 49277 KB/s, 8 seconds passed
+... 62%, 436672 KB, 49280 KB/s, 8 seconds passed
+... 62%, 436704 KB, 49282 KB/s, 8 seconds passed
+... 62%, 436736 KB, 49284 KB/s, 8 seconds passed
+... 62%, 436768 KB, 49286 KB/s, 8 seconds passed
+... 62%, 436800 KB, 49289 KB/s, 8 seconds passed
+... 62%, 436832 KB, 49291 KB/s, 8 seconds passed
+... 62%, 436864 KB, 49293 KB/s, 8 seconds passed
+... 62%, 436896 KB, 49296 KB/s, 8 seconds passed
+... 62%, 436928 KB, 49298 KB/s, 8 seconds passed
+... 62%, 436960 KB, 49300 KB/s, 8 seconds passed
+... 62%, 436992 KB, 49303 KB/s, 8 seconds passed
+... 62%, 437024 KB, 49305 KB/s, 8 seconds passed
+... 62%, 437056 KB, 49307 KB/s, 8 seconds passed
+... 62%, 437088 KB, 49310 KB/s, 8 seconds passed
+... 62%, 437120 KB, 49312 KB/s, 8 seconds passed
+... 62%, 437152 KB, 49314 KB/s, 8 seconds passed
+... 62%, 437184 KB, 49317 KB/s, 8 seconds passed
+... 62%, 437216 KB, 49319 KB/s, 8 seconds passed
+... 62%, 437248 KB, 49321 KB/s, 8 seconds passed
+... 62%, 437280 KB, 49324 KB/s, 8 seconds passed
+... 62%, 437312 KB, 49326 KB/s, 8 seconds passed
+... 62%, 437344 KB, 49328 KB/s, 8 seconds passed
+... 62%, 437376 KB, 49331 KB/s, 8 seconds passed
+... 62%, 437408 KB, 49333 KB/s, 8 seconds passed
+... 62%, 437440 KB, 49335 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 62%, 437472 KB, 49338 KB/s, 8 seconds passed
+... 62%, 437504 KB, 49340 KB/s, 8 seconds passed
+... 62%, 437536 KB, 49342 KB/s, 8 seconds passed
+... 62%, 437568 KB, 49344 KB/s, 8 seconds passed
+... 62%, 437600 KB, 49347 KB/s, 8 seconds passed
+... 62%, 437632 KB, 49349 KB/s, 8 seconds passed
+... 62%, 437664 KB, 49352 KB/s, 8 seconds passed
+... 62%, 437696 KB, 49354 KB/s, 8 seconds passed
+... 62%, 437728 KB, 49356 KB/s, 8 seconds passed
+... 62%, 437760 KB, 49359 KB/s, 8 seconds passed
+... 62%, 437792 KB, 49362 KB/s, 8 seconds passed
+... 62%, 437824 KB, 49364 KB/s, 8 seconds passed
+... 62%, 437856 KB, 49367 KB/s, 8 seconds passed
+... 62%, 437888 KB, 49368 KB/s, 8 seconds passed
+... 62%, 437920 KB, 49370 KB/s, 8 seconds passed
+... 62%, 437952 KB, 49372 KB/s, 8 seconds passed
+... 62%, 437984 KB, 49375 KB/s, 8 seconds passed
+... 62%, 438016 KB, 49377 KB/s, 8 seconds passed
+... 62%, 438048 KB, 49379 KB/s, 8 seconds passed
+... 62%, 438080 KB, 49381 KB/s, 8 seconds passed
+... 62%, 438112 KB, 49383 KB/s, 8 seconds passed
+... 62%, 438144 KB, 49385 KB/s, 8 seconds passed
+... 63%, 438176 KB, 49388 KB/s, 8 seconds passed
+... 63%, 438208 KB, 49389 KB/s, 8 seconds passed
+... 63%, 438240 KB, 49391 KB/s, 8 seconds passed
+... 63%, 438272 KB, 49393 KB/s, 8 seconds passed
+... 63%, 438304 KB, 49395 KB/s, 8 seconds passed
+... 63%, 438336 KB, 49397 KB/s, 8 seconds passed
+... 63%, 438368 KB, 49400 KB/s, 8 seconds passed
+... 63%, 438400 KB, 49402 KB/s, 8 seconds passed
+... 63%, 438432 KB, 49404 KB/s, 8 seconds passed
+... 63%, 438464 KB, 49406 KB/s, 8 seconds passed
+... 63%, 438496 KB, 49408 KB/s, 8 seconds passed
+... 63%, 438528 KB, 49410 KB/s, 8 seconds passed
+... 63%, 438560 KB, 49413 KB/s, 8 seconds passed
+... 63%, 438592 KB, 49415 KB/s, 8 seconds passed
+... 63%, 438624 KB, 49415 KB/s, 8 seconds passed
+... 63%, 438656 KB, 49417 KB/s, 8 seconds passed
+... 63%, 438688 KB, 49420 KB/s, 8 seconds passed
+... 63%, 438720 KB, 49422 KB/s, 8 seconds passed
+... 63%, 438752 KB, 49424 KB/s, 8 seconds passed
+... 63%, 438784 KB, 49426 KB/s, 8 seconds passed
+... 63%, 438816 KB, 49429 KB/s, 8 seconds passed
+... 63%, 438848 KB, 49430 KB/s, 8 seconds passed
+... 63%, 438880 KB, 49432 KB/s, 8 seconds passed
+... 63%, 438912 KB, 49434 KB/s, 8 seconds passed
+... 63%, 438944 KB, 49436 KB/s, 8 seconds passed
+... 63%, 438976 KB, 49438 KB/s, 8 seconds passed
+... 63%, 439008 KB, 49440 KB/s, 8 seconds passed
+... 63%, 439040 KB, 49443 KB/s, 8 seconds passed
+... 63%, 439072 KB, 49445 KB/s, 8 seconds passed
+... 63%, 439104 KB, 49435 KB/s, 8 seconds passed
+... 63%, 439136 KB, 49437 KB/s, 8 seconds passed
+... 63%, 439168 KB, 49440 KB/s, 8 seconds passed
+... 63%, 439200 KB, 49442 KB/s, 8 seconds passed
+... 63%, 439232 KB, 49444 KB/s, 8 seconds passed
+... 63%, 439264 KB, 49445 KB/s, 8 seconds passed
+... 63%, 439296 KB, 49446 KB/s, 8 seconds passed
+... 63%, 439328 KB, 49449 KB/s, 8 seconds passed
+... 63%, 439360 KB, 49452 KB/s, 8 seconds passed
+... 63%, 439392 KB, 49454 KB/s, 8 seconds passed
+... 63%, 439424 KB, 49456 KB/s, 8 seconds passed
+... 63%, 439456 KB, 49457 KB/s, 8 seconds passed
+... 63%, 439488 KB, 49459 KB/s, 8 seconds passed
+... 63%, 439520 KB, 49461 KB/s, 8 seconds passed
+... 63%, 439552 KB, 49463 KB/s, 8 seconds passed
+... 63%, 439584 KB, 49466 KB/s, 8 seconds passed
+... 63%, 439616 KB, 49467 KB/s, 8 seconds passed
+... 63%, 439648 KB, 49469 KB/s, 8 seconds passed
+... 63%, 439680 KB, 49472 KB/s, 8 seconds passed
+... 63%, 439712 KB, 49474 KB/s, 8 seconds passed
+... 63%, 439744 KB, 49477 KB/s, 8 seconds passed
+... 63%, 439776 KB, 49479 KB/s, 8 seconds passed
+... 63%, 439808 KB, 49481 KB/s, 8 seconds passed
+... 63%, 439840 KB, 49482 KB/s, 8 seconds passed
+... 63%, 439872 KB, 49483 KB/s, 8 seconds passed
+... 63%, 439904 KB, 49486 KB/s, 8 seconds passed
+... 63%, 439936 KB, 49488 KB/s, 8 seconds passed
+... 63%, 439968 KB, 49490 KB/s, 8 seconds passed
+... 63%, 440000 KB, 49493 KB/s, 8 seconds passed
+... 63%, 440032 KB, 49495 KB/s, 8 seconds passed
+... 63%, 440064 KB, 49497 KB/s, 8 seconds passed
+... 63%, 440096 KB, 49498 KB/s, 8 seconds passed
+... 63%, 440128 KB, 49499 KB/s, 8 seconds passed
+... 63%, 440160 KB, 49502 KB/s, 8 seconds passed
+... 63%, 440192 KB, 49505 KB/s, 8 seconds passed
+... 63%, 440224 KB, 49507 KB/s, 8 seconds passed
+... 63%, 440256 KB, 49509 KB/s, 8 seconds passed
+... 63%, 440288 KB, 49511 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 63%, 440320 KB, 49290 KB/s, 8 seconds passed
+... 63%, 440352 KB, 49291 KB/s, 8 seconds passed
+... 63%, 440384 KB, 49292 KB/s, 8 seconds passed
+... 63%, 440416 KB, 49294 KB/s, 8 seconds passed
+... 63%, 440448 KB, 49295 KB/s, 8 seconds passed
+... 63%, 440480 KB, 49297 KB/s, 8 seconds passed
+... 63%, 440512 KB, 49298 KB/s, 8 seconds passed
+... 63%, 440544 KB, 49300 KB/s, 8 seconds passed
+... 63%, 440576 KB, 49302 KB/s, 8 seconds passed
+... 63%, 440608 KB, 49303 KB/s, 8 seconds passed
+... 63%, 440640 KB, 49305 KB/s, 8 seconds passed
+... 63%, 440672 KB, 49306 KB/s, 8 seconds passed
+... 63%, 440704 KB, 49308 KB/s, 8 seconds passed
+... 63%, 440736 KB, 49310 KB/s, 8 seconds passed
+... 63%, 440768 KB, 49311 KB/s, 8 seconds passed
+... 63%, 440800 KB, 49313 KB/s, 8 seconds passed
+... 63%, 440832 KB, 49314 KB/s, 8 seconds passed
+... 63%, 440864 KB, 49316 KB/s, 8 seconds passed
+... 63%, 440896 KB, 49317 KB/s, 8 seconds passed
+... 63%, 440928 KB, 49319 KB/s, 8 seconds passed
+... 63%, 440960 KB, 49321 KB/s, 8 seconds passed
+... 63%, 440992 KB, 49323 KB/s, 8 seconds passed
+... 63%, 441024 KB, 49324 KB/s, 8 seconds passed
+... 63%, 441056 KB, 49326 KB/s, 8 seconds passed
+... 63%, 441088 KB, 49327 KB/s, 8 seconds passed
+... 63%, 441120 KB, 49329 KB/s, 8 seconds passed
+... 63%, 441152 KB, 49331 KB/s, 8 seconds passed
+... 63%, 441184 KB, 49332 KB/s, 8 seconds passed
+... 63%, 441216 KB, 49334 KB/s, 8 seconds passed
+... 63%, 441248 KB, 49335 KB/s, 8 seconds passed
+... 63%, 441280 KB, 49337 KB/s, 8 seconds passed
+... 63%, 441312 KB, 49338 KB/s, 8 seconds passed
+... 63%, 441344 KB, 49340 KB/s, 8 seconds passed
+... 63%, 441376 KB, 49342 KB/s, 8 seconds passed
+... 63%, 441408 KB, 49343 KB/s, 8 seconds passed
+... 63%, 441440 KB, 49345 KB/s, 8 seconds passed
+... 63%, 441472 KB, 49347 KB/s, 8 seconds passed
+... 63%, 441504 KB, 49348 KB/s, 8 seconds passed
+... 63%, 441536 KB, 49350 KB/s, 8 seconds passed
+... 63%, 441568 KB, 49351 KB/s, 8 seconds passed
+... 63%, 441600 KB, 49353 KB/s, 8 seconds passed
+... 63%, 441632 KB, 49355 KB/s, 8 seconds passed
+... 63%, 441664 KB, 49356 KB/s, 8 seconds passed
+... 63%, 441696 KB, 49358 KB/s, 8 seconds passed
+... 63%, 441728 KB, 49360 KB/s, 8 seconds passed
+... 63%, 441760 KB, 49363 KB/s, 8 seconds passed
+... 63%, 441792 KB, 49365 KB/s, 8 seconds passed
+... 63%, 441824 KB, 49367 KB/s, 8 seconds passed
+... 63%, 441856 KB, 49369 KB/s, 8 seconds passed
+... 63%, 441888 KB, 49372 KB/s, 8 seconds passed
+... 63%, 441920 KB, 49374 KB/s, 8 seconds passed
+... 63%, 441952 KB, 49376 KB/s, 8 seconds passed
+... 63%, 441984 KB, 49378 KB/s, 8 seconds passed
+... 63%, 442016 KB, 49381 KB/s, 8 seconds passed
+... 63%, 442048 KB, 49383 KB/s, 8 seconds passed
+... 63%, 442080 KB, 49385 KB/s, 8 seconds passed
+... 63%, 442112 KB, 49387 KB/s, 8 seconds passed
+... 63%, 442144 KB, 49390 KB/s, 8 seconds passed
+... 63%, 442176 KB, 49392 KB/s, 8 seconds passed
+... 63%, 442208 KB, 49394 KB/s, 8 seconds passed
+... 63%, 442240 KB, 49397 KB/s, 8 seconds passed
+... 63%, 442272 KB, 49399 KB/s, 8 seconds passed
+... 63%, 442304 KB, 49401 KB/s, 8 seconds passed
+... 63%, 442336 KB, 49403 KB/s, 8 seconds passed
+... 63%, 442368 KB, 49406 KB/s, 8 seconds passed
+... 63%, 442400 KB, 49408 KB/s, 8 seconds passed
+... 63%, 442432 KB, 49410 KB/s, 8 seconds passed
+... 63%, 442464 KB, 49413 KB/s, 8 seconds passed
+... 63%, 442496 KB, 49415 KB/s, 8 seconds passed
+... 63%, 442528 KB, 49417 KB/s, 8 seconds passed
+... 63%, 442560 KB, 49420 KB/s, 8 seconds passed
+... 63%, 442592 KB, 49422 KB/s, 8 seconds passed
+... 63%, 442624 KB, 49424 KB/s, 8 seconds passed
+... 63%, 442656 KB, 49426 KB/s, 8 seconds passed
+... 63%, 442688 KB, 49429 KB/s, 8 seconds passed
+... 63%, 442720 KB, 49431 KB/s, 8 seconds passed
+... 63%, 442752 KB, 49433 KB/s, 8 seconds passed
+... 63%, 442784 KB, 49436 KB/s, 8 seconds passed
+... 63%, 442816 KB, 49438 KB/s, 8 seconds passed
+... 63%, 442848 KB, 49440 KB/s, 8 seconds passed
+... 63%, 442880 KB, 49443 KB/s, 8 seconds passed
+... 63%, 442912 KB, 49445 KB/s, 8 seconds passed
+... 63%, 442944 KB, 49447 KB/s, 8 seconds passed
+... 63%, 442976 KB, 49450 KB/s, 8 seconds passed
+... 63%, 443008 KB, 49452 KB/s, 8 seconds passed
+... 63%, 443040 KB, 49454 KB/s, 8 seconds passed
+... 63%, 443072 KB, 49456 KB/s, 8 seconds passed
+... 63%, 443104 KB, 49459 KB/s, 8 seconds passed
+... 63%, 443136 KB, 49461 KB/s, 8 seconds passed
+... 63%, 443168 KB, 49463 KB/s, 8 seconds passed
+... 63%, 443200 KB, 49466 KB/s, 8 seconds passed
+... 63%, 443232 KB, 49468 KB/s, 8 seconds passed
+... 63%, 443264 KB, 49470 KB/s, 8 seconds passed
+... 63%, 443296 KB, 49473 KB/s, 8 seconds passed
+... 63%, 443328 KB, 49476 KB/s, 8 seconds passed
+... 63%, 443360 KB, 49479 KB/s, 8 seconds passed
+... 63%, 443392 KB, 49481 KB/s, 8 seconds passed
+... 63%, 443424 KB, 49484 KB/s, 8 seconds passed
+... 63%, 443456 KB, 49487 KB/s, 8 seconds passed
+... 63%, 443488 KB, 49490 KB/s, 8 seconds passed
+... 63%, 443520 KB, 49492 KB/s, 8 seconds passed
+... 63%, 443552 KB, 49495 KB/s, 8 seconds passed
+... 63%, 443584 KB, 49497 KB/s, 8 seconds passed
+... 63%, 443616 KB, 49499 KB/s, 8 seconds passed
+... 63%, 443648 KB, 49502 KB/s, 8 seconds passed
+... 63%, 443680 KB, 49504 KB/s, 8 seconds passed
+... 63%, 443712 KB, 49506 KB/s, 8 seconds passed
+... 63%, 443744 KB, 49508 KB/s, 8 seconds passed
+... 63%, 443776 KB, 49509 KB/s, 8 seconds passed
+... 63%, 443808 KB, 49510 KB/s, 8 seconds passed
+... 63%, 443840 KB, 49512 KB/s, 8 seconds passed
+... 63%, 443872 KB, 49515 KB/s, 8 seconds passed
+... 63%, 443904 KB, 49517 KB/s, 8 seconds passed
+... 63%, 443936 KB, 49520 KB/s, 8 seconds passed
+... 63%, 443968 KB, 49522 KB/s, 8 seconds passed
+... 63%, 444000 KB, 49524 KB/s, 8 seconds passed
+
+.. parsed-literal::
+
+    ... 63%, 444032 KB, 49452 KB/s, 8 seconds passed
+... 63%, 444064 KB, 49453 KB/s, 8 seconds passed
+... 63%, 444096 KB, 49455 KB/s, 8 seconds passed
+... 63%, 444128 KB, 49456 KB/s, 8 seconds passed
+... 63%, 444160 KB, 49458 KB/s, 8 seconds passed
+... 63%, 444192 KB, 49460 KB/s, 8 seconds passed
+... 63%, 444224 KB, 49461 KB/s, 8 seconds passed
+... 63%, 444256 KB, 49463 KB/s, 8 seconds passed
+... 63%, 444288 KB, 49464 KB/s, 8 seconds passed
+... 63%, 444320 KB, 49466 KB/s, 8 seconds passed
+... 63%, 444352 KB, 49468 KB/s, 8 seconds passed
+... 63%, 444384 KB, 49469 KB/s, 8 seconds passed
+... 63%, 444416 KB, 49471 KB/s, 8 seconds passed
+... 63%, 444448 KB, 49473 KB/s, 8 seconds passed
+... 63%, 444480 KB, 49474 KB/s, 8 seconds passed
+... 63%, 444512 KB, 49476 KB/s, 8 seconds passed
+... 63%, 444544 KB, 49478 KB/s, 8 seconds passed
+... 63%, 444576 KB, 49479 KB/s, 8 seconds passed
+... 63%, 444608 KB, 49481 KB/s, 8 seconds passed
+... 63%, 444640 KB, 49483 KB/s, 8 seconds passed
+... 63%, 444672 KB, 49484 KB/s, 8 seconds passed
+... 63%, 444704 KB, 49486 KB/s, 8 seconds passed
+... 63%, 444736 KB, 49488 KB/s, 8 seconds passed
+... 63%, 444768 KB, 49489 KB/s, 8 seconds passed
+... 63%, 444800 KB, 49491 KB/s, 8 seconds passed
+... 63%, 444832 KB, 49493 KB/s, 8 seconds passed
+... 63%, 444864 KB, 49494 KB/s, 8 seconds passed
+... 63%, 444896 KB, 49496 KB/s, 8 seconds passed
+... 63%, 444928 KB, 49498 KB/s, 8 seconds passed
+... 63%, 444960 KB, 49499 KB/s, 8 seconds passed
+... 63%, 444992 KB, 49501 KB/s, 8 seconds passed
+... 63%, 445024 KB, 49503 KB/s, 8 seconds passed
+... 63%, 445056 KB, 49505 KB/s, 8 seconds passed
+... 63%, 445088 KB, 49507 KB/s, 8 seconds passed
+... 63%, 445120 KB, 49510 KB/s, 8 seconds passed
+... 64%, 445152 KB, 49512 KB/s, 8 seconds passed
+... 64%, 445184 KB, 49514 KB/s, 8 seconds passed
+... 64%, 445216 KB, 49517 KB/s, 8 seconds passed
+... 64%, 445248 KB, 49519 KB/s, 8 seconds passed
+... 64%, 445280 KB, 49521 KB/s, 8 seconds passed
+... 64%, 445312 KB, 49524 KB/s, 8 seconds passed
+... 64%, 445344 KB, 49526 KB/s, 8 seconds passed
+... 64%, 445376 KB, 49529 KB/s, 8 seconds passed
+... 64%, 445408 KB, 49531 KB/s, 8 seconds passed
+... 64%, 445440 KB, 49379 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
+    ... 64%, 445472 KB, 49380 KB/s, 9 seconds passed
+... 64%, 445504 KB, 49382 KB/s, 9 seconds passed
+... 64%, 445536 KB, 49384 KB/s, 9 seconds passed
+... 64%, 445568 KB, 49385 KB/s, 9 seconds passed
+... 64%, 445600 KB, 49387 KB/s, 9 seconds passed
+... 64%, 445632 KB, 49389 KB/s, 9 seconds passed
+... 64%, 445664 KB, 49390 KB/s, 9 seconds passed
+... 64%, 445696 KB, 49392 KB/s, 9 seconds passed
+... 64%, 445728 KB, 49394 KB/s, 9 seconds passed
+... 64%, 445760 KB, 49396 KB/s, 9 seconds passed
+... 64%, 445792 KB, 49397 KB/s, 9 seconds passed
+... 64%, 445824 KB, 49399 KB/s, 9 seconds passed
+... 64%, 445856 KB, 49401 KB/s, 9 seconds passed
+... 64%, 445888 KB, 49403 KB/s, 9 seconds passed
+... 64%, 445920 KB, 49405 KB/s, 9 seconds passed
+... 64%, 445952 KB, 49407 KB/s, 9 seconds passed
+... 64%, 445984 KB, 49408 KB/s, 9 seconds passed
+... 64%, 446016 KB, 49410 KB/s, 9 seconds passed
+... 64%, 446048 KB, 49412 KB/s, 9 seconds passed
+... 64%, 446080 KB, 49414 KB/s, 9 seconds passed
+... 64%, 446112 KB, 49416 KB/s, 9 seconds passed
+... 64%, 446144 KB, 49417 KB/s, 9 seconds passed
+... 64%, 446176 KB, 49419 KB/s, 9 seconds passed
+... 64%, 446208 KB, 49421 KB/s, 9 seconds passed
+... 64%, 446240 KB, 49423 KB/s, 9 seconds passed
+... 64%, 446272 KB, 49425 KB/s, 9 seconds passed
+... 64%, 446304 KB, 49427 KB/s, 9 seconds passed
+... 64%, 446336 KB, 49429 KB/s, 9 seconds passed
+... 64%, 446368 KB, 49431 KB/s, 9 seconds passed
+... 64%, 446400 KB, 49433 KB/s, 9 seconds passed
+... 64%, 446432 KB, 49435 KB/s, 9 seconds passed
+... 64%, 446464 KB, 49437 KB/s, 9 seconds passed
+... 64%, 446496 KB, 49439 KB/s, 9 seconds passed
+... 64%, 446528 KB, 49441 KB/s, 9 seconds passed
+... 64%, 446560 KB, 49443 KB/s, 9 seconds passed
+... 64%, 446592 KB, 49445 KB/s, 9 seconds passed
+... 64%, 446624 KB, 49448 KB/s, 9 seconds passed
+... 64%, 446656 KB, 49450 KB/s, 9 seconds passed
+... 64%, 446688 KB, 49452 KB/s, 9 seconds passed
+... 64%, 446720 KB, 49454 KB/s, 9 seconds passed
+... 64%, 446752 KB, 49456 KB/s, 9 seconds passed
+... 64%, 446784 KB, 49458 KB/s, 9 seconds passed
+... 64%, 446816 KB, 49460 KB/s, 9 seconds passed
+... 64%, 446848 KB, 49463 KB/s, 9 seconds passed
+... 64%, 446880 KB, 49465 KB/s, 9 seconds passed
+... 64%, 446912 KB, 49467 KB/s, 9 seconds passed
+... 64%, 446944 KB, 49469 KB/s, 9 seconds passed
+... 64%, 446976 KB, 49471 KB/s, 9 seconds passed
+... 64%, 447008 KB, 49473 KB/s, 9 seconds passed
+... 64%, 447040 KB, 49475 KB/s, 9 seconds passed
+... 64%, 447072 KB, 49478 KB/s, 9 seconds passed
+... 64%, 447104 KB, 49480 KB/s, 9 seconds passed
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+... 64%, 447200 KB, 49486 KB/s, 9 seconds passed
+... 64%, 447232 KB, 49488 KB/s, 9 seconds passed
+... 64%, 447264 KB, 49491 KB/s, 9 seconds passed
+... 64%, 447296 KB, 49493 KB/s, 9 seconds passed
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+... 64%, 447360 KB, 49497 KB/s, 9 seconds passed
+... 64%, 447392 KB, 49499 KB/s, 9 seconds passed
+... 64%, 447424 KB, 49501 KB/s, 9 seconds passed
+... 64%, 447456 KB, 49504 KB/s, 9 seconds passed
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+... 64%, 447520 KB, 49508 KB/s, 9 seconds passed
+... 64%, 447552 KB, 49510 KB/s, 9 seconds passed
+... 64%, 447584 KB, 49512 KB/s, 9 seconds passed
+... 64%, 447616 KB, 49515 KB/s, 9 seconds passed
+... 64%, 447648 KB, 49518 KB/s, 9 seconds passed
+... 64%, 447680 KB, 49520 KB/s, 9 seconds passed
+... 64%, 447712 KB, 49522 KB/s, 9 seconds passed
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+... 64%, 447776 KB, 49526 KB/s, 9 seconds passed
+... 64%, 447808 KB, 49529 KB/s, 9 seconds passed
+... 64%, 447840 KB, 49531 KB/s, 9 seconds passed
+... 64%, 447872 KB, 49533 KB/s, 9 seconds passed
+... 64%, 447904 KB, 49535 KB/s, 9 seconds passed
+... 64%, 447936 KB, 49537 KB/s, 9 seconds passed
+... 64%, 447968 KB, 49533 KB/s, 9 seconds passed
+... 64%, 448000 KB, 49534 KB/s, 9 seconds passed
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+... 64%, 448064 KB, 49539 KB/s, 9 seconds passed
+... 64%, 448096 KB, 49541 KB/s, 9 seconds passed
+... 64%, 448128 KB, 49542 KB/s, 9 seconds passed
+... 64%, 448160 KB, 49544 KB/s, 9 seconds passed
+... 64%, 448192 KB, 49546 KB/s, 9 seconds passed
+... 64%, 448224 KB, 49549 KB/s, 9 seconds passed
+... 64%, 448256 KB, 49551 KB/s, 9 seconds passed
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+... 64%, 448320 KB, 49555 KB/s, 9 seconds passed
+... 64%, 448352 KB, 49556 KB/s, 9 seconds passed
+... 64%, 448384 KB, 49557 KB/s, 9 seconds passed
+... 64%, 448416 KB, 49558 KB/s, 9 seconds passed
+... 64%, 448448 KB, 49560 KB/s, 9 seconds passed
+... 64%, 448480 KB, 49563 KB/s, 9 seconds passed
+... 64%, 448512 KB, 49565 KB/s, 9 seconds passed
+... 64%, 448544 KB, 49568 KB/s, 9 seconds passed
+... 64%, 448576 KB, 49571 KB/s, 9 seconds passed
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+... 64%, 448768 KB, 49581 KB/s, 9 seconds passed
+... 64%, 448800 KB, 49583 KB/s, 9 seconds passed
+... 64%, 448832 KB, 49585 KB/s, 9 seconds passed
+... 64%, 448864 KB, 49587 KB/s, 9 seconds passed
+... 64%, 448896 KB, 49590 KB/s, 9 seconds passed
+... 64%, 448928 KB, 49518 KB/s, 9 seconds passed
+... 64%, 448960 KB, 49519 KB/s, 9 seconds passed
+... 64%, 448992 KB, 49520 KB/s, 9 seconds passed
+... 64%, 449024 KB, 49521 KB/s, 9 seconds passed
+... 64%, 449056 KB, 49522 KB/s, 9 seconds passed
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+... 64%, 449120 KB, 49525 KB/s, 9 seconds passed
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+... 64%, 449376 KB, 49537 KB/s, 9 seconds passed
+... 64%, 449408 KB, 49538 KB/s, 9 seconds passed
+... 64%, 449440 KB, 49540 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
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+... 64%, 449504 KB, 49543 KB/s, 9 seconds passed
+... 64%, 449536 KB, 49544 KB/s, 9 seconds passed
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+... 64%, 449600 KB, 49548 KB/s, 9 seconds passed
+... 64%, 449632 KB, 49550 KB/s, 9 seconds passed
+... 64%, 449664 KB, 49552 KB/s, 9 seconds passed
+... 64%, 449696 KB, 49554 KB/s, 9 seconds passed
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+... 64%, 449760 KB, 49496 KB/s, 9 seconds passed
+... 64%, 449792 KB, 49497 KB/s, 9 seconds passed
+... 64%, 449824 KB, 49499 KB/s, 9 seconds passed
+... 64%, 449856 KB, 49500 KB/s, 9 seconds passed
+... 64%, 449888 KB, 49502 KB/s, 9 seconds passed
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+... 64%, 449952 KB, 49504 KB/s, 9 seconds passed
+... 64%, 449984 KB, 49506 KB/s, 9 seconds passed
+... 64%, 450016 KB, 49507 KB/s, 9 seconds passed
+... 64%, 450048 KB, 49509 KB/s, 9 seconds passed
+... 64%, 450080 KB, 49510 KB/s, 9 seconds passed
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+... 64%, 450272 KB, 49519 KB/s, 9 seconds passed
+... 64%, 450304 KB, 49520 KB/s, 9 seconds passed
+... 64%, 450336 KB, 49522 KB/s, 9 seconds passed
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+... 64%, 450432 KB, 49528 KB/s, 9 seconds passed
+... 64%, 450464 KB, 49530 KB/s, 9 seconds passed
+... 64%, 450496 KB, 49532 KB/s, 9 seconds passed
+... 64%, 450528 KB, 49534 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
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+... 64%, 450592 KB, 49169 KB/s, 9 seconds passed
+... 64%, 450624 KB, 49170 KB/s, 9 seconds passed
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+... 64%, 450720 KB, 49174 KB/s, 9 seconds passed
+... 64%, 450752 KB, 49175 KB/s, 9 seconds passed
+... 64%, 450784 KB, 49177 KB/s, 9 seconds passed
+... 64%, 450816 KB, 49178 KB/s, 9 seconds passed
+... 64%, 450848 KB, 49180 KB/s, 9 seconds passed
+... 64%, 450880 KB, 49181 KB/s, 9 seconds passed
+... 64%, 450912 KB, 49183 KB/s, 9 seconds passed
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+... 64%, 451008 KB, 49187 KB/s, 9 seconds passed
+... 64%, 451040 KB, 49188 KB/s, 9 seconds passed
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+... 64%, 451104 KB, 49191 KB/s, 9 seconds passed
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+... 64%, 451296 KB, 49200 KB/s, 9 seconds passed
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+... 64%, 451360 KB, 49203 KB/s, 9 seconds passed
+... 64%, 451392 KB, 49204 KB/s, 9 seconds passed
+... 64%, 451424 KB, 49206 KB/s, 9 seconds passed
+... 64%, 451456 KB, 49207 KB/s, 9 seconds passed
+... 64%, 451488 KB, 49209 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
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+... 64%, 451680 KB, 49217 KB/s, 9 seconds passed
+... 64%, 451712 KB, 49218 KB/s, 9 seconds passed
+... 64%, 451744 KB, 49220 KB/s, 9 seconds passed
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+... 64%, 451936 KB, 49229 KB/s, 9 seconds passed
+... 64%, 451968 KB, 49230 KB/s, 9 seconds passed
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+... 64%, 452032 KB, 49234 KB/s, 9 seconds passed
+... 64%, 452064 KB, 49236 KB/s, 9 seconds passed
+... 65%, 452096 KB, 49238 KB/s, 9 seconds passed
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+... 65%, 452256 KB, 49249 KB/s, 9 seconds passed
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+... 65%, 452352 KB, 49255 KB/s, 9 seconds passed
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+... 65%, 452480 KB, 49264 KB/s, 9 seconds passed
+... 65%, 452512 KB, 49266 KB/s, 9 seconds passed
+... 65%, 452544 KB, 49269 KB/s, 9 seconds passed
+... 65%, 452576 KB, 49271 KB/s, 9 seconds passed
+... 65%, 452608 KB, 49273 KB/s, 9 seconds passed
+... 65%, 452640 KB, 49275 KB/s, 9 seconds passed
+... 65%, 452672 KB, 49277 KB/s, 9 seconds passed
+... 65%, 452704 KB, 49280 KB/s, 9 seconds passed
+... 65%, 452736 KB, 49282 KB/s, 9 seconds passed
+... 65%, 452768 KB, 49285 KB/s, 9 seconds passed
+... 65%, 452800 KB, 49287 KB/s, 9 seconds passed
+... 65%, 452832 KB, 49289 KB/s, 9 seconds passed
+... 65%, 452864 KB, 49291 KB/s, 9 seconds passed
+... 65%, 452896 KB, 49294 KB/s, 9 seconds passed
+... 65%, 452928 KB, 49296 KB/s, 9 seconds passed
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+... 65%, 453216 KB, 49238 KB/s, 9 seconds passed
+... 65%, 453248 KB, 49208 KB/s, 9 seconds passed
+... 65%, 453280 KB, 49209 KB/s, 9 seconds passed
+... 65%, 453312 KB, 49210 KB/s, 9 seconds passed
+... 65%, 453344 KB, 49212 KB/s, 9 seconds passed
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+... 65%, 453440 KB, 49216 KB/s, 9 seconds passed
+... 65%, 453472 KB, 49218 KB/s, 9 seconds passed
+... 65%, 453504 KB, 49219 KB/s, 9 seconds passed
+... 65%, 453536 KB, 49220 KB/s, 9 seconds passed
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+... 65%, 453632 KB, 49225 KB/s, 9 seconds passed
+... 65%, 453664 KB, 49226 KB/s, 9 seconds passed
+... 65%, 453696 KB, 49227 KB/s, 9 seconds passed
+... 65%, 453728 KB, 49229 KB/s, 9 seconds passed
+... 65%, 453760 KB, 49230 KB/s, 9 seconds passed
+... 65%, 453792 KB, 49232 KB/s, 9 seconds passed
+... 65%, 453824 KB, 49233 KB/s, 9 seconds passed
+... 65%, 453856 KB, 49234 KB/s, 9 seconds passed
+... 65%, 453888 KB, 49236 KB/s, 9 seconds passed
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+... 65%, 453984 KB, 49240 KB/s, 9 seconds passed
+... 65%, 454016 KB, 49241 KB/s, 9 seconds passed
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+... 65%, 454080 KB, 49244 KB/s, 9 seconds passed
+... 65%, 454112 KB, 49246 KB/s, 9 seconds passed
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+... 65%, 454176 KB, 49250 KB/s, 9 seconds passed
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+... 65%, 454272 KB, 49257 KB/s, 9 seconds passed
+... 65%, 454304 KB, 49259 KB/s, 9 seconds passed
+... 65%, 454336 KB, 49261 KB/s, 9 seconds passed
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+... 65%, 454464 KB, 49269 KB/s, 9 seconds passed
+... 65%, 454496 KB, 49272 KB/s, 9 seconds passed
+... 65%, 454528 KB, 49274 KB/s, 9 seconds passed
+... 65%, 454560 KB, 49276 KB/s, 9 seconds passed
+... 65%, 454592 KB, 49278 KB/s, 9 seconds passed
+... 65%, 454624 KB, 49280 KB/s, 9 seconds passed
+... 65%, 454656 KB, 49282 KB/s, 9 seconds passed
+... 65%, 454688 KB, 49285 KB/s, 9 seconds passed
+... 65%, 454720 KB, 49287 KB/s, 9 seconds passed
+... 65%, 454752 KB, 49289 KB/s, 9 seconds passed
+
+.. parsed-literal::
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+... 65%, 454944 KB, 49302 KB/s, 9 seconds passed
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+... 65%, 455104 KB, 49313 KB/s, 9 seconds passed
+... 65%, 455136 KB, 49315 KB/s, 9 seconds passed
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+... 65%, 455264 KB, 49324 KB/s, 9 seconds passed
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+... 65%, 455520 KB, 49342 KB/s, 9 seconds passed
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+... 65%, 455712 KB, 49200 KB/s, 9 seconds passed
+... 65%, 455744 KB, 49201 KB/s, 9 seconds passed
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+... 65%, 455808 KB, 49191 KB/s, 9 seconds passed
+... 65%, 455840 KB, 49192 KB/s, 9 seconds passed
+... 65%, 455872 KB, 49193 KB/s, 9 seconds passed
+... 65%, 455904 KB, 49194 KB/s, 9 seconds passed
+... 65%, 455936 KB, 49195 KB/s, 9 seconds passed
+... 65%, 455968 KB, 49197 KB/s, 9 seconds passed
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+... 65%, 456064 KB, 49201 KB/s, 9 seconds passed
+... 65%, 456096 KB, 49202 KB/s, 9 seconds passed
+... 65%, 456128 KB, 49204 KB/s, 9 seconds passed
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+... 65%, 456192 KB, 49202 KB/s, 9 seconds passed
+... 65%, 456224 KB, 49203 KB/s, 9 seconds passed
+... 65%, 456256 KB, 49204 KB/s, 9 seconds passed
+... 65%, 456288 KB, 49206 KB/s, 9 seconds passed
+... 65%, 456320 KB, 49207 KB/s, 9 seconds passed
+... 65%, 456352 KB, 49207 KB/s, 9 seconds passed
+... 65%, 456384 KB, 49209 KB/s, 9 seconds passed
+... 65%, 456416 KB, 49210 KB/s, 9 seconds passed
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+... 65%, 456480 KB, 49213 KB/s, 9 seconds passed
+... 65%, 456512 KB, 49214 KB/s, 9 seconds passed
+... 65%, 456544 KB, 49216 KB/s, 9 seconds passed
+... 65%, 456576 KB, 49217 KB/s, 9 seconds passed
+... 65%, 456608 KB, 49218 KB/s, 9 seconds passed
+
+.. parsed-literal::
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+... 65%, 456704 KB, 49222 KB/s, 9 seconds passed
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+... 65%, 456768 KB, 49225 KB/s, 9 seconds passed
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+... 65%, 456896 KB, 49231 KB/s, 9 seconds passed
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+... 65%, 456960 KB, 49233 KB/s, 9 seconds passed
+... 65%, 456992 KB, 49235 KB/s, 9 seconds passed
+... 65%, 457024 KB, 49236 KB/s, 9 seconds passed
+... 65%, 457056 KB, 49238 KB/s, 9 seconds passed
+... 65%, 457088 KB, 49240 KB/s, 9 seconds passed
+... 65%, 457120 KB, 49242 KB/s, 9 seconds passed
+... 65%, 457152 KB, 49244 KB/s, 9 seconds passed
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+... 65%, 457248 KB, 49251 KB/s, 9 seconds passed
+... 65%, 457280 KB, 49253 KB/s, 9 seconds passed
+... 65%, 457312 KB, 49255 KB/s, 9 seconds passed
+... 65%, 457344 KB, 49257 KB/s, 9 seconds passed
+... 65%, 457376 KB, 49259 KB/s, 9 seconds passed
+... 65%, 457408 KB, 49262 KB/s, 9 seconds passed
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+... 65%, 457472 KB, 49266 KB/s, 9 seconds passed
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+... 65%, 457536 KB, 49166 KB/s, 9 seconds passed
+... 65%, 457568 KB, 49167 KB/s, 9 seconds passed
+... 65%, 457600 KB, 49168 KB/s, 9 seconds passed
+... 65%, 457632 KB, 49170 KB/s, 9 seconds passed
+... 65%, 457664 KB, 49171 KB/s, 9 seconds passed
+... 65%, 457696 KB, 49172 KB/s, 9 seconds passed
+... 65%, 457728 KB, 49174 KB/s, 9 seconds passed
+... 65%, 457760 KB, 49175 KB/s, 9 seconds passed
+... 65%, 457792 KB, 49176 KB/s, 9 seconds passed
+... 65%, 457824 KB, 49178 KB/s, 9 seconds passed
+... 65%, 457856 KB, 49179 KB/s, 9 seconds passed
+... 65%, 457888 KB, 49180 KB/s, 9 seconds passed
+... 65%, 457920 KB, 49182 KB/s, 9 seconds passed
+... 65%, 457952 KB, 49183 KB/s, 9 seconds passed
+... 65%, 457984 KB, 49185 KB/s, 9 seconds passed
+... 65%, 458016 KB, 49187 KB/s, 9 seconds passed
+... 65%, 458048 KB, 49189 KB/s, 9 seconds passed
+... 65%, 458080 KB, 49190 KB/s, 9 seconds passed
+... 65%, 458112 KB, 49192 KB/s, 9 seconds passed
+... 65%, 458144 KB, 49193 KB/s, 9 seconds passed
+... 65%, 458176 KB, 49195 KB/s, 9 seconds passed
+... 65%, 458208 KB, 49196 KB/s, 9 seconds passed
+... 65%, 458240 KB, 49197 KB/s, 9 seconds passed
+... 65%, 458272 KB, 49199 KB/s, 9 seconds passed
+... 65%, 458304 KB, 49200 KB/s, 9 seconds passed
+... 65%, 458336 KB, 49202 KB/s, 9 seconds passed
+... 65%, 458368 KB, 49203 KB/s, 9 seconds passed
+... 65%, 458400 KB, 49205 KB/s, 9 seconds passed
+... 65%, 458432 KB, 49206 KB/s, 9 seconds passed
+... 65%, 458464 KB, 49207 KB/s, 9 seconds passed
+... 65%, 458496 KB, 49209 KB/s, 9 seconds passed
+... 65%, 458528 KB, 49210 KB/s, 9 seconds passed
+... 65%, 458560 KB, 49211 KB/s, 9 seconds passed
+... 65%, 458592 KB, 49213 KB/s, 9 seconds passed
+... 65%, 458624 KB, 49214 KB/s, 9 seconds passed
+... 65%, 458656 KB, 49215 KB/s, 9 seconds passed
+... 65%, 458688 KB, 49217 KB/s, 9 seconds passed
+... 65%, 458720 KB, 49218 KB/s, 9 seconds passed
+... 65%, 458752 KB, 49219 KB/s, 9 seconds passed
+... 65%, 458784 KB, 49221 KB/s, 9 seconds passed
+... 65%, 458816 KB, 49222 KB/s, 9 seconds passed
+... 65%, 458848 KB, 49224 KB/s, 9 seconds passed
+... 65%, 458880 KB, 49225 KB/s, 9 seconds passed
+... 65%, 458912 KB, 49226 KB/s, 9 seconds passed
+... 65%, 458944 KB, 49228 KB/s, 9 seconds passed
+... 65%, 458976 KB, 49230 KB/s, 9 seconds passed
+... 65%, 459008 KB, 49232 KB/s, 9 seconds passed
+... 66%, 459040 KB, 49234 KB/s, 9 seconds passed
+... 66%, 459072 KB, 49237 KB/s, 9 seconds passed
+... 66%, 459104 KB, 49239 KB/s, 9 seconds passed
+... 66%, 459136 KB, 49241 KB/s, 9 seconds passed
+... 66%, 459168 KB, 49243 KB/s, 9 seconds passed
+... 66%, 459200 KB, 49245 KB/s, 9 seconds passed
+... 66%, 459232 KB, 49247 KB/s, 9 seconds passed
+... 66%, 459264 KB, 49249 KB/s, 9 seconds passed
+... 66%, 459296 KB, 49251 KB/s, 9 seconds passed
+... 66%, 459328 KB, 49254 KB/s, 9 seconds passed
+... 66%, 459360 KB, 49256 KB/s, 9 seconds passed
+... 66%, 459392 KB, 49258 KB/s, 9 seconds passed
+... 66%, 459424 KB, 49260 KB/s, 9 seconds passed
+... 66%, 459456 KB, 49262 KB/s, 9 seconds passed
+... 66%, 459488 KB, 49264 KB/s, 9 seconds passed
+... 66%, 459520 KB, 49266 KB/s, 9 seconds passed
+... 66%, 459552 KB, 49268 KB/s, 9 seconds passed
+... 66%, 459584 KB, 49270 KB/s, 9 seconds passed
+... 66%, 459616 KB, 49272 KB/s, 9 seconds passed
+... 66%, 459648 KB, 49275 KB/s, 9 seconds passed
+... 66%, 459680 KB, 49277 KB/s, 9 seconds passed
+... 66%, 459712 KB, 49279 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
+    ... 66%, 459744 KB, 49281 KB/s, 9 seconds passed
+... 66%, 459776 KB, 49283 KB/s, 9 seconds passed
+... 66%, 459808 KB, 49285 KB/s, 9 seconds passed
+... 66%, 459840 KB, 49287 KB/s, 9 seconds passed
+... 66%, 459872 KB, 49290 KB/s, 9 seconds passed
+... 66%, 459904 KB, 49292 KB/s, 9 seconds passed
+... 66%, 459936 KB, 49294 KB/s, 9 seconds passed
+... 66%, 459968 KB, 49296 KB/s, 9 seconds passed
+... 66%, 460000 KB, 49298 KB/s, 9 seconds passed
+... 66%, 460032 KB, 49300 KB/s, 9 seconds passed
+... 66%, 460064 KB, 49302 KB/s, 9 seconds passed
+... 66%, 460096 KB, 49304 KB/s, 9 seconds passed
+... 66%, 460128 KB, 49307 KB/s, 9 seconds passed
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+... 66%, 460192 KB, 49311 KB/s, 9 seconds passed
+... 66%, 460224 KB, 49313 KB/s, 9 seconds passed
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+... 66%, 460288 KB, 49318 KB/s, 9 seconds passed
+... 66%, 460320 KB, 49321 KB/s, 9 seconds passed
+... 66%, 460352 KB, 49323 KB/s, 9 seconds passed
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+... 66%, 460416 KB, 49328 KB/s, 9 seconds passed
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+... 66%, 460512 KB, 49336 KB/s, 9 seconds passed
+... 66%, 460544 KB, 49338 KB/s, 9 seconds passed
+... 66%, 460576 KB, 49341 KB/s, 9 seconds passed
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+... 66%, 460800 KB, 49316 KB/s, 9 seconds passed
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+... 66%, 461120 KB, 49330 KB/s, 9 seconds passed
+... 66%, 461152 KB, 49331 KB/s, 9 seconds passed
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+... 66%, 461216 KB, 49334 KB/s, 9 seconds passed
+... 66%, 461248 KB, 49336 KB/s, 9 seconds passed
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+... 66%, 461344 KB, 49340 KB/s, 9 seconds passed
+... 66%, 461376 KB, 49341 KB/s, 9 seconds passed
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+... 66%, 461440 KB, 49344 KB/s, 9 seconds passed
+... 66%, 461472 KB, 49345 KB/s, 9 seconds passed
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+... 66%, 461664 KB, 49357 KB/s, 9 seconds passed
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+... 66%, 461792 KB, 49344 KB/s, 9 seconds passed
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+... 66%, 462112 KB, 49303 KB/s, 9 seconds passed
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+... 66%, 462400 KB, 49315 KB/s, 9 seconds passed
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+... 66%, 462528 KB, 49321 KB/s, 9 seconds passed
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+... 66%, 462592 KB, 49324 KB/s, 9 seconds passed
+... 66%, 462624 KB, 49325 KB/s, 9 seconds passed
+... 66%, 462656 KB, 49326 KB/s, 9 seconds passed
+... 66%, 462688 KB, 49328 KB/s, 9 seconds passed
+
+.. parsed-literal::
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+... 66%, 462784 KB, 49332 KB/s, 9 seconds passed
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+... 66%, 463776 KB, 49394 KB/s, 9 seconds passed
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+... 66%, 464000 KB, 49409 KB/s, 9 seconds passed
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+... 66%, 464128 KB, 49418 KB/s, 9 seconds passed
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+... 66%, 464192 KB, 49422 KB/s, 9 seconds passed
+... 66%, 464224 KB, 49424 KB/s, 9 seconds passed
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+... 66%, 464288 KB, 49429 KB/s, 9 seconds passed
+... 66%, 464320 KB, 49432 KB/s, 9 seconds passed
+... 66%, 464352 KB, 49434 KB/s, 9 seconds passed
+... 66%, 464384 KB, 49437 KB/s, 9 seconds passed
+... 66%, 464416 KB, 49439 KB/s, 9 seconds passed
+... 66%, 464448 KB, 49442 KB/s, 9 seconds passed
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+... 66%, 464800 KB, 49470 KB/s, 9 seconds passed
+... 66%, 464832 KB, 49472 KB/s, 9 seconds passed
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+... 66%, 464960 KB, 49482 KB/s, 9 seconds passed
+... 66%, 464992 KB, 49485 KB/s, 9 seconds passed
+... 66%, 465024 KB, 49487 KB/s, 9 seconds passed
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+... 66%, 465568 KB, 49519 KB/s, 9 seconds passed
+... 66%, 465600 KB, 49521 KB/s, 9 seconds passed
+... 66%, 465632 KB, 49524 KB/s, 9 seconds passed
+... 66%, 465664 KB, 49525 KB/s, 9 seconds passed
+... 66%, 465696 KB, 49527 KB/s, 9 seconds passed
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+... 66%, 465760 KB, 49531 KB/s, 9 seconds passed
+... 66%, 465792 KB, 49533 KB/s, 9 seconds passed
+... 66%, 465824 KB, 49535 KB/s, 9 seconds passed
+... 66%, 465856 KB, 49537 KB/s, 9 seconds passed
+... 66%, 465888 KB, 49539 KB/s, 9 seconds passed
+
+.. parsed-literal::
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+... 66%, 465984 KB, 49232 KB/s, 9 seconds passed
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+... 67%, 467072 KB, 49281 KB/s, 9 seconds passed
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+... 67%, 467456 KB, 49301 KB/s, 9 seconds passed
+... 67%, 467488 KB, 49302 KB/s, 9 seconds passed
+... 67%, 467520 KB, 49304 KB/s, 9 seconds passed
+
+.. parsed-literal::
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+... 67%, 468576 KB, 49332 KB/s, 9 seconds passed
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+... 67%, 468864 KB, 49348 KB/s, 9 seconds passed
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+... 67%, 469504 KB, 49389 KB/s, 9 seconds passed
+... 67%, 469536 KB, 49391 KB/s, 9 seconds passed
+... 67%, 469568 KB, 49393 KB/s, 9 seconds passed
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+... 67%, 469664 KB, 49400 KB/s, 9 seconds passed
+... 67%, 469696 KB, 49402 KB/s, 9 seconds passed
+... 67%, 469728 KB, 49404 KB/s, 9 seconds passed
+... 67%, 469760 KB, 49406 KB/s, 9 seconds passed
+... 67%, 469792 KB, 49408 KB/s, 9 seconds passed
+... 67%, 469824 KB, 49410 KB/s, 9 seconds passed
+... 67%, 469856 KB, 49412 KB/s, 9 seconds passed
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+... 67%, 469920 KB, 49416 KB/s, 9 seconds passed
+... 67%, 469952 KB, 49418 KB/s, 9 seconds passed
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+... 67%, 470016 KB, 49422 KB/s, 9 seconds passed
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+... 67%, 470080 KB, 49426 KB/s, 9 seconds passed
+... 67%, 470112 KB, 49428 KB/s, 9 seconds passed
+... 67%, 470144 KB, 49430 KB/s, 9 seconds passed
+... 67%, 470176 KB, 49432 KB/s, 9 seconds passed
+... 67%, 470208 KB, 49433 KB/s, 9 seconds passed
+... 67%, 470240 KB, 49435 KB/s, 9 seconds passed
+... 67%, 470272 KB, 49437 KB/s, 9 seconds passed
+... 67%, 470304 KB, 49439 KB/s, 9 seconds passed
+... 67%, 470336 KB, 49441 KB/s, 9 seconds passed
+... 67%, 470368 KB, 49443 KB/s, 9 seconds passed
+... 67%, 470400 KB, 49445 KB/s, 9 seconds passed
+... 67%, 470432 KB, 49447 KB/s, 9 seconds passed
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+... 67%, 470528 KB, 49452 KB/s, 9 seconds passed
+... 67%, 470560 KB, 49454 KB/s, 9 seconds passed
+... 67%, 470592 KB, 49456 KB/s, 9 seconds passed
+... 67%, 470624 KB, 49458 KB/s, 9 seconds passed
+... 67%, 470656 KB, 49460 KB/s, 9 seconds passed
+... 67%, 470688 KB, 49462 KB/s, 9 seconds passed
+... 67%, 470720 KB, 49464 KB/s, 9 seconds passed
+... 67%, 470752 KB, 49466 KB/s, 9 seconds passed
+... 67%, 470784 KB, 49468 KB/s, 9 seconds passed
+... 67%, 470816 KB, 49470 KB/s, 9 seconds passed
+... 67%, 470848 KB, 49472 KB/s, 9 seconds passed
+... 67%, 470880 KB, 49474 KB/s, 9 seconds passed
+... 67%, 470912 KB, 49476 KB/s, 9 seconds passed
+... 67%, 470944 KB, 49477 KB/s, 9 seconds passed
+... 67%, 470976 KB, 49479 KB/s, 9 seconds passed
+... 67%, 471008 KB, 49481 KB/s, 9 seconds passed
+
+.. parsed-literal::
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+... 67%, 471392 KB, 49322 KB/s, 9 seconds passed
+... 67%, 471424 KB, 49323 KB/s, 9 seconds passed
+... 67%, 471456 KB, 49325 KB/s, 9 seconds passed
+... 67%, 471488 KB, 49326 KB/s, 9 seconds passed
+... 67%, 471520 KB, 49327 KB/s, 9 seconds passed
+... 67%, 471552 KB, 49329 KB/s, 9 seconds passed
+... 67%, 471584 KB, 49330 KB/s, 9 seconds passed
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+... 67%, 471648 KB, 49333 KB/s, 9 seconds passed
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+... 67%, 471808 KB, 49340 KB/s, 9 seconds passed
+... 67%, 471840 KB, 49342 KB/s, 9 seconds passed
+... 67%, 471872 KB, 49343 KB/s, 9 seconds passed
+... 67%, 471904 KB, 49344 KB/s, 9 seconds passed
+... 67%, 471936 KB, 49346 KB/s, 9 seconds passed
+... 67%, 471968 KB, 49347 KB/s, 9 seconds passed
+... 67%, 472000 KB, 49349 KB/s, 9 seconds passed
+... 67%, 472032 KB, 49351 KB/s, 9 seconds passed
+... 67%, 472064 KB, 49353 KB/s, 9 seconds passed
+... 67%, 472096 KB, 49355 KB/s, 9 seconds passed
+... 67%, 472128 KB, 49357 KB/s, 9 seconds passed
+... 67%, 472160 KB, 49359 KB/s, 9 seconds passed
+... 67%, 472192 KB, 49361 KB/s, 9 seconds passed
+... 67%, 472224 KB, 49363 KB/s, 9 seconds passed
+... 67%, 472256 KB, 49365 KB/s, 9 seconds passed
+... 67%, 472288 KB, 49366 KB/s, 9 seconds passed
+... 67%, 472320 KB, 49368 KB/s, 9 seconds passed
+... 67%, 472352 KB, 49370 KB/s, 9 seconds passed
+... 67%, 472384 KB, 49372 KB/s, 9 seconds passed
+... 67%, 472416 KB, 49374 KB/s, 9 seconds passed
+... 67%, 472448 KB, 49376 KB/s, 9 seconds passed
+... 67%, 472480 KB, 49378 KB/s, 9 seconds passed
+... 67%, 472512 KB, 49380 KB/s, 9 seconds passed
+... 67%, 472544 KB, 49382 KB/s, 9 seconds passed
+... 67%, 472576 KB, 49384 KB/s, 9 seconds passed
+... 67%, 472608 KB, 49386 KB/s, 9 seconds passed
+... 67%, 472640 KB, 49388 KB/s, 9 seconds passed
+... 67%, 472672 KB, 49390 KB/s, 9 seconds passed
+... 67%, 472704 KB, 49392 KB/s, 9 seconds passed
+... 67%, 472736 KB, 49394 KB/s, 9 seconds passed
+... 67%, 472768 KB, 49396 KB/s, 9 seconds passed
+... 67%, 472800 KB, 49398 KB/s, 9 seconds passed
+... 67%, 472832 KB, 49399 KB/s, 9 seconds passed
+... 67%, 472864 KB, 49401 KB/s, 9 seconds passed
+... 67%, 472896 KB, 49403 KB/s, 9 seconds passed
+... 67%, 472928 KB, 49405 KB/s, 9 seconds passed
+... 68%, 472960 KB, 49407 KB/s, 9 seconds passed
+... 68%, 472992 KB, 49409 KB/s, 9 seconds passed
+... 68%, 473024 KB, 49411 KB/s, 9 seconds passed
+... 68%, 473056 KB, 49413 KB/s, 9 seconds passed
+... 68%, 473088 KB, 49415 KB/s, 9 seconds passed
+... 68%, 473120 KB, 49417 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
+    ... 68%, 473152 KB, 48812 KB/s, 9 seconds passed
+... 68%, 473184 KB, 48813 KB/s, 9 seconds passed
+... 68%, 473216 KB, 48814 KB/s, 9 seconds passed
+... 68%, 473248 KB, 48815 KB/s, 9 seconds passed
+... 68%, 473280 KB, 48817 KB/s, 9 seconds passed
+... 68%, 473312 KB, 48818 KB/s, 9 seconds passed
+... 68%, 473344 KB, 48819 KB/s, 9 seconds passed
+... 68%, 473376 KB, 48820 KB/s, 9 seconds passed
+... 68%, 473408 KB, 48822 KB/s, 9 seconds passed
+... 68%, 473440 KB, 48823 KB/s, 9 seconds passed
+... 68%, 473472 KB, 48824 KB/s, 9 seconds passed
+... 68%, 473504 KB, 48826 KB/s, 9 seconds passed
+... 68%, 473536 KB, 48827 KB/s, 9 seconds passed
+... 68%, 473568 KB, 48829 KB/s, 9 seconds passed
+... 68%, 473600 KB, 48830 KB/s, 9 seconds passed
+... 68%, 473632 KB, 48831 KB/s, 9 seconds passed
+... 68%, 473664 KB, 48832 KB/s, 9 seconds passed
+... 68%, 473696 KB, 48834 KB/s, 9 seconds passed
+... 68%, 473728 KB, 48835 KB/s, 9 seconds passed
+... 68%, 473760 KB, 48837 KB/s, 9 seconds passed
+... 68%, 473792 KB, 48838 KB/s, 9 seconds passed
+... 68%, 473824 KB, 48840 KB/s, 9 seconds passed
+... 68%, 473856 KB, 48841 KB/s, 9 seconds passed
+... 68%, 473888 KB, 48842 KB/s, 9 seconds passed
+... 68%, 473920 KB, 48844 KB/s, 9 seconds passed
+... 68%, 473952 KB, 48845 KB/s, 9 seconds passed
+... 68%, 473984 KB, 48847 KB/s, 9 seconds passed
+... 68%, 474016 KB, 48848 KB/s, 9 seconds passed
+... 68%, 474048 KB, 48849 KB/s, 9 seconds passed
+... 68%, 474080 KB, 48851 KB/s, 9 seconds passed
+... 68%, 474112 KB, 48852 KB/s, 9 seconds passed
+... 68%, 474144 KB, 48853 KB/s, 9 seconds passed
+... 68%, 474176 KB, 48855 KB/s, 9 seconds passed
+... 68%, 474208 KB, 48856 KB/s, 9 seconds passed
+... 68%, 474240 KB, 48858 KB/s, 9 seconds passed
+... 68%, 474272 KB, 48860 KB/s, 9 seconds passed
+... 68%, 474304 KB, 48862 KB/s, 9 seconds passed
+... 68%, 474336 KB, 48864 KB/s, 9 seconds passed
+... 68%, 474368 KB, 48866 KB/s, 9 seconds passed
+... 68%, 474400 KB, 48868 KB/s, 9 seconds passed
+... 68%, 474432 KB, 48870 KB/s, 9 seconds passed
+... 68%, 474464 KB, 48872 KB/s, 9 seconds passed
+... 68%, 474496 KB, 48874 KB/s, 9 seconds passed
+... 68%, 474528 KB, 48876 KB/s, 9 seconds passed
+... 68%, 474560 KB, 48878 KB/s, 9 seconds passed
+... 68%, 474592 KB, 48880 KB/s, 9 seconds passed
+... 68%, 474624 KB, 48882 KB/s, 9 seconds passed
+... 68%, 474656 KB, 48884 KB/s, 9 seconds passed
+... 68%, 474688 KB, 48886 KB/s, 9 seconds passed
+... 68%, 474720 KB, 48888 KB/s, 9 seconds passed
+... 68%, 474752 KB, 48889 KB/s, 9 seconds passed
+... 68%, 474784 KB, 48890 KB/s, 9 seconds passed
+... 68%, 474816 KB, 48890 KB/s, 9 seconds passed
+... 68%, 474848 KB, 48892 KB/s, 9 seconds passed
+... 68%, 474880 KB, 48893 KB/s, 9 seconds passed
+... 68%, 474912 KB, 48895 KB/s, 9 seconds passed
+... 68%, 474944 KB, 48896 KB/s, 9 seconds passed
+... 68%, 474976 KB, 48898 KB/s, 9 seconds passed
+... 68%, 475008 KB, 48899 KB/s, 9 seconds passed
+... 68%, 475040 KB, 48901 KB/s, 9 seconds passed
+... 68%, 475072 KB, 48897 KB/s, 9 seconds passed
+... 68%, 475104 KB, 48898 KB/s, 9 seconds passed
+... 68%, 475136 KB, 48900 KB/s, 9 seconds passed
+... 68%, 475168 KB, 48901 KB/s, 9 seconds passed
+... 68%, 475200 KB, 48903 KB/s, 9 seconds passed
+... 68%, 475232 KB, 48904 KB/s, 9 seconds passed
+... 68%, 475264 KB, 48906 KB/s, 9 seconds passed
+... 68%, 475296 KB, 48908 KB/s, 9 seconds passed
+... 68%, 475328 KB, 48810 KB/s, 9 seconds passed
+... 68%, 475360 KB, 48811 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
+    ... 68%, 475392 KB, 48813 KB/s, 9 seconds passed
+... 68%, 475424 KB, 48814 KB/s, 9 seconds passed
+... 68%, 475456 KB, 48816 KB/s, 9 seconds passed
+... 68%, 475488 KB, 48817 KB/s, 9 seconds passed
+... 68%, 475520 KB, 48819 KB/s, 9 seconds passed
+... 68%, 475552 KB, 48820 KB/s, 9 seconds passed
+... 68%, 475584 KB, 48822 KB/s, 9 seconds passed
+... 68%, 475616 KB, 48823 KB/s, 9 seconds passed
+... 68%, 475648 KB, 48825 KB/s, 9 seconds passed
+... 68%, 475680 KB, 48826 KB/s, 9 seconds passed
+... 68%, 475712 KB, 48828 KB/s, 9 seconds passed
+... 68%, 475744 KB, 48829 KB/s, 9 seconds passed
+... 68%, 475776 KB, 48831 KB/s, 9 seconds passed
+... 68%, 475808 KB, 48832 KB/s, 9 seconds passed
+... 68%, 475840 KB, 48834 KB/s, 9 seconds passed
+... 68%, 475872 KB, 48835 KB/s, 9 seconds passed
+... 68%, 475904 KB, 48837 KB/s, 9 seconds passed
+... 68%, 475936 KB, 48839 KB/s, 9 seconds passed
+... 68%, 475968 KB, 48840 KB/s, 9 seconds passed
+... 68%, 476000 KB, 48842 KB/s, 9 seconds passed
+... 68%, 476032 KB, 48843 KB/s, 9 seconds passed
+... 68%, 476064 KB, 48845 KB/s, 9 seconds passed
+... 68%, 476096 KB, 48847 KB/s, 9 seconds passed
+... 68%, 476128 KB, 48849 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
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+... 68%, 476192 KB, 48554 KB/s, 9 seconds passed
+... 68%, 476224 KB, 48555 KB/s, 9 seconds passed
+... 68%, 476256 KB, 48556 KB/s, 9 seconds passed
+... 68%, 476288 KB, 48557 KB/s, 9 seconds passed
+... 68%, 476320 KB, 48559 KB/s, 9 seconds passed
+... 68%, 476352 KB, 48560 KB/s, 9 seconds passed
+... 68%, 476384 KB, 48562 KB/s, 9 seconds passed
+... 68%, 476416 KB, 48563 KB/s, 9 seconds passed
+... 68%, 476448 KB, 48564 KB/s, 9 seconds passed
+... 68%, 476480 KB, 48565 KB/s, 9 seconds passed
+... 68%, 476512 KB, 48567 KB/s, 9 seconds passed
+... 68%, 476544 KB, 48568 KB/s, 9 seconds passed
+... 68%, 476576 KB, 48569 KB/s, 9 seconds passed
+... 68%, 476608 KB, 48571 KB/s, 9 seconds passed
+... 68%, 476640 KB, 48572 KB/s, 9 seconds passed
+... 68%, 476672 KB, 48574 KB/s, 9 seconds passed
+... 68%, 476704 KB, 48575 KB/s, 9 seconds passed
+... 68%, 476736 KB, 48576 KB/s, 9 seconds passed
+... 68%, 476768 KB, 48578 KB/s, 9 seconds passed
+... 68%, 476800 KB, 48579 KB/s, 9 seconds passed
+... 68%, 476832 KB, 48580 KB/s, 9 seconds passed
+... 68%, 476864 KB, 48582 KB/s, 9 seconds passed
+... 68%, 476896 KB, 48583 KB/s, 9 seconds passed
+... 68%, 476928 KB, 48584 KB/s, 9 seconds passed
+... 68%, 476960 KB, 48586 KB/s, 9 seconds passed
+... 68%, 476992 KB, 48587 KB/s, 9 seconds passed
+... 68%, 477024 KB, 48589 KB/s, 9 seconds passed
+... 68%, 477056 KB, 48591 KB/s, 9 seconds passed
+... 68%, 477088 KB, 48592 KB/s, 9 seconds passed
+... 68%, 477120 KB, 48594 KB/s, 9 seconds passed
+... 68%, 477152 KB, 48596 KB/s, 9 seconds passed
+... 68%, 477184 KB, 48598 KB/s, 9 seconds passed
+... 68%, 477216 KB, 48599 KB/s, 9 seconds passed
+... 68%, 477248 KB, 48601 KB/s, 9 seconds passed
+... 68%, 477280 KB, 48603 KB/s, 9 seconds passed
+... 68%, 477312 KB, 48605 KB/s, 9 seconds passed
+... 68%, 477344 KB, 48607 KB/s, 9 seconds passed
+... 68%, 477376 KB, 48608 KB/s, 9 seconds passed
+... 68%, 477408 KB, 48610 KB/s, 9 seconds passed
+... 68%, 477440 KB, 48612 KB/s, 9 seconds passed
+... 68%, 477472 KB, 48614 KB/s, 9 seconds passed
+... 68%, 477504 KB, 48615 KB/s, 9 seconds passed
+... 68%, 477536 KB, 48617 KB/s, 9 seconds passed
+... 68%, 477568 KB, 48619 KB/s, 9 seconds passed
+... 68%, 477600 KB, 48621 KB/s, 9 seconds passed
+... 68%, 477632 KB, 48623 KB/s, 9 seconds passed
+... 68%, 477664 KB, 48625 KB/s, 9 seconds passed
+... 68%, 477696 KB, 48626 KB/s, 9 seconds passed
+... 68%, 477728 KB, 48628 KB/s, 9 seconds passed
+... 68%, 477760 KB, 48630 KB/s, 9 seconds passed
+... 68%, 477792 KB, 48632 KB/s, 9 seconds passed
+... 68%, 477824 KB, 48634 KB/s, 9 seconds passed
+... 68%, 477856 KB, 48636 KB/s, 9 seconds passed
+... 68%, 477888 KB, 48638 KB/s, 9 seconds passed
+... 68%, 477920 KB, 48640 KB/s, 9 seconds passed
+... 68%, 477952 KB, 48641 KB/s, 9 seconds passed
+... 68%, 477984 KB, 48643 KB/s, 9 seconds passed
+... 68%, 478016 KB, 48645 KB/s, 9 seconds passed
+... 68%, 478048 KB, 48647 KB/s, 9 seconds passed
+... 68%, 478080 KB, 48649 KB/s, 9 seconds passed
+... 68%, 478112 KB, 48651 KB/s, 9 seconds passed
+... 68%, 478144 KB, 48653 KB/s, 9 seconds passed
+... 68%, 478176 KB, 48655 KB/s, 9 seconds passed
+... 68%, 478208 KB, 48658 KB/s, 9 seconds passed
+... 68%, 478240 KB, 48660 KB/s, 9 seconds passed
+... 68%, 478272 KB, 48663 KB/s, 9 seconds passed
+... 68%, 478304 KB, 48665 KB/s, 9 seconds passed
+... 68%, 478336 KB, 48667 KB/s, 9 seconds passed
+... 68%, 478368 KB, 48669 KB/s, 9 seconds passed
+... 68%, 478400 KB, 48672 KB/s, 9 seconds passed
+... 68%, 478432 KB, 48674 KB/s, 9 seconds passed
+... 68%, 478464 KB, 48676 KB/s, 9 seconds passed
+... 68%, 478496 KB, 48679 KB/s, 9 seconds passed
+... 68%, 478528 KB, 48681 KB/s, 9 seconds passed
+... 68%, 478560 KB, 48683 KB/s, 9 seconds passed
+... 68%, 478592 KB, 48686 KB/s, 9 seconds passed
+... 68%, 478624 KB, 48688 KB/s, 9 seconds passed
+... 68%, 478656 KB, 48690 KB/s, 9 seconds passed
+... 68%, 478688 KB, 48693 KB/s, 9 seconds passed
+... 68%, 478720 KB, 48695 KB/s, 9 seconds passed
+... 68%, 478752 KB, 48697 KB/s, 9 seconds passed
+... 68%, 478784 KB, 48700 KB/s, 9 seconds passed
+... 68%, 478816 KB, 48702 KB/s, 9 seconds passed
+... 68%, 478848 KB, 48704 KB/s, 9 seconds passed
+... 68%, 478880 KB, 48707 KB/s, 9 seconds passed
+... 68%, 478912 KB, 48709 KB/s, 9 seconds passed
+... 68%, 478944 KB, 48711 KB/s, 9 seconds passed
+... 68%, 478976 KB, 48714 KB/s, 9 seconds passed
+... 68%, 479008 KB, 48716 KB/s, 9 seconds passed
+... 68%, 479040 KB, 48718 KB/s, 9 seconds passed
+... 68%, 479072 KB, 48721 KB/s, 9 seconds passed
+... 68%, 479104 KB, 48723 KB/s, 9 seconds passed
+... 68%, 479136 KB, 48725 KB/s, 9 seconds passed
+... 68%, 479168 KB, 48728 KB/s, 9 seconds passed
+... 68%, 479200 KB, 48730 KB/s, 9 seconds passed
+... 68%, 479232 KB, 48732 KB/s, 9 seconds passed
+... 68%, 479264 KB, 48735 KB/s, 9 seconds passed
+... 68%, 479296 KB, 48737 KB/s, 9 seconds passed
+... 68%, 479328 KB, 48739 KB/s, 9 seconds passed
+... 68%, 479360 KB, 48741 KB/s, 9 seconds passed
+... 68%, 479392 KB, 48743 KB/s, 9 seconds passed
+... 68%, 479424 KB, 48745 KB/s, 9 seconds passed
+... 68%, 479456 KB, 48747 KB/s, 9 seconds passed
+... 68%, 479488 KB, 48749 KB/s, 9 seconds passed
+... 68%, 479520 KB, 48751 KB/s, 9 seconds passed
+... 68%, 479552 KB, 48752 KB/s, 9 seconds passed
+... 68%, 479584 KB, 48754 KB/s, 9 seconds passed
+... 68%, 479616 KB, 48756 KB/s, 9 seconds passed
+... 68%, 479648 KB, 48758 KB/s, 9 seconds passed
+... 68%, 479680 KB, 48760 KB/s, 9 seconds passed
+... 68%, 479712 KB, 48762 KB/s, 9 seconds passed
+... 68%, 479744 KB, 48764 KB/s, 9 seconds passed
+... 68%, 479776 KB, 48766 KB/s, 9 seconds passed
+... 68%, 479808 KB, 48767 KB/s, 9 seconds passed
+... 68%, 479840 KB, 48769 KB/s, 9 seconds passed
+... 68%, 479872 KB, 48771 KB/s, 9 seconds passed
+... 69%, 479904 KB, 48772 KB/s, 9 seconds passed
+... 69%, 479936 KB, 48773 KB/s, 9 seconds passed
+... 69%, 479968 KB, 48775 KB/s, 9 seconds passed
+... 69%, 480000 KB, 48777 KB/s, 9 seconds passed
+... 69%, 480032 KB, 48779 KB/s, 9 seconds passed
+... 69%, 480064 KB, 48782 KB/s, 9 seconds passed
+... 69%, 480096 KB, 48784 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
+    ... 69%, 480128 KB, 48786 KB/s, 9 seconds passed
+... 69%, 480160 KB, 48787 KB/s, 9 seconds passed
+... 69%, 480192 KB, 48788 KB/s, 9 seconds passed
+... 69%, 480224 KB, 48790 KB/s, 9 seconds passed
+... 69%, 480256 KB, 48792 KB/s, 9 seconds passed
+... 69%, 480288 KB, 48795 KB/s, 9 seconds passed
+... 69%, 480320 KB, 48797 KB/s, 9 seconds passed
+... 69%, 480352 KB, 48799 KB/s, 9 seconds passed
+... 69%, 480384 KB, 48801 KB/s, 9 seconds passed
+... 69%, 480416 KB, 48802 KB/s, 9 seconds passed
+... 69%, 480448 KB, 48804 KB/s, 9 seconds passed
+... 69%, 480480 KB, 48806 KB/s, 9 seconds passed
+... 69%, 480512 KB, 48808 KB/s, 9 seconds passed
+... 69%, 480544 KB, 48810 KB/s, 9 seconds passed
+... 69%, 480576 KB, 48812 KB/s, 9 seconds passed
+... 69%, 480608 KB, 48814 KB/s, 9 seconds passed
+... 69%, 480640 KB, 48816 KB/s, 9 seconds passed
+... 69%, 480672 KB, 48817 KB/s, 9 seconds passed
+... 69%, 480704 KB, 48819 KB/s, 9 seconds passed
+... 69%, 480736 KB, 48821 KB/s, 9 seconds passed
+... 69%, 480768 KB, 48823 KB/s, 9 seconds passed
+... 69%, 480800 KB, 48825 KB/s, 9 seconds passed
+... 69%, 480832 KB, 48827 KB/s, 9 seconds passed
+... 69%, 480864 KB, 48828 KB/s, 9 seconds passed
+... 69%, 480896 KB, 48830 KB/s, 9 seconds passed
+... 69%, 480928 KB, 48832 KB/s, 9 seconds passed
+... 69%, 480960 KB, 48834 KB/s, 9 seconds passed
+... 69%, 480992 KB, 48836 KB/s, 9 seconds passed
+... 69%, 481024 KB, 48838 KB/s, 9 seconds passed
+... 69%, 481056 KB, 48840 KB/s, 9 seconds passed
+... 69%, 481088 KB, 48842 KB/s, 9 seconds passed
+... 69%, 481120 KB, 48844 KB/s, 9 seconds passed
+... 69%, 481152 KB, 48845 KB/s, 9 seconds passed
+... 69%, 481184 KB, 48847 KB/s, 9 seconds passed
+... 69%, 481216 KB, 48849 KB/s, 9 seconds passed
+... 69%, 481248 KB, 48851 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
+    ... 69%, 481280 KB, 48633 KB/s, 9 seconds passed
+... 69%, 481312 KB, 48586 KB/s, 9 seconds passed
+... 69%, 481344 KB, 48587 KB/s, 9 seconds passed
+... 69%, 481376 KB, 48588 KB/s, 9 seconds passed
+... 69%, 481408 KB, 48590 KB/s, 9 seconds passed
+... 69%, 481440 KB, 48591 KB/s, 9 seconds passed
+... 69%, 481472 KB, 48592 KB/s, 9 seconds passed
+... 69%, 481504 KB, 48593 KB/s, 9 seconds passed
+... 69%, 481536 KB, 48595 KB/s, 9 seconds passed
+... 69%, 481568 KB, 48596 KB/s, 9 seconds passed
+... 69%, 481600 KB, 48598 KB/s, 9 seconds passed
+... 69%, 481632 KB, 48599 KB/s, 9 seconds passed
+... 69%, 481664 KB, 48600 KB/s, 9 seconds passed
+... 69%, 481696 KB, 48602 KB/s, 9 seconds passed
+... 69%, 481728 KB, 48603 KB/s, 9 seconds passed
+... 69%, 481760 KB, 48604 KB/s, 9 seconds passed
+... 69%, 481792 KB, 48606 KB/s, 9 seconds passed
+... 69%, 481824 KB, 48607 KB/s, 9 seconds passed
+... 69%, 481856 KB, 48608 KB/s, 9 seconds passed
+... 69%, 481888 KB, 48610 KB/s, 9 seconds passed
+... 69%, 481920 KB, 48611 KB/s, 9 seconds passed
+... 69%, 481952 KB, 48612 KB/s, 9 seconds passed
+... 69%, 481984 KB, 48614 KB/s, 9 seconds passed
+... 69%, 482016 KB, 48615 KB/s, 9 seconds passed
+... 69%, 482048 KB, 48616 KB/s, 9 seconds passed
+... 69%, 482080 KB, 48618 KB/s, 9 seconds passed
+... 69%, 482112 KB, 48619 KB/s, 9 seconds passed
+... 69%, 482144 KB, 48620 KB/s, 9 seconds passed
+... 69%, 482176 KB, 48622 KB/s, 9 seconds passed
+... 69%, 482208 KB, 48623 KB/s, 9 seconds passed
+... 69%, 482240 KB, 48624 KB/s, 9 seconds passed
+... 69%, 482272 KB, 48626 KB/s, 9 seconds passed
+... 69%, 482304 KB, 48627 KB/s, 9 seconds passed
+... 69%, 482336 KB, 48629 KB/s, 9 seconds passed
+... 69%, 482368 KB, 48631 KB/s, 9 seconds passed
+... 69%, 482400 KB, 48632 KB/s, 9 seconds passed
+... 69%, 482432 KB, 48634 KB/s, 9 seconds passed
+... 69%, 482464 KB, 48636 KB/s, 9 seconds passed
+... 69%, 482496 KB, 48638 KB/s, 9 seconds passed
+... 69%, 482528 KB, 48640 KB/s, 9 seconds passed
+... 69%, 482560 KB, 48642 KB/s, 9 seconds passed
+... 69%, 482592 KB, 48644 KB/s, 9 seconds passed
+... 69%, 482624 KB, 48646 KB/s, 9 seconds passed
+... 69%, 482656 KB, 48648 KB/s, 9 seconds passed
+... 69%, 482688 KB, 48650 KB/s, 9 seconds passed
+... 69%, 482720 KB, 48652 KB/s, 9 seconds passed
+... 69%, 482752 KB, 48654 KB/s, 9 seconds passed
+... 69%, 482784 KB, 48657 KB/s, 9 seconds passed
+... 69%, 482816 KB, 48659 KB/s, 9 seconds passed
+... 69%, 482848 KB, 48661 KB/s, 9 seconds passed
+... 69%, 482880 KB, 48663 KB/s, 9 seconds passed
+... 69%, 482912 KB, 48665 KB/s, 9 seconds passed
+... 69%, 482944 KB, 48667 KB/s, 9 seconds passed
+... 69%, 482976 KB, 48669 KB/s, 9 seconds passed
+... 69%, 483008 KB, 48671 KB/s, 9 seconds passed
+... 69%, 483040 KB, 48673 KB/s, 9 seconds passed
+... 69%, 483072 KB, 48675 KB/s, 9 seconds passed
+... 69%, 483104 KB, 48677 KB/s, 9 seconds passed
+... 69%, 483136 KB, 48679 KB/s, 9 seconds passed
+... 69%, 483168 KB, 48681 KB/s, 9 seconds passed
+... 69%, 483200 KB, 48683 KB/s, 9 seconds passed
+... 69%, 483232 KB, 48685 KB/s, 9 seconds passed
+... 69%, 483264 KB, 48687 KB/s, 9 seconds passed
+... 69%, 483296 KB, 48689 KB/s, 9 seconds passed
+... 69%, 483328 KB, 48691 KB/s, 9 seconds passed
+... 69%, 483360 KB, 48693 KB/s, 9 seconds passed
+... 69%, 483392 KB, 48695 KB/s, 9 seconds passed
+... 69%, 483424 KB, 48697 KB/s, 9 seconds passed
+... 69%, 483456 KB, 48699 KB/s, 9 seconds passed
+... 69%, 483488 KB, 48701 KB/s, 9 seconds passed
+... 69%, 483520 KB, 48703 KB/s, 9 seconds passed
+... 69%, 483552 KB, 48705 KB/s, 9 seconds passed
+... 69%, 483584 KB, 48707 KB/s, 9 seconds passed
+... 69%, 483616 KB, 48709 KB/s, 9 seconds passed
+... 69%, 483648 KB, 48711 KB/s, 9 seconds passed
+... 69%, 483680 KB, 48714 KB/s, 9 seconds passed
+... 69%, 483712 KB, 48716 KB/s, 9 seconds passed
+... 69%, 483744 KB, 48718 KB/s, 9 seconds passed
+... 69%, 483776 KB, 48721 KB/s, 9 seconds passed
+... 69%, 483808 KB, 48723 KB/s, 9 seconds passed
+... 69%, 483840 KB, 48726 KB/s, 9 seconds passed
+... 69%, 483872 KB, 48728 KB/s, 9 seconds passed
+... 69%, 483904 KB, 48730 KB/s, 9 seconds passed
+... 69%, 483936 KB, 48733 KB/s, 9 seconds passed
+... 69%, 483968 KB, 48735 KB/s, 9 seconds passed
+... 69%, 484000 KB, 48738 KB/s, 9 seconds passed
+... 69%, 484032 KB, 48740 KB/s, 9 seconds passed
+... 69%, 484064 KB, 48743 KB/s, 9 seconds passed
+... 69%, 484096 KB, 48745 KB/s, 9 seconds passed
+... 69%, 484128 KB, 48747 KB/s, 9 seconds passed
+... 69%, 484160 KB, 48750 KB/s, 9 seconds passed
+... 69%, 484192 KB, 48752 KB/s, 9 seconds passed
+... 69%, 484224 KB, 48754 KB/s, 9 seconds passed
+... 69%, 484256 KB, 48757 KB/s, 9 seconds passed
+... 69%, 484288 KB, 48759 KB/s, 9 seconds passed
+... 69%, 484320 KB, 48761 KB/s, 9 seconds passed
+... 69%, 484352 KB, 48763 KB/s, 9 seconds passed
+... 69%, 484384 KB, 48764 KB/s, 9 seconds passed
+... 69%, 484416 KB, 48766 KB/s, 9 seconds passed
+... 69%, 484448 KB, 48768 KB/s, 9 seconds passed
+... 69%, 484480 KB, 48770 KB/s, 9 seconds passed
+... 69%, 484512 KB, 48772 KB/s, 9 seconds passed
+... 69%, 484544 KB, 48774 KB/s, 9 seconds passed
+... 69%, 484576 KB, 48776 KB/s, 9 seconds passed
+... 69%, 484608 KB, 48777 KB/s, 9 seconds passed
+... 69%, 484640 KB, 48780 KB/s, 9 seconds passed
+... 69%, 484672 KB, 48781 KB/s, 9 seconds passed
+... 69%, 484704 KB, 48783 KB/s, 9 seconds passed
+... 69%, 484736 KB, 48785 KB/s, 9 seconds passed
+... 69%, 484768 KB, 48786 KB/s, 9 seconds passed
+... 69%, 484800 KB, 48789 KB/s, 9 seconds passed
+... 69%, 484832 KB, 48791 KB/s, 9 seconds passed
+... 69%, 484864 KB, 48793 KB/s, 9 seconds passed
+... 69%, 484896 KB, 48794 KB/s, 9 seconds passed
+... 69%, 484928 KB, 48796 KB/s, 9 seconds passed
+... 69%, 484960 KB, 48797 KB/s, 9 seconds passed
+... 69%, 484992 KB, 48799 KB/s, 9 seconds passed
+... 69%, 485024 KB, 48801 KB/s, 9 seconds passed
+... 69%, 485056 KB, 48803 KB/s, 9 seconds passed
+... 69%, 485088 KB, 48805 KB/s, 9 seconds passed
+... 69%, 485120 KB, 48807 KB/s, 9 seconds passed
+... 69%, 485152 KB, 48809 KB/s, 9 seconds passed
+... 69%, 485184 KB, 48810 KB/s, 9 seconds passed
+... 69%, 485216 KB, 48812 KB/s, 9 seconds passed
+... 69%, 485248 KB, 48814 KB/s, 9 seconds passed
+... 69%, 485280 KB, 48816 KB/s, 9 seconds passed
+... 69%, 485312 KB, 48818 KB/s, 9 seconds passed
+... 69%, 485344 KB, 48819 KB/s, 9 seconds passed
+... 69%, 485376 KB, 48820 KB/s, 9 seconds passed
+... 69%, 485408 KB, 48821 KB/s, 9 seconds passed
+... 69%, 485440 KB, 48824 KB/s, 9 seconds passed
+... 69%, 485472 KB, 48826 KB/s, 9 seconds passed
+... 69%, 485504 KB, 48829 KB/s, 9 seconds passed
+... 69%, 485536 KB, 48831 KB/s, 9 seconds passed
+... 69%, 485568 KB, 48833 KB/s, 9 seconds passed
+... 69%, 485600 KB, 48835 KB/s, 9 seconds passed
+... 69%, 485632 KB, 48837 KB/s, 9 seconds passed
+
+.. parsed-literal::
+
+    ... 69%, 485664 KB, 48839 KB/s, 9 seconds passed
+... 69%, 485696 KB, 48841 KB/s, 9 seconds passed
+... 69%, 485728 KB, 48843 KB/s, 9 seconds passed
+... 69%, 485760 KB, 48844 KB/s, 9 seconds passed
+... 69%, 485792 KB, 48846 KB/s, 9 seconds passed
+... 69%, 485824 KB, 48848 KB/s, 9 seconds passed
+... 69%, 485856 KB, 48850 KB/s, 9 seconds passed
+... 69%, 485888 KB, 48852 KB/s, 9 seconds passed
+... 69%, 485920 KB, 48854 KB/s, 9 seconds passed
+... 69%, 485952 KB, 48855 KB/s, 9 seconds passed
+... 69%, 485984 KB, 48857 KB/s, 9 seconds passed
+... 69%, 486016 KB, 48859 KB/s, 9 seconds passed
+... 69%, 486048 KB, 48861 KB/s, 9 seconds passed
+... 69%, 486080 KB, 48863 KB/s, 9 seconds passed
+... 69%, 486112 KB, 48865 KB/s, 9 seconds passed
+... 69%, 486144 KB, 48866 KB/s, 9 seconds passed
+... 69%, 486176 KB, 48868 KB/s, 9 seconds passed
+... 69%, 486208 KB, 48870 KB/s, 9 seconds passed
+... 69%, 486240 KB, 48872 KB/s, 9 seconds passed
+... 69%, 486272 KB, 48874 KB/s, 9 seconds passed
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+... 69%, 486784 KB, 48900 KB/s, 9 seconds passed
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+... 70%, 486880 KB, 48906 KB/s, 9 seconds passed
+... 70%, 486912 KB, 48907 KB/s, 9 seconds passed
+... 70%, 486944 KB, 48908 KB/s, 9 seconds passed
+... 70%, 486976 KB, 48909 KB/s, 9 seconds passed
+... 70%, 487008 KB, 48912 KB/s, 9 seconds passed
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 71%, 499456 KB, 49292 KB/s, 10 seconds passed
+... 71%, 499488 KB, 49294 KB/s, 10 seconds passed
+... 71%, 499520 KB, 49296 KB/s, 10 seconds passed
+... 71%, 499552 KB, 49298 KB/s, 10 seconds passed
+... 71%, 499584 KB, 49300 KB/s, 10 seconds passed
+... 71%, 499616 KB, 49301 KB/s, 10 seconds passed
+... 71%, 499648 KB, 49304 KB/s, 10 seconds passed
+... 71%, 499680 KB, 49305 KB/s, 10 seconds passed
+... 71%, 499712 KB, 49307 KB/s, 10 seconds passed
+... 71%, 499744 KB, 49309 KB/s, 10 seconds passed
+... 71%, 499776 KB, 49311 KB/s, 10 seconds passed
+... 71%, 499808 KB, 49313 KB/s, 10 seconds passed
+... 71%, 499840 KB, 49315 KB/s, 10 seconds passed
+... 71%, 499872 KB, 49316 KB/s, 10 seconds passed
+... 71%, 499904 KB, 49318 KB/s, 10 seconds passed
+... 71%, 499936 KB, 49320 KB/s, 10 seconds passed
+... 71%, 499968 KB, 49322 KB/s, 10 seconds passed
+... 71%, 500000 KB, 49324 KB/s, 10 seconds passed
+... 71%, 500032 KB, 49325 KB/s, 10 seconds passed
+... 71%, 500064 KB, 49326 KB/s, 10 seconds passed
+... 71%, 500096 KB, 49327 KB/s, 10 seconds passed
+... 71%, 500128 KB, 49329 KB/s, 10 seconds passed
+... 71%, 500160 KB, 49331 KB/s, 10 seconds passed
+... 71%, 500192 KB, 49334 KB/s, 10 seconds passed
+... 71%, 500224 KB, 49336 KB/s, 10 seconds passed
+... 71%, 500256 KB, 49338 KB/s, 10 seconds passed
+... 71%, 500288 KB, 49339 KB/s, 10 seconds passed
+... 71%, 500320 KB, 49340 KB/s, 10 seconds passed
+... 71%, 500352 KB, 49342 KB/s, 10 seconds passed
+... 71%, 500384 KB, 49345 KB/s, 10 seconds passed
+... 71%, 500416 KB, 49347 KB/s, 10 seconds passed
+... 71%, 500448 KB, 49348 KB/s, 10 seconds passed
+... 71%, 500480 KB, 49350 KB/s, 10 seconds passed
+... 71%, 500512 KB, 49349 KB/s, 10 seconds passed
+... 71%, 500544 KB, 49351 KB/s, 10 seconds passed
+... 71%, 500576 KB, 49353 KB/s, 10 seconds passed
+... 71%, 500608 KB, 49355 KB/s, 10 seconds passed
+... 71%, 500640 KB, 49357 KB/s, 10 seconds passed
+... 71%, 500672 KB, 49358 KB/s, 10 seconds passed
+... 71%, 500704 KB, 49359 KB/s, 10 seconds passed
+... 71%, 500736 KB, 49360 KB/s, 10 seconds passed
+... 72%, 500768 KB, 49362 KB/s, 10 seconds passed
+... 72%, 500800 KB, 49365 KB/s, 10 seconds passed
+... 72%, 500832 KB, 49367 KB/s, 10 seconds passed
+... 72%, 500864 KB, 49354 KB/s, 10 seconds passed
+... 72%, 500896 KB, 49355 KB/s, 10 seconds passed
+... 72%, 500928 KB, 49358 KB/s, 10 seconds passed
+... 72%, 500960 KB, 49360 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
+    ... 72%, 500992 KB, 49361 KB/s, 10 seconds passed
+... 72%, 501024 KB, 49363 KB/s, 10 seconds passed
+... 72%, 501056 KB, 49365 KB/s, 10 seconds passed
+... 72%, 501088 KB, 49367 KB/s, 10 seconds passed
+... 72%, 501120 KB, 49369 KB/s, 10 seconds passed
+... 72%, 501152 KB, 49370 KB/s, 10 seconds passed
+... 72%, 501184 KB, 49372 KB/s, 10 seconds passed
+... 72%, 501216 KB, 49374 KB/s, 10 seconds passed
+... 72%, 501248 KB, 49376 KB/s, 10 seconds passed
+... 72%, 501280 KB, 49378 KB/s, 10 seconds passed
+... 72%, 501312 KB, 49379 KB/s, 10 seconds passed
+... 72%, 501344 KB, 49381 KB/s, 10 seconds passed
+... 72%, 501376 KB, 49383 KB/s, 10 seconds passed
+... 72%, 501408 KB, 49385 KB/s, 10 seconds passed
+... 72%, 501440 KB, 49386 KB/s, 10 seconds passed
+... 72%, 501472 KB, 49388 KB/s, 10 seconds passed
+... 72%, 501504 KB, 49390 KB/s, 10 seconds passed
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+... 72%, 501568 KB, 49394 KB/s, 10 seconds passed
+... 72%, 501600 KB, 49396 KB/s, 10 seconds passed
+... 72%, 501632 KB, 49397 KB/s, 10 seconds passed
+... 72%, 501664 KB, 49399 KB/s, 10 seconds passed
+... 72%, 501696 KB, 49401 KB/s, 10 seconds passed
+... 72%, 501728 KB, 49403 KB/s, 10 seconds passed
+... 72%, 501760 KB, 49324 KB/s, 10 seconds passed
+... 72%, 501792 KB, 49325 KB/s, 10 seconds passed
+... 72%, 501824 KB, 49326 KB/s, 10 seconds passed
+... 72%, 501856 KB, 49327 KB/s, 10 seconds passed
+... 72%, 501888 KB, 49329 KB/s, 10 seconds passed
+... 72%, 501920 KB, 49302 KB/s, 10 seconds passed
+... 72%, 501952 KB, 49303 KB/s, 10 seconds passed
+... 72%, 501984 KB, 49273 KB/s, 10 seconds passed
+... 72%, 502016 KB, 49274 KB/s, 10 seconds passed
+... 72%, 502048 KB, 49275 KB/s, 10 seconds passed
+... 72%, 502080 KB, 49276 KB/s, 10 seconds passed
+... 72%, 502112 KB, 49278 KB/s, 10 seconds passed
+... 72%, 502144 KB, 49279 KB/s, 10 seconds passed
+... 72%, 502176 KB, 49280 KB/s, 10 seconds passed
+... 72%, 502208 KB, 49282 KB/s, 10 seconds passed
+... 72%, 502240 KB, 49283 KB/s, 10 seconds passed
+... 72%, 502272 KB, 49284 KB/s, 10 seconds passed
+... 72%, 502304 KB, 49285 KB/s, 10 seconds passed
+... 72%, 502336 KB, 49287 KB/s, 10 seconds passed
+... 72%, 502368 KB, 49288 KB/s, 10 seconds passed
+... 72%, 502400 KB, 49289 KB/s, 10 seconds passed
+... 72%, 502432 KB, 49291 KB/s, 10 seconds passed
+... 72%, 502464 KB, 49292 KB/s, 10 seconds passed
+... 72%, 502496 KB, 49293 KB/s, 10 seconds passed
+... 72%, 502528 KB, 49294 KB/s, 10 seconds passed
+... 72%, 502560 KB, 49296 KB/s, 10 seconds passed
+... 72%, 502592 KB, 49297 KB/s, 10 seconds passed
+... 72%, 502624 KB, 49298 KB/s, 10 seconds passed
+... 72%, 502656 KB, 49299 KB/s, 10 seconds passed
+... 72%, 502688 KB, 49301 KB/s, 10 seconds passed
+... 72%, 502720 KB, 49302 KB/s, 10 seconds passed
+... 72%, 502752 KB, 49303 KB/s, 10 seconds passed
+... 72%, 502784 KB, 49305 KB/s, 10 seconds passed
+... 72%, 502816 KB, 49306 KB/s, 10 seconds passed
+... 72%, 502848 KB, 49307 KB/s, 10 seconds passed
+... 72%, 502880 KB, 49309 KB/s, 10 seconds passed
+... 72%, 502912 KB, 49310 KB/s, 10 seconds passed
+... 72%, 502944 KB, 49311 KB/s, 10 seconds passed
+... 72%, 502976 KB, 49312 KB/s, 10 seconds passed
+... 72%, 503008 KB, 49314 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
+    ... 72%, 503040 KB, 49315 KB/s, 10 seconds passed
+... 72%, 503072 KB, 49316 KB/s, 10 seconds passed
+... 72%, 503104 KB, 49318 KB/s, 10 seconds passed
+... 72%, 503136 KB, 49320 KB/s, 10 seconds passed
+... 72%, 503168 KB, 49322 KB/s, 10 seconds passed
+... 72%, 503200 KB, 49324 KB/s, 10 seconds passed
+... 72%, 503232 KB, 49326 KB/s, 10 seconds passed
+... 72%, 503264 KB, 49328 KB/s, 10 seconds passed
+... 72%, 503296 KB, 49330 KB/s, 10 seconds passed
+... 72%, 503328 KB, 49331 KB/s, 10 seconds passed
+... 72%, 503360 KB, 49333 KB/s, 10 seconds passed
+... 72%, 503392 KB, 49335 KB/s, 10 seconds passed
+... 72%, 503424 KB, 49337 KB/s, 10 seconds passed
+... 72%, 503456 KB, 49339 KB/s, 10 seconds passed
+... 72%, 503488 KB, 49341 KB/s, 10 seconds passed
+... 72%, 503520 KB, 49343 KB/s, 10 seconds passed
+... 72%, 503552 KB, 49345 KB/s, 10 seconds passed
+... 72%, 503584 KB, 49347 KB/s, 10 seconds passed
+... 72%, 503616 KB, 49349 KB/s, 10 seconds passed
+... 72%, 503648 KB, 49351 KB/s, 10 seconds passed
+... 72%, 503680 KB, 49353 KB/s, 10 seconds passed
+... 72%, 503712 KB, 49355 KB/s, 10 seconds passed
+... 72%, 503744 KB, 49357 KB/s, 10 seconds passed
+... 72%, 503776 KB, 49359 KB/s, 10 seconds passed
+... 72%, 503808 KB, 49361 KB/s, 10 seconds passed
+... 72%, 503840 KB, 49363 KB/s, 10 seconds passed
+... 72%, 503872 KB, 49365 KB/s, 10 seconds passed
+... 72%, 503904 KB, 49366 KB/s, 10 seconds passed
+... 72%, 503936 KB, 49368 KB/s, 10 seconds passed
+... 72%, 503968 KB, 49370 KB/s, 10 seconds passed
+... 72%, 504000 KB, 49372 KB/s, 10 seconds passed
+... 72%, 504032 KB, 49374 KB/s, 10 seconds passed
+... 72%, 504064 KB, 49376 KB/s, 10 seconds passed
+... 72%, 504096 KB, 49378 KB/s, 10 seconds passed
+... 72%, 504128 KB, 49380 KB/s, 10 seconds passed
+... 72%, 504160 KB, 49382 KB/s, 10 seconds passed
+... 72%, 504192 KB, 49384 KB/s, 10 seconds passed
+... 72%, 504224 KB, 49386 KB/s, 10 seconds passed
+... 72%, 504256 KB, 49388 KB/s, 10 seconds passed
+... 72%, 504288 KB, 49390 KB/s, 10 seconds passed
+... 72%, 504320 KB, 49392 KB/s, 10 seconds passed
+... 72%, 504352 KB, 49394 KB/s, 10 seconds passed
+... 72%, 504384 KB, 49396 KB/s, 10 seconds passed
+... 72%, 504416 KB, 49398 KB/s, 10 seconds passed
+... 72%, 504448 KB, 49401 KB/s, 10 seconds passed
+... 72%, 504480 KB, 49403 KB/s, 10 seconds passed
+... 72%, 504512 KB, 49405 KB/s, 10 seconds passed
+... 72%, 504544 KB, 49407 KB/s, 10 seconds passed
+... 72%, 504576 KB, 49410 KB/s, 10 seconds passed
+... 72%, 504608 KB, 49412 KB/s, 10 seconds passed
+... 72%, 504640 KB, 49414 KB/s, 10 seconds passed
+... 72%, 504672 KB, 49417 KB/s, 10 seconds passed
+... 72%, 504704 KB, 49419 KB/s, 10 seconds passed
+... 72%, 504736 KB, 49421 KB/s, 10 seconds passed
+... 72%, 504768 KB, 49424 KB/s, 10 seconds passed
+... 72%, 504800 KB, 49426 KB/s, 10 seconds passed
+... 72%, 504832 KB, 49428 KB/s, 10 seconds passed
+... 72%, 504864 KB, 49431 KB/s, 10 seconds passed
+... 72%, 504896 KB, 49433 KB/s, 10 seconds passed
+... 72%, 504928 KB, 49435 KB/s, 10 seconds passed
+... 72%, 504960 KB, 49438 KB/s, 10 seconds passed
+... 72%, 504992 KB, 49440 KB/s, 10 seconds passed
+... 72%, 505024 KB, 49443 KB/s, 10 seconds passed
+... 72%, 505056 KB, 49445 KB/s, 10 seconds passed
+... 72%, 505088 KB, 49446 KB/s, 10 seconds passed
+... 72%, 505120 KB, 49448 KB/s, 10 seconds passed
+... 72%, 505152 KB, 49450 KB/s, 10 seconds passed
+... 72%, 505184 KB, 49452 KB/s, 10 seconds passed
+... 72%, 505216 KB, 49454 KB/s, 10 seconds passed
+... 72%, 505248 KB, 49455 KB/s, 10 seconds passed
+... 72%, 505280 KB, 49457 KB/s, 10 seconds passed
+... 72%, 505312 KB, 49459 KB/s, 10 seconds passed
+... 72%, 505344 KB, 49460 KB/s, 10 seconds passed
+... 72%, 505376 KB, 49462 KB/s, 10 seconds passed
+... 72%, 505408 KB, 49464 KB/s, 10 seconds passed
+... 72%, 505440 KB, 49466 KB/s, 10 seconds passed
+... 72%, 505472 KB, 49468 KB/s, 10 seconds passed
+... 72%, 505504 KB, 49470 KB/s, 10 seconds passed
+... 72%, 505536 KB, 49472 KB/s, 10 seconds passed
+... 72%, 505568 KB, 49473 KB/s, 10 seconds passed
+... 72%, 505600 KB, 49475 KB/s, 10 seconds passed
+... 72%, 505632 KB, 49477 KB/s, 10 seconds passed
+... 72%, 505664 KB, 49479 KB/s, 10 seconds passed
+... 72%, 505696 KB, 49480 KB/s, 10 seconds passed
+... 72%, 505728 KB, 49482 KB/s, 10 seconds passed
+... 72%, 505760 KB, 49484 KB/s, 10 seconds passed
+... 72%, 505792 KB, 49486 KB/s, 10 seconds passed
+... 72%, 505824 KB, 49488 KB/s, 10 seconds passed
+... 72%, 505856 KB, 49490 KB/s, 10 seconds passed
+... 72%, 505888 KB, 49491 KB/s, 10 seconds passed
+... 72%, 505920 KB, 49493 KB/s, 10 seconds passed
+... 72%, 505952 KB, 49495 KB/s, 10 seconds passed
+... 72%, 505984 KB, 49497 KB/s, 10 seconds passed
+... 72%, 506016 KB, 49498 KB/s, 10 seconds passed
+... 72%, 506048 KB, 49500 KB/s, 10 seconds passed
+... 72%, 506080 KB, 49502 KB/s, 10 seconds passed
+... 72%, 506112 KB, 49504 KB/s, 10 seconds passed
+... 72%, 506144 KB, 49505 KB/s, 10 seconds passed
+... 72%, 506176 KB, 49507 KB/s, 10 seconds passed
+... 72%, 506208 KB, 49509 KB/s, 10 seconds passed
+... 72%, 506240 KB, 49511 KB/s, 10 seconds passed
+... 72%, 506272 KB, 49513 KB/s, 10 seconds passed
+... 72%, 506304 KB, 49515 KB/s, 10 seconds passed
+... 72%, 506336 KB, 49516 KB/s, 10 seconds passed
+... 72%, 506368 KB, 49517 KB/s, 10 seconds passed
+... 72%, 506400 KB, 49519 KB/s, 10 seconds passed
+... 72%, 506432 KB, 49521 KB/s, 10 seconds passed
+... 72%, 506464 KB, 49523 KB/s, 10 seconds passed
+... 72%, 506496 KB, 49525 KB/s, 10 seconds passed
+... 72%, 506528 KB, 49527 KB/s, 10 seconds passed
+... 72%, 506560 KB, 49529 KB/s, 10 seconds passed
+... 72%, 506592 KB, 49530 KB/s, 10 seconds passed
+... 72%, 506624 KB, 49532 KB/s, 10 seconds passed
+... 72%, 506656 KB, 49534 KB/s, 10 seconds passed
+... 72%, 506688 KB, 49536 KB/s, 10 seconds passed
+... 72%, 506720 KB, 49538 KB/s, 10 seconds passed
+... 72%, 506752 KB, 49539 KB/s, 10 seconds passed
+... 72%, 506784 KB, 49541 KB/s, 10 seconds passed
+... 72%, 506816 KB, 49543 KB/s, 10 seconds passed
+... 72%, 506848 KB, 49545 KB/s, 10 seconds passed
+... 72%, 506880 KB, 49456 KB/s, 10 seconds passed
+... 72%, 506912 KB, 49457 KB/s, 10 seconds passed
+... 72%, 506944 KB, 49458 KB/s, 10 seconds passed
+... 72%, 506976 KB, 49460 KB/s, 10 seconds passed
+... 72%, 507008 KB, 49461 KB/s, 10 seconds passed
+... 72%, 507040 KB, 49463 KB/s, 10 seconds passed
+... 72%, 507072 KB, 49464 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
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+... 72%, 507168 KB, 49467 KB/s, 10 seconds passed
+... 72%, 507200 KB, 49468 KB/s, 10 seconds passed
+... 72%, 507232 KB, 49470 KB/s, 10 seconds passed
+... 72%, 507264 KB, 49464 KB/s, 10 seconds passed
+... 72%, 507296 KB, 49465 KB/s, 10 seconds passed
+... 72%, 507328 KB, 49466 KB/s, 10 seconds passed
+... 72%, 507360 KB, 49468 KB/s, 10 seconds passed
+... 72%, 507392 KB, 49469 KB/s, 10 seconds passed
+... 72%, 507424 KB, 49470 KB/s, 10 seconds passed
+... 72%, 507456 KB, 49471 KB/s, 10 seconds passed
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+... 72%, 507520 KB, 49474 KB/s, 10 seconds passed
+... 72%, 507552 KB, 49476 KB/s, 10 seconds passed
+... 72%, 507584 KB, 49477 KB/s, 10 seconds passed
+... 72%, 507616 KB, 49478 KB/s, 10 seconds passed
+... 72%, 507648 KB, 49479 KB/s, 10 seconds passed
+... 72%, 507680 KB, 49481 KB/s, 10 seconds passed
+... 72%, 507712 KB, 49482 KB/s, 10 seconds passed
+... 73%, 507744 KB, 49484 KB/s, 10 seconds passed
+... 73%, 507776 KB, 49485 KB/s, 10 seconds passed
+... 73%, 507808 KB, 49487 KB/s, 10 seconds passed
+... 73%, 507840 KB, 49488 KB/s, 10 seconds passed
+... 73%, 507872 KB, 49490 KB/s, 10 seconds passed
+... 73%, 507904 KB, 49492 KB/s, 10 seconds passed
+... 73%, 507936 KB, 49493 KB/s, 10 seconds passed
+... 73%, 507968 KB, 49495 KB/s, 10 seconds passed
+... 73%, 508000 KB, 49497 KB/s, 10 seconds passed
+... 73%, 508032 KB, 49498 KB/s, 10 seconds passed
+... 73%, 508064 KB, 49500 KB/s, 10 seconds passed
+... 73%, 508096 KB, 49502 KB/s, 10 seconds passed
+... 73%, 508128 KB, 49503 KB/s, 10 seconds passed
+... 73%, 508160 KB, 49505 KB/s, 10 seconds passed
+... 73%, 508192 KB, 49507 KB/s, 10 seconds passed
+... 73%, 508224 KB, 49508 KB/s, 10 seconds passed
+... 73%, 508256 KB, 49510 KB/s, 10 seconds passed
+... 73%, 508288 KB, 49512 KB/s, 10 seconds passed
+... 73%, 508320 KB, 49514 KB/s, 10 seconds passed
+... 73%, 508352 KB, 49515 KB/s, 10 seconds passed
+... 73%, 508384 KB, 49517 KB/s, 10 seconds passed
+... 73%, 508416 KB, 49518 KB/s, 10 seconds passed
+... 73%, 508448 KB, 49520 KB/s, 10 seconds passed
+... 73%, 508480 KB, 49522 KB/s, 10 seconds passed
+... 73%, 508512 KB, 49523 KB/s, 10 seconds passed
+... 73%, 508544 KB, 49525 KB/s, 10 seconds passed
+... 73%, 508576 KB, 49527 KB/s, 10 seconds passed
+... 73%, 508608 KB, 49529 KB/s, 10 seconds passed
+... 73%, 508640 KB, 49530 KB/s, 10 seconds passed
+... 73%, 508672 KB, 49532 KB/s, 10 seconds passed
+... 73%, 508704 KB, 49534 KB/s, 10 seconds passed
+... 73%, 508736 KB, 49535 KB/s, 10 seconds passed
+... 73%, 508768 KB, 49537 KB/s, 10 seconds passed
+... 73%, 508800 KB, 49539 KB/s, 10 seconds passed
+... 73%, 508832 KB, 49540 KB/s, 10 seconds passed
+... 73%, 508864 KB, 49542 KB/s, 10 seconds passed
+... 73%, 508896 KB, 49544 KB/s, 10 seconds passed
+... 73%, 508928 KB, 49546 KB/s, 10 seconds passed
+... 73%, 508960 KB, 49548 KB/s, 10 seconds passed
+... 73%, 508992 KB, 49551 KB/s, 10 seconds passed
+... 73%, 509024 KB, 49553 KB/s, 10 seconds passed
+... 73%, 509056 KB, 49555 KB/s, 10 seconds passed
+... 73%, 509088 KB, 49557 KB/s, 10 seconds passed
+... 73%, 509120 KB, 49559 KB/s, 10 seconds passed
+... 73%, 509152 KB, 49561 KB/s, 10 seconds passed
+... 73%, 509184 KB, 49564 KB/s, 10 seconds passed
+... 73%, 509216 KB, 49566 KB/s, 10 seconds passed
+... 73%, 509248 KB, 49568 KB/s, 10 seconds passed
+... 73%, 509280 KB, 49570 KB/s, 10 seconds passed
+... 73%, 509312 KB, 49572 KB/s, 10 seconds passed
+... 73%, 509344 KB, 49574 KB/s, 10 seconds passed
+... 73%, 509376 KB, 49577 KB/s, 10 seconds passed
+... 73%, 509408 KB, 49579 KB/s, 10 seconds passed
+... 73%, 509440 KB, 49581 KB/s, 10 seconds passed
+... 73%, 509472 KB, 49583 KB/s, 10 seconds passed
+... 73%, 509504 KB, 49585 KB/s, 10 seconds passed
+... 73%, 509536 KB, 49587 KB/s, 10 seconds passed
+... 73%, 509568 KB, 49589 KB/s, 10 seconds passed
+... 73%, 509600 KB, 49592 KB/s, 10 seconds passed
+... 73%, 509632 KB, 49594 KB/s, 10 seconds passed
+... 73%, 509664 KB, 49596 KB/s, 10 seconds passed
+... 73%, 509696 KB, 49598 KB/s, 10 seconds passed
+... 73%, 509728 KB, 49600 KB/s, 10 seconds passed
+... 73%, 509760 KB, 49602 KB/s, 10 seconds passed
+... 73%, 509792 KB, 49604 KB/s, 10 seconds passed
+... 73%, 509824 KB, 49605 KB/s, 10 seconds passed
+... 73%, 509856 KB, 49607 KB/s, 10 seconds passed
+... 73%, 509888 KB, 49609 KB/s, 10 seconds passed
+... 73%, 509920 KB, 49611 KB/s, 10 seconds passed
+... 73%, 509952 KB, 49613 KB/s, 10 seconds passed
+... 73%, 509984 KB, 49614 KB/s, 10 seconds passed
+... 73%, 510016 KB, 49616 KB/s, 10 seconds passed
+... 73%, 510048 KB, 49617 KB/s, 10 seconds passed
+... 73%, 510080 KB, 49619 KB/s, 10 seconds passed
+... 73%, 510112 KB, 49621 KB/s, 10 seconds passed
+... 73%, 510144 KB, 49623 KB/s, 10 seconds passed
+... 73%, 510176 KB, 49624 KB/s, 10 seconds passed
+... 73%, 510208 KB, 49626 KB/s, 10 seconds passed
+... 73%, 510240 KB, 49628 KB/s, 10 seconds passed
+... 73%, 510272 KB, 49630 KB/s, 10 seconds passed
+... 73%, 510304 KB, 49631 KB/s, 10 seconds passed
+... 73%, 510336 KB, 49633 KB/s, 10 seconds passed
+... 73%, 510368 KB, 49635 KB/s, 10 seconds passed
+... 73%, 510400 KB, 49637 KB/s, 10 seconds passed
+... 73%, 510432 KB, 49639 KB/s, 10 seconds passed
+... 73%, 510464 KB, 49640 KB/s, 10 seconds passed
+... 73%, 510496 KB, 49642 KB/s, 10 seconds passed
+... 73%, 510528 KB, 49644 KB/s, 10 seconds passed
+... 73%, 510560 KB, 49646 KB/s, 10 seconds passed
+... 73%, 510592 KB, 49648 KB/s, 10 seconds passed
+... 73%, 510624 KB, 49650 KB/s, 10 seconds passed
+... 73%, 510656 KB, 49652 KB/s, 10 seconds passed
+... 73%, 510688 KB, 49653 KB/s, 10 seconds passed
+... 73%, 510720 KB, 49655 KB/s, 10 seconds passed
+... 73%, 510752 KB, 49657 KB/s, 10 seconds passed
+... 73%, 510784 KB, 49658 KB/s, 10 seconds passed
+... 73%, 510816 KB, 49660 KB/s, 10 seconds passed
+... 73%, 510848 KB, 49650 KB/s, 10 seconds passed
+... 73%, 510880 KB, 49651 KB/s, 10 seconds passed
+... 73%, 510912 KB, 49654 KB/s, 10 seconds passed
+... 73%, 510944 KB, 49655 KB/s, 10 seconds passed
+... 73%, 510976 KB, 49657 KB/s, 10 seconds passed
+... 73%, 511008 KB, 49659 KB/s, 10 seconds passed
+... 73%, 511040 KB, 49660 KB/s, 10 seconds passed
+... 73%, 511072 KB, 49663 KB/s, 10 seconds passed
+... 73%, 511104 KB, 49664 KB/s, 10 seconds passed
+... 73%, 511136 KB, 49666 KB/s, 10 seconds passed
+... 73%, 511168 KB, 49652 KB/s, 10 seconds passed
+... 73%, 511200 KB, 49653 KB/s, 10 seconds passed
+... 73%, 511232 KB, 49654 KB/s, 10 seconds passed
+... 73%, 511264 KB, 49657 KB/s, 10 seconds passed
+... 73%, 511296 KB, 49657 KB/s, 10 seconds passed
+... 73%, 511328 KB, 49648 KB/s, 10 seconds passed
+... 73%, 511360 KB, 49650 KB/s, 10 seconds passed
+... 73%, 511392 KB, 49652 KB/s, 10 seconds passed
+... 73%, 511424 KB, 49654 KB/s, 10 seconds passed
+... 73%, 511456 KB, 49655 KB/s, 10 seconds passed
+... 73%, 511488 KB, 49657 KB/s, 10 seconds passed
+... 73%, 511520 KB, 49659 KB/s, 10 seconds passed
+... 73%, 511552 KB, 49661 KB/s, 10 seconds passed
+... 73%, 511584 KB, 49663 KB/s, 10 seconds passed
+... 73%, 511616 KB, 49664 KB/s, 10 seconds passed
+... 73%, 511648 KB, 49666 KB/s, 10 seconds passed
+... 73%, 511680 KB, 49667 KB/s, 10 seconds passed
+... 73%, 511712 KB, 49669 KB/s, 10 seconds passed
+... 73%, 511744 KB, 49671 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
+    ... 73%, 511776 KB, 49673 KB/s, 10 seconds passed
+... 73%, 511808 KB, 49674 KB/s, 10 seconds passed
+... 73%, 511840 KB, 49676 KB/s, 10 seconds passed
+... 73%, 511872 KB, 49678 KB/s, 10 seconds passed
+... 73%, 511904 KB, 49680 KB/s, 10 seconds passed
+... 73%, 511936 KB, 49682 KB/s, 10 seconds passed
+... 73%, 511968 KB, 49684 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
+    ... 73%, 512000 KB, 49383 KB/s, 10 seconds passed
+... 73%, 512032 KB, 49384 KB/s, 10 seconds passed
+... 73%, 512064 KB, 49385 KB/s, 10 seconds passed
+... 73%, 512096 KB, 49386 KB/s, 10 seconds passed
+... 73%, 512128 KB, 49388 KB/s, 10 seconds passed
+... 73%, 512160 KB, 49389 KB/s, 10 seconds passed
+... 73%, 512192 KB, 49390 KB/s, 10 seconds passed
+... 73%, 512224 KB, 49391 KB/s, 10 seconds passed
+... 73%, 512256 KB, 49392 KB/s, 10 seconds passed
+... 73%, 512288 KB, 49394 KB/s, 10 seconds passed
+... 73%, 512320 KB, 49395 KB/s, 10 seconds passed
+... 73%, 512352 KB, 49396 KB/s, 10 seconds passed
+... 73%, 512384 KB, 49397 KB/s, 10 seconds passed
+... 73%, 512416 KB, 49398 KB/s, 10 seconds passed
+... 73%, 512448 KB, 49400 KB/s, 10 seconds passed
+... 73%, 512480 KB, 49401 KB/s, 10 seconds passed
+... 73%, 512512 KB, 49402 KB/s, 10 seconds passed
+... 73%, 512544 KB, 49403 KB/s, 10 seconds passed
+... 73%, 512576 KB, 49404 KB/s, 10 seconds passed
+... 73%, 512608 KB, 49406 KB/s, 10 seconds passed
+... 73%, 512640 KB, 49407 KB/s, 10 seconds passed
+... 73%, 512672 KB, 49409 KB/s, 10 seconds passed
+... 73%, 512704 KB, 49411 KB/s, 10 seconds passed
+... 73%, 512736 KB, 49412 KB/s, 10 seconds passed
+... 73%, 512768 KB, 49387 KB/s, 10 seconds passed
+... 73%, 512800 KB, 49388 KB/s, 10 seconds passed
+... 73%, 512832 KB, 49389 KB/s, 10 seconds passed
+... 73%, 512864 KB, 49391 KB/s, 10 seconds passed
+... 73%, 512896 KB, 49392 KB/s, 10 seconds passed
+... 73%, 512928 KB, 49393 KB/s, 10 seconds passed
+... 73%, 512960 KB, 49394 KB/s, 10 seconds passed
+... 73%, 512992 KB, 49396 KB/s, 10 seconds passed
+... 73%, 513024 KB, 49397 KB/s, 10 seconds passed
+... 73%, 513056 KB, 49398 KB/s, 10 seconds passed
+... 73%, 513088 KB, 49399 KB/s, 10 seconds passed
+... 73%, 513120 KB, 49400 KB/s, 10 seconds passed
+... 73%, 513152 KB, 49402 KB/s, 10 seconds passed
+... 73%, 513184 KB, 49403 KB/s, 10 seconds passed
+... 73%, 513216 KB, 49404 KB/s, 10 seconds passed
+... 73%, 513248 KB, 49405 KB/s, 10 seconds passed
+... 73%, 513280 KB, 49406 KB/s, 10 seconds passed
+... 73%, 513312 KB, 49408 KB/s, 10 seconds passed
+... 73%, 513344 KB, 49409 KB/s, 10 seconds passed
+... 73%, 513376 KB, 49410 KB/s, 10 seconds passed
+... 73%, 513408 KB, 49411 KB/s, 10 seconds passed
+... 73%, 513440 KB, 49413 KB/s, 10 seconds passed
+... 73%, 513472 KB, 49414 KB/s, 10 seconds passed
+... 73%, 513504 KB, 49415 KB/s, 10 seconds passed
+... 73%, 513536 KB, 49416 KB/s, 10 seconds passed
+... 73%, 513568 KB, 49417 KB/s, 10 seconds passed
+... 73%, 513600 KB, 49419 KB/s, 10 seconds passed
+... 73%, 513632 KB, 49421 KB/s, 10 seconds passed
+... 73%, 513664 KB, 49423 KB/s, 10 seconds passed
+... 73%, 513696 KB, 49425 KB/s, 10 seconds passed
+... 73%, 513728 KB, 49426 KB/s, 10 seconds passed
+... 73%, 513760 KB, 49428 KB/s, 10 seconds passed
+... 73%, 513792 KB, 49430 KB/s, 10 seconds passed
+... 73%, 513824 KB, 49432 KB/s, 10 seconds passed
+... 73%, 513856 KB, 49433 KB/s, 10 seconds passed
+... 73%, 513888 KB, 49435 KB/s, 10 seconds passed
+... 73%, 513920 KB, 49437 KB/s, 10 seconds passed
+... 73%, 513952 KB, 49439 KB/s, 10 seconds passed
+... 73%, 513984 KB, 49440 KB/s, 10 seconds passed
+... 73%, 514016 KB, 49442 KB/s, 10 seconds passed
+... 73%, 514048 KB, 49444 KB/s, 10 seconds passed
+... 73%, 514080 KB, 49446 KB/s, 10 seconds passed
+... 73%, 514112 KB, 49447 KB/s, 10 seconds passed
+... 73%, 514144 KB, 49449 KB/s, 10 seconds passed
+... 73%, 514176 KB, 49451 KB/s, 10 seconds passed
+... 73%, 514208 KB, 49452 KB/s, 10 seconds passed
+... 73%, 514240 KB, 49454 KB/s, 10 seconds passed
+... 73%, 514272 KB, 49456 KB/s, 10 seconds passed
+... 73%, 514304 KB, 49458 KB/s, 10 seconds passed
+... 73%, 514336 KB, 49459 KB/s, 10 seconds passed
+... 73%, 514368 KB, 49461 KB/s, 10 seconds passed
+... 73%, 514400 KB, 49463 KB/s, 10 seconds passed
+... 73%, 514432 KB, 49465 KB/s, 10 seconds passed
+... 73%, 514464 KB, 49467 KB/s, 10 seconds passed
+... 73%, 514496 KB, 49468 KB/s, 10 seconds passed
+... 73%, 514528 KB, 49470 KB/s, 10 seconds passed
+... 73%, 514560 KB, 49472 KB/s, 10 seconds passed
+... 73%, 514592 KB, 49474 KB/s, 10 seconds passed
+... 73%, 514624 KB, 49475 KB/s, 10 seconds passed
+... 73%, 514656 KB, 49477 KB/s, 10 seconds passed
+... 74%, 514688 KB, 49479 KB/s, 10 seconds passed
+... 74%, 514720 KB, 49481 KB/s, 10 seconds passed
+... 74%, 514752 KB, 49484 KB/s, 10 seconds passed
+... 74%, 514784 KB, 49486 KB/s, 10 seconds passed
+... 74%, 514816 KB, 49488 KB/s, 10 seconds passed
+... 74%, 514848 KB, 49491 KB/s, 10 seconds passed
+... 74%, 514880 KB, 49493 KB/s, 10 seconds passed
+... 74%, 514912 KB, 49495 KB/s, 10 seconds passed
+... 74%, 514944 KB, 49498 KB/s, 10 seconds passed
+... 74%, 514976 KB, 49500 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
+    ... 74%, 515008 KB, 49437 KB/s, 10 seconds passed
+... 74%, 515040 KB, 49405 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
+    ... 74%, 515072 KB, 49195 KB/s, 10 seconds passed
+... 74%, 515104 KB, 49197 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
+    ... 74%, 515136 KB, 48956 KB/s, 10 seconds passed
+... 74%, 515168 KB, 48821 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
+    ... 74%, 515200 KB, 48789 KB/s, 10 seconds passed
+... 74%, 515232 KB, 48621 KB/s, 10 seconds passed
+
+.. parsed-literal::
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+    ... 74%, 515264 KB, 48519 KB/s, 10 seconds passed
+... 74%, 515296 KB, 48482 KB/s, 10 seconds passed
+... 74%, 515328 KB, 48482 KB/s, 10 seconds passed
+... 74%, 515360 KB, 48483 KB/s, 10 seconds passed
+... 74%, 515392 KB, 48484 KB/s, 10 seconds passed
+... 74%, 515424 KB, 48486 KB/s, 10 seconds passed
+... 74%, 515456 KB, 48487 KB/s, 10 seconds passed
+... 74%, 515488 KB, 48488 KB/s, 10 seconds passed
+... 74%, 515520 KB, 48489 KB/s, 10 seconds passed
+... 74%, 515552 KB, 48490 KB/s, 10 seconds passed
+... 74%, 515584 KB, 48492 KB/s, 10 seconds passed
+... 74%, 515616 KB, 48493 KB/s, 10 seconds passed
+... 74%, 515648 KB, 48494 KB/s, 10 seconds passed
+... 74%, 515680 KB, 48495 KB/s, 10 seconds passed
+... 74%, 515712 KB, 48496 KB/s, 10 seconds passed
+... 74%, 515744 KB, 48498 KB/s, 10 seconds passed
+... 74%, 515776 KB, 48499 KB/s, 10 seconds passed
+... 74%, 515808 KB, 48500 KB/s, 10 seconds passed
+... 74%, 515840 KB, 48502 KB/s, 10 seconds passed
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+... 74%, 515904 KB, 48505 KB/s, 10 seconds passed
+... 74%, 515936 KB, 48507 KB/s, 10 seconds passed
+... 74%, 515968 KB, 48508 KB/s, 10 seconds passed
+... 74%, 516000 KB, 48510 KB/s, 10 seconds passed
+... 74%, 516032 KB, 48512 KB/s, 10 seconds passed
+... 74%, 516064 KB, 48507 KB/s, 10 seconds passed
+... 74%, 516096 KB, 48508 KB/s, 10 seconds passed
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+... 74%, 516192 KB, 48512 KB/s, 10 seconds passed
+... 74%, 516224 KB, 48513 KB/s, 10 seconds passed
+... 74%, 516256 KB, 48514 KB/s, 10 seconds passed
+... 74%, 516288 KB, 48515 KB/s, 10 seconds passed
+... 74%, 516320 KB, 48517 KB/s, 10 seconds passed
+... 74%, 516352 KB, 48518 KB/s, 10 seconds passed
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+... 74%, 516512 KB, 48524 KB/s, 10 seconds passed
+... 74%, 516544 KB, 48525 KB/s, 10 seconds passed
+... 74%, 516576 KB, 48527 KB/s, 10 seconds passed
+... 74%, 516608 KB, 48529 KB/s, 10 seconds passed
+... 74%, 516640 KB, 48531 KB/s, 10 seconds passed
+... 74%, 516672 KB, 48532 KB/s, 10 seconds passed
+... 74%, 516704 KB, 48534 KB/s, 10 seconds passed
+... 74%, 516736 KB, 48536 KB/s, 10 seconds passed
+... 74%, 516768 KB, 48537 KB/s, 10 seconds passed
+... 74%, 516800 KB, 48539 KB/s, 10 seconds passed
+... 74%, 516832 KB, 48541 KB/s, 10 seconds passed
+... 74%, 516864 KB, 48542 KB/s, 10 seconds passed
+... 74%, 516896 KB, 48544 KB/s, 10 seconds passed
+... 74%, 516928 KB, 48546 KB/s, 10 seconds passed
+... 74%, 516960 KB, 48548 KB/s, 10 seconds passed
+... 74%, 516992 KB, 48549 KB/s, 10 seconds passed
+... 74%, 517024 KB, 48551 KB/s, 10 seconds passed
+... 74%, 517056 KB, 48553 KB/s, 10 seconds passed
+... 74%, 517088 KB, 48554 KB/s, 10 seconds passed
+... 74%, 517120 KB, 48556 KB/s, 10 seconds passed
+... 74%, 517152 KB, 48557 KB/s, 10 seconds passed
+... 74%, 517184 KB, 48559 KB/s, 10 seconds passed
+... 74%, 517216 KB, 48561 KB/s, 10 seconds passed
+... 74%, 517248 KB, 48563 KB/s, 10 seconds passed
+... 74%, 517280 KB, 48564 KB/s, 10 seconds passed
+... 74%, 517312 KB, 48566 KB/s, 10 seconds passed
+... 74%, 517344 KB, 48568 KB/s, 10 seconds passed
+... 74%, 517376 KB, 48569 KB/s, 10 seconds passed
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+... 74%, 517440 KB, 48573 KB/s, 10 seconds passed
+... 74%, 517472 KB, 48574 KB/s, 10 seconds passed
+... 74%, 517504 KB, 48576 KB/s, 10 seconds passed
+... 74%, 517536 KB, 48578 KB/s, 10 seconds passed
+... 74%, 517568 KB, 48579 KB/s, 10 seconds passed
+... 74%, 517600 KB, 48581 KB/s, 10 seconds passed
+... 74%, 517632 KB, 48583 KB/s, 10 seconds passed
+... 74%, 517664 KB, 48585 KB/s, 10 seconds passed
+... 74%, 517696 KB, 48587 KB/s, 10 seconds passed
+... 74%, 517728 KB, 48589 KB/s, 10 seconds passed
+... 74%, 517760 KB, 48591 KB/s, 10 seconds passed
+... 74%, 517792 KB, 48593 KB/s, 10 seconds passed
+... 74%, 517824 KB, 48595 KB/s, 10 seconds passed
+... 74%, 517856 KB, 48597 KB/s, 10 seconds passed
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+... 74%, 517920 KB, 48601 KB/s, 10 seconds passed
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+... 74%, 518016 KB, 48608 KB/s, 10 seconds passed
+... 74%, 518048 KB, 48610 KB/s, 10 seconds passed
+... 74%, 518080 KB, 48612 KB/s, 10 seconds passed
+... 74%, 518112 KB, 48614 KB/s, 10 seconds passed
+... 74%, 518144 KB, 48616 KB/s, 10 seconds passed
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+... 74%, 518240 KB, 48623 KB/s, 10 seconds passed
+... 74%, 518272 KB, 48625 KB/s, 10 seconds passed
+... 74%, 518304 KB, 48627 KB/s, 10 seconds passed
+... 74%, 518336 KB, 48629 KB/s, 10 seconds passed
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+... 74%, 518400 KB, 48633 KB/s, 10 seconds passed
+... 74%, 518432 KB, 48635 KB/s, 10 seconds passed
+... 74%, 518464 KB, 48637 KB/s, 10 seconds passed
+... 74%, 518496 KB, 48639 KB/s, 10 seconds passed
+... 74%, 518528 KB, 48641 KB/s, 10 seconds passed
+... 74%, 518560 KB, 48642 KB/s, 10 seconds passed
+... 74%, 518592 KB, 48644 KB/s, 10 seconds passed
+... 74%, 518624 KB, 48646 KB/s, 10 seconds passed
+... 74%, 518656 KB, 48648 KB/s, 10 seconds passed
+... 74%, 518688 KB, 48649 KB/s, 10 seconds passed
+... 74%, 518720 KB, 48650 KB/s, 10 seconds passed
+
+.. parsed-literal::
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+... 74%, 518816 KB, 48655 KB/s, 10 seconds passed
+... 74%, 518848 KB, 48657 KB/s, 10 seconds passed
+... 74%, 518880 KB, 48660 KB/s, 10 seconds passed
+... 74%, 518912 KB, 48661 KB/s, 10 seconds passed
+... 74%, 518944 KB, 48663 KB/s, 10 seconds passed
+... 74%, 518976 KB, 48665 KB/s, 10 seconds passed
+... 74%, 519008 KB, 48666 KB/s, 10 seconds passed
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+... 74%, 519200 KB, 48677 KB/s, 10 seconds passed
+... 74%, 519232 KB, 48679 KB/s, 10 seconds passed
+... 74%, 519264 KB, 48680 KB/s, 10 seconds passed
+... 74%, 519296 KB, 48682 KB/s, 10 seconds passed
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+... 74%, 519616 KB, 48699 KB/s, 10 seconds passed
+... 74%, 519648 KB, 48701 KB/s, 10 seconds passed
+... 74%, 519680 KB, 48702 KB/s, 10 seconds passed
+... 74%, 519712 KB, 48704 KB/s, 10 seconds passed
+... 74%, 519744 KB, 48706 KB/s, 10 seconds passed
+... 74%, 519776 KB, 48708 KB/s, 10 seconds passed
+... 74%, 519808 KB, 48709 KB/s, 10 seconds passed
+... 74%, 519840 KB, 48711 KB/s, 10 seconds passed
+... 74%, 519872 KB, 48713 KB/s, 10 seconds passed
+... 74%, 519904 KB, 48715 KB/s, 10 seconds passed
+... 74%, 519936 KB, 48716 KB/s, 10 seconds passed
+... 74%, 519968 KB, 48718 KB/s, 10 seconds passed
+... 74%, 520000 KB, 48720 KB/s, 10 seconds passed
+... 74%, 520032 KB, 48722 KB/s, 10 seconds passed
+... 74%, 520064 KB, 48724 KB/s, 10 seconds passed
+... 74%, 520096 KB, 48725 KB/s, 10 seconds passed
+... 74%, 520128 KB, 48727 KB/s, 10 seconds passed
+... 74%, 520160 KB, 48729 KB/s, 10 seconds passed
+... 74%, 520192 KB, 48731 KB/s, 10 seconds passed
+... 74%, 520224 KB, 48732 KB/s, 10 seconds passed
+... 74%, 520256 KB, 48734 KB/s, 10 seconds passed
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+... 74%, 520320 KB, 48738 KB/s, 10 seconds passed
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+... 74%, 520416 KB, 48742 KB/s, 10 seconds passed
+... 74%, 520448 KB, 48744 KB/s, 10 seconds passed
+... 74%, 520480 KB, 48746 KB/s, 10 seconds passed
+... 74%, 520512 KB, 48748 KB/s, 10 seconds passed
+... 74%, 520544 KB, 48749 KB/s, 10 seconds passed
+... 74%, 520576 KB, 48750 KB/s, 10 seconds passed
+... 74%, 520608 KB, 48752 KB/s, 10 seconds passed
+... 74%, 520640 KB, 48754 KB/s, 10 seconds passed
+... 74%, 520672 KB, 48745 KB/s, 10 seconds passed
+... 74%, 520704 KB, 48743 KB/s, 10 seconds passed
+... 74%, 520736 KB, 48741 KB/s, 10 seconds passed
+... 74%, 520768 KB, 48725 KB/s, 10 seconds passed
+... 74%, 520800 KB, 48726 KB/s, 10 seconds passed
+... 74%, 520832 KB, 48727 KB/s, 10 seconds passed
+... 74%, 520864 KB, 48729 KB/s, 10 seconds passed
+... 74%, 520896 KB, 48730 KB/s, 10 seconds passed
+... 74%, 520928 KB, 48732 KB/s, 10 seconds passed
+... 74%, 520960 KB, 48733 KB/s, 10 seconds passed
+... 74%, 520992 KB, 48734 KB/s, 10 seconds passed
+... 74%, 521024 KB, 48736 KB/s, 10 seconds passed
+... 74%, 521056 KB, 48737 KB/s, 10 seconds passed
+... 74%, 521088 KB, 48739 KB/s, 10 seconds passed
+... 74%, 521120 KB, 48689 KB/s, 10 seconds passed
+... 74%, 521152 KB, 48689 KB/s, 10 seconds passed
+... 74%, 521184 KB, 48690 KB/s, 10 seconds passed
+... 74%, 521216 KB, 48692 KB/s, 10 seconds passed
+... 74%, 521248 KB, 48693 KB/s, 10 seconds passed
+... 74%, 521280 KB, 48694 KB/s, 10 seconds passed
+... 74%, 521312 KB, 48696 KB/s, 10 seconds passed
+... 74%, 521344 KB, 48697 KB/s, 10 seconds passed
+... 74%, 521376 KB, 48698 KB/s, 10 seconds passed
+... 74%, 521408 KB, 48700 KB/s, 10 seconds passed
+... 74%, 521440 KB, 48701 KB/s, 10 seconds passed
+... 74%, 521472 KB, 48702 KB/s, 10 seconds passed
+... 74%, 521504 KB, 48703 KB/s, 10 seconds passed
+... 74%, 521536 KB, 48705 KB/s, 10 seconds passed
+... 74%, 521568 KB, 48706 KB/s, 10 seconds passed
+... 74%, 521600 KB, 48707 KB/s, 10 seconds passed
+... 75%, 521632 KB, 48709 KB/s, 10 seconds passed
+... 75%, 521664 KB, 48710 KB/s, 10 seconds passed
+... 75%, 521696 KB, 48711 KB/s, 10 seconds passed
+... 75%, 521728 KB, 48713 KB/s, 10 seconds passed
+... 75%, 521760 KB, 48714 KB/s, 10 seconds passed
+... 75%, 521792 KB, 48716 KB/s, 10 seconds passed
+... 75%, 521824 KB, 48717 KB/s, 10 seconds passed
+... 75%, 521856 KB, 48718 KB/s, 10 seconds passed
+... 75%, 521888 KB, 48719 KB/s, 10 seconds passed
+... 75%, 521920 KB, 48721 KB/s, 10 seconds passed
+... 75%, 521952 KB, 48722 KB/s, 10 seconds passed
+... 75%, 521984 KB, 48723 KB/s, 10 seconds passed
+... 75%, 522016 KB, 48724 KB/s, 10 seconds passed
+
+.. parsed-literal::
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+... 75%, 522112 KB, 48728 KB/s, 10 seconds passed
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+... 75%, 522528 KB, 48752 KB/s, 10 seconds passed
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+... 75%, 522592 KB, 48756 KB/s, 10 seconds passed
+... 75%, 522624 KB, 48758 KB/s, 10 seconds passed
+... 75%, 522656 KB, 48760 KB/s, 10 seconds passed
+... 75%, 522688 KB, 48761 KB/s, 10 seconds passed
+... 75%, 522720 KB, 48763 KB/s, 10 seconds passed
+... 75%, 522752 KB, 48765 KB/s, 10 seconds passed
+... 75%, 522784 KB, 48767 KB/s, 10 seconds passed
+... 75%, 522816 KB, 48769 KB/s, 10 seconds passed
+... 75%, 522848 KB, 48771 KB/s, 10 seconds passed
+... 75%, 522880 KB, 48773 KB/s, 10 seconds passed
+... 75%, 522912 KB, 48775 KB/s, 10 seconds passed
+... 75%, 522944 KB, 48777 KB/s, 10 seconds passed
+... 75%, 522976 KB, 48779 KB/s, 10 seconds passed
+... 75%, 523008 KB, 48780 KB/s, 10 seconds passed
+... 75%, 523040 KB, 48782 KB/s, 10 seconds passed
+... 75%, 523072 KB, 48784 KB/s, 10 seconds passed
+... 75%, 523104 KB, 48786 KB/s, 10 seconds passed
+... 75%, 523136 KB, 48788 KB/s, 10 seconds passed
+... 75%, 523168 KB, 48790 KB/s, 10 seconds passed
+... 75%, 523200 KB, 48792 KB/s, 10 seconds passed
+... 75%, 523232 KB, 48794 KB/s, 10 seconds passed
+... 75%, 523264 KB, 48796 KB/s, 10 seconds passed
+... 75%, 523296 KB, 48798 KB/s, 10 seconds passed
+... 75%, 523328 KB, 48799 KB/s, 10 seconds passed
+... 75%, 523360 KB, 48801 KB/s, 10 seconds passed
+... 75%, 523392 KB, 48803 KB/s, 10 seconds passed
+... 75%, 523424 KB, 48805 KB/s, 10 seconds passed
+... 75%, 523456 KB, 48807 KB/s, 10 seconds passed
+... 75%, 523488 KB, 48809 KB/s, 10 seconds passed
+... 75%, 523520 KB, 48811 KB/s, 10 seconds passed
+... 75%, 523552 KB, 48813 KB/s, 10 seconds passed
+... 75%, 523584 KB, 48815 KB/s, 10 seconds passed
+... 75%, 523616 KB, 48817 KB/s, 10 seconds passed
+... 75%, 523648 KB, 48819 KB/s, 10 seconds passed
+... 75%, 523680 KB, 48822 KB/s, 10 seconds passed
+... 75%, 523712 KB, 48824 KB/s, 10 seconds passed
+... 75%, 523744 KB, 48826 KB/s, 10 seconds passed
+... 75%, 523776 KB, 48828 KB/s, 10 seconds passed
+... 75%, 523808 KB, 48831 KB/s, 10 seconds passed
+... 75%, 523840 KB, 48833 KB/s, 10 seconds passed
+... 75%, 523872 KB, 48835 KB/s, 10 seconds passed
+... 75%, 523904 KB, 48837 KB/s, 10 seconds passed
+... 75%, 523936 KB, 48840 KB/s, 10 seconds passed
+... 75%, 523968 KB, 48842 KB/s, 10 seconds passed
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+... 75%, 524032 KB, 48840 KB/s, 10 seconds passed
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+... 75%, 524320 KB, 48856 KB/s, 10 seconds passed
+... 75%, 524352 KB, 48858 KB/s, 10 seconds passed
+... 75%, 524384 KB, 48859 KB/s, 10 seconds passed
+... 75%, 524416 KB, 48861 KB/s, 10 seconds passed
+... 75%, 524448 KB, 48863 KB/s, 10 seconds passed
+... 75%, 524480 KB, 48865 KB/s, 10 seconds passed
+... 75%, 524512 KB, 48866 KB/s, 10 seconds passed
+... 75%, 524544 KB, 48867 KB/s, 10 seconds passed
+... 75%, 524576 KB, 48869 KB/s, 10 seconds passed
+... 75%, 524608 KB, 48871 KB/s, 10 seconds passed
+... 75%, 524640 KB, 48873 KB/s, 10 seconds passed
+... 75%, 524672 KB, 48875 KB/s, 10 seconds passed
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+... 75%, 524736 KB, 48878 KB/s, 10 seconds passed
+... 75%, 524768 KB, 48880 KB/s, 10 seconds passed
+... 75%, 524800 KB, 48882 KB/s, 10 seconds passed
+... 75%, 524832 KB, 48883 KB/s, 10 seconds passed
+... 75%, 524864 KB, 48884 KB/s, 10 seconds passed
+... 75%, 524896 KB, 48885 KB/s, 10 seconds passed
+... 75%, 524928 KB, 48886 KB/s, 10 seconds passed
+... 75%, 524960 KB, 48889 KB/s, 10 seconds passed
+... 75%, 524992 KB, 48891 KB/s, 10 seconds passed
+... 75%, 525024 KB, 48893 KB/s, 10 seconds passed
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+... 75%, 525088 KB, 48897 KB/s, 10 seconds passed
+... 75%, 525120 KB, 48899 KB/s, 10 seconds passed
+... 75%, 525152 KB, 48900 KB/s, 10 seconds passed
+... 75%, 525184 KB, 48902 KB/s, 10 seconds passed
+... 75%, 525216 KB, 48904 KB/s, 10 seconds passed
+... 75%, 525248 KB, 48906 KB/s, 10 seconds passed
+... 75%, 525280 KB, 48907 KB/s, 10 seconds passed
+... 75%, 525312 KB, 48909 KB/s, 10 seconds passed
+... 75%, 525344 KB, 48911 KB/s, 10 seconds passed
+... 75%, 525376 KB, 48912 KB/s, 10 seconds passed
+... 75%, 525408 KB, 48914 KB/s, 10 seconds passed
+... 75%, 525440 KB, 48916 KB/s, 10 seconds passed
+... 75%, 525472 KB, 48917 KB/s, 10 seconds passed
+... 75%, 525504 KB, 48919 KB/s, 10 seconds passed
+... 75%, 525536 KB, 48921 KB/s, 10 seconds passed
+... 75%, 525568 KB, 48922 KB/s, 10 seconds passed
+... 75%, 525600 KB, 48924 KB/s, 10 seconds passed
+... 75%, 525632 KB, 48926 KB/s, 10 seconds passed
+... 75%, 525664 KB, 48927 KB/s, 10 seconds passed
+... 75%, 525696 KB, 48928 KB/s, 10 seconds passed
+... 75%, 525728 KB, 48929 KB/s, 10 seconds passed
+... 75%, 525760 KB, 48931 KB/s, 10 seconds passed
+... 75%, 525792 KB, 48934 KB/s, 10 seconds passed
+... 75%, 525824 KB, 48936 KB/s, 10 seconds passed
+... 75%, 525856 KB, 48938 KB/s, 10 seconds passed
+... 75%, 525888 KB, 48939 KB/s, 10 seconds passed
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+
+.. parsed-literal::
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+... 76%, 532512 KB, 49291 KB/s, 10 seconds passed
+... 76%, 532544 KB, 49292 KB/s, 10 seconds passed
+... 76%, 532576 KB, 49294 KB/s, 10 seconds passed
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+... 76%, 533984 KB, 49369 KB/s, 10 seconds passed
+... 76%, 534016 KB, 49370 KB/s, 10 seconds passed
+... 76%, 534048 KB, 49372 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
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+... 77%, 538784 KB, 49621 KB/s, 10 seconds passed
+... 77%, 538816 KB, 49622 KB/s, 10 seconds passed
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+... 77%, 539168 KB, 49639 KB/s, 10 seconds passed
+... 77%, 539200 KB, 49642 KB/s, 10 seconds passed
+... 77%, 539232 KB, 49644 KB/s, 10 seconds passed
+... 77%, 539264 KB, 49646 KB/s, 10 seconds passed
+... 77%, 539296 KB, 49647 KB/s, 10 seconds passed
+... 77%, 539328 KB, 49649 KB/s, 10 seconds passed
+... 77%, 539360 KB, 49651 KB/s, 10 seconds passed
+... 77%, 539392 KB, 49652 KB/s, 10 seconds passed
+... 77%, 539424 KB, 49654 KB/s, 10 seconds passed
+... 77%, 539456 KB, 49655 KB/s, 10 seconds passed
+... 77%, 539488 KB, 49656 KB/s, 10 seconds passed
+... 77%, 539520 KB, 49658 KB/s, 10 seconds passed
+... 77%, 539552 KB, 49661 KB/s, 10 seconds passed
+... 77%, 539584 KB, 49663 KB/s, 10 seconds passed
+... 77%, 539616 KB, 49664 KB/s, 10 seconds passed
+... 77%, 539648 KB, 49665 KB/s, 10 seconds passed
+... 77%, 539680 KB, 49666 KB/s, 10 seconds passed
+... 77%, 539712 KB, 49667 KB/s, 10 seconds passed
+... 77%, 539744 KB, 49668 KB/s, 10 seconds passed
+... 77%, 539776 KB, 49670 KB/s, 10 seconds passed
+... 77%, 539808 KB, 49671 KB/s, 10 seconds passed
+... 77%, 539840 KB, 49672 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
+    ... 77%, 539872 KB, 49673 KB/s, 10 seconds passed
+... 77%, 539904 KB, 49675 KB/s, 10 seconds passed
+... 77%, 539936 KB, 49676 KB/s, 10 seconds passed
+... 77%, 539968 KB, 49677 KB/s, 10 seconds passed
+... 77%, 540000 KB, 49678 KB/s, 10 seconds passed
+... 77%, 540032 KB, 49680 KB/s, 10 seconds passed
+... 77%, 540064 KB, 49681 KB/s, 10 seconds passed
+... 77%, 540096 KB, 49682 KB/s, 10 seconds passed
+... 77%, 540128 KB, 49683 KB/s, 10 seconds passed
+... 77%, 540160 KB, 49684 KB/s, 10 seconds passed
+... 77%, 540192 KB, 49686 KB/s, 10 seconds passed
+... 77%, 540224 KB, 49687 KB/s, 10 seconds passed
+... 77%, 540256 KB, 49688 KB/s, 10 seconds passed
+... 77%, 540288 KB, 49689 KB/s, 10 seconds passed
+... 77%, 540320 KB, 49691 KB/s, 10 seconds passed
+... 77%, 540352 KB, 49692 KB/s, 10 seconds passed
+... 77%, 540384 KB, 49693 KB/s, 10 seconds passed
+... 77%, 540416 KB, 49695 KB/s, 10 seconds passed
+... 77%, 540448 KB, 49696 KB/s, 10 seconds passed
+... 77%, 540480 KB, 49697 KB/s, 10 seconds passed
+... 77%, 540512 KB, 49698 KB/s, 10 seconds passed
+... 77%, 540544 KB, 49700 KB/s, 10 seconds passed
+... 77%, 540576 KB, 49701 KB/s, 10 seconds passed
+... 77%, 540608 KB, 49703 KB/s, 10 seconds passed
+... 77%, 540640 KB, 49705 KB/s, 10 seconds passed
+... 77%, 540672 KB, 49707 KB/s, 10 seconds passed
+... 77%, 540704 KB, 49708 KB/s, 10 seconds passed
+... 77%, 540736 KB, 49710 KB/s, 10 seconds passed
+... 77%, 540768 KB, 49712 KB/s, 10 seconds passed
+... 77%, 540800 KB, 49714 KB/s, 10 seconds passed
+... 77%, 540832 KB, 49716 KB/s, 10 seconds passed
+... 77%, 540864 KB, 49718 KB/s, 10 seconds passed
+... 77%, 540896 KB, 49720 KB/s, 10 seconds passed
+... 77%, 540928 KB, 49721 KB/s, 10 seconds passed
+... 77%, 540960 KB, 49723 KB/s, 10 seconds passed
+... 77%, 540992 KB, 49725 KB/s, 10 seconds passed
+... 77%, 541024 KB, 49727 KB/s, 10 seconds passed
+... 77%, 541056 KB, 49729 KB/s, 10 seconds passed
+... 77%, 541088 KB, 49730 KB/s, 10 seconds passed
+... 77%, 541120 KB, 49732 KB/s, 10 seconds passed
+... 77%, 541152 KB, 49734 KB/s, 10 seconds passed
+... 77%, 541184 KB, 49736 KB/s, 10 seconds passed
+... 77%, 541216 KB, 49738 KB/s, 10 seconds passed
+... 77%, 541248 KB, 49740 KB/s, 10 seconds passed
+... 77%, 541280 KB, 49741 KB/s, 10 seconds passed
+... 77%, 541312 KB, 49743 KB/s, 10 seconds passed
+... 77%, 541344 KB, 49745 KB/s, 10 seconds passed
+... 77%, 541376 KB, 49747 KB/s, 10 seconds passed
+... 77%, 541408 KB, 49749 KB/s, 10 seconds passed
+... 77%, 541440 KB, 49751 KB/s, 10 seconds passed
+... 77%, 541472 KB, 49753 KB/s, 10 seconds passed
+... 77%, 541504 KB, 49754 KB/s, 10 seconds passed
+... 77%, 541536 KB, 49756 KB/s, 10 seconds passed
+... 77%, 541568 KB, 49758 KB/s, 10 seconds passed
+... 77%, 541600 KB, 49760 KB/s, 10 seconds passed
+... 77%, 541632 KB, 49762 KB/s, 10 seconds passed
+... 77%, 541664 KB, 49764 KB/s, 10 seconds passed
+... 77%, 541696 KB, 49766 KB/s, 10 seconds passed
+... 77%, 541728 KB, 49768 KB/s, 10 seconds passed
+... 77%, 541760 KB, 49769 KB/s, 10 seconds passed
+... 77%, 541792 KB, 49771 KB/s, 10 seconds passed
+... 77%, 541824 KB, 49773 KB/s, 10 seconds passed
+... 77%, 541856 KB, 49775 KB/s, 10 seconds passed
+... 77%, 541888 KB, 49777 KB/s, 10 seconds passed
+... 77%, 541920 KB, 49779 KB/s, 10 seconds passed
+... 77%, 541952 KB, 49781 KB/s, 10 seconds passed
+... 77%, 541984 KB, 49783 KB/s, 10 seconds passed
+... 77%, 542016 KB, 49784 KB/s, 10 seconds passed
+... 77%, 542048 KB, 49787 KB/s, 10 seconds passed
+... 77%, 542080 KB, 49789 KB/s, 10 seconds passed
+... 77%, 542112 KB, 49791 KB/s, 10 seconds passed
+... 77%, 542144 KB, 49793 KB/s, 10 seconds passed
+... 77%, 542176 KB, 49796 KB/s, 10 seconds passed
+... 77%, 542208 KB, 49798 KB/s, 10 seconds passed
+... 77%, 542240 KB, 49800 KB/s, 10 seconds passed
+... 77%, 542272 KB, 49802 KB/s, 10 seconds passed
+... 77%, 542304 KB, 49804 KB/s, 10 seconds passed
+... 77%, 542336 KB, 49806 KB/s, 10 seconds passed
+... 77%, 542368 KB, 49807 KB/s, 10 seconds passed
+... 77%, 542400 KB, 49809 KB/s, 10 seconds passed
+... 77%, 542432 KB, 49811 KB/s, 10 seconds passed
+... 77%, 542464 KB, 49812 KB/s, 10 seconds passed
+... 78%, 542496 KB, 49814 KB/s, 10 seconds passed
+... 78%, 542528 KB, 49815 KB/s, 10 seconds passed
+... 78%, 542560 KB, 49816 KB/s, 10 seconds passed
+... 78%, 542592 KB, 49818 KB/s, 10 seconds passed
+... 78%, 542624 KB, 49820 KB/s, 10 seconds passed
+... 78%, 542656 KB, 49822 KB/s, 10 seconds passed
+... 78%, 542688 KB, 49824 KB/s, 10 seconds passed
+
+.. parsed-literal::
+
+    ... 78%, 542720 KB, 49461 KB/s, 10 seconds passed
+... 78%, 542752 KB, 49461 KB/s, 10 seconds passed
+... 78%, 542784 KB, 49462 KB/s, 10 seconds passed
+... 78%, 542816 KB, 49464 KB/s, 10 seconds passed
+... 78%, 542848 KB, 49465 KB/s, 10 seconds passed
+... 78%, 542880 KB, 49466 KB/s, 10 seconds passed
+... 78%, 542912 KB, 49468 KB/s, 10 seconds passed
+... 78%, 542944 KB, 49469 KB/s, 10 seconds passed
+... 78%, 542976 KB, 49466 KB/s, 10 seconds passed
+... 78%, 543008 KB, 49466 KB/s, 10 seconds passed
+... 78%, 543040 KB, 49467 KB/s, 10 seconds passed
+... 78%, 543072 KB, 49468 KB/s, 10 seconds passed
+... 78%, 543104 KB, 49469 KB/s, 10 seconds passed
+... 78%, 543136 KB, 49471 KB/s, 10 seconds passed
+... 78%, 543168 KB, 49471 KB/s, 10 seconds passed
+... 78%, 543200 KB, 49473 KB/s, 10 seconds passed
+... 78%, 543232 KB, 49474 KB/s, 10 seconds passed
+... 78%, 543264 KB, 49475 KB/s, 10 seconds passed
+... 78%, 543296 KB, 49476 KB/s, 10 seconds passed
+... 78%, 543328 KB, 49477 KB/s, 10 seconds passed
+... 78%, 543360 KB, 49478 KB/s, 10 seconds passed
+... 78%, 543392 KB, 49479 KB/s, 10 seconds passed
+... 78%, 543424 KB, 49481 KB/s, 10 seconds passed
+... 78%, 543456 KB, 49482 KB/s, 10 seconds passed
+... 78%, 543488 KB, 49483 KB/s, 10 seconds passed
+... 78%, 543520 KB, 49484 KB/s, 10 seconds passed
+... 78%, 543552 KB, 49485 KB/s, 10 seconds passed
+... 78%, 543584 KB, 49486 KB/s, 10 seconds passed
+... 78%, 543616 KB, 49488 KB/s, 10 seconds passed
+... 78%, 543648 KB, 49489 KB/s, 10 seconds passed
+... 78%, 543680 KB, 49490 KB/s, 10 seconds passed
+... 78%, 543712 KB, 49491 KB/s, 10 seconds passed
+... 78%, 543744 KB, 49492 KB/s, 10 seconds passed
+... 78%, 543776 KB, 49494 KB/s, 10 seconds passed
+... 78%, 543808 KB, 49495 KB/s, 10 seconds passed
+... 78%, 543840 KB, 49496 KB/s, 10 seconds passed
+... 78%, 543872 KB, 49498 KB/s, 10 seconds passed
+... 78%, 543904 KB, 49500 KB/s, 10 seconds passed
+... 78%, 543936 KB, 49502 KB/s, 10 seconds passed
+... 78%, 543968 KB, 49503 KB/s, 10 seconds passed
+... 78%, 544000 KB, 49505 KB/s, 10 seconds passed
+... 78%, 544032 KB, 49507 KB/s, 10 seconds passed
+... 78%, 544064 KB, 49509 KB/s, 10 seconds passed
+... 78%, 544096 KB, 49511 KB/s, 10 seconds passed
+... 78%, 544128 KB, 49512 KB/s, 10 seconds passed
+... 78%, 544160 KB, 49514 KB/s, 10 seconds passed
+... 78%, 544192 KB, 49516 KB/s, 10 seconds passed
+... 78%, 544224 KB, 49518 KB/s, 10 seconds passed
+... 78%, 544256 KB, 49520 KB/s, 10 seconds passed
+... 78%, 544288 KB, 49521 KB/s, 10 seconds passed
+... 78%, 544320 KB, 49523 KB/s, 10 seconds passed
+... 78%, 544352 KB, 49525 KB/s, 10 seconds passed
+... 78%, 544384 KB, 49527 KB/s, 10 seconds passed
+... 78%, 544416 KB, 49529 KB/s, 10 seconds passed
+... 78%, 544448 KB, 49530 KB/s, 10 seconds passed
+... 78%, 544480 KB, 49532 KB/s, 10 seconds passed
+... 78%, 544512 KB, 49534 KB/s, 10 seconds passed
+... 78%, 544544 KB, 49536 KB/s, 10 seconds passed
+... 78%, 544576 KB, 49538 KB/s, 10 seconds passed
+... 78%, 544608 KB, 49539 KB/s, 10 seconds passed
+... 78%, 544640 KB, 49541 KB/s, 10 seconds passed
+... 78%, 544672 KB, 49543 KB/s, 10 seconds passed
+... 78%, 544704 KB, 49545 KB/s, 10 seconds passed
+... 78%, 544736 KB, 49547 KB/s, 10 seconds passed
+... 78%, 544768 KB, 49548 KB/s, 10 seconds passed
+... 78%, 544800 KB, 49550 KB/s, 10 seconds passed
+... 78%, 544832 KB, 49552 KB/s, 10 seconds passed
+... 78%, 544864 KB, 49554 KB/s, 10 seconds passed
+... 78%, 544896 KB, 49556 KB/s, 10 seconds passed
+... 78%, 544928 KB, 49557 KB/s, 10 seconds passed
+... 78%, 544960 KB, 49559 KB/s, 10 seconds passed
+... 78%, 544992 KB, 49561 KB/s, 10 seconds passed
+... 78%, 545024 KB, 49563 KB/s, 10 seconds passed
+... 78%, 545056 KB, 49565 KB/s, 10 seconds passed
+... 78%, 545088 KB, 49567 KB/s, 10 seconds passed
+... 78%, 545120 KB, 49568 KB/s, 10 seconds passed
+... 78%, 545152 KB, 49571 KB/s, 10 seconds passed
+... 78%, 545184 KB, 49573 KB/s, 10 seconds passed
+... 78%, 545216 KB, 49575 KB/s, 10 seconds passed
+... 78%, 545248 KB, 49577 KB/s, 10 seconds passed
+... 78%, 545280 KB, 49579 KB/s, 10 seconds passed
+... 78%, 545312 KB, 49581 KB/s, 10 seconds passed
+... 78%, 545344 KB, 49583 KB/s, 10 seconds passed
+... 78%, 545376 KB, 49586 KB/s, 10 seconds passed
+... 78%, 545408 KB, 49588 KB/s, 10 seconds passed
+... 78%, 545440 KB, 49590 KB/s, 10 seconds passed
+... 78%, 545472 KB, 49592 KB/s, 10 seconds passed
+... 78%, 545504 KB, 49594 KB/s, 10 seconds passed
+... 78%, 545536 KB, 49596 KB/s, 10 seconds passed
+... 78%, 545568 KB, 49599 KB/s, 10 seconds passed
+... 78%, 545600 KB, 49601 KB/s, 10 seconds passed
+... 78%, 545632 KB, 49603 KB/s, 10 seconds passed
+... 78%, 545664 KB, 49604 KB/s, 11 seconds passed
+... 78%, 545696 KB, 49606 KB/s, 11 seconds passed
+... 78%, 545728 KB, 49608 KB/s, 11 seconds passed
+... 78%, 545760 KB, 49609 KB/s, 11 seconds passed
+... 78%, 545792 KB, 49611 KB/s, 11 seconds passed
+... 78%, 545824 KB, 49613 KB/s, 11 seconds passed
+... 78%, 545856 KB, 49615 KB/s, 11 seconds passed
+... 78%, 545888 KB, 49616 KB/s, 11 seconds passed
+... 78%, 545920 KB, 49618 KB/s, 11 seconds passed
+... 78%, 545952 KB, 49620 KB/s, 11 seconds passed
+... 78%, 545984 KB, 49621 KB/s, 11 seconds passed
+... 78%, 546016 KB, 49623 KB/s, 11 seconds passed
+... 78%, 546048 KB, 49625 KB/s, 11 seconds passed
+... 78%, 546080 KB, 49626 KB/s, 11 seconds passed
+... 78%, 546112 KB, 49627 KB/s, 11 seconds passed
+... 78%, 546144 KB, 49629 KB/s, 11 seconds passed
+... 78%, 546176 KB, 49630 KB/s, 11 seconds passed
+... 78%, 546208 KB, 49633 KB/s, 11 seconds passed
+... 78%, 546240 KB, 49635 KB/s, 11 seconds passed
+... 78%, 546272 KB, 49636 KB/s, 11 seconds passed
+... 78%, 546304 KB, 49638 KB/s, 11 seconds passed
+... 78%, 546336 KB, 49640 KB/s, 11 seconds passed
+... 78%, 546368 KB, 49641 KB/s, 11 seconds passed
+... 78%, 546400 KB, 49643 KB/s, 11 seconds passed
+... 78%, 546432 KB, 49645 KB/s, 11 seconds passed
+... 78%, 546464 KB, 49646 KB/s, 11 seconds passed
+... 78%, 546496 KB, 49647 KB/s, 11 seconds passed
+... 78%, 546528 KB, 49649 KB/s, 11 seconds passed
+... 78%, 546560 KB, 49650 KB/s, 11 seconds passed
+... 78%, 546592 KB, 49651 KB/s, 11 seconds passed
+... 78%, 546624 KB, 49654 KB/s, 11 seconds passed
+... 78%, 546656 KB, 49656 KB/s, 11 seconds passed
+... 78%, 546688 KB, 49658 KB/s, 11 seconds passed
+... 78%, 546720 KB, 49660 KB/s, 11 seconds passed
+... 78%, 546752 KB, 49661 KB/s, 11 seconds passed
+... 78%, 546784 KB, 49663 KB/s, 11 seconds passed
+... 78%, 546816 KB, 49664 KB/s, 11 seconds passed
+... 78%, 546848 KB, 49666 KB/s, 11 seconds passed
+... 78%, 546880 KB, 49667 KB/s, 11 seconds passed
+... 78%, 546912 KB, 49666 KB/s, 11 seconds passed
+... 78%, 546944 KB, 49667 KB/s, 11 seconds passed
+... 78%, 546976 KB, 49669 KB/s, 11 seconds passed
+... 78%, 547008 KB, 49672 KB/s, 11 seconds passed
+... 78%, 547040 KB, 49673 KB/s, 11 seconds passed
+... 78%, 547072 KB, 49675 KB/s, 11 seconds passed
+... 78%, 547104 KB, 49677 KB/s, 11 seconds passed
+... 78%, 547136 KB, 49678 KB/s, 11 seconds passed
+... 78%, 547168 KB, 49680 KB/s, 11 seconds passed
+... 78%, 547200 KB, 49682 KB/s, 11 seconds passed
+... 78%, 547232 KB, 49683 KB/s, 11 seconds passed
+... 78%, 547264 KB, 49685 KB/s, 11 seconds passed
+... 78%, 547296 KB, 49686 KB/s, 11 seconds passed
+... 78%, 547328 KB, 49686 KB/s, 11 seconds passed
+... 78%, 547360 KB, 49688 KB/s, 11 seconds passed
+... 78%, 547392 KB, 49691 KB/s, 11 seconds passed
+... 78%, 547424 KB, 49693 KB/s, 11 seconds passed
+... 78%, 547456 KB, 49695 KB/s, 11 seconds passed
+... 78%, 547488 KB, 49696 KB/s, 11 seconds passed
+... 78%, 547520 KB, 49698 KB/s, 11 seconds passed
+... 78%, 547552 KB, 49700 KB/s, 11 seconds passed
+... 78%, 547584 KB, 49701 KB/s, 11 seconds passed
+... 78%, 547616 KB, 49703 KB/s, 11 seconds passed
+... 78%, 547648 KB, 49704 KB/s, 11 seconds passed
+... 78%, 547680 KB, 49705 KB/s, 11 seconds passed
+... 78%, 547712 KB, 49707 KB/s, 11 seconds passed
+... 78%, 547744 KB, 49709 KB/s, 11 seconds passed
+... 78%, 547776 KB, 49711 KB/s, 11 seconds passed
+... 78%, 547808 KB, 49713 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 78%, 547840 KB, 49340 KB/s, 11 seconds passed
+... 78%, 547872 KB, 49341 KB/s, 11 seconds passed
+... 78%, 547904 KB, 49342 KB/s, 11 seconds passed
+... 78%, 547936 KB, 49343 KB/s, 11 seconds passed
+... 78%, 547968 KB, 49344 KB/s, 11 seconds passed
+... 78%, 548000 KB, 49345 KB/s, 11 seconds passed
+... 78%, 548032 KB, 49346 KB/s, 11 seconds passed
+... 78%, 548064 KB, 49348 KB/s, 11 seconds passed
+... 78%, 548096 KB, 49349 KB/s, 11 seconds passed
+... 78%, 548128 KB, 49350 KB/s, 11 seconds passed
+... 78%, 548160 KB, 49351 KB/s, 11 seconds passed
+... 78%, 548192 KB, 49352 KB/s, 11 seconds passed
+... 78%, 548224 KB, 49353 KB/s, 11 seconds passed
+... 78%, 548256 KB, 49354 KB/s, 11 seconds passed
+... 78%, 548288 KB, 49355 KB/s, 11 seconds passed
+... 78%, 548320 KB, 49357 KB/s, 11 seconds passed
+... 78%, 548352 KB, 49358 KB/s, 11 seconds passed
+... 78%, 548384 KB, 49359 KB/s, 11 seconds passed
+... 78%, 548416 KB, 49360 KB/s, 11 seconds passed
+... 78%, 548448 KB, 49361 KB/s, 11 seconds passed
+... 78%, 548480 KB, 49362 KB/s, 11 seconds passed
+... 78%, 548512 KB, 49363 KB/s, 11 seconds passed
+... 78%, 548544 KB, 49364 KB/s, 11 seconds passed
+... 78%, 548576 KB, 49366 KB/s, 11 seconds passed
+... 78%, 548608 KB, 49367 KB/s, 11 seconds passed
+... 78%, 548640 KB, 49368 KB/s, 11 seconds passed
+... 78%, 548672 KB, 49369 KB/s, 11 seconds passed
+... 78%, 548704 KB, 49370 KB/s, 11 seconds passed
+... 78%, 548736 KB, 49372 KB/s, 11 seconds passed
+... 78%, 548768 KB, 49373 KB/s, 11 seconds passed
+... 78%, 548800 KB, 49375 KB/s, 11 seconds passed
+... 78%, 548832 KB, 49375 KB/s, 11 seconds passed
+... 78%, 548864 KB, 49377 KB/s, 11 seconds passed
+... 78%, 548896 KB, 49378 KB/s, 11 seconds passed
+... 78%, 548928 KB, 49379 KB/s, 11 seconds passed
+... 78%, 548960 KB, 49380 KB/s, 11 seconds passed
+... 78%, 548992 KB, 49381 KB/s, 11 seconds passed
+... 78%, 549024 KB, 49382 KB/s, 11 seconds passed
+... 78%, 549056 KB, 49384 KB/s, 11 seconds passed
+... 78%, 549088 KB, 49385 KB/s, 11 seconds passed
+... 78%, 549120 KB, 49386 KB/s, 11 seconds passed
+... 78%, 549152 KB, 49388 KB/s, 11 seconds passed
+... 78%, 549184 KB, 49390 KB/s, 11 seconds passed
+... 78%, 549216 KB, 49392 KB/s, 11 seconds passed
+... 78%, 549248 KB, 49393 KB/s, 11 seconds passed
+... 78%, 549280 KB, 49395 KB/s, 11 seconds passed
+... 78%, 549312 KB, 49397 KB/s, 11 seconds passed
+... 78%, 549344 KB, 49399 KB/s, 11 seconds passed
+... 78%, 549376 KB, 49400 KB/s, 11 seconds passed
+... 78%, 549408 KB, 49402 KB/s, 11 seconds passed
+... 78%, 549440 KB, 49404 KB/s, 11 seconds passed
+... 79%, 549472 KB, 49406 KB/s, 11 seconds passed
+... 79%, 549504 KB, 49408 KB/s, 11 seconds passed
+... 79%, 549536 KB, 49410 KB/s, 11 seconds passed
+... 79%, 549568 KB, 49411 KB/s, 11 seconds passed
+... 79%, 549600 KB, 49413 KB/s, 11 seconds passed
+... 79%, 549632 KB, 49415 KB/s, 11 seconds passed
+... 79%, 549664 KB, 49417 KB/s, 11 seconds passed
+... 79%, 549696 KB, 49418 KB/s, 11 seconds passed
+... 79%, 549728 KB, 49420 KB/s, 11 seconds passed
+... 79%, 549760 KB, 49422 KB/s, 11 seconds passed
+... 79%, 549792 KB, 49424 KB/s, 11 seconds passed
+... 79%, 549824 KB, 49426 KB/s, 11 seconds passed
+... 79%, 549856 KB, 49427 KB/s, 11 seconds passed
+... 79%, 549888 KB, 49429 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 79%, 549920 KB, 49431 KB/s, 11 seconds passed
+... 79%, 549952 KB, 49433 KB/s, 11 seconds passed
+... 79%, 549984 KB, 49435 KB/s, 11 seconds passed
+... 79%, 550016 KB, 49436 KB/s, 11 seconds passed
+... 79%, 550048 KB, 49438 KB/s, 11 seconds passed
+... 79%, 550080 KB, 49440 KB/s, 11 seconds passed
+... 79%, 550112 KB, 49442 KB/s, 11 seconds passed
+... 79%, 550144 KB, 49443 KB/s, 11 seconds passed
+... 79%, 550176 KB, 49445 KB/s, 11 seconds passed
+... 79%, 550208 KB, 49447 KB/s, 11 seconds passed
+... 79%, 550240 KB, 49449 KB/s, 11 seconds passed
+... 79%, 550272 KB, 49450 KB/s, 11 seconds passed
+... 79%, 550304 KB, 49452 KB/s, 11 seconds passed
+... 79%, 550336 KB, 49454 KB/s, 11 seconds passed
+... 79%, 550368 KB, 49456 KB/s, 11 seconds passed
+... 79%, 550400 KB, 49458 KB/s, 11 seconds passed
+... 79%, 550432 KB, 49460 KB/s, 11 seconds passed
+... 79%, 550464 KB, 49462 KB/s, 11 seconds passed
+... 79%, 550496 KB, 49464 KB/s, 11 seconds passed
+... 79%, 550528 KB, 49466 KB/s, 11 seconds passed
+... 79%, 550560 KB, 49468 KB/s, 11 seconds passed
+... 79%, 550592 KB, 49470 KB/s, 11 seconds passed
+... 79%, 550624 KB, 49472 KB/s, 11 seconds passed
+... 79%, 550656 KB, 49475 KB/s, 11 seconds passed
+... 79%, 550688 KB, 49477 KB/s, 11 seconds passed
+... 79%, 550720 KB, 49479 KB/s, 11 seconds passed
+... 79%, 550752 KB, 49481 KB/s, 11 seconds passed
+... 79%, 550784 KB, 49483 KB/s, 11 seconds passed
+... 79%, 550816 KB, 49485 KB/s, 11 seconds passed
+... 79%, 550848 KB, 49487 KB/s, 11 seconds passed
+... 79%, 550880 KB, 49489 KB/s, 11 seconds passed
+... 79%, 550912 KB, 49492 KB/s, 11 seconds passed
+... 79%, 550944 KB, 49494 KB/s, 11 seconds passed
+... 79%, 550976 KB, 49496 KB/s, 11 seconds passed
+... 79%, 551008 KB, 49498 KB/s, 11 seconds passed
+... 79%, 551040 KB, 49500 KB/s, 11 seconds passed
+... 79%, 551072 KB, 49502 KB/s, 11 seconds passed
+... 79%, 551104 KB, 49504 KB/s, 11 seconds passed
+... 79%, 551136 KB, 49505 KB/s, 11 seconds passed
+... 79%, 551168 KB, 49507 KB/s, 11 seconds passed
+... 79%, 551200 KB, 49509 KB/s, 11 seconds passed
+... 79%, 551232 KB, 49510 KB/s, 11 seconds passed
+... 79%, 551264 KB, 49512 KB/s, 11 seconds passed
+... 79%, 551296 KB, 49514 KB/s, 11 seconds passed
+... 79%, 551328 KB, 49515 KB/s, 11 seconds passed
+... 79%, 551360 KB, 49517 KB/s, 11 seconds passed
+... 79%, 551392 KB, 49519 KB/s, 11 seconds passed
+... 79%, 551424 KB, 49520 KB/s, 11 seconds passed
+... 79%, 551456 KB, 49522 KB/s, 11 seconds passed
+... 79%, 551488 KB, 49523 KB/s, 11 seconds passed
+... 79%, 551520 KB, 49525 KB/s, 11 seconds passed
+... 79%, 551552 KB, 49526 KB/s, 11 seconds passed
+... 79%, 551584 KB, 49528 KB/s, 11 seconds passed
+... 79%, 551616 KB, 49530 KB/s, 11 seconds passed
+... 79%, 551648 KB, 49532 KB/s, 11 seconds passed
+... 79%, 551680 KB, 49533 KB/s, 11 seconds passed
+... 79%, 551712 KB, 49535 KB/s, 11 seconds passed
+... 79%, 551744 KB, 49536 KB/s, 11 seconds passed
+... 79%, 551776 KB, 49538 KB/s, 11 seconds passed
+... 79%, 551808 KB, 49540 KB/s, 11 seconds passed
+... 79%, 551840 KB, 49541 KB/s, 11 seconds passed
+... 79%, 551872 KB, 49543 KB/s, 11 seconds passed
+... 79%, 551904 KB, 49545 KB/s, 11 seconds passed
+... 79%, 551936 KB, 49546 KB/s, 11 seconds passed
+... 79%, 551968 KB, 49548 KB/s, 11 seconds passed
+... 79%, 552000 KB, 49549 KB/s, 11 seconds passed
+... 79%, 552032 KB, 49551 KB/s, 11 seconds passed
+... 79%, 552064 KB, 49537 KB/s, 11 seconds passed
+... 79%, 552096 KB, 49539 KB/s, 11 seconds passed
+... 79%, 552128 KB, 49541 KB/s, 11 seconds passed
+... 79%, 552160 KB, 49543 KB/s, 11 seconds passed
+... 79%, 552192 KB, 49544 KB/s, 11 seconds passed
+... 79%, 552224 KB, 49546 KB/s, 11 seconds passed
+... 79%, 552256 KB, 49548 KB/s, 11 seconds passed
+... 79%, 552288 KB, 49549 KB/s, 11 seconds passed
+... 79%, 552320 KB, 49551 KB/s, 11 seconds passed
+... 79%, 552352 KB, 49553 KB/s, 11 seconds passed
+... 79%, 552384 KB, 49554 KB/s, 11 seconds passed
+... 79%, 552416 KB, 49556 KB/s, 11 seconds passed
+... 79%, 552448 KB, 49557 KB/s, 11 seconds passed
+... 79%, 552480 KB, 49559 KB/s, 11 seconds passed
+... 79%, 552512 KB, 49561 KB/s, 11 seconds passed
+... 79%, 552544 KB, 49562 KB/s, 11 seconds passed
+... 79%, 552576 KB, 49564 KB/s, 11 seconds passed
+... 79%, 552608 KB, 49564 KB/s, 11 seconds passed
+... 79%, 552640 KB, 49567 KB/s, 11 seconds passed
+... 79%, 552672 KB, 49569 KB/s, 11 seconds passed
+... 79%, 552704 KB, 49570 KB/s, 11 seconds passed
+... 79%, 552736 KB, 49572 KB/s, 11 seconds passed
+... 79%, 552768 KB, 49573 KB/s, 11 seconds passed
+... 79%, 552800 KB, 49575 KB/s, 11 seconds passed
+... 79%, 552832 KB, 49577 KB/s, 11 seconds passed
+... 79%, 552864 KB, 49578 KB/s, 11 seconds passed
+... 79%, 552896 KB, 49579 KB/s, 11 seconds passed
+... 79%, 552928 KB, 49580 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 79%, 552960 KB, 49363 KB/s, 11 seconds passed
+... 79%, 552992 KB, 49363 KB/s, 11 seconds passed
+... 79%, 553024 KB, 49364 KB/s, 11 seconds passed
+... 79%, 553056 KB, 49364 KB/s, 11 seconds passed
+... 79%, 553088 KB, 49365 KB/s, 11 seconds passed
+... 79%, 553120 KB, 49366 KB/s, 11 seconds passed
+... 79%, 553152 KB, 49368 KB/s, 11 seconds passed
+... 79%, 553184 KB, 49352 KB/s, 11 seconds passed
+... 79%, 553216 KB, 49353 KB/s, 11 seconds passed
+... 79%, 553248 KB, 49354 KB/s, 11 seconds passed
+... 79%, 553280 KB, 49355 KB/s, 11 seconds passed
+... 79%, 553312 KB, 49356 KB/s, 11 seconds passed
+... 79%, 553344 KB, 49357 KB/s, 11 seconds passed
+... 79%, 553376 KB, 49358 KB/s, 11 seconds passed
+... 79%, 553408 KB, 49360 KB/s, 11 seconds passed
+... 79%, 553440 KB, 49361 KB/s, 11 seconds passed
+... 79%, 553472 KB, 49362 KB/s, 11 seconds passed
+... 79%, 553504 KB, 49363 KB/s, 11 seconds passed
+... 79%, 553536 KB, 49364 KB/s, 11 seconds passed
+... 79%, 553568 KB, 49365 KB/s, 11 seconds passed
+... 79%, 553600 KB, 49367 KB/s, 11 seconds passed
+... 79%, 553632 KB, 49368 KB/s, 11 seconds passed
+... 79%, 553664 KB, 49369 KB/s, 11 seconds passed
+... 79%, 553696 KB, 49370 KB/s, 11 seconds passed
+... 79%, 553728 KB, 49371 KB/s, 11 seconds passed
+... 79%, 553760 KB, 49372 KB/s, 11 seconds passed
+... 79%, 553792 KB, 49374 KB/s, 11 seconds passed
+... 79%, 553824 KB, 49375 KB/s, 11 seconds passed
+... 79%, 553856 KB, 49376 KB/s, 11 seconds passed
+... 79%, 553888 KB, 49377 KB/s, 11 seconds passed
+... 79%, 553920 KB, 49378 KB/s, 11 seconds passed
+... 79%, 553952 KB, 49379 KB/s, 11 seconds passed
+... 79%, 553984 KB, 49381 KB/s, 11 seconds passed
+... 79%, 554016 KB, 49382 KB/s, 11 seconds passed
+... 79%, 554048 KB, 49383 KB/s, 11 seconds passed
+... 79%, 554080 KB, 49384 KB/s, 11 seconds passed
+... 79%, 554112 KB, 49386 KB/s, 11 seconds passed
+... 79%, 554144 KB, 49388 KB/s, 11 seconds passed
+... 79%, 554176 KB, 49390 KB/s, 11 seconds passed
+... 79%, 554208 KB, 49391 KB/s, 11 seconds passed
+... 79%, 554240 KB, 49393 KB/s, 11 seconds passed
+... 79%, 554272 KB, 49395 KB/s, 11 seconds passed
+... 79%, 554304 KB, 49397 KB/s, 11 seconds passed
+... 79%, 554336 KB, 49398 KB/s, 11 seconds passed
+... 79%, 554368 KB, 49400 KB/s, 11 seconds passed
+... 79%, 554400 KB, 49402 KB/s, 11 seconds passed
+... 79%, 554432 KB, 49404 KB/s, 11 seconds passed
+... 79%, 554464 KB, 49405 KB/s, 11 seconds passed
+... 79%, 554496 KB, 49407 KB/s, 11 seconds passed
+... 79%, 554528 KB, 49409 KB/s, 11 seconds passed
+... 79%, 554560 KB, 49411 KB/s, 11 seconds passed
+... 79%, 554592 KB, 49412 KB/s, 11 seconds passed
+... 79%, 554624 KB, 49414 KB/s, 11 seconds passed
+... 79%, 554656 KB, 49416 KB/s, 11 seconds passed
+... 79%, 554688 KB, 49418 KB/s, 11 seconds passed
+... 79%, 554720 KB, 49420 KB/s, 11 seconds passed
+... 79%, 554752 KB, 49422 KB/s, 11 seconds passed
+... 79%, 554784 KB, 49424 KB/s, 11 seconds passed
+... 79%, 554816 KB, 49426 KB/s, 11 seconds passed
+... 79%, 554848 KB, 49428 KB/s, 11 seconds passed
+... 79%, 554880 KB, 49427 KB/s, 11 seconds passed
+... 79%, 554912 KB, 49428 KB/s, 11 seconds passed
+... 79%, 554944 KB, 49430 KB/s, 11 seconds passed
+... 79%, 554976 KB, 49431 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 79%, 555008 KB, 49433 KB/s, 11 seconds passed
+... 79%, 555040 KB, 49435 KB/s, 11 seconds passed
+... 79%, 555072 KB, 49436 KB/s, 11 seconds passed
+... 79%, 555104 KB, 49438 KB/s, 11 seconds passed
+... 79%, 555136 KB, 49440 KB/s, 11 seconds passed
+... 79%, 555168 KB, 49441 KB/s, 11 seconds passed
+... 79%, 555200 KB, 49443 KB/s, 11 seconds passed
+... 79%, 555232 KB, 49445 KB/s, 11 seconds passed
+... 79%, 555264 KB, 49446 KB/s, 11 seconds passed
+... 79%, 555296 KB, 49447 KB/s, 11 seconds passed
+... 79%, 555328 KB, 49449 KB/s, 11 seconds passed
+... 79%, 555360 KB, 49451 KB/s, 11 seconds passed
+... 79%, 555392 KB, 49453 KB/s, 11 seconds passed
+... 79%, 555424 KB, 49454 KB/s, 11 seconds passed
+... 79%, 555456 KB, 49456 KB/s, 11 seconds passed
+... 79%, 555488 KB, 49457 KB/s, 11 seconds passed
+... 79%, 555520 KB, 49458 KB/s, 11 seconds passed
+... 79%, 555552 KB, 49460 KB/s, 11 seconds passed
+... 79%, 555584 KB, 49462 KB/s, 11 seconds passed
+... 79%, 555616 KB, 49464 KB/s, 11 seconds passed
+... 79%, 555648 KB, 49465 KB/s, 11 seconds passed
+... 79%, 555680 KB, 49467 KB/s, 11 seconds passed
+... 79%, 555712 KB, 49469 KB/s, 11 seconds passed
+... 79%, 555744 KB, 49470 KB/s, 11 seconds passed
+... 79%, 555776 KB, 49472 KB/s, 11 seconds passed
+... 79%, 555808 KB, 49474 KB/s, 11 seconds passed
+... 79%, 555840 KB, 49475 KB/s, 11 seconds passed
+... 79%, 555872 KB, 49477 KB/s, 11 seconds passed
+... 79%, 555904 KB, 49478 KB/s, 11 seconds passed
+... 79%, 555936 KB, 49432 KB/s, 11 seconds passed
+... 79%, 555968 KB, 49433 KB/s, 11 seconds passed
+... 79%, 556000 KB, 49434 KB/s, 11 seconds passed
+... 79%, 556032 KB, 49435 KB/s, 11 seconds passed
+... 79%, 556064 KB, 49436 KB/s, 11 seconds passed
+... 79%, 556096 KB, 49438 KB/s, 11 seconds passed
+... 79%, 556128 KB, 49439 KB/s, 11 seconds passed
+... 79%, 556160 KB, 49440 KB/s, 11 seconds passed
+... 79%, 556192 KB, 49441 KB/s, 11 seconds passed
+... 79%, 556224 KB, 49442 KB/s, 11 seconds passed
+... 79%, 556256 KB, 49444 KB/s, 11 seconds passed
+... 79%, 556288 KB, 49445 KB/s, 11 seconds passed
+... 79%, 556320 KB, 49446 KB/s, 11 seconds passed
+... 79%, 556352 KB, 49447 KB/s, 11 seconds passed
+... 79%, 556384 KB, 49448 KB/s, 11 seconds passed
+... 80%, 556416 KB, 49449 KB/s, 11 seconds passed
+... 80%, 556448 KB, 49451 KB/s, 11 seconds passed
+... 80%, 556480 KB, 49452 KB/s, 11 seconds passed
+... 80%, 556512 KB, 49453 KB/s, 11 seconds passed
+... 80%, 556544 KB, 49454 KB/s, 11 seconds passed
+... 80%, 556576 KB, 49455 KB/s, 11 seconds passed
+... 80%, 556608 KB, 49457 KB/s, 11 seconds passed
+... 80%, 556640 KB, 49458 KB/s, 11 seconds passed
+... 80%, 556672 KB, 49459 KB/s, 11 seconds passed
+... 80%, 556704 KB, 49460 KB/s, 11 seconds passed
+... 80%, 556736 KB, 49461 KB/s, 11 seconds passed
+... 80%, 556768 KB, 49462 KB/s, 11 seconds passed
+... 80%, 556800 KB, 49464 KB/s, 11 seconds passed
+... 80%, 556832 KB, 49465 KB/s, 11 seconds passed
+... 80%, 556864 KB, 49466 KB/s, 11 seconds passed
+... 80%, 556896 KB, 49467 KB/s, 11 seconds passed
+... 80%, 556928 KB, 49468 KB/s, 11 seconds passed
+... 80%, 556960 KB, 49470 KB/s, 11 seconds passed
+... 80%, 556992 KB, 49471 KB/s, 11 seconds passed
+... 80%, 557024 KB, 49472 KB/s, 11 seconds passed
+... 80%, 557056 KB, 49473 KB/s, 11 seconds passed
+... 80%, 557088 KB, 49475 KB/s, 11 seconds passed
+... 80%, 557120 KB, 49477 KB/s, 11 seconds passed
+... 80%, 557152 KB, 49478 KB/s, 11 seconds passed
+... 80%, 557184 KB, 49480 KB/s, 11 seconds passed
+... 80%, 557216 KB, 49482 KB/s, 11 seconds passed
+... 80%, 557248 KB, 49484 KB/s, 11 seconds passed
+... 80%, 557280 KB, 49486 KB/s, 11 seconds passed
+... 80%, 557312 KB, 49487 KB/s, 11 seconds passed
+... 80%, 557344 KB, 49489 KB/s, 11 seconds passed
+... 80%, 557376 KB, 49491 KB/s, 11 seconds passed
+... 80%, 557408 KB, 49493 KB/s, 11 seconds passed
+... 80%, 557440 KB, 49494 KB/s, 11 seconds passed
+... 80%, 557472 KB, 49496 KB/s, 11 seconds passed
+... 80%, 557504 KB, 49498 KB/s, 11 seconds passed
+... 80%, 557536 KB, 49500 KB/s, 11 seconds passed
+... 80%, 557568 KB, 49501 KB/s, 11 seconds passed
+... 80%, 557600 KB, 49503 KB/s, 11 seconds passed
+... 80%, 557632 KB, 49505 KB/s, 11 seconds passed
+... 80%, 557664 KB, 49507 KB/s, 11 seconds passed
+... 80%, 557696 KB, 49508 KB/s, 11 seconds passed
+... 80%, 557728 KB, 49510 KB/s, 11 seconds passed
+... 80%, 557760 KB, 49512 KB/s, 11 seconds passed
+... 80%, 557792 KB, 49514 KB/s, 11 seconds passed
+... 80%, 557824 KB, 49516 KB/s, 11 seconds passed
+... 80%, 557856 KB, 49518 KB/s, 11 seconds passed
+... 80%, 557888 KB, 49520 KB/s, 11 seconds passed
+... 80%, 557920 KB, 49522 KB/s, 11 seconds passed
+... 80%, 557952 KB, 49524 KB/s, 11 seconds passed
+... 80%, 557984 KB, 49526 KB/s, 11 seconds passed
+... 80%, 558016 KB, 49528 KB/s, 11 seconds passed
+... 80%, 558048 KB, 49530 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 80%, 558080 KB, 49404 KB/s, 11 seconds passed
+... 80%, 558112 KB, 49404 KB/s, 11 seconds passed
+... 80%, 558144 KB, 49405 KB/s, 11 seconds passed
+... 80%, 558176 KB, 49407 KB/s, 11 seconds passed
+... 80%, 558208 KB, 49408 KB/s, 11 seconds passed
+... 80%, 558240 KB, 49409 KB/s, 11 seconds passed
+... 80%, 558272 KB, 49410 KB/s, 11 seconds passed
+... 80%, 558304 KB, 49412 KB/s, 11 seconds passed
+... 80%, 558336 KB, 49406 KB/s, 11 seconds passed
+... 80%, 558368 KB, 49407 KB/s, 11 seconds passed
+... 80%, 558400 KB, 49408 KB/s, 11 seconds passed
+... 80%, 558432 KB, 49409 KB/s, 11 seconds passed
+... 80%, 558464 KB, 49410 KB/s, 11 seconds passed
+... 80%, 558496 KB, 49411 KB/s, 11 seconds passed
+... 80%, 558528 KB, 49413 KB/s, 11 seconds passed
+... 80%, 558560 KB, 49414 KB/s, 11 seconds passed
+... 80%, 558592 KB, 49415 KB/s, 11 seconds passed
+... 80%, 558624 KB, 49416 KB/s, 11 seconds passed
+... 80%, 558656 KB, 49417 KB/s, 11 seconds passed
+... 80%, 558688 KB, 49418 KB/s, 11 seconds passed
+... 80%, 558720 KB, 49420 KB/s, 11 seconds passed
+... 80%, 558752 KB, 49421 KB/s, 11 seconds passed
+... 80%, 558784 KB, 49422 KB/s, 11 seconds passed
+... 80%, 558816 KB, 49423 KB/s, 11 seconds passed
+... 80%, 558848 KB, 49424 KB/s, 11 seconds passed
+... 80%, 558880 KB, 49425 KB/s, 11 seconds passed
+... 80%, 558912 KB, 49426 KB/s, 11 seconds passed
+... 80%, 558944 KB, 49428 KB/s, 11 seconds passed
+... 80%, 558976 KB, 49429 KB/s, 11 seconds passed
+... 80%, 559008 KB, 49430 KB/s, 11 seconds passed
+... 80%, 559040 KB, 49431 KB/s, 11 seconds passed
+... 80%, 559072 KB, 49432 KB/s, 11 seconds passed
+... 80%, 559104 KB, 49433 KB/s, 11 seconds passed
+... 80%, 559136 KB, 49435 KB/s, 11 seconds passed
+... 80%, 559168 KB, 49436 KB/s, 11 seconds passed
+... 80%, 559200 KB, 49438 KB/s, 11 seconds passed
+... 80%, 559232 KB, 49439 KB/s, 11 seconds passed
+... 80%, 559264 KB, 49441 KB/s, 11 seconds passed
+... 80%, 559296 KB, 49442 KB/s, 11 seconds passed
+... 80%, 559328 KB, 49443 KB/s, 11 seconds passed
+... 80%, 559360 KB, 49445 KB/s, 11 seconds passed
+... 80%, 559392 KB, 49447 KB/s, 11 seconds passed
+... 80%, 559424 KB, 49448 KB/s, 11 seconds passed
+... 80%, 559456 KB, 49449 KB/s, 11 seconds passed
+... 80%, 559488 KB, 49451 KB/s, 11 seconds passed
+... 80%, 559520 KB, 49453 KB/s, 11 seconds passed
+... 80%, 559552 KB, 49454 KB/s, 11 seconds passed
+... 80%, 559584 KB, 49455 KB/s, 11 seconds passed
+... 80%, 559616 KB, 49457 KB/s, 11 seconds passed
+... 80%, 559648 KB, 49458 KB/s, 11 seconds passed
+... 80%, 559680 KB, 49460 KB/s, 11 seconds passed
+... 80%, 559712 KB, 49461 KB/s, 11 seconds passed
+... 80%, 559744 KB, 49463 KB/s, 11 seconds passed
+... 80%, 559776 KB, 49464 KB/s, 11 seconds passed
+... 80%, 559808 KB, 49466 KB/s, 11 seconds passed
+... 80%, 559840 KB, 49467 KB/s, 11 seconds passed
+... 80%, 559872 KB, 49469 KB/s, 11 seconds passed
+... 80%, 559904 KB, 49470 KB/s, 11 seconds passed
+... 80%, 559936 KB, 49472 KB/s, 11 seconds passed
+... 80%, 559968 KB, 49473 KB/s, 11 seconds passed
+... 80%, 560000 KB, 49475 KB/s, 11 seconds passed
+... 80%, 560032 KB, 49476 KB/s, 11 seconds passed
+... 80%, 560064 KB, 49478 KB/s, 11 seconds passed
+... 80%, 560096 KB, 49479 KB/s, 11 seconds passed
+... 80%, 560128 KB, 49481 KB/s, 11 seconds passed
+... 80%, 560160 KB, 49482 KB/s, 11 seconds passed
+... 80%, 560192 KB, 49484 KB/s, 11 seconds passed
+... 80%, 560224 KB, 49486 KB/s, 11 seconds passed
+... 80%, 560256 KB, 49488 KB/s, 11 seconds passed
+... 80%, 560288 KB, 49489 KB/s, 11 seconds passed
+... 80%, 560320 KB, 49491 KB/s, 11 seconds passed
+... 80%, 560352 KB, 49493 KB/s, 11 seconds passed
+... 80%, 560384 KB, 49495 KB/s, 11 seconds passed
+... 80%, 560416 KB, 49497 KB/s, 11 seconds passed
+... 80%, 560448 KB, 49499 KB/s, 11 seconds passed
+... 80%, 560480 KB, 49501 KB/s, 11 seconds passed
+... 80%, 560512 KB, 49503 KB/s, 11 seconds passed
+... 80%, 560544 KB, 49505 KB/s, 11 seconds passed
+... 80%, 560576 KB, 49507 KB/s, 11 seconds passed
+... 80%, 560608 KB, 49509 KB/s, 11 seconds passed
+... 80%, 560640 KB, 49511 KB/s, 11 seconds passed
+... 80%, 560672 KB, 49513 KB/s, 11 seconds passed
+... 80%, 560704 KB, 49515 KB/s, 11 seconds passed
+... 80%, 560736 KB, 49517 KB/s, 11 seconds passed
+... 80%, 560768 KB, 49519 KB/s, 11 seconds passed
+... 80%, 560800 KB, 49520 KB/s, 11 seconds passed
+... 80%, 560832 KB, 49522 KB/s, 11 seconds passed
+... 80%, 560864 KB, 49524 KB/s, 11 seconds passed
+... 80%, 560896 KB, 49526 KB/s, 11 seconds passed
+... 80%, 560928 KB, 49528 KB/s, 11 seconds passed
+... 80%, 560960 KB, 49530 KB/s, 11 seconds passed
+... 80%, 560992 KB, 49532 KB/s, 11 seconds passed
+... 80%, 561024 KB, 49534 KB/s, 11 seconds passed
+... 80%, 561056 KB, 49536 KB/s, 11 seconds passed
+... 80%, 561088 KB, 49538 KB/s, 11 seconds passed
+... 80%, 561120 KB, 49540 KB/s, 11 seconds passed
+... 80%, 561152 KB, 49542 KB/s, 11 seconds passed
+... 80%, 561184 KB, 49544 KB/s, 11 seconds passed
+... 80%, 561216 KB, 49546 KB/s, 11 seconds passed
+... 80%, 561248 KB, 49548 KB/s, 11 seconds passed
+... 80%, 561280 KB, 49550 KB/s, 11 seconds passed
+... 80%, 561312 KB, 49552 KB/s, 11 seconds passed
+... 80%, 561344 KB, 49554 KB/s, 11 seconds passed
+... 80%, 561376 KB, 49556 KB/s, 11 seconds passed
+... 80%, 561408 KB, 49558 KB/s, 11 seconds passed
+... 80%, 561440 KB, 49559 KB/s, 11 seconds passed
+... 80%, 561472 KB, 49561 KB/s, 11 seconds passed
+... 80%, 561504 KB, 49563 KB/s, 11 seconds passed
+... 80%, 561536 KB, 49565 KB/s, 11 seconds passed
+... 80%, 561568 KB, 49567 KB/s, 11 seconds passed
+... 80%, 561600 KB, 49569 KB/s, 11 seconds passed
+... 80%, 561632 KB, 49571 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 80%, 561664 KB, 49573 KB/s, 11 seconds passed
+... 80%, 561696 KB, 49575 KB/s, 11 seconds passed
+... 80%, 561728 KB, 49576 KB/s, 11 seconds passed
+... 80%, 561760 KB, 49578 KB/s, 11 seconds passed
+... 80%, 561792 KB, 49579 KB/s, 11 seconds passed
+... 80%, 561824 KB, 49581 KB/s, 11 seconds passed
+... 80%, 561856 KB, 49583 KB/s, 11 seconds passed
+... 80%, 561888 KB, 49584 KB/s, 11 seconds passed
+... 80%, 561920 KB, 49586 KB/s, 11 seconds passed
+... 80%, 561952 KB, 49588 KB/s, 11 seconds passed
+... 80%, 561984 KB, 49589 KB/s, 11 seconds passed
+... 80%, 562016 KB, 49591 KB/s, 11 seconds passed
+... 80%, 562048 KB, 49592 KB/s, 11 seconds passed
+... 80%, 562080 KB, 49594 KB/s, 11 seconds passed
+... 80%, 562112 KB, 49586 KB/s, 11 seconds passed
+... 80%, 562144 KB, 49588 KB/s, 11 seconds passed
+... 80%, 562176 KB, 49589 KB/s, 11 seconds passed
+... 80%, 562208 KB, 49591 KB/s, 11 seconds passed
+... 80%, 562240 KB, 49592 KB/s, 11 seconds passed
+... 80%, 562272 KB, 49594 KB/s, 11 seconds passed
+... 80%, 562304 KB, 49596 KB/s, 11 seconds passed
+... 80%, 562336 KB, 49597 KB/s, 11 seconds passed
+... 80%, 562368 KB, 49599 KB/s, 11 seconds passed
+... 80%, 562400 KB, 49600 KB/s, 11 seconds passed
+... 80%, 562432 KB, 49602 KB/s, 11 seconds passed
+... 80%, 562464 KB, 49604 KB/s, 11 seconds passed
+... 80%, 562496 KB, 49605 KB/s, 11 seconds passed
+... 80%, 562528 KB, 49607 KB/s, 11 seconds passed
+... 80%, 562560 KB, 49608 KB/s, 11 seconds passed
+... 80%, 562592 KB, 49610 KB/s, 11 seconds passed
+... 80%, 562624 KB, 49612 KB/s, 11 seconds passed
+... 80%, 562656 KB, 49613 KB/s, 11 seconds passed
+... 80%, 562688 KB, 49615 KB/s, 11 seconds passed
+... 80%, 562720 KB, 49616 KB/s, 11 seconds passed
+... 80%, 562752 KB, 49618 KB/s, 11 seconds passed
+... 80%, 562784 KB, 49620 KB/s, 11 seconds passed
+... 80%, 562816 KB, 49621 KB/s, 11 seconds passed
+... 80%, 562848 KB, 49623 KB/s, 11 seconds passed
+... 80%, 562880 KB, 49624 KB/s, 11 seconds passed
+... 80%, 562912 KB, 49626 KB/s, 11 seconds passed
+... 80%, 562944 KB, 49628 KB/s, 11 seconds passed
+... 80%, 562976 KB, 49629 KB/s, 11 seconds passed
+... 80%, 563008 KB, 49631 KB/s, 11 seconds passed
+... 80%, 563040 KB, 49632 KB/s, 11 seconds passed
+... 80%, 563072 KB, 49634 KB/s, 11 seconds passed
+... 80%, 563104 KB, 49636 KB/s, 11 seconds passed
+... 80%, 563136 KB, 49637 KB/s, 11 seconds passed
+... 80%, 563168 KB, 49639 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 80%, 563200 KB, 49448 KB/s, 11 seconds passed
+... 80%, 563232 KB, 49448 KB/s, 11 seconds passed
+... 80%, 563264 KB, 49445 KB/s, 11 seconds passed
+... 80%, 563296 KB, 49446 KB/s, 11 seconds passed
+... 80%, 563328 KB, 49447 KB/s, 11 seconds passed
+... 80%, 563360 KB, 49448 KB/s, 11 seconds passed
+... 81%, 563392 KB, 49449 KB/s, 11 seconds passed
+... 81%, 563424 KB, 49451 KB/s, 11 seconds passed
+... 81%, 563456 KB, 49446 KB/s, 11 seconds passed
+... 81%, 563488 KB, 49446 KB/s, 11 seconds passed
+... 81%, 563520 KB, 49447 KB/s, 11 seconds passed
+... 81%, 563552 KB, 49448 KB/s, 11 seconds passed
+... 81%, 563584 KB, 49450 KB/s, 11 seconds passed
+... 81%, 563616 KB, 49451 KB/s, 11 seconds passed
+... 81%, 563648 KB, 49452 KB/s, 11 seconds passed
+... 81%, 563680 KB, 49453 KB/s, 11 seconds passed
+... 81%, 563712 KB, 49454 KB/s, 11 seconds passed
+... 81%, 563744 KB, 49455 KB/s, 11 seconds passed
+... 81%, 563776 KB, 49456 KB/s, 11 seconds passed
+... 81%, 563808 KB, 49457 KB/s, 11 seconds passed
+... 81%, 563840 KB, 49459 KB/s, 11 seconds passed
+... 81%, 563872 KB, 49460 KB/s, 11 seconds passed
+... 81%, 563904 KB, 49461 KB/s, 11 seconds passed
+... 81%, 563936 KB, 49462 KB/s, 11 seconds passed
+... 81%, 563968 KB, 49463 KB/s, 11 seconds passed
+... 81%, 564000 KB, 49465 KB/s, 11 seconds passed
+... 81%, 564032 KB, 49466 KB/s, 11 seconds passed
+... 81%, 564064 KB, 49468 KB/s, 11 seconds passed
+... 81%, 564096 KB, 49469 KB/s, 11 seconds passed
+... 81%, 564128 KB, 49470 KB/s, 11 seconds passed
+... 81%, 564160 KB, 49472 KB/s, 11 seconds passed
+... 81%, 564192 KB, 49473 KB/s, 11 seconds passed
+... 81%, 564224 KB, 49475 KB/s, 11 seconds passed
+... 81%, 564256 KB, 49476 KB/s, 11 seconds passed
+... 81%, 564288 KB, 49477 KB/s, 11 seconds passed
+... 81%, 564320 KB, 49479 KB/s, 11 seconds passed
+... 81%, 564352 KB, 49480 KB/s, 11 seconds passed
+... 81%, 564384 KB, 49481 KB/s, 11 seconds passed
+... 81%, 564416 KB, 49483 KB/s, 11 seconds passed
+... 81%, 564448 KB, 49484 KB/s, 11 seconds passed
+... 81%, 564480 KB, 49485 KB/s, 11 seconds passed
+... 81%, 564512 KB, 49486 KB/s, 11 seconds passed
+... 81%, 564544 KB, 49488 KB/s, 11 seconds passed
+... 81%, 564576 KB, 49489 KB/s, 11 seconds passed
+... 81%, 564608 KB, 49491 KB/s, 11 seconds passed
+... 81%, 564640 KB, 49492 KB/s, 11 seconds passed
+... 81%, 564672 KB, 49493 KB/s, 11 seconds passed
+... 81%, 564704 KB, 49495 KB/s, 11 seconds passed
+... 81%, 564736 KB, 49496 KB/s, 11 seconds passed
+... 81%, 564768 KB, 49498 KB/s, 11 seconds passed
+... 81%, 564800 KB, 49499 KB/s, 11 seconds passed
+... 81%, 564832 KB, 49500 KB/s, 11 seconds passed
+... 81%, 564864 KB, 49502 KB/s, 11 seconds passed
+... 81%, 564896 KB, 49503 KB/s, 11 seconds passed
+... 81%, 564928 KB, 49505 KB/s, 11 seconds passed
+... 81%, 564960 KB, 49507 KB/s, 11 seconds passed
+... 81%, 564992 KB, 49509 KB/s, 11 seconds passed
+... 81%, 565024 KB, 49511 KB/s, 11 seconds passed
+... 81%, 565056 KB, 49513 KB/s, 11 seconds passed
+... 81%, 565088 KB, 49515 KB/s, 11 seconds passed
+... 81%, 565120 KB, 49516 KB/s, 11 seconds passed
+... 81%, 565152 KB, 49518 KB/s, 11 seconds passed
+... 81%, 565184 KB, 49520 KB/s, 11 seconds passed
+... 81%, 565216 KB, 49522 KB/s, 11 seconds passed
+... 81%, 565248 KB, 49524 KB/s, 11 seconds passed
+... 81%, 565280 KB, 49526 KB/s, 11 seconds passed
+... 81%, 565312 KB, 49528 KB/s, 11 seconds passed
+... 81%, 565344 KB, 49530 KB/s, 11 seconds passed
+... 81%, 565376 KB, 49531 KB/s, 11 seconds passed
+... 81%, 565408 KB, 49533 KB/s, 11 seconds passed
+... 81%, 565440 KB, 49535 KB/s, 11 seconds passed
+... 81%, 565472 KB, 49537 KB/s, 11 seconds passed
+... 81%, 565504 KB, 49539 KB/s, 11 seconds passed
+... 81%, 565536 KB, 49541 KB/s, 11 seconds passed
+... 81%, 565568 KB, 49543 KB/s, 11 seconds passed
+... 81%, 565600 KB, 49544 KB/s, 11 seconds passed
+... 81%, 565632 KB, 49546 KB/s, 11 seconds passed
+... 81%, 565664 KB, 49548 KB/s, 11 seconds passed
+... 81%, 565696 KB, 49550 KB/s, 11 seconds passed
+... 81%, 565728 KB, 49552 KB/s, 11 seconds passed
+... 81%, 565760 KB, 49554 KB/s, 11 seconds passed
+... 81%, 565792 KB, 49556 KB/s, 11 seconds passed
+... 81%, 565824 KB, 49558 KB/s, 11 seconds passed
+... 81%, 565856 KB, 49560 KB/s, 11 seconds passed
+... 81%, 565888 KB, 49561 KB/s, 11 seconds passed
+... 81%, 565920 KB, 49563 KB/s, 11 seconds passed
+... 81%, 565952 KB, 49565 KB/s, 11 seconds passed
+... 81%, 565984 KB, 49567 KB/s, 11 seconds passed
+... 81%, 566016 KB, 49569 KB/s, 11 seconds passed
+... 81%, 566048 KB, 49571 KB/s, 11 seconds passed
+... 81%, 566080 KB, 49573 KB/s, 11 seconds passed
+... 81%, 566112 KB, 49575 KB/s, 11 seconds passed
+... 81%, 566144 KB, 49577 KB/s, 11 seconds passed
+... 81%, 566176 KB, 49578 KB/s, 11 seconds passed
+... 81%, 566208 KB, 49580 KB/s, 11 seconds passed
+... 81%, 566240 KB, 49582 KB/s, 11 seconds passed
+... 81%, 566272 KB, 49584 KB/s, 11 seconds passed
+... 81%, 566304 KB, 49586 KB/s, 11 seconds passed
+... 81%, 566336 KB, 49588 KB/s, 11 seconds passed
+... 81%, 566368 KB, 49590 KB/s, 11 seconds passed
+... 81%, 566400 KB, 49592 KB/s, 11 seconds passed
+... 81%, 566432 KB, 49594 KB/s, 11 seconds passed
+... 81%, 566464 KB, 49595 KB/s, 11 seconds passed
+... 81%, 566496 KB, 49597 KB/s, 11 seconds passed
+... 81%, 566528 KB, 49599 KB/s, 11 seconds passed
+... 81%, 566560 KB, 49601 KB/s, 11 seconds passed
+... 81%, 566592 KB, 49602 KB/s, 11 seconds passed
+... 81%, 566624 KB, 49604 KB/s, 11 seconds passed
+... 81%, 566656 KB, 49606 KB/s, 11 seconds passed
+... 81%, 566688 KB, 49607 KB/s, 11 seconds passed
+... 81%, 566720 KB, 49609 KB/s, 11 seconds passed
+... 81%, 566752 KB, 49610 KB/s, 11 seconds passed
+... 81%, 566784 KB, 49612 KB/s, 11 seconds passed
+... 81%, 566816 KB, 49613 KB/s, 11 seconds passed
+... 81%, 566848 KB, 49615 KB/s, 11 seconds passed
+... 81%, 566880 KB, 49616 KB/s, 11 seconds passed
+... 81%, 566912 KB, 49618 KB/s, 11 seconds passed
+... 81%, 566944 KB, 49620 KB/s, 11 seconds passed
+... 81%, 566976 KB, 49621 KB/s, 11 seconds passed
+... 81%, 567008 KB, 49623 KB/s, 11 seconds passed
+... 81%, 567040 KB, 49624 KB/s, 11 seconds passed
+... 81%, 567072 KB, 49626 KB/s, 11 seconds passed
+... 81%, 567104 KB, 49627 KB/s, 11 seconds passed
+... 81%, 567136 KB, 49627 KB/s, 11 seconds passed
+... 81%, 567168 KB, 49628 KB/s, 11 seconds passed
+... 81%, 567200 KB, 49630 KB/s, 11 seconds passed
+... 81%, 567232 KB, 49631 KB/s, 11 seconds passed
+... 81%, 567264 KB, 49633 KB/s, 11 seconds passed
+... 81%, 567296 KB, 49635 KB/s, 11 seconds passed
+... 81%, 567328 KB, 49636 KB/s, 11 seconds passed
+... 81%, 567360 KB, 49638 KB/s, 11 seconds passed
+... 81%, 567392 KB, 49639 KB/s, 11 seconds passed
+... 81%, 567424 KB, 49641 KB/s, 11 seconds passed
+... 81%, 567456 KB, 49643 KB/s, 11 seconds passed
+... 81%, 567488 KB, 49644 KB/s, 11 seconds passed
+... 81%, 567520 KB, 49646 KB/s, 11 seconds passed
+... 81%, 567552 KB, 49647 KB/s, 11 seconds passed
+... 81%, 567584 KB, 49649 KB/s, 11 seconds passed
+... 81%, 567616 KB, 49650 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 81%, 567648 KB, 49651 KB/s, 11 seconds passed
+... 81%, 567680 KB, 49652 KB/s, 11 seconds passed
+... 81%, 567712 KB, 49653 KB/s, 11 seconds passed
+... 81%, 567744 KB, 49655 KB/s, 11 seconds passed
+... 81%, 567776 KB, 49657 KB/s, 11 seconds passed
+... 81%, 567808 KB, 49659 KB/s, 11 seconds passed
+... 81%, 567840 KB, 49662 KB/s, 11 seconds passed
+... 81%, 567872 KB, 49663 KB/s, 11 seconds passed
+... 81%, 567904 KB, 49665 KB/s, 11 seconds passed
+... 81%, 567936 KB, 49666 KB/s, 11 seconds passed
+... 81%, 567968 KB, 49668 KB/s, 11 seconds passed
+... 81%, 568000 KB, 49670 KB/s, 11 seconds passed
+... 81%, 568032 KB, 49671 KB/s, 11 seconds passed
+... 81%, 568064 KB, 49673 KB/s, 11 seconds passed
+... 81%, 568096 KB, 49674 KB/s, 11 seconds passed
+... 81%, 568128 KB, 49676 KB/s, 11 seconds passed
+... 81%, 568160 KB, 49678 KB/s, 11 seconds passed
+... 81%, 568192 KB, 49679 KB/s, 11 seconds passed
+... 81%, 568224 KB, 49681 KB/s, 11 seconds passed
+... 81%, 568256 KB, 49682 KB/s, 11 seconds passed
+... 81%, 568288 KB, 49684 KB/s, 11 seconds passed
+... 81%, 568320 KB, 49519 KB/s, 11 seconds passed
+... 81%, 568352 KB, 49520 KB/s, 11 seconds passed
+... 81%, 568384 KB, 49521 KB/s, 11 seconds passed
+... 81%, 568416 KB, 49522 KB/s, 11 seconds passed
+... 81%, 568448 KB, 49523 KB/s, 11 seconds passed
+... 81%, 568480 KB, 49524 KB/s, 11 seconds passed
+... 81%, 568512 KB, 49525 KB/s, 11 seconds passed
+... 81%, 568544 KB, 49526 KB/s, 11 seconds passed
+... 81%, 568576 KB, 49528 KB/s, 11 seconds passed
+... 81%, 568608 KB, 49529 KB/s, 11 seconds passed
+... 81%, 568640 KB, 49530 KB/s, 11 seconds passed
+... 81%, 568672 KB, 49531 KB/s, 11 seconds passed
+... 81%, 568704 KB, 49532 KB/s, 11 seconds passed
+... 81%, 568736 KB, 49533 KB/s, 11 seconds passed
+... 81%, 568768 KB, 49534 KB/s, 11 seconds passed
+... 81%, 568800 KB, 49536 KB/s, 11 seconds passed
+... 81%, 568832 KB, 49537 KB/s, 11 seconds passed
+... 81%, 568864 KB, 49538 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 81%, 568896 KB, 49539 KB/s, 11 seconds passed
+... 81%, 568928 KB, 49540 KB/s, 11 seconds passed
+... 81%, 568960 KB, 49541 KB/s, 11 seconds passed
+... 81%, 568992 KB, 49542 KB/s, 11 seconds passed
+... 81%, 569024 KB, 49543 KB/s, 11 seconds passed
+... 81%, 569056 KB, 49544 KB/s, 11 seconds passed
+... 81%, 569088 KB, 49546 KB/s, 11 seconds passed
+... 81%, 569120 KB, 49547 KB/s, 11 seconds passed
+... 81%, 569152 KB, 49548 KB/s, 11 seconds passed
+... 81%, 569184 KB, 49549 KB/s, 11 seconds passed
+... 81%, 569216 KB, 49550 KB/s, 11 seconds passed
+... 81%, 569248 KB, 49551 KB/s, 11 seconds passed
+... 81%, 569280 KB, 49552 KB/s, 11 seconds passed
+... 81%, 569312 KB, 49553 KB/s, 11 seconds passed
+... 81%, 569344 KB, 49555 KB/s, 11 seconds passed
+... 81%, 569376 KB, 49556 KB/s, 11 seconds passed
+... 81%, 569408 KB, 49557 KB/s, 11 seconds passed
+... 81%, 569440 KB, 49558 KB/s, 11 seconds passed
+... 81%, 569472 KB, 49559 KB/s, 11 seconds passed
+... 81%, 569504 KB, 49560 KB/s, 11 seconds passed
+... 81%, 569536 KB, 49561 KB/s, 11 seconds passed
+... 81%, 569568 KB, 49563 KB/s, 11 seconds passed
+... 81%, 569600 KB, 49564 KB/s, 11 seconds passed
+... 81%, 569632 KB, 49566 KB/s, 11 seconds passed
+... 81%, 569664 KB, 49567 KB/s, 11 seconds passed
+... 81%, 569696 KB, 49569 KB/s, 11 seconds passed
+... 81%, 569728 KB, 49571 KB/s, 11 seconds passed
+... 81%, 569760 KB, 49572 KB/s, 11 seconds passed
+... 81%, 569792 KB, 49574 KB/s, 11 seconds passed
+... 81%, 569824 KB, 49576 KB/s, 11 seconds passed
+... 81%, 569856 KB, 49577 KB/s, 11 seconds passed
+... 81%, 569888 KB, 49579 KB/s, 11 seconds passed
+... 81%, 569920 KB, 49581 KB/s, 11 seconds passed
+... 81%, 569952 KB, 49583 KB/s, 11 seconds passed
+... 81%, 569984 KB, 49584 KB/s, 11 seconds passed
+... 81%, 570016 KB, 49586 KB/s, 11 seconds passed
+... 81%, 570048 KB, 49588 KB/s, 11 seconds passed
+... 81%, 570080 KB, 49589 KB/s, 11 seconds passed
+... 81%, 570112 KB, 49591 KB/s, 11 seconds passed
+... 81%, 570144 KB, 49593 KB/s, 11 seconds passed
+... 81%, 570176 KB, 49595 KB/s, 11 seconds passed
+... 81%, 570208 KB, 49596 KB/s, 11 seconds passed
+... 81%, 570240 KB, 49598 KB/s, 11 seconds passed
+... 81%, 570272 KB, 49600 KB/s, 11 seconds passed
+... 81%, 570304 KB, 49602 KB/s, 11 seconds passed
+... 82%, 570336 KB, 49603 KB/s, 11 seconds passed
+... 82%, 570368 KB, 49605 KB/s, 11 seconds passed
+... 82%, 570400 KB, 49607 KB/s, 11 seconds passed
+... 82%, 570432 KB, 49609 KB/s, 11 seconds passed
+... 82%, 570464 KB, 49610 KB/s, 11 seconds passed
+... 82%, 570496 KB, 49612 KB/s, 11 seconds passed
+... 82%, 570528 KB, 49614 KB/s, 11 seconds passed
+... 82%, 570560 KB, 49615 KB/s, 11 seconds passed
+... 82%, 570592 KB, 49617 KB/s, 11 seconds passed
+... 82%, 570624 KB, 49619 KB/s, 11 seconds passed
+... 82%, 570656 KB, 49621 KB/s, 11 seconds passed
+... 82%, 570688 KB, 49622 KB/s, 11 seconds passed
+... 82%, 570720 KB, 49624 KB/s, 11 seconds passed
+... 82%, 570752 KB, 49626 KB/s, 11 seconds passed
+... 82%, 570784 KB, 49627 KB/s, 11 seconds passed
+... 82%, 570816 KB, 49629 KB/s, 11 seconds passed
+... 82%, 570848 KB, 49631 KB/s, 11 seconds passed
+... 82%, 570880 KB, 49633 KB/s, 11 seconds passed
+... 82%, 570912 KB, 49634 KB/s, 11 seconds passed
+... 82%, 570944 KB, 49636 KB/s, 11 seconds passed
+... 82%, 570976 KB, 49638 KB/s, 11 seconds passed
+... 82%, 571008 KB, 49640 KB/s, 11 seconds passed
+... 82%, 571040 KB, 49642 KB/s, 11 seconds passed
+... 82%, 571072 KB, 49644 KB/s, 11 seconds passed
+... 82%, 571104 KB, 49646 KB/s, 11 seconds passed
+... 82%, 571136 KB, 49649 KB/s, 11 seconds passed
+... 82%, 571168 KB, 49651 KB/s, 11 seconds passed
+... 82%, 571200 KB, 49653 KB/s, 11 seconds passed
+... 82%, 571232 KB, 49655 KB/s, 11 seconds passed
+... 82%, 571264 KB, 49657 KB/s, 11 seconds passed
+... 82%, 571296 KB, 49659 KB/s, 11 seconds passed
+... 82%, 571328 KB, 49661 KB/s, 11 seconds passed
+... 82%, 571360 KB, 49663 KB/s, 11 seconds passed
+... 82%, 571392 KB, 49665 KB/s, 11 seconds passed
+... 82%, 571424 KB, 49667 KB/s, 11 seconds passed
+... 82%, 571456 KB, 49669 KB/s, 11 seconds passed
+... 82%, 571488 KB, 49671 KB/s, 11 seconds passed
+... 82%, 571520 KB, 49673 KB/s, 11 seconds passed
+... 82%, 571552 KB, 49676 KB/s, 11 seconds passed
+... 82%, 571584 KB, 49678 KB/s, 11 seconds passed
+... 82%, 571616 KB, 49680 KB/s, 11 seconds passed
+... 82%, 571648 KB, 49682 KB/s, 11 seconds passed
+... 82%, 571680 KB, 49683 KB/s, 11 seconds passed
+... 82%, 571712 KB, 49685 KB/s, 11 seconds passed
+... 82%, 571744 KB, 49686 KB/s, 11 seconds passed
+... 82%, 571776 KB, 49688 KB/s, 11 seconds passed
+... 82%, 571808 KB, 49689 KB/s, 11 seconds passed
+... 82%, 571840 KB, 49691 KB/s, 11 seconds passed
+... 82%, 571872 KB, 49693 KB/s, 11 seconds passed
+... 82%, 571904 KB, 49694 KB/s, 11 seconds passed
+... 82%, 571936 KB, 49696 KB/s, 11 seconds passed
+... 82%, 571968 KB, 49697 KB/s, 11 seconds passed
+... 82%, 572000 KB, 49698 KB/s, 11 seconds passed
+... 82%, 572032 KB, 49700 KB/s, 11 seconds passed
+... 82%, 572064 KB, 49701 KB/s, 11 seconds passed
+... 82%, 572096 KB, 49702 KB/s, 11 seconds passed
+... 82%, 572128 KB, 49704 KB/s, 11 seconds passed
+... 82%, 572160 KB, 49706 KB/s, 11 seconds passed
+... 82%, 572192 KB, 49708 KB/s, 11 seconds passed
+... 82%, 572224 KB, 49710 KB/s, 11 seconds passed
+... 82%, 572256 KB, 49711 KB/s, 11 seconds passed
+... 82%, 572288 KB, 49712 KB/s, 11 seconds passed
+... 82%, 572320 KB, 49713 KB/s, 11 seconds passed
+... 82%, 572352 KB, 49692 KB/s, 11 seconds passed
+... 82%, 572384 KB, 49694 KB/s, 11 seconds passed
+... 82%, 572416 KB, 49695 KB/s, 11 seconds passed
+... 82%, 572448 KB, 49697 KB/s, 11 seconds passed
+... 82%, 572480 KB, 49699 KB/s, 11 seconds passed
+... 82%, 572512 KB, 49700 KB/s, 11 seconds passed
+... 82%, 572544 KB, 49702 KB/s, 11 seconds passed
+... 82%, 572576 KB, 49703 KB/s, 11 seconds passed
+... 82%, 572608 KB, 49705 KB/s, 11 seconds passed
+... 82%, 572640 KB, 49707 KB/s, 11 seconds passed
+... 82%, 572672 KB, 49708 KB/s, 11 seconds passed
+... 82%, 572704 KB, 49710 KB/s, 11 seconds passed
+... 82%, 572736 KB, 49711 KB/s, 11 seconds passed
+... 82%, 572768 KB, 49713 KB/s, 11 seconds passed
+... 82%, 572800 KB, 49714 KB/s, 11 seconds passed
+... 82%, 572832 KB, 49716 KB/s, 11 seconds passed
+... 82%, 572864 KB, 49717 KB/s, 11 seconds passed
+... 82%, 572896 KB, 49718 KB/s, 11 seconds passed
+... 82%, 572928 KB, 49721 KB/s, 11 seconds passed
+... 82%, 572960 KB, 49722 KB/s, 11 seconds passed
+... 82%, 572992 KB, 49723 KB/s, 11 seconds passed
+... 82%, 573024 KB, 49724 KB/s, 11 seconds passed
+... 82%, 573056 KB, 49725 KB/s, 11 seconds passed
+... 82%, 573088 KB, 49727 KB/s, 11 seconds passed
+... 82%, 573120 KB, 49729 KB/s, 11 seconds passed
+... 82%, 573152 KB, 49731 KB/s, 11 seconds passed
+... 82%, 573184 KB, 49733 KB/s, 11 seconds passed
+... 82%, 573216 KB, 49735 KB/s, 11 seconds passed
+... 82%, 573248 KB, 49736 KB/s, 11 seconds passed
+... 82%, 573280 KB, 49738 KB/s, 11 seconds passed
+... 82%, 573312 KB, 49740 KB/s, 11 seconds passed
+... 82%, 573344 KB, 49741 KB/s, 11 seconds passed
+... 82%, 573376 KB, 49743 KB/s, 11 seconds passed
+... 82%, 573408 KB, 49744 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
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+... 82%, 573472 KB, 49640 KB/s, 11 seconds passed
+... 82%, 573504 KB, 49637 KB/s, 11 seconds passed
+... 82%, 573536 KB, 49638 KB/s, 11 seconds passed
+... 82%, 573568 KB, 49639 KB/s, 11 seconds passed
+... 82%, 573600 KB, 49640 KB/s, 11 seconds passed
+... 82%, 573632 KB, 49641 KB/s, 11 seconds passed
+... 82%, 573664 KB, 49642 KB/s, 11 seconds passed
+... 82%, 573696 KB, 49643 KB/s, 11 seconds passed
+... 82%, 573728 KB, 49645 KB/s, 11 seconds passed
+... 82%, 573760 KB, 49646 KB/s, 11 seconds passed
+... 82%, 573792 KB, 49647 KB/s, 11 seconds passed
+... 82%, 573824 KB, 49648 KB/s, 11 seconds passed
+... 82%, 573856 KB, 49649 KB/s, 11 seconds passed
+... 82%, 573888 KB, 49650 KB/s, 11 seconds passed
+... 82%, 573920 KB, 49651 KB/s, 11 seconds passed
+... 82%, 573952 KB, 49652 KB/s, 11 seconds passed
+... 82%, 573984 KB, 49653 KB/s, 11 seconds passed
+... 82%, 574016 KB, 49655 KB/s, 11 seconds passed
+... 82%, 574048 KB, 49656 KB/s, 11 seconds passed
+... 82%, 574080 KB, 49657 KB/s, 11 seconds passed
+... 82%, 574112 KB, 49658 KB/s, 11 seconds passed
+... 82%, 574144 KB, 49659 KB/s, 11 seconds passed
+... 82%, 574176 KB, 49660 KB/s, 11 seconds passed
+... 82%, 574208 KB, 49662 KB/s, 11 seconds passed
+... 82%, 574240 KB, 49663 KB/s, 11 seconds passed
+... 82%, 574272 KB, 49665 KB/s, 11 seconds passed
+... 82%, 574304 KB, 49666 KB/s, 11 seconds passed
+... 82%, 574336 KB, 49668 KB/s, 11 seconds passed
+... 82%, 574368 KB, 49669 KB/s, 11 seconds passed
+... 82%, 574400 KB, 49671 KB/s, 11 seconds passed
+... 82%, 574432 KB, 49672 KB/s, 11 seconds passed
+... 82%, 574464 KB, 49674 KB/s, 11 seconds passed
+... 82%, 574496 KB, 49675 KB/s, 11 seconds passed
+... 82%, 574528 KB, 49677 KB/s, 11 seconds passed
+... 82%, 574560 KB, 49678 KB/s, 11 seconds passed
+... 82%, 574592 KB, 49680 KB/s, 11 seconds passed
+... 82%, 574624 KB, 49681 KB/s, 11 seconds passed
+... 82%, 574656 KB, 49683 KB/s, 11 seconds passed
+... 82%, 574688 KB, 49685 KB/s, 11 seconds passed
+... 82%, 574720 KB, 49686 KB/s, 11 seconds passed
+... 82%, 574752 KB, 49688 KB/s, 11 seconds passed
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+... 82%, 574816 KB, 49691 KB/s, 11 seconds passed
+... 82%, 574848 KB, 49692 KB/s, 11 seconds passed
+... 82%, 574880 KB, 49694 KB/s, 11 seconds passed
+... 82%, 574912 KB, 49695 KB/s, 11 seconds passed
+... 82%, 574944 KB, 49697 KB/s, 11 seconds passed
+... 82%, 574976 KB, 49698 KB/s, 11 seconds passed
+... 82%, 575008 KB, 49700 KB/s, 11 seconds passed
+... 82%, 575040 KB, 49701 KB/s, 11 seconds passed
+... 82%, 575072 KB, 49703 KB/s, 11 seconds passed
+... 82%, 575104 KB, 49704 KB/s, 11 seconds passed
+... 82%, 575136 KB, 49706 KB/s, 11 seconds passed
+... 82%, 575168 KB, 49708 KB/s, 11 seconds passed
+... 82%, 575200 KB, 49709 KB/s, 11 seconds passed
+... 82%, 575232 KB, 49711 KB/s, 11 seconds passed
+... 82%, 575264 KB, 49712 KB/s, 11 seconds passed
+... 82%, 575296 KB, 49714 KB/s, 11 seconds passed
+... 82%, 575328 KB, 49716 KB/s, 11 seconds passed
+... 82%, 575360 KB, 49717 KB/s, 11 seconds passed
+... 82%, 575392 KB, 49719 KB/s, 11 seconds passed
+... 82%, 575424 KB, 49721 KB/s, 11 seconds passed
+... 82%, 575456 KB, 49723 KB/s, 11 seconds passed
+... 82%, 575488 KB, 49725 KB/s, 11 seconds passed
+... 82%, 575520 KB, 49727 KB/s, 11 seconds passed
+... 82%, 575552 KB, 49729 KB/s, 11 seconds passed
+... 82%, 575584 KB, 49731 KB/s, 11 seconds passed
+... 82%, 575616 KB, 49733 KB/s, 11 seconds passed
+... 82%, 575648 KB, 49735 KB/s, 11 seconds passed
+... 82%, 575680 KB, 49737 KB/s, 11 seconds passed
+... 82%, 575712 KB, 49739 KB/s, 11 seconds passed
+... 82%, 575744 KB, 49741 KB/s, 11 seconds passed
+... 82%, 575776 KB, 49743 KB/s, 11 seconds passed
+... 82%, 575808 KB, 49745 KB/s, 11 seconds passed
+... 82%, 575840 KB, 49747 KB/s, 11 seconds passed
+... 82%, 575872 KB, 49749 KB/s, 11 seconds passed
+... 82%, 575904 KB, 49751 KB/s, 11 seconds passed
+... 82%, 575936 KB, 49753 KB/s, 11 seconds passed
+... 82%, 575968 KB, 49754 KB/s, 11 seconds passed
+... 82%, 576000 KB, 49756 KB/s, 11 seconds passed
+... 82%, 576032 KB, 49758 KB/s, 11 seconds passed
+... 82%, 576064 KB, 49760 KB/s, 11 seconds passed
+... 82%, 576096 KB, 49762 KB/s, 11 seconds passed
+... 82%, 576128 KB, 49759 KB/s, 11 seconds passed
+... 82%, 576160 KB, 49761 KB/s, 11 seconds passed
+... 82%, 576192 KB, 49762 KB/s, 11 seconds passed
+... 82%, 576224 KB, 49764 KB/s, 11 seconds passed
+... 82%, 576256 KB, 49766 KB/s, 11 seconds passed
+... 82%, 576288 KB, 49767 KB/s, 11 seconds passed
+... 82%, 576320 KB, 49769 KB/s, 11 seconds passed
+... 82%, 576352 KB, 49770 KB/s, 11 seconds passed
+... 82%, 576384 KB, 49772 KB/s, 11 seconds passed
+... 82%, 576416 KB, 49773 KB/s, 11 seconds passed
+... 82%, 576448 KB, 49775 KB/s, 11 seconds passed
+... 82%, 576480 KB, 49776 KB/s, 11 seconds passed
+... 82%, 576512 KB, 49778 KB/s, 11 seconds passed
+... 82%, 576544 KB, 49780 KB/s, 11 seconds passed
+... 82%, 576576 KB, 49781 KB/s, 11 seconds passed
+... 82%, 576608 KB, 49783 KB/s, 11 seconds passed
+... 82%, 576640 KB, 49784 KB/s, 11 seconds passed
+... 82%, 576672 KB, 49786 KB/s, 11 seconds passed
+... 82%, 576704 KB, 49787 KB/s, 11 seconds passed
+... 82%, 576736 KB, 49789 KB/s, 11 seconds passed
+... 82%, 576768 KB, 49790 KB/s, 11 seconds passed
+... 82%, 576800 KB, 49792 KB/s, 11 seconds passed
+... 82%, 576832 KB, 49794 KB/s, 11 seconds passed
+... 82%, 576864 KB, 49795 KB/s, 11 seconds passed
+... 82%, 576896 KB, 49797 KB/s, 11 seconds passed
+... 82%, 576928 KB, 49798 KB/s, 11 seconds passed
+... 82%, 576960 KB, 49799 KB/s, 11 seconds passed
+... 82%, 576992 KB, 49801 KB/s, 11 seconds passed
+... 82%, 577024 KB, 49803 KB/s, 11 seconds passed
+... 82%, 577056 KB, 49804 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 82%, 577088 KB, 49806 KB/s, 11 seconds passed
+... 82%, 577120 KB, 49808 KB/s, 11 seconds passed
+... 82%, 577152 KB, 49809 KB/s, 11 seconds passed
+... 82%, 577184 KB, 49810 KB/s, 11 seconds passed
+... 82%, 577216 KB, 49755 KB/s, 11 seconds passed
+... 82%, 577248 KB, 49756 KB/s, 11 seconds passed
+... 83%, 577280 KB, 49757 KB/s, 11 seconds passed
+... 83%, 577312 KB, 49758 KB/s, 11 seconds passed
+... 83%, 577344 KB, 49760 KB/s, 11 seconds passed
+... 83%, 577376 KB, 49761 KB/s, 11 seconds passed
+... 83%, 577408 KB, 49762 KB/s, 11 seconds passed
+... 83%, 577440 KB, 49764 KB/s, 11 seconds passed
+... 83%, 577472 KB, 49765 KB/s, 11 seconds passed
+... 83%, 577504 KB, 49766 KB/s, 11 seconds passed
+... 83%, 577536 KB, 49768 KB/s, 11 seconds passed
+... 83%, 577568 KB, 49769 KB/s, 11 seconds passed
+... 83%, 577600 KB, 49771 KB/s, 11 seconds passed
+... 83%, 577632 KB, 49772 KB/s, 11 seconds passed
+... 83%, 577664 KB, 49773 KB/s, 11 seconds passed
+... 83%, 577696 KB, 49775 KB/s, 11 seconds passed
+... 83%, 577728 KB, 49776 KB/s, 11 seconds passed
+... 83%, 577760 KB, 49778 KB/s, 11 seconds passed
+... 83%, 577792 KB, 49779 KB/s, 11 seconds passed
+... 83%, 577824 KB, 49779 KB/s, 11 seconds passed
+... 83%, 577856 KB, 49780 KB/s, 11 seconds passed
+... 83%, 577888 KB, 49782 KB/s, 11 seconds passed
+... 83%, 577920 KB, 49783 KB/s, 11 seconds passed
+... 83%, 577952 KB, 49784 KB/s, 11 seconds passed
+... 83%, 577984 KB, 49786 KB/s, 11 seconds passed
+... 83%, 578016 KB, 49787 KB/s, 11 seconds passed
+... 83%, 578048 KB, 49789 KB/s, 11 seconds passed
+... 83%, 578080 KB, 49790 KB/s, 11 seconds passed
+... 83%, 578112 KB, 49792 KB/s, 11 seconds passed
+... 83%, 578144 KB, 49793 KB/s, 11 seconds passed
+... 83%, 578176 KB, 49795 KB/s, 11 seconds passed
+... 83%, 578208 KB, 49796 KB/s, 11 seconds passed
+... 83%, 578240 KB, 49797 KB/s, 11 seconds passed
+... 83%, 578272 KB, 49799 KB/s, 11 seconds passed
+... 83%, 578304 KB, 49800 KB/s, 11 seconds passed
+... 83%, 578336 KB, 49802 KB/s, 11 seconds passed
+... 83%, 578368 KB, 49803 KB/s, 11 seconds passed
+... 83%, 578400 KB, 49805 KB/s, 11 seconds passed
+... 83%, 578432 KB, 49806 KB/s, 11 seconds passed
+... 83%, 578464 KB, 49808 KB/s, 11 seconds passed
+... 83%, 578496 KB, 49810 KB/s, 11 seconds passed
+... 83%, 578528 KB, 49811 KB/s, 11 seconds passed
+... 83%, 578560 KB, 49733 KB/s, 11 seconds passed
+... 83%, 578592 KB, 49734 KB/s, 11 seconds passed
+... 83%, 578624 KB, 49735 KB/s, 11 seconds passed
+... 83%, 578656 KB, 49737 KB/s, 11 seconds passed
+... 83%, 578688 KB, 49738 KB/s, 11 seconds passed
+... 83%, 578720 KB, 49740 KB/s, 11 seconds passed
+... 83%, 578752 KB, 49741 KB/s, 11 seconds passed
+... 83%, 578784 KB, 49742 KB/s, 11 seconds passed
+... 83%, 578816 KB, 49743 KB/s, 11 seconds passed
+... 83%, 578848 KB, 49744 KB/s, 11 seconds passed
+... 83%, 578880 KB, 49745 KB/s, 11 seconds passed
+... 83%, 578912 KB, 49747 KB/s, 11 seconds passed
+... 83%, 578944 KB, 49748 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 83%, 578976 KB, 49750 KB/s, 11 seconds passed
+... 83%, 579008 KB, 49751 KB/s, 11 seconds passed
+... 83%, 579040 KB, 49753 KB/s, 11 seconds passed
+... 83%, 579072 KB, 49754 KB/s, 11 seconds passed
+... 83%, 579104 KB, 49755 KB/s, 11 seconds passed
+... 83%, 579136 KB, 49757 KB/s, 11 seconds passed
+... 83%, 579168 KB, 49758 KB/s, 11 seconds passed
+... 83%, 579200 KB, 49760 KB/s, 11 seconds passed
+... 83%, 579232 KB, 49761 KB/s, 11 seconds passed
+... 83%, 579264 KB, 49763 KB/s, 11 seconds passed
+... 83%, 579296 KB, 49764 KB/s, 11 seconds passed
+... 83%, 579328 KB, 49766 KB/s, 11 seconds passed
+... 83%, 579360 KB, 49767 KB/s, 11 seconds passed
+... 83%, 579392 KB, 49768 KB/s, 11 seconds passed
+... 83%, 579424 KB, 49770 KB/s, 11 seconds passed
+... 83%, 579456 KB, 49771 KB/s, 11 seconds passed
+... 83%, 579488 KB, 49773 KB/s, 11 seconds passed
+... 83%, 579520 KB, 49774 KB/s, 11 seconds passed
+... 83%, 579552 KB, 49776 KB/s, 11 seconds passed
+... 83%, 579584 KB, 49778 KB/s, 11 seconds passed
+... 83%, 579616 KB, 49779 KB/s, 11 seconds passed
+... 83%, 579648 KB, 49781 KB/s, 11 seconds passed
+... 83%, 579680 KB, 49783 KB/s, 11 seconds passed
+... 83%, 579712 KB, 49784 KB/s, 11 seconds passed
+... 83%, 579744 KB, 49786 KB/s, 11 seconds passed
+... 83%, 579776 KB, 49788 KB/s, 11 seconds passed
+... 83%, 579808 KB, 49789 KB/s, 11 seconds passed
+... 83%, 579840 KB, 49791 KB/s, 11 seconds passed
+... 83%, 579872 KB, 49793 KB/s, 11 seconds passed
+... 83%, 579904 KB, 49794 KB/s, 11 seconds passed
+... 83%, 579936 KB, 49796 KB/s, 11 seconds passed
+... 83%, 579968 KB, 49798 KB/s, 11 seconds passed
+... 83%, 580000 KB, 49799 KB/s, 11 seconds passed
+... 83%, 580032 KB, 49801 KB/s, 11 seconds passed
+... 83%, 580064 KB, 49803 KB/s, 11 seconds passed
+... 83%, 580096 KB, 49804 KB/s, 11 seconds passed
+... 83%, 580128 KB, 49806 KB/s, 11 seconds passed
+... 83%, 580160 KB, 49807 KB/s, 11 seconds passed
+... 83%, 580192 KB, 49809 KB/s, 11 seconds passed
+... 83%, 580224 KB, 49810 KB/s, 11 seconds passed
+... 83%, 580256 KB, 49812 KB/s, 11 seconds passed
+... 83%, 580288 KB, 49814 KB/s, 11 seconds passed
+... 83%, 580320 KB, 49815 KB/s, 11 seconds passed
+... 83%, 580352 KB, 49817 KB/s, 11 seconds passed
+... 83%, 580384 KB, 49818 KB/s, 11 seconds passed
+... 83%, 580416 KB, 49820 KB/s, 11 seconds passed
+... 83%, 580448 KB, 49822 KB/s, 11 seconds passed
+... 83%, 580480 KB, 49823 KB/s, 11 seconds passed
+... 83%, 580512 KB, 49825 KB/s, 11 seconds passed
+... 83%, 580544 KB, 49826 KB/s, 11 seconds passed
+... 83%, 580576 KB, 49828 KB/s, 11 seconds passed
+... 83%, 580608 KB, 49829 KB/s, 11 seconds passed
+... 83%, 580640 KB, 49831 KB/s, 11 seconds passed
+... 83%, 580672 KB, 49833 KB/s, 11 seconds passed
+... 83%, 580704 KB, 49834 KB/s, 11 seconds passed
+... 83%, 580736 KB, 49835 KB/s, 11 seconds passed
+... 83%, 580768 KB, 49837 KB/s, 11 seconds passed
+... 83%, 580800 KB, 49839 KB/s, 11 seconds passed
+... 83%, 580832 KB, 49840 KB/s, 11 seconds passed
+... 83%, 580864 KB, 49842 KB/s, 11 seconds passed
+... 83%, 580896 KB, 49844 KB/s, 11 seconds passed
+... 83%, 580928 KB, 49845 KB/s, 11 seconds passed
+... 83%, 580960 KB, 49847 KB/s, 11 seconds passed
+... 83%, 580992 KB, 49848 KB/s, 11 seconds passed
+... 83%, 581024 KB, 49849 KB/s, 11 seconds passed
+... 83%, 581056 KB, 49851 KB/s, 11 seconds passed
+... 83%, 581088 KB, 49853 KB/s, 11 seconds passed
+... 83%, 581120 KB, 49854 KB/s, 11 seconds passed
+... 83%, 581152 KB, 49856 KB/s, 11 seconds passed
+... 83%, 581184 KB, 49857 KB/s, 11 seconds passed
+... 83%, 581216 KB, 49859 KB/s, 11 seconds passed
+... 83%, 581248 KB, 49860 KB/s, 11 seconds passed
+... 83%, 581280 KB, 49861 KB/s, 11 seconds passed
+... 83%, 581312 KB, 49863 KB/s, 11 seconds passed
+... 83%, 581344 KB, 49864 KB/s, 11 seconds passed
+... 83%, 581376 KB, 49866 KB/s, 11 seconds passed
+... 83%, 581408 KB, 49868 KB/s, 11 seconds passed
+... 83%, 581440 KB, 49870 KB/s, 11 seconds passed
+... 83%, 581472 KB, 49871 KB/s, 11 seconds passed
+... 83%, 581504 KB, 49873 KB/s, 11 seconds passed
+... 83%, 581536 KB, 49875 KB/s, 11 seconds passed
+... 83%, 581568 KB, 49876 KB/s, 11 seconds passed
+... 83%, 581600 KB, 49878 KB/s, 11 seconds passed
+... 83%, 581632 KB, 49879 KB/s, 11 seconds passed
+... 83%, 581664 KB, 49880 KB/s, 11 seconds passed
+... 83%, 581696 KB, 49882 KB/s, 11 seconds passed
+... 83%, 581728 KB, 49884 KB/s, 11 seconds passed
+... 83%, 581760 KB, 49885 KB/s, 11 seconds passed
+... 83%, 581792 KB, 49886 KB/s, 11 seconds passed
+... 83%, 581824 KB, 49888 KB/s, 11 seconds passed
+... 83%, 581856 KB, 49890 KB/s, 11 seconds passed
+... 83%, 581888 KB, 49892 KB/s, 11 seconds passed
+... 83%, 581920 KB, 49893 KB/s, 11 seconds passed
+... 83%, 581952 KB, 49895 KB/s, 11 seconds passed
+... 83%, 581984 KB, 49896 KB/s, 11 seconds passed
+... 83%, 582016 KB, 49898 KB/s, 11 seconds passed
+... 83%, 582048 KB, 49899 KB/s, 11 seconds passed
+... 83%, 582080 KB, 49901 KB/s, 11 seconds passed
+... 83%, 582112 KB, 49902 KB/s, 11 seconds passed
+... 83%, 582144 KB, 49904 KB/s, 11 seconds passed
+... 83%, 582176 KB, 49905 KB/s, 11 seconds passed
+... 83%, 582208 KB, 49907 KB/s, 11 seconds passed
+... 83%, 582240 KB, 49908 KB/s, 11 seconds passed
+... 83%, 582272 KB, 49910 KB/s, 11 seconds passed
+... 83%, 582304 KB, 49912 KB/s, 11 seconds passed
+... 83%, 582336 KB, 49913 KB/s, 11 seconds passed
+... 83%, 582368 KB, 49915 KB/s, 11 seconds passed
+... 83%, 582400 KB, 49917 KB/s, 11 seconds passed
+... 83%, 582432 KB, 49918 KB/s, 11 seconds passed
+... 83%, 582464 KB, 49914 KB/s, 11 seconds passed
+... 83%, 582496 KB, 49915 KB/s, 11 seconds passed
+... 83%, 582528 KB, 49917 KB/s, 11 seconds passed
+... 83%, 582560 KB, 49918 KB/s, 11 seconds passed
+... 83%, 582592 KB, 49920 KB/s, 11 seconds passed
+... 83%, 582624 KB, 49921 KB/s, 11 seconds passed
+... 83%, 582656 KB, 49922 KB/s, 11 seconds passed
+... 83%, 582688 KB, 49917 KB/s, 11 seconds passed
+... 83%, 582720 KB, 49919 KB/s, 11 seconds passed
+... 83%, 582752 KB, 49920 KB/s, 11 seconds passed
+... 83%, 582784 KB, 49922 KB/s, 11 seconds passed
+... 83%, 582816 KB, 49923 KB/s, 11 seconds passed
+... 83%, 582848 KB, 49925 KB/s, 11 seconds passed
+... 83%, 582880 KB, 49926 KB/s, 11 seconds passed
+... 83%, 582912 KB, 49927 KB/s, 11 seconds passed
+... 83%, 582944 KB, 49929 KB/s, 11 seconds passed
+... 83%, 582976 KB, 49930 KB/s, 11 seconds passed
+... 83%, 583008 KB, 49932 KB/s, 11 seconds passed
+... 83%, 583040 KB, 49933 KB/s, 11 seconds passed
+... 83%, 583072 KB, 49935 KB/s, 11 seconds passed
+... 83%, 583104 KB, 49936 KB/s, 11 seconds passed
+... 83%, 583136 KB, 49938 KB/s, 11 seconds passed
+... 83%, 583168 KB, 49939 KB/s, 11 seconds passed
+... 83%, 583200 KB, 49941 KB/s, 11 seconds passed
+... 83%, 583232 KB, 49942 KB/s, 11 seconds passed
+... 83%, 583264 KB, 49944 KB/s, 11 seconds passed
+... 83%, 583296 KB, 49945 KB/s, 11 seconds passed
+... 83%, 583328 KB, 49947 KB/s, 11 seconds passed
+... 83%, 583360 KB, 49948 KB/s, 11 seconds passed
+... 83%, 583392 KB, 49949 KB/s, 11 seconds passed
+... 83%, 583424 KB, 49951 KB/s, 11 seconds passed
+... 83%, 583456 KB, 49952 KB/s, 11 seconds passed
+... 83%, 583488 KB, 49954 KB/s, 11 seconds passed
+... 83%, 583520 KB, 49955 KB/s, 11 seconds passed
+... 83%, 583552 KB, 49957 KB/s, 11 seconds passed
+... 83%, 583584 KB, 49958 KB/s, 11 seconds passed
+... 83%, 583616 KB, 49960 KB/s, 11 seconds passed
+... 83%, 583648 KB, 49961 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 83%, 583680 KB, 49751 KB/s, 11 seconds passed
+... 83%, 583712 KB, 49752 KB/s, 11 seconds passed
+... 83%, 583744 KB, 49753 KB/s, 11 seconds passed
+... 83%, 583776 KB, 49754 KB/s, 11 seconds passed
+... 83%, 583808 KB, 49756 KB/s, 11 seconds passed
+... 83%, 583840 KB, 49757 KB/s, 11 seconds passed
+... 83%, 583872 KB, 49758 KB/s, 11 seconds passed
+... 83%, 583904 KB, 49760 KB/s, 11 seconds passed
+... 83%, 583936 KB, 49761 KB/s, 11 seconds passed
+... 83%, 583968 KB, 49763 KB/s, 11 seconds passed
+... 83%, 584000 KB, 49764 KB/s, 11 seconds passed
+... 83%, 584032 KB, 49765 KB/s, 11 seconds passed
+... 83%, 584064 KB, 49767 KB/s, 11 seconds passed
+... 83%, 584096 KB, 49768 KB/s, 11 seconds passed
+... 83%, 584128 KB, 49770 KB/s, 11 seconds passed
+... 83%, 584160 KB, 49771 KB/s, 11 seconds passed
+... 83%, 584192 KB, 49773 KB/s, 11 seconds passed
+... 83%, 584224 KB, 49774 KB/s, 11 seconds passed
+... 84%, 584256 KB, 49775 KB/s, 11 seconds passed
+... 84%, 584288 KB, 49777 KB/s, 11 seconds passed
+... 84%, 584320 KB, 49778 KB/s, 11 seconds passed
+... 84%, 584352 KB, 49780 KB/s, 11 seconds passed
+... 84%, 584384 KB, 49781 KB/s, 11 seconds passed
+... 84%, 584416 KB, 49783 KB/s, 11 seconds passed
+... 84%, 584448 KB, 49784 KB/s, 11 seconds passed
+... 84%, 584480 KB, 49785 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 84%, 584512 KB, 49787 KB/s, 11 seconds passed
+... 84%, 584544 KB, 49788 KB/s, 11 seconds passed
+... 84%, 584576 KB, 49790 KB/s, 11 seconds passed
+... 84%, 584608 KB, 49791 KB/s, 11 seconds passed
+... 84%, 584640 KB, 49792 KB/s, 11 seconds passed
+... 84%, 584672 KB, 49794 KB/s, 11 seconds passed
+... 84%, 584704 KB, 49795 KB/s, 11 seconds passed
+... 84%, 584736 KB, 49797 KB/s, 11 seconds passed
+... 84%, 584768 KB, 49798 KB/s, 11 seconds passed
+... 84%, 584800 KB, 49800 KB/s, 11 seconds passed
+... 84%, 584832 KB, 49801 KB/s, 11 seconds passed
+... 84%, 584864 KB, 49802 KB/s, 11 seconds passed
+... 84%, 584896 KB, 49804 KB/s, 11 seconds passed
+... 84%, 584928 KB, 49806 KB/s, 11 seconds passed
+... 84%, 584960 KB, 49808 KB/s, 11 seconds passed
+... 84%, 584992 KB, 49810 KB/s, 11 seconds passed
+... 84%, 585024 KB, 49811 KB/s, 11 seconds passed
+... 84%, 585056 KB, 49813 KB/s, 11 seconds passed
+... 84%, 585088 KB, 49815 KB/s, 11 seconds passed
+... 84%, 585120 KB, 49817 KB/s, 11 seconds passed
+... 84%, 585152 KB, 49819 KB/s, 11 seconds passed
+... 84%, 585184 KB, 49821 KB/s, 11 seconds passed
+... 84%, 585216 KB, 49823 KB/s, 11 seconds passed
+... 84%, 585248 KB, 49824 KB/s, 11 seconds passed
+... 84%, 585280 KB, 49826 KB/s, 11 seconds passed
+... 84%, 585312 KB, 49828 KB/s, 11 seconds passed
+... 84%, 585344 KB, 49830 KB/s, 11 seconds passed
+... 84%, 585376 KB, 49832 KB/s, 11 seconds passed
+... 84%, 585408 KB, 49834 KB/s, 11 seconds passed
+... 84%, 585440 KB, 49836 KB/s, 11 seconds passed
+... 84%, 585472 KB, 49837 KB/s, 11 seconds passed
+... 84%, 585504 KB, 49838 KB/s, 11 seconds passed
+... 84%, 585536 KB, 49840 KB/s, 11 seconds passed
+... 84%, 585568 KB, 49842 KB/s, 11 seconds passed
+... 84%, 585600 KB, 49843 KB/s, 11 seconds passed
+... 84%, 585632 KB, 49844 KB/s, 11 seconds passed
+... 84%, 585664 KB, 49846 KB/s, 11 seconds passed
+... 84%, 585696 KB, 49848 KB/s, 11 seconds passed
+... 84%, 585728 KB, 49849 KB/s, 11 seconds passed
+... 84%, 585760 KB, 49851 KB/s, 11 seconds passed
+... 84%, 585792 KB, 49852 KB/s, 11 seconds passed
+... 84%, 585824 KB, 49854 KB/s, 11 seconds passed
+... 84%, 585856 KB, 49855 KB/s, 11 seconds passed
+... 84%, 585888 KB, 49857 KB/s, 11 seconds passed
+... 84%, 585920 KB, 49858 KB/s, 11 seconds passed
+... 84%, 585952 KB, 49860 KB/s, 11 seconds passed
+... 84%, 585984 KB, 49861 KB/s, 11 seconds passed
+... 84%, 586016 KB, 49863 KB/s, 11 seconds passed
+... 84%, 586048 KB, 49865 KB/s, 11 seconds passed
+... 84%, 586080 KB, 49866 KB/s, 11 seconds passed
+... 84%, 586112 KB, 49867 KB/s, 11 seconds passed
+... 84%, 586144 KB, 49869 KB/s, 11 seconds passed
+... 84%, 586176 KB, 49870 KB/s, 11 seconds passed
+... 84%, 586208 KB, 49872 KB/s, 11 seconds passed
+... 84%, 586240 KB, 49873 KB/s, 11 seconds passed
+... 84%, 586272 KB, 49875 KB/s, 11 seconds passed
+... 84%, 586304 KB, 49877 KB/s, 11 seconds passed
+... 84%, 586336 KB, 49879 KB/s, 11 seconds passed
+... 84%, 586368 KB, 49880 KB/s, 11 seconds passed
+... 84%, 586400 KB, 49882 KB/s, 11 seconds passed
+... 84%, 586432 KB, 49883 KB/s, 11 seconds passed
+... 84%, 586464 KB, 49885 KB/s, 11 seconds passed
+... 84%, 586496 KB, 49886 KB/s, 11 seconds passed
+... 84%, 586528 KB, 49887 KB/s, 11 seconds passed
+... 84%, 586560 KB, 49889 KB/s, 11 seconds passed
+... 84%, 586592 KB, 49890 KB/s, 11 seconds passed
+... 84%, 586624 KB, 49892 KB/s, 11 seconds passed
+... 84%, 586656 KB, 49893 KB/s, 11 seconds passed
+... 84%, 586688 KB, 49895 KB/s, 11 seconds passed
+... 84%, 586720 KB, 49896 KB/s, 11 seconds passed
+... 84%, 586752 KB, 49898 KB/s, 11 seconds passed
+... 84%, 586784 KB, 49900 KB/s, 11 seconds passed
+... 84%, 586816 KB, 49902 KB/s, 11 seconds passed
+... 84%, 586848 KB, 49903 KB/s, 11 seconds passed
+... 84%, 586880 KB, 49905 KB/s, 11 seconds passed
+... 84%, 586912 KB, 49906 KB/s, 11 seconds passed
+... 84%, 586944 KB, 49908 KB/s, 11 seconds passed
+... 84%, 586976 KB, 49909 KB/s, 11 seconds passed
+... 84%, 587008 KB, 49911 KB/s, 11 seconds passed
+... 84%, 587040 KB, 49912 KB/s, 11 seconds passed
+... 84%, 587072 KB, 49913 KB/s, 11 seconds passed
+... 84%, 587104 KB, 49915 KB/s, 11 seconds passed
+... 84%, 587136 KB, 49917 KB/s, 11 seconds passed
+... 84%, 587168 KB, 49918 KB/s, 11 seconds passed
+... 84%, 587200 KB, 49919 KB/s, 11 seconds passed
+... 84%, 587232 KB, 49921 KB/s, 11 seconds passed
+... 84%, 587264 KB, 49922 KB/s, 11 seconds passed
+... 84%, 587296 KB, 49924 KB/s, 11 seconds passed
+... 84%, 587328 KB, 49925 KB/s, 11 seconds passed
+... 84%, 587360 KB, 49927 KB/s, 11 seconds passed
+... 84%, 587392 KB, 49929 KB/s, 11 seconds passed
+... 84%, 587424 KB, 49930 KB/s, 11 seconds passed
+... 84%, 587456 KB, 49932 KB/s, 11 seconds passed
+... 84%, 587488 KB, 49921 KB/s, 11 seconds passed
+... 84%, 587520 KB, 49923 KB/s, 11 seconds passed
+... 84%, 587552 KB, 49924 KB/s, 11 seconds passed
+... 84%, 587584 KB, 49926 KB/s, 11 seconds passed
+... 84%, 587616 KB, 49927 KB/s, 11 seconds passed
+... 84%, 587648 KB, 49926 KB/s, 11 seconds passed
+... 84%, 587680 KB, 49927 KB/s, 11 seconds passed
+... 84%, 587712 KB, 49929 KB/s, 11 seconds passed
+... 84%, 587744 KB, 49930 KB/s, 11 seconds passed
+... 84%, 587776 KB, 49932 KB/s, 11 seconds passed
+... 84%, 587808 KB, 49933 KB/s, 11 seconds passed
+... 84%, 587840 KB, 49935 KB/s, 11 seconds passed
+... 84%, 587872 KB, 49937 KB/s, 11 seconds passed
+... 84%, 587904 KB, 49938 KB/s, 11 seconds passed
+... 84%, 587936 KB, 49931 KB/s, 11 seconds passed
+... 84%, 587968 KB, 49932 KB/s, 11 seconds passed
+... 84%, 588000 KB, 49934 KB/s, 11 seconds passed
+... 84%, 588032 KB, 49935 KB/s, 11 seconds passed
+... 84%, 588064 KB, 49937 KB/s, 11 seconds passed
+... 84%, 588096 KB, 49938 KB/s, 11 seconds passed
+... 84%, 588128 KB, 49939 KB/s, 11 seconds passed
+... 84%, 588160 KB, 49941 KB/s, 11 seconds passed
+... 84%, 588192 KB, 49942 KB/s, 11 seconds passed
+... 84%, 588224 KB, 49944 KB/s, 11 seconds passed
+... 84%, 588256 KB, 49945 KB/s, 11 seconds passed
+... 84%, 588288 KB, 49947 KB/s, 11 seconds passed
+... 84%, 588320 KB, 49948 KB/s, 11 seconds passed
+... 84%, 588352 KB, 49949 KB/s, 11 seconds passed
+... 84%, 588384 KB, 49951 KB/s, 11 seconds passed
+... 84%, 588416 KB, 49952 KB/s, 11 seconds passed
+... 84%, 588448 KB, 49954 KB/s, 11 seconds passed
+... 84%, 588480 KB, 49955 KB/s, 11 seconds passed
+... 84%, 588512 KB, 49956 KB/s, 11 seconds passed
+... 84%, 588544 KB, 49958 KB/s, 11 seconds passed
+... 84%, 588576 KB, 49959 KB/s, 11 seconds passed
+... 84%, 588608 KB, 49961 KB/s, 11 seconds passed
+... 84%, 588640 KB, 49962 KB/s, 11 seconds passed
+... 84%, 588672 KB, 49964 KB/s, 11 seconds passed
+... 84%, 588704 KB, 49965 KB/s, 11 seconds passed
+... 84%, 588736 KB, 49967 KB/s, 11 seconds passed
+... 84%, 588768 KB, 49968 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 84%, 588800 KB, 49667 KB/s, 11 seconds passed
+... 84%, 588832 KB, 49668 KB/s, 11 seconds passed
+... 84%, 588864 KB, 49669 KB/s, 11 seconds passed
+... 84%, 588896 KB, 49671 KB/s, 11 seconds passed
+... 84%, 588928 KB, 49672 KB/s, 11 seconds passed
+... 84%, 588960 KB, 49666 KB/s, 11 seconds passed
+... 84%, 588992 KB, 49667 KB/s, 11 seconds passed
+... 84%, 589024 KB, 49668 KB/s, 11 seconds passed
+... 84%, 589056 KB, 49669 KB/s, 11 seconds passed
+... 84%, 589088 KB, 49671 KB/s, 11 seconds passed
+... 84%, 589120 KB, 49672 KB/s, 11 seconds passed
+... 84%, 589152 KB, 49674 KB/s, 11 seconds passed
+... 84%, 589184 KB, 49675 KB/s, 11 seconds passed
+... 84%, 589216 KB, 49676 KB/s, 11 seconds passed
+... 84%, 589248 KB, 49678 KB/s, 11 seconds passed
+... 84%, 589280 KB, 49679 KB/s, 11 seconds passed
+... 84%, 589312 KB, 49681 KB/s, 11 seconds passed
+... 84%, 589344 KB, 49682 KB/s, 11 seconds passed
+... 84%, 589376 KB, 49684 KB/s, 11 seconds passed
+... 84%, 589408 KB, 49685 KB/s, 11 seconds passed
+... 84%, 589440 KB, 49686 KB/s, 11 seconds passed
+... 84%, 589472 KB, 49688 KB/s, 11 seconds passed
+... 84%, 589504 KB, 49689 KB/s, 11 seconds passed
+... 84%, 589536 KB, 49690 KB/s, 11 seconds passed
+... 84%, 589568 KB, 49692 KB/s, 11 seconds passed
+... 84%, 589600 KB, 49693 KB/s, 11 seconds passed
+... 84%, 589632 KB, 49695 KB/s, 11 seconds passed
+... 84%, 589664 KB, 49696 KB/s, 11 seconds passed
+... 84%, 589696 KB, 49698 KB/s, 11 seconds passed
+... 84%, 589728 KB, 49699 KB/s, 11 seconds passed
+... 84%, 589760 KB, 49701 KB/s, 11 seconds passed
+... 84%, 589792 KB, 49703 KB/s, 11 seconds passed
+... 84%, 589824 KB, 49704 KB/s, 11 seconds passed
+... 84%, 589856 KB, 49706 KB/s, 11 seconds passed
+... 84%, 589888 KB, 49707 KB/s, 11 seconds passed
+... 84%, 589920 KB, 49709 KB/s, 11 seconds passed
+... 84%, 589952 KB, 49710 KB/s, 11 seconds passed
+... 84%, 589984 KB, 49712 KB/s, 11 seconds passed
+... 84%, 590016 KB, 49713 KB/s, 11 seconds passed
+... 84%, 590048 KB, 49715 KB/s, 11 seconds passed
+... 84%, 590080 KB, 49717 KB/s, 11 seconds passed
+... 84%, 590112 KB, 49718 KB/s, 11 seconds passed
+... 84%, 590144 KB, 49720 KB/s, 11 seconds passed
+... 84%, 590176 KB, 49721 KB/s, 11 seconds passed
+... 84%, 590208 KB, 49723 KB/s, 11 seconds passed
+... 84%, 590240 KB, 49724 KB/s, 11 seconds passed
+... 84%, 590272 KB, 49726 KB/s, 11 seconds passed
+... 84%, 590304 KB, 49727 KB/s, 11 seconds passed
+... 84%, 590336 KB, 49729 KB/s, 11 seconds passed
+... 84%, 590368 KB, 49730 KB/s, 11 seconds passed
+... 84%, 590400 KB, 49732 KB/s, 11 seconds passed
+... 84%, 590432 KB, 49734 KB/s, 11 seconds passed
+... 84%, 590464 KB, 49735 KB/s, 11 seconds passed
+... 84%, 590496 KB, 49737 KB/s, 11 seconds passed
+... 84%, 590528 KB, 49738 KB/s, 11 seconds passed
+... 84%, 590560 KB, 49740 KB/s, 11 seconds passed
+... 84%, 590592 KB, 49741 KB/s, 11 seconds passed
+... 84%, 590624 KB, 49743 KB/s, 11 seconds passed
+... 84%, 590656 KB, 49745 KB/s, 11 seconds passed
+... 84%, 590688 KB, 49746 KB/s, 11 seconds passed
+... 84%, 590720 KB, 49748 KB/s, 11 seconds passed
+... 84%, 590752 KB, 49749 KB/s, 11 seconds passed
+... 84%, 590784 KB, 49751 KB/s, 11 seconds passed
+... 84%, 590816 KB, 49753 KB/s, 11 seconds passed
+... 84%, 590848 KB, 49754 KB/s, 11 seconds passed
+... 84%, 590880 KB, 49756 KB/s, 11 seconds passed
+... 84%, 590912 KB, 49757 KB/s, 11 seconds passed
+... 84%, 590944 KB, 49759 KB/s, 11 seconds passed
+... 84%, 590976 KB, 49760 KB/s, 11 seconds passed
+... 84%, 591008 KB, 49762 KB/s, 11 seconds passed
+... 84%, 591040 KB, 49763 KB/s, 11 seconds passed
+... 84%, 591072 KB, 49764 KB/s, 11 seconds passed
+... 84%, 591104 KB, 49766 KB/s, 11 seconds passed
+... 84%, 591136 KB, 49768 KB/s, 11 seconds passed
+... 84%, 591168 KB, 49770 KB/s, 11 seconds passed
+... 85%, 591200 KB, 49771 KB/s, 11 seconds passed
+... 85%, 591232 KB, 49773 KB/s, 11 seconds passed
+... 85%, 591264 KB, 49774 KB/s, 11 seconds passed
+... 85%, 591296 KB, 49776 KB/s, 11 seconds passed
+... 85%, 591328 KB, 49777 KB/s, 11 seconds passed
+... 85%, 591360 KB, 49779 KB/s, 11 seconds passed
+... 85%, 591392 KB, 49781 KB/s, 11 seconds passed
+... 85%, 591424 KB, 49782 KB/s, 11 seconds passed
+... 85%, 591456 KB, 49783 KB/s, 11 seconds passed
+... 85%, 591488 KB, 49785 KB/s, 11 seconds passed
+... 85%, 591520 KB, 49786 KB/s, 11 seconds passed
+... 85%, 591552 KB, 49788 KB/s, 11 seconds passed
+... 85%, 591584 KB, 49790 KB/s, 11 seconds passed
+... 85%, 591616 KB, 49791 KB/s, 11 seconds passed
+... 85%, 591648 KB, 49793 KB/s, 11 seconds passed
+... 85%, 591680 KB, 49794 KB/s, 11 seconds passed
+... 85%, 591712 KB, 49796 KB/s, 11 seconds passed
+... 85%, 591744 KB, 49797 KB/s, 11 seconds passed
+... 85%, 591776 KB, 49799 KB/s, 11 seconds passed
+... 85%, 591808 KB, 49800 KB/s, 11 seconds passed
+... 85%, 591840 KB, 49802 KB/s, 11 seconds passed
+... 85%, 591872 KB, 49803 KB/s, 11 seconds passed
+... 85%, 591904 KB, 49805 KB/s, 11 seconds passed
+... 85%, 591936 KB, 49806 KB/s, 11 seconds passed
+... 85%, 591968 KB, 49808 KB/s, 11 seconds passed
+... 85%, 592000 KB, 49809 KB/s, 11 seconds passed
+... 85%, 592032 KB, 49811 KB/s, 11 seconds passed
+... 85%, 592064 KB, 49813 KB/s, 11 seconds passed
+... 85%, 592096 KB, 49814 KB/s, 11 seconds passed
+... 85%, 592128 KB, 49815 KB/s, 11 seconds passed
+... 85%, 592160 KB, 49817 KB/s, 11 seconds passed
+... 85%, 592192 KB, 49819 KB/s, 11 seconds passed
+... 85%, 592224 KB, 49820 KB/s, 11 seconds passed
+... 85%, 592256 KB, 49822 KB/s, 11 seconds passed
+... 85%, 592288 KB, 49823 KB/s, 11 seconds passed
+... 85%, 592320 KB, 49825 KB/s, 11 seconds passed
+... 85%, 592352 KB, 49826 KB/s, 11 seconds passed
+... 85%, 592384 KB, 49828 KB/s, 11 seconds passed
+... 85%, 592416 KB, 49829 KB/s, 11 seconds passed
+... 85%, 592448 KB, 49831 KB/s, 11 seconds passed
+... 85%, 592480 KB, 49832 KB/s, 11 seconds passed
+... 85%, 592512 KB, 49834 KB/s, 11 seconds passed
+... 85%, 592544 KB, 49835 KB/s, 11 seconds passed
+... 85%, 592576 KB, 49837 KB/s, 11 seconds passed
+... 85%, 592608 KB, 49838 KB/s, 11 seconds passed
+... 85%, 592640 KB, 49840 KB/s, 11 seconds passed
+... 85%, 592672 KB, 49841 KB/s, 11 seconds passed
+... 85%, 592704 KB, 49843 KB/s, 11 seconds passed
+... 85%, 592736 KB, 49844 KB/s, 11 seconds passed
+... 85%, 592768 KB, 49846 KB/s, 11 seconds passed
+... 85%, 592800 KB, 49847 KB/s, 11 seconds passed
+... 85%, 592832 KB, 49849 KB/s, 11 seconds passed
+... 85%, 592864 KB, 49850 KB/s, 11 seconds passed
+... 85%, 592896 KB, 49851 KB/s, 11 seconds passed
+... 85%, 592928 KB, 49853 KB/s, 11 seconds passed
+... 85%, 592960 KB, 49855 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 85%, 592992 KB, 49857 KB/s, 11 seconds passed
+... 85%, 593024 KB, 49858 KB/s, 11 seconds passed
+... 85%, 593056 KB, 49859 KB/s, 11 seconds passed
+... 85%, 593088 KB, 49861 KB/s, 11 seconds passed
+... 85%, 593120 KB, 49863 KB/s, 11 seconds passed
+... 85%, 593152 KB, 49864 KB/s, 11 seconds passed
+... 85%, 593184 KB, 49865 KB/s, 11 seconds passed
+... 85%, 593216 KB, 49867 KB/s, 11 seconds passed
+... 85%, 593248 KB, 49869 KB/s, 11 seconds passed
+... 85%, 593280 KB, 49870 KB/s, 11 seconds passed
+... 85%, 593312 KB, 49872 KB/s, 11 seconds passed
+... 85%, 593344 KB, 49873 KB/s, 11 seconds passed
+... 85%, 593376 KB, 49875 KB/s, 11 seconds passed
+... 85%, 593408 KB, 49876 KB/s, 11 seconds passed
+... 85%, 593440 KB, 49878 KB/s, 11 seconds passed
+... 85%, 593472 KB, 49879 KB/s, 11 seconds passed
+... 85%, 593504 KB, 49881 KB/s, 11 seconds passed
+... 85%, 593536 KB, 49882 KB/s, 11 seconds passed
+... 85%, 593568 KB, 49884 KB/s, 11 seconds passed
+... 85%, 593600 KB, 49885 KB/s, 11 seconds passed
+... 85%, 593632 KB, 49887 KB/s, 11 seconds passed
+... 85%, 593664 KB, 49888 KB/s, 11 seconds passed
+... 85%, 593696 KB, 49890 KB/s, 11 seconds passed
+... 85%, 593728 KB, 49891 KB/s, 11 seconds passed
+... 85%, 593760 KB, 49893 KB/s, 11 seconds passed
+... 85%, 593792 KB, 49894 KB/s, 11 seconds passed
+... 85%, 593824 KB, 49895 KB/s, 11 seconds passed
+... 85%, 593856 KB, 49897 KB/s, 11 seconds passed
+... 85%, 593888 KB, 49899 KB/s, 11 seconds passed
+... 85%, 593920 KB, 49744 KB/s, 11 seconds passed
+... 85%, 593952 KB, 49745 KB/s, 11 seconds passed
+... 85%, 593984 KB, 49745 KB/s, 11 seconds passed
+... 85%, 594016 KB, 49746 KB/s, 11 seconds passed
+... 85%, 594048 KB, 49744 KB/s, 11 seconds passed
+... 85%, 594080 KB, 49745 KB/s, 11 seconds passed
+... 85%, 594112 KB, 49746 KB/s, 11 seconds passed
+... 85%, 594144 KB, 49748 KB/s, 11 seconds passed
+... 85%, 594176 KB, 49749 KB/s, 11 seconds passed
+... 85%, 594208 KB, 49750 KB/s, 11 seconds passed
+... 85%, 594240 KB, 49752 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 85%, 594272 KB, 49744 KB/s, 11 seconds passed
+... 85%, 594304 KB, 49745 KB/s, 11 seconds passed
+... 85%, 594336 KB, 49747 KB/s, 11 seconds passed
+... 85%, 594368 KB, 49748 KB/s, 11 seconds passed
+... 85%, 594400 KB, 49750 KB/s, 11 seconds passed
+... 85%, 594432 KB, 49751 KB/s, 11 seconds passed
+... 85%, 594464 KB, 49752 KB/s, 11 seconds passed
+... 85%, 594496 KB, 49754 KB/s, 11 seconds passed
+... 85%, 594528 KB, 49755 KB/s, 11 seconds passed
+... 85%, 594560 KB, 49757 KB/s, 11 seconds passed
+... 85%, 594592 KB, 49758 KB/s, 11 seconds passed
+... 85%, 594624 KB, 49759 KB/s, 11 seconds passed
+... 85%, 594656 KB, 49761 KB/s, 11 seconds passed
+... 85%, 594688 KB, 49762 KB/s, 11 seconds passed
+... 85%, 594720 KB, 49763 KB/s, 11 seconds passed
+... 85%, 594752 KB, 49765 KB/s, 11 seconds passed
+... 85%, 594784 KB, 49766 KB/s, 11 seconds passed
+... 85%, 594816 KB, 49768 KB/s, 11 seconds passed
+... 85%, 594848 KB, 49769 KB/s, 11 seconds passed
+... 85%, 594880 KB, 49770 KB/s, 11 seconds passed
+... 85%, 594912 KB, 49772 KB/s, 11 seconds passed
+... 85%, 594944 KB, 49773 KB/s, 11 seconds passed
+... 85%, 594976 KB, 49775 KB/s, 11 seconds passed
+... 85%, 595008 KB, 49776 KB/s, 11 seconds passed
+... 85%, 595040 KB, 49777 KB/s, 11 seconds passed
+... 85%, 595072 KB, 49779 KB/s, 11 seconds passed
+... 85%, 595104 KB, 49780 KB/s, 11 seconds passed
+... 85%, 595136 KB, 49782 KB/s, 11 seconds passed
+... 85%, 595168 KB, 49783 KB/s, 11 seconds passed
+... 85%, 595200 KB, 49784 KB/s, 11 seconds passed
+... 85%, 595232 KB, 49786 KB/s, 11 seconds passed
+... 85%, 595264 KB, 49787 KB/s, 11 seconds passed
+... 85%, 595296 KB, 49789 KB/s, 11 seconds passed
+... 85%, 595328 KB, 49790 KB/s, 11 seconds passed
+... 85%, 595360 KB, 49792 KB/s, 11 seconds passed
+... 85%, 595392 KB, 49794 KB/s, 11 seconds passed
+... 85%, 595424 KB, 49795 KB/s, 11 seconds passed
+... 85%, 595456 KB, 49797 KB/s, 11 seconds passed
+... 85%, 595488 KB, 49799 KB/s, 11 seconds passed
+... 85%, 595520 KB, 49800 KB/s, 11 seconds passed
+... 85%, 595552 KB, 49802 KB/s, 11 seconds passed
+... 85%, 595584 KB, 49804 KB/s, 11 seconds passed
+... 85%, 595616 KB, 49805 KB/s, 11 seconds passed
+... 85%, 595648 KB, 49807 KB/s, 11 seconds passed
+... 85%, 595680 KB, 49809 KB/s, 11 seconds passed
+... 85%, 595712 KB, 49810 KB/s, 11 seconds passed
+... 85%, 595744 KB, 49812 KB/s, 11 seconds passed
+... 85%, 595776 KB, 49814 KB/s, 11 seconds passed
+... 85%, 595808 KB, 49815 KB/s, 11 seconds passed
+... 85%, 595840 KB, 49817 KB/s, 11 seconds passed
+... 85%, 595872 KB, 49819 KB/s, 11 seconds passed
+... 85%, 595904 KB, 49820 KB/s, 11 seconds passed
+... 85%, 595936 KB, 49822 KB/s, 11 seconds passed
+... 85%, 595968 KB, 49824 KB/s, 11 seconds passed
+... 85%, 596000 KB, 49825 KB/s, 11 seconds passed
+... 85%, 596032 KB, 49827 KB/s, 11 seconds passed
+... 85%, 596064 KB, 49829 KB/s, 11 seconds passed
+... 85%, 596096 KB, 49830 KB/s, 11 seconds passed
+... 85%, 596128 KB, 49832 KB/s, 11 seconds passed
+... 85%, 596160 KB, 49834 KB/s, 11 seconds passed
+... 85%, 596192 KB, 49835 KB/s, 11 seconds passed
+... 85%, 596224 KB, 49837 KB/s, 11 seconds passed
+... 85%, 596256 KB, 49838 KB/s, 11 seconds passed
+... 85%, 596288 KB, 49840 KB/s, 11 seconds passed
+... 85%, 596320 KB, 49841 KB/s, 11 seconds passed
+... 85%, 596352 KB, 49842 KB/s, 11 seconds passed
+... 85%, 596384 KB, 49844 KB/s, 11 seconds passed
+... 85%, 596416 KB, 49846 KB/s, 11 seconds passed
+... 85%, 596448 KB, 49847 KB/s, 11 seconds passed
+... 85%, 596480 KB, 49849 KB/s, 11 seconds passed
+... 85%, 596512 KB, 49851 KB/s, 11 seconds passed
+... 85%, 596544 KB, 49852 KB/s, 11 seconds passed
+... 85%, 596576 KB, 49854 KB/s, 11 seconds passed
+... 85%, 596608 KB, 49855 KB/s, 11 seconds passed
+... 85%, 596640 KB, 49857 KB/s, 11 seconds passed
+... 85%, 596672 KB, 49858 KB/s, 11 seconds passed
+... 85%, 596704 KB, 49859 KB/s, 11 seconds passed
+... 85%, 596736 KB, 49861 KB/s, 11 seconds passed
+... 85%, 596768 KB, 49863 KB/s, 11 seconds passed
+... 85%, 596800 KB, 49864 KB/s, 11 seconds passed
+... 85%, 596832 KB, 49865 KB/s, 11 seconds passed
+... 85%, 596864 KB, 49867 KB/s, 11 seconds passed
+... 85%, 596896 KB, 49868 KB/s, 11 seconds passed
+... 85%, 596928 KB, 49870 KB/s, 11 seconds passed
+... 85%, 596960 KB, 49872 KB/s, 11 seconds passed
+... 85%, 596992 KB, 49873 KB/s, 11 seconds passed
+... 85%, 597024 KB, 49874 KB/s, 11 seconds passed
+... 85%, 597056 KB, 49876 KB/s, 11 seconds passed
+... 85%, 597088 KB, 49878 KB/s, 11 seconds passed
+... 85%, 597120 KB, 49879 KB/s, 11 seconds passed
+... 85%, 597152 KB, 49881 KB/s, 11 seconds passed
+... 85%, 597184 KB, 49882 KB/s, 11 seconds passed
+... 85%, 597216 KB, 49884 KB/s, 11 seconds passed
+... 85%, 597248 KB, 49885 KB/s, 11 seconds passed
+... 85%, 597280 KB, 49887 KB/s, 11 seconds passed
+... 85%, 597312 KB, 49888 KB/s, 11 seconds passed
+... 85%, 597344 KB, 49890 KB/s, 11 seconds passed
+... 85%, 597376 KB, 49891 KB/s, 11 seconds passed
+... 85%, 597408 KB, 49893 KB/s, 11 seconds passed
+... 85%, 597440 KB, 49894 KB/s, 11 seconds passed
+... 85%, 597472 KB, 49896 KB/s, 11 seconds passed
+... 85%, 597504 KB, 49897 KB/s, 11 seconds passed
+... 85%, 597536 KB, 49899 KB/s, 11 seconds passed
+... 85%, 597568 KB, 49900 KB/s, 11 seconds passed
+... 85%, 597600 KB, 49902 KB/s, 11 seconds passed
+... 85%, 597632 KB, 49903 KB/s, 11 seconds passed
+... 85%, 597664 KB, 49905 KB/s, 11 seconds passed
+... 85%, 597696 KB, 49906 KB/s, 11 seconds passed
+... 85%, 597728 KB, 49908 KB/s, 11 seconds passed
+... 85%, 597760 KB, 49909 KB/s, 11 seconds passed
+... 85%, 597792 KB, 49911 KB/s, 11 seconds passed
+... 85%, 597824 KB, 49912 KB/s, 11 seconds passed
+... 85%, 597856 KB, 49912 KB/s, 11 seconds passed
+... 85%, 597888 KB, 49914 KB/s, 11 seconds passed
+... 85%, 597920 KB, 49916 KB/s, 11 seconds passed
+... 85%, 597952 KB, 49917 KB/s, 11 seconds passed
+... 85%, 597984 KB, 49918 KB/s, 11 seconds passed
+... 85%, 598016 KB, 49920 KB/s, 11 seconds passed
+... 85%, 598048 KB, 49922 KB/s, 11 seconds passed
+... 85%, 598080 KB, 49923 KB/s, 11 seconds passed
+... 85%, 598112 KB, 49915 KB/s, 11 seconds passed
+... 86%, 598144 KB, 49917 KB/s, 11 seconds passed
+... 86%, 598176 KB, 49918 KB/s, 11 seconds passed
+... 86%, 598208 KB, 49920 KB/s, 11 seconds passed
+... 86%, 598240 KB, 49921 KB/s, 11 seconds passed
+... 86%, 598272 KB, 49923 KB/s, 11 seconds passed
+... 86%, 598304 KB, 49924 KB/s, 11 seconds passed
+... 86%, 598336 KB, 49926 KB/s, 11 seconds passed
+... 86%, 598368 KB, 49927 KB/s, 11 seconds passed
+... 86%, 598400 KB, 49929 KB/s, 11 seconds passed
+... 86%, 598432 KB, 49931 KB/s, 11 seconds passed
+... 86%, 598464 KB, 49932 KB/s, 11 seconds passed
+... 86%, 598496 KB, 49933 KB/s, 11 seconds passed
+... 86%, 598528 KB, 49935 KB/s, 11 seconds passed
+... 86%, 598560 KB, 49937 KB/s, 11 seconds passed
+... 86%, 598592 KB, 49938 KB/s, 11 seconds passed
+... 86%, 598624 KB, 49939 KB/s, 11 seconds passed
+... 86%, 598656 KB, 49941 KB/s, 11 seconds passed
+... 86%, 598688 KB, 49943 KB/s, 11 seconds passed
+... 86%, 598720 KB, 49944 KB/s, 11 seconds passed
+... 86%, 598752 KB, 49945 KB/s, 11 seconds passed
+... 86%, 598784 KB, 49947 KB/s, 11 seconds passed
+... 86%, 598816 KB, 49949 KB/s, 11 seconds passed
+... 86%, 598848 KB, 49950 KB/s, 11 seconds passed
+... 86%, 598880 KB, 49951 KB/s, 11 seconds passed
+... 86%, 598912 KB, 49953 KB/s, 11 seconds passed
+... 86%, 598944 KB, 49955 KB/s, 11 seconds passed
+... 86%, 598976 KB, 49956 KB/s, 11 seconds passed
+... 86%, 599008 KB, 49958 KB/s, 11 seconds passed
+
+.. parsed-literal::
+
+    ... 86%, 599040 KB, 49825 KB/s, 12 seconds passed
+... 86%, 599072 KB, 49825 KB/s, 12 seconds passed
+... 86%, 599104 KB, 49826 KB/s, 12 seconds passed
+... 86%, 599136 KB, 49828 KB/s, 12 seconds passed
+... 86%, 599168 KB, 49829 KB/s, 12 seconds passed
+... 86%, 599200 KB, 49831 KB/s, 12 seconds passed
+... 86%, 599232 KB, 49832 KB/s, 12 seconds passed
+... 86%, 599264 KB, 49833 KB/s, 12 seconds passed
+... 86%, 599296 KB, 49835 KB/s, 12 seconds passed
+... 86%, 599328 KB, 49836 KB/s, 12 seconds passed
+... 86%, 599360 KB, 49837 KB/s, 12 seconds passed
+... 86%, 599392 KB, 49839 KB/s, 12 seconds passed
+... 86%, 599424 KB, 49829 KB/s, 12 seconds passed
+... 86%, 599456 KB, 49830 KB/s, 12 seconds passed
+... 86%, 599488 KB, 49831 KB/s, 12 seconds passed
+... 86%, 599520 KB, 49832 KB/s, 12 seconds passed
+... 86%, 599552 KB, 49833 KB/s, 12 seconds passed
+... 86%, 599584 KB, 49835 KB/s, 12 seconds passed
+... 86%, 599616 KB, 49836 KB/s, 12 seconds passed
+... 86%, 599648 KB, 49837 KB/s, 12 seconds passed
+... 86%, 599680 KB, 49839 KB/s, 12 seconds passed
+... 86%, 599712 KB, 49840 KB/s, 12 seconds passed
+... 86%, 599744 KB, 49842 KB/s, 12 seconds passed
+... 86%, 599776 KB, 49843 KB/s, 12 seconds passed
+... 86%, 599808 KB, 49844 KB/s, 12 seconds passed
+... 86%, 599840 KB, 49846 KB/s, 12 seconds passed
+... 86%, 599872 KB, 49847 KB/s, 12 seconds passed
+... 86%, 599904 KB, 49849 KB/s, 12 seconds passed
+... 86%, 599936 KB, 49850 KB/s, 12 seconds passed
+... 86%, 599968 KB, 49852 KB/s, 12 seconds passed
+... 86%, 600000 KB, 49853 KB/s, 12 seconds passed
+... 86%, 600032 KB, 49854 KB/s, 12 seconds passed
+... 86%, 600064 KB, 49855 KB/s, 12 seconds passed
+... 86%, 600096 KB, 49857 KB/s, 12 seconds passed
+... 86%, 600128 KB, 49858 KB/s, 12 seconds passed
+... 86%, 600160 KB, 49860 KB/s, 12 seconds passed
+... 86%, 600192 KB, 49861 KB/s, 12 seconds passed
+... 86%, 600224 KB, 49863 KB/s, 12 seconds passed
+... 86%, 600256 KB, 49864 KB/s, 12 seconds passed
+... 86%, 600288 KB, 49866 KB/s, 12 seconds passed
+... 86%, 600320 KB, 49867 KB/s, 12 seconds passed
+... 86%, 600352 KB, 49869 KB/s, 12 seconds passed
+... 86%, 600384 KB, 49851 KB/s, 12 seconds passed
+... 86%, 600416 KB, 49852 KB/s, 12 seconds passed
+... 86%, 600448 KB, 49853 KB/s, 12 seconds passed
+... 86%, 600480 KB, 49855 KB/s, 12 seconds passed
+... 86%, 600512 KB, 49856 KB/s, 12 seconds passed
+... 86%, 600544 KB, 49858 KB/s, 12 seconds passed
+... 86%, 600576 KB, 49859 KB/s, 12 seconds passed
+... 86%, 600608 KB, 49861 KB/s, 12 seconds passed
+... 86%, 600640 KB, 49862 KB/s, 12 seconds passed
+... 86%, 600672 KB, 49864 KB/s, 12 seconds passed
+... 86%, 600704 KB, 49865 KB/s, 12 seconds passed
+... 86%, 600736 KB, 49867 KB/s, 12 seconds passed
+... 86%, 600768 KB, 49868 KB/s, 12 seconds passed
+... 86%, 600800 KB, 49869 KB/s, 12 seconds passed
+... 86%, 600832 KB, 49870 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 86%, 600864 KB, 49872 KB/s, 12 seconds passed
+... 86%, 600896 KB, 49873 KB/s, 12 seconds passed
+... 86%, 600928 KB, 49875 KB/s, 12 seconds passed
+... 86%, 600960 KB, 49876 KB/s, 12 seconds passed
+... 86%, 600992 KB, 49877 KB/s, 12 seconds passed
+... 86%, 601024 KB, 49878 KB/s, 12 seconds passed
+... 86%, 601056 KB, 49880 KB/s, 12 seconds passed
+... 86%, 601088 KB, 49881 KB/s, 12 seconds passed
+... 86%, 601120 KB, 49883 KB/s, 12 seconds passed
+... 86%, 601152 KB, 49884 KB/s, 12 seconds passed
+... 86%, 601184 KB, 49885 KB/s, 12 seconds passed
+... 86%, 601216 KB, 49887 KB/s, 12 seconds passed
+... 86%, 601248 KB, 49888 KB/s, 12 seconds passed
+... 86%, 601280 KB, 49889 KB/s, 12 seconds passed
+... 86%, 601312 KB, 49891 KB/s, 12 seconds passed
+... 86%, 601344 KB, 49892 KB/s, 12 seconds passed
+... 86%, 601376 KB, 49894 KB/s, 12 seconds passed
+... 86%, 601408 KB, 49895 KB/s, 12 seconds passed
+... 86%, 601440 KB, 49896 KB/s, 12 seconds passed
+... 86%, 601472 KB, 49898 KB/s, 12 seconds passed
+... 86%, 601504 KB, 49899 KB/s, 12 seconds passed
+... 86%, 601536 KB, 49901 KB/s, 12 seconds passed
+... 86%, 601568 KB, 49902 KB/s, 12 seconds passed
+... 86%, 601600 KB, 49903 KB/s, 12 seconds passed
+... 86%, 601632 KB, 49905 KB/s, 12 seconds passed
+... 86%, 601664 KB, 49906 KB/s, 12 seconds passed
+... 86%, 601696 KB, 49907 KB/s, 12 seconds passed
+... 86%, 601728 KB, 49909 KB/s, 12 seconds passed
+... 86%, 601760 KB, 49910 KB/s, 12 seconds passed
+... 86%, 601792 KB, 49912 KB/s, 12 seconds passed
+... 86%, 601824 KB, 49914 KB/s, 12 seconds passed
+... 86%, 601856 KB, 49916 KB/s, 12 seconds passed
+... 86%, 601888 KB, 49918 KB/s, 12 seconds passed
+... 86%, 601920 KB, 49919 KB/s, 12 seconds passed
+... 86%, 601952 KB, 49921 KB/s, 12 seconds passed
+... 86%, 601984 KB, 49923 KB/s, 12 seconds passed
+... 86%, 602016 KB, 49925 KB/s, 12 seconds passed
+... 86%, 602048 KB, 49926 KB/s, 12 seconds passed
+... 86%, 602080 KB, 49928 KB/s, 12 seconds passed
+... 86%, 602112 KB, 49930 KB/s, 12 seconds passed
+... 86%, 602144 KB, 49932 KB/s, 12 seconds passed
+... 86%, 602176 KB, 49933 KB/s, 12 seconds passed
+... 86%, 602208 KB, 49935 KB/s, 12 seconds passed
+... 86%, 602240 KB, 49937 KB/s, 12 seconds passed
+... 86%, 602272 KB, 49939 KB/s, 12 seconds passed
+... 86%, 602304 KB, 49941 KB/s, 12 seconds passed
+... 86%, 602336 KB, 49943 KB/s, 12 seconds passed
+... 86%, 602368 KB, 49944 KB/s, 12 seconds passed
+... 86%, 602400 KB, 49946 KB/s, 12 seconds passed
+... 86%, 602432 KB, 49948 KB/s, 12 seconds passed
+... 86%, 602464 KB, 49949 KB/s, 12 seconds passed
+... 86%, 602496 KB, 49951 KB/s, 12 seconds passed
+... 86%, 602528 KB, 49952 KB/s, 12 seconds passed
+... 86%, 602560 KB, 49953 KB/s, 12 seconds passed
+... 86%, 602592 KB, 49955 KB/s, 12 seconds passed
+... 86%, 602624 KB, 49956 KB/s, 12 seconds passed
+... 86%, 602656 KB, 49958 KB/s, 12 seconds passed
+... 86%, 602688 KB, 49960 KB/s, 12 seconds passed
+... 86%, 602720 KB, 49961 KB/s, 12 seconds passed
+... 86%, 602752 KB, 49963 KB/s, 12 seconds passed
+... 86%, 602784 KB, 49964 KB/s, 12 seconds passed
+... 86%, 602816 KB, 49965 KB/s, 12 seconds passed
+... 86%, 602848 KB, 49967 KB/s, 12 seconds passed
+... 86%, 602880 KB, 49968 KB/s, 12 seconds passed
+... 86%, 602912 KB, 49970 KB/s, 12 seconds passed
+... 86%, 602944 KB, 49971 KB/s, 12 seconds passed
+... 86%, 602976 KB, 49973 KB/s, 12 seconds passed
+... 86%, 603008 KB, 49974 KB/s, 12 seconds passed
+... 86%, 603040 KB, 49976 KB/s, 12 seconds passed
+... 86%, 603072 KB, 49977 KB/s, 12 seconds passed
+... 86%, 603104 KB, 49979 KB/s, 12 seconds passed
+... 86%, 603136 KB, 49980 KB/s, 12 seconds passed
+... 86%, 603168 KB, 49982 KB/s, 12 seconds passed
+... 86%, 603200 KB, 49983 KB/s, 12 seconds passed
+... 86%, 603232 KB, 49985 KB/s, 12 seconds passed
+... 86%, 603264 KB, 49986 KB/s, 12 seconds passed
+... 86%, 603296 KB, 49988 KB/s, 12 seconds passed
+... 86%, 603328 KB, 49989 KB/s, 12 seconds passed
+... 86%, 603360 KB, 49991 KB/s, 12 seconds passed
+... 86%, 603392 KB, 49993 KB/s, 12 seconds passed
+... 86%, 603424 KB, 49994 KB/s, 12 seconds passed
+... 86%, 603456 KB, 49996 KB/s, 12 seconds passed
+... 86%, 603488 KB, 49997 KB/s, 12 seconds passed
+... 86%, 603520 KB, 49999 KB/s, 12 seconds passed
+... 86%, 603552 KB, 50000 KB/s, 12 seconds passed
+... 86%, 603584 KB, 50001 KB/s, 12 seconds passed
+... 86%, 603616 KB, 50003 KB/s, 12 seconds passed
+... 86%, 603648 KB, 50004 KB/s, 12 seconds passed
+... 86%, 603680 KB, 50006 KB/s, 12 seconds passed
+... 86%, 603712 KB, 50007 KB/s, 12 seconds passed
+... 86%, 603744 KB, 50009 KB/s, 12 seconds passed
+... 86%, 603776 KB, 50010 KB/s, 12 seconds passed
+... 86%, 603808 KB, 50012 KB/s, 12 seconds passed
+... 86%, 603840 KB, 50013 KB/s, 12 seconds passed
+... 86%, 603872 KB, 50015 KB/s, 12 seconds passed
+... 86%, 603904 KB, 50016 KB/s, 12 seconds passed
+... 86%, 603936 KB, 50018 KB/s, 12 seconds passed
+... 86%, 603968 KB, 50019 KB/s, 12 seconds passed
+... 86%, 604000 KB, 50021 KB/s, 12 seconds passed
+... 86%, 604032 KB, 50022 KB/s, 12 seconds passed
+... 86%, 604064 KB, 50024 KB/s, 12 seconds passed
+... 86%, 604096 KB, 50025 KB/s, 12 seconds passed
+... 86%, 604128 KB, 50027 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 86%, 604160 KB, 49798 KB/s, 12 seconds passed
+... 86%, 604192 KB, 49799 KB/s, 12 seconds passed
+... 86%, 604224 KB, 49799 KB/s, 12 seconds passed
+... 86%, 604256 KB, 49801 KB/s, 12 seconds passed
+... 86%, 604288 KB, 49797 KB/s, 12 seconds passed
+... 86%, 604320 KB, 49798 KB/s, 12 seconds passed
+... 86%, 604352 KB, 49800 KB/s, 12 seconds passed
+... 86%, 604384 KB, 49799 KB/s, 12 seconds passed
+... 86%, 604416 KB, 49801 KB/s, 12 seconds passed
+... 86%, 604448 KB, 49802 KB/s, 12 seconds passed
+... 86%, 604480 KB, 49803 KB/s, 12 seconds passed
+... 86%, 604512 KB, 49805 KB/s, 12 seconds passed
+... 86%, 604544 KB, 49804 KB/s, 12 seconds passed
+... 86%, 604576 KB, 49805 KB/s, 12 seconds passed
+... 86%, 604608 KB, 49806 KB/s, 12 seconds passed
+... 86%, 604640 KB, 49808 KB/s, 12 seconds passed
+... 86%, 604672 KB, 49809 KB/s, 12 seconds passed
+... 86%, 604704 KB, 49810 KB/s, 12 seconds passed
+... 86%, 604736 KB, 49812 KB/s, 12 seconds passed
+... 86%, 604768 KB, 49813 KB/s, 12 seconds passed
+... 86%, 604800 KB, 49814 KB/s, 12 seconds passed
+... 86%, 604832 KB, 49816 KB/s, 12 seconds passed
+... 86%, 604864 KB, 49817 KB/s, 12 seconds passed
+... 86%, 604896 KB, 49819 KB/s, 12 seconds passed
+... 86%, 604928 KB, 49820 KB/s, 12 seconds passed
+... 86%, 604960 KB, 49821 KB/s, 12 seconds passed
+... 86%, 604992 KB, 49823 KB/s, 12 seconds passed
+... 86%, 605024 KB, 49824 KB/s, 12 seconds passed
+... 86%, 605056 KB, 49821 KB/s, 12 seconds passed
+... 86%, 605088 KB, 49823 KB/s, 12 seconds passed
+... 87%, 605120 KB, 49824 KB/s, 12 seconds passed
+... 87%, 605152 KB, 49826 KB/s, 12 seconds passed
+... 87%, 605184 KB, 49827 KB/s, 12 seconds passed
+... 87%, 605216 KB, 49828 KB/s, 12 seconds passed
+... 87%, 605248 KB, 49820 KB/s, 12 seconds passed
+... 87%, 605280 KB, 49821 KB/s, 12 seconds passed
+... 87%, 605312 KB, 49823 KB/s, 12 seconds passed
+... 87%, 605344 KB, 49824 KB/s, 12 seconds passed
+... 87%, 605376 KB, 49824 KB/s, 12 seconds passed
+... 87%, 605408 KB, 49826 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 87%, 605440 KB, 49827 KB/s, 12 seconds passed
+... 87%, 605472 KB, 49829 KB/s, 12 seconds passed
+... 87%, 605504 KB, 49830 KB/s, 12 seconds passed
+... 87%, 605536 KB, 49832 KB/s, 12 seconds passed
+... 87%, 605568 KB, 49833 KB/s, 12 seconds passed
+... 87%, 605600 KB, 49834 KB/s, 12 seconds passed
+... 87%, 605632 KB, 49836 KB/s, 12 seconds passed
+... 87%, 605664 KB, 49837 KB/s, 12 seconds passed
+... 87%, 605696 KB, 49839 KB/s, 12 seconds passed
+... 87%, 605728 KB, 49840 KB/s, 12 seconds passed
+... 87%, 605760 KB, 49842 KB/s, 12 seconds passed
+... 87%, 605792 KB, 49843 KB/s, 12 seconds passed
+... 87%, 605824 KB, 49844 KB/s, 12 seconds passed
+... 87%, 605856 KB, 49846 KB/s, 12 seconds passed
+... 87%, 605888 KB, 49847 KB/s, 12 seconds passed
+... 87%, 605920 KB, 49849 KB/s, 12 seconds passed
+... 87%, 605952 KB, 49850 KB/s, 12 seconds passed
+... 87%, 605984 KB, 49852 KB/s, 12 seconds passed
+... 87%, 606016 KB, 49853 KB/s, 12 seconds passed
+... 87%, 606048 KB, 49854 KB/s, 12 seconds passed
+... 87%, 606080 KB, 49856 KB/s, 12 seconds passed
+... 87%, 606112 KB, 49857 KB/s, 12 seconds passed
+... 87%, 606144 KB, 49859 KB/s, 12 seconds passed
+... 87%, 606176 KB, 49860 KB/s, 12 seconds passed
+... 87%, 606208 KB, 49862 KB/s, 12 seconds passed
+... 87%, 606240 KB, 49863 KB/s, 12 seconds passed
+... 87%, 606272 KB, 49865 KB/s, 12 seconds passed
+... 87%, 606304 KB, 49866 KB/s, 12 seconds passed
+... 87%, 606336 KB, 49864 KB/s, 12 seconds passed
+... 87%, 606368 KB, 49865 KB/s, 12 seconds passed
+... 87%, 606400 KB, 49867 KB/s, 12 seconds passed
+... 87%, 606432 KB, 49868 KB/s, 12 seconds passed
+... 87%, 606464 KB, 49867 KB/s, 12 seconds passed
+... 87%, 606496 KB, 49869 KB/s, 12 seconds passed
+... 87%, 606528 KB, 49870 KB/s, 12 seconds passed
+... 87%, 606560 KB, 49872 KB/s, 12 seconds passed
+... 87%, 606592 KB, 49874 KB/s, 12 seconds passed
+... 87%, 606624 KB, 49875 KB/s, 12 seconds passed
+... 87%, 606656 KB, 49876 KB/s, 12 seconds passed
+... 87%, 606688 KB, 49878 KB/s, 12 seconds passed
+... 87%, 606720 KB, 49879 KB/s, 12 seconds passed
+... 87%, 606752 KB, 49881 KB/s, 12 seconds passed
+... 87%, 606784 KB, 49882 KB/s, 12 seconds passed
+... 87%, 606816 KB, 49884 KB/s, 12 seconds passed
+... 87%, 606848 KB, 49885 KB/s, 12 seconds passed
+... 87%, 606880 KB, 49886 KB/s, 12 seconds passed
+... 87%, 606912 KB, 49888 KB/s, 12 seconds passed
+... 87%, 606944 KB, 49889 KB/s, 12 seconds passed
+... 87%, 606976 KB, 49891 KB/s, 12 seconds passed
+... 87%, 607008 KB, 49892 KB/s, 12 seconds passed
+... 87%, 607040 KB, 49894 KB/s, 12 seconds passed
+... 87%, 607072 KB, 49895 KB/s, 12 seconds passed
+... 87%, 607104 KB, 49896 KB/s, 12 seconds passed
+... 87%, 607136 KB, 49898 KB/s, 12 seconds passed
+... 87%, 607168 KB, 49900 KB/s, 12 seconds passed
+... 87%, 607200 KB, 49901 KB/s, 12 seconds passed
+... 87%, 607232 KB, 49902 KB/s, 12 seconds passed
+... 87%, 607264 KB, 49904 KB/s, 12 seconds passed
+... 87%, 607296 KB, 49905 KB/s, 12 seconds passed
+... 87%, 607328 KB, 49907 KB/s, 12 seconds passed
+... 87%, 607360 KB, 49908 KB/s, 12 seconds passed
+... 87%, 607392 KB, 49910 KB/s, 12 seconds passed
+... 87%, 607424 KB, 49912 KB/s, 12 seconds passed
+... 87%, 607456 KB, 49913 KB/s, 12 seconds passed
+... 87%, 607488 KB, 49914 KB/s, 12 seconds passed
+... 87%, 607520 KB, 49916 KB/s, 12 seconds passed
+... 87%, 607552 KB, 49917 KB/s, 12 seconds passed
+... 87%, 607584 KB, 49919 KB/s, 12 seconds passed
+... 87%, 607616 KB, 49920 KB/s, 12 seconds passed
+... 87%, 607648 KB, 49922 KB/s, 12 seconds passed
+... 87%, 607680 KB, 49923 KB/s, 12 seconds passed
+... 87%, 607712 KB, 49924 KB/s, 12 seconds passed
+... 87%, 607744 KB, 49926 KB/s, 12 seconds passed
+... 87%, 607776 KB, 49927 KB/s, 12 seconds passed
+... 87%, 607808 KB, 49928 KB/s, 12 seconds passed
+... 87%, 607840 KB, 49930 KB/s, 12 seconds passed
+... 87%, 607872 KB, 49931 KB/s, 12 seconds passed
+... 87%, 607904 KB, 49933 KB/s, 12 seconds passed
+... 87%, 607936 KB, 49935 KB/s, 12 seconds passed
+... 87%, 607968 KB, 49937 KB/s, 12 seconds passed
+... 87%, 608000 KB, 49938 KB/s, 12 seconds passed
+... 87%, 608032 KB, 49940 KB/s, 12 seconds passed
+... 87%, 608064 KB, 49941 KB/s, 12 seconds passed
+... 87%, 608096 KB, 49943 KB/s, 12 seconds passed
+... 87%, 608128 KB, 49944 KB/s, 12 seconds passed
+... 87%, 608160 KB, 49945 KB/s, 12 seconds passed
+... 87%, 608192 KB, 49947 KB/s, 12 seconds passed
+... 87%, 608224 KB, 49948 KB/s, 12 seconds passed
+... 87%, 608256 KB, 49950 KB/s, 12 seconds passed
+... 87%, 608288 KB, 49951 KB/s, 12 seconds passed
+... 87%, 608320 KB, 49953 KB/s, 12 seconds passed
+... 87%, 608352 KB, 49954 KB/s, 12 seconds passed
+... 87%, 608384 KB, 49956 KB/s, 12 seconds passed
+... 87%, 608416 KB, 49958 KB/s, 12 seconds passed
+... 87%, 608448 KB, 49953 KB/s, 12 seconds passed
+... 87%, 608480 KB, 49954 KB/s, 12 seconds passed
+... 87%, 608512 KB, 49956 KB/s, 12 seconds passed
+... 87%, 608544 KB, 49957 KB/s, 12 seconds passed
+... 87%, 608576 KB, 49959 KB/s, 12 seconds passed
+... 87%, 608608 KB, 49960 KB/s, 12 seconds passed
+... 87%, 608640 KB, 49962 KB/s, 12 seconds passed
+... 87%, 608672 KB, 49963 KB/s, 12 seconds passed
+... 87%, 608704 KB, 49965 KB/s, 12 seconds passed
+... 87%, 608736 KB, 49966 KB/s, 12 seconds passed
+... 87%, 608768 KB, 49967 KB/s, 12 seconds passed
+... 87%, 608800 KB, 49969 KB/s, 12 seconds passed
+... 87%, 608832 KB, 49970 KB/s, 12 seconds passed
+... 87%, 608864 KB, 49972 KB/s, 12 seconds passed
+... 87%, 608896 KB, 49974 KB/s, 12 seconds passed
+... 87%, 608928 KB, 49975 KB/s, 12 seconds passed
+... 87%, 608960 KB, 49977 KB/s, 12 seconds passed
+... 87%, 608992 KB, 49978 KB/s, 12 seconds passed
+... 87%, 609024 KB, 49980 KB/s, 12 seconds passed
+... 87%, 609056 KB, 49981 KB/s, 12 seconds passed
+... 87%, 609088 KB, 49982 KB/s, 12 seconds passed
+... 87%, 609120 KB, 49984 KB/s, 12 seconds passed
+... 87%, 609152 KB, 49986 KB/s, 12 seconds passed
+... 87%, 609184 KB, 49987 KB/s, 12 seconds passed
+... 87%, 609216 KB, 49988 KB/s, 12 seconds passed
+... 87%, 609248 KB, 49989 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 87%, 609280 KB, 49721 KB/s, 12 seconds passed
+... 87%, 609312 KB, 49721 KB/s, 12 seconds passed
+... 87%, 609344 KB, 49722 KB/s, 12 seconds passed
+... 87%, 609376 KB, 49723 KB/s, 12 seconds passed
+... 87%, 609408 KB, 49725 KB/s, 12 seconds passed
+... 87%, 609440 KB, 49721 KB/s, 12 seconds passed
+... 87%, 609472 KB, 49721 KB/s, 12 seconds passed
+... 87%, 609504 KB, 49723 KB/s, 12 seconds passed
+... 87%, 609536 KB, 49724 KB/s, 12 seconds passed
+... 87%, 609568 KB, 49725 KB/s, 12 seconds passed
+... 87%, 609600 KB, 49727 KB/s, 12 seconds passed
+... 87%, 609632 KB, 49728 KB/s, 12 seconds passed
+... 87%, 609664 KB, 49729 KB/s, 12 seconds passed
+... 87%, 609696 KB, 49731 KB/s, 12 seconds passed
+... 87%, 609728 KB, 49732 KB/s, 12 seconds passed
+... 87%, 609760 KB, 49733 KB/s, 12 seconds passed
+... 87%, 609792 KB, 49735 KB/s, 12 seconds passed
+... 87%, 609824 KB, 49736 KB/s, 12 seconds passed
+... 87%, 609856 KB, 49737 KB/s, 12 seconds passed
+... 87%, 609888 KB, 49739 KB/s, 12 seconds passed
+... 87%, 609920 KB, 49740 KB/s, 12 seconds passed
+... 87%, 609952 KB, 49742 KB/s, 12 seconds passed
+... 87%, 609984 KB, 49743 KB/s, 12 seconds passed
+... 87%, 610016 KB, 49744 KB/s, 12 seconds passed
+... 87%, 610048 KB, 49746 KB/s, 12 seconds passed
+... 87%, 610080 KB, 49747 KB/s, 12 seconds passed
+... 87%, 610112 KB, 49748 KB/s, 12 seconds passed
+... 87%, 610144 KB, 49750 KB/s, 12 seconds passed
+... 87%, 610176 KB, 49751 KB/s, 12 seconds passed
+... 87%, 610208 KB, 49753 KB/s, 12 seconds passed
+... 87%, 610240 KB, 49754 KB/s, 12 seconds passed
+... 87%, 610272 KB, 49755 KB/s, 12 seconds passed
+... 87%, 610304 KB, 49757 KB/s, 12 seconds passed
+... 87%, 610336 KB, 49758 KB/s, 12 seconds passed
+... 87%, 610368 KB, 49759 KB/s, 12 seconds passed
+... 87%, 610400 KB, 49761 KB/s, 12 seconds passed
+... 87%, 610432 KB, 49762 KB/s, 12 seconds passed
+... 87%, 610464 KB, 49763 KB/s, 12 seconds passed
+... 87%, 610496 KB, 49765 KB/s, 12 seconds passed
+... 87%, 610528 KB, 49766 KB/s, 12 seconds passed
+... 87%, 610560 KB, 49767 KB/s, 12 seconds passed
+... 87%, 610592 KB, 49769 KB/s, 12 seconds passed
+... 87%, 610624 KB, 49770 KB/s, 12 seconds passed
+... 87%, 610656 KB, 49772 KB/s, 12 seconds passed
+... 87%, 610688 KB, 49774 KB/s, 12 seconds passed
+... 87%, 610720 KB, 49776 KB/s, 12 seconds passed
+... 87%, 610752 KB, 49777 KB/s, 12 seconds passed
+... 87%, 610784 KB, 49779 KB/s, 12 seconds passed
+... 87%, 610816 KB, 49781 KB/s, 12 seconds passed
+... 87%, 610848 KB, 49783 KB/s, 12 seconds passed
+... 87%, 610880 KB, 49784 KB/s, 12 seconds passed
+... 87%, 610912 KB, 49786 KB/s, 12 seconds passed
+... 87%, 610944 KB, 49788 KB/s, 12 seconds passed
+... 87%, 610976 KB, 49790 KB/s, 12 seconds passed
+... 87%, 611008 KB, 49791 KB/s, 12 seconds passed
+... 87%, 611040 KB, 49793 KB/s, 12 seconds passed
+... 87%, 611072 KB, 49795 KB/s, 12 seconds passed
+... 87%, 611104 KB, 49796 KB/s, 12 seconds passed
+... 87%, 611136 KB, 49798 KB/s, 12 seconds passed
+... 87%, 611168 KB, 49799 KB/s, 12 seconds passed
+... 87%, 611200 KB, 49801 KB/s, 12 seconds passed
+... 87%, 611232 KB, 49802 KB/s, 12 seconds passed
+... 87%, 611264 KB, 49804 KB/s, 12 seconds passed
+... 87%, 611296 KB, 49805 KB/s, 12 seconds passed
+... 87%, 611328 KB, 49807 KB/s, 12 seconds passed
+... 87%, 611360 KB, 49808 KB/s, 12 seconds passed
+... 87%, 611392 KB, 49809 KB/s, 12 seconds passed
+... 87%, 611424 KB, 49811 KB/s, 12 seconds passed
+... 87%, 611456 KB, 49812 KB/s, 12 seconds passed
+... 87%, 611488 KB, 49814 KB/s, 12 seconds passed
+... 87%, 611520 KB, 49816 KB/s, 12 seconds passed
+... 87%, 611552 KB, 49817 KB/s, 12 seconds passed
+... 87%, 611584 KB, 49818 KB/s, 12 seconds passed
+... 87%, 611616 KB, 49820 KB/s, 12 seconds passed
+... 87%, 611648 KB, 49821 KB/s, 12 seconds passed
+... 87%, 611680 KB, 49823 KB/s, 12 seconds passed
+... 87%, 611712 KB, 49824 KB/s, 12 seconds passed
+... 87%, 611744 KB, 49826 KB/s, 12 seconds passed
+... 87%, 611776 KB, 49827 KB/s, 12 seconds passed
+... 87%, 611808 KB, 49829 KB/s, 12 seconds passed
+... 87%, 611840 KB, 49830 KB/s, 12 seconds passed
+... 87%, 611872 KB, 49832 KB/s, 12 seconds passed
+... 87%, 611904 KB, 49833 KB/s, 12 seconds passed
+... 87%, 611936 KB, 49835 KB/s, 12 seconds passed
+... 87%, 611968 KB, 49836 KB/s, 12 seconds passed
+... 87%, 612000 KB, 49838 KB/s, 12 seconds passed
+... 87%, 612032 KB, 49839 KB/s, 12 seconds passed
+... 88%, 612064 KB, 49841 KB/s, 12 seconds passed
+... 88%, 612096 KB, 49842 KB/s, 12 seconds passed
+... 88%, 612128 KB, 49843 KB/s, 12 seconds passed
+... 88%, 612160 KB, 49845 KB/s, 12 seconds passed
+... 88%, 612192 KB, 49846 KB/s, 12 seconds passed
+... 88%, 612224 KB, 49848 KB/s, 12 seconds passed
+... 88%, 612256 KB, 49849 KB/s, 12 seconds passed
+... 88%, 612288 KB, 49851 KB/s, 12 seconds passed
+... 88%, 612320 KB, 49852 KB/s, 12 seconds passed
+... 88%, 612352 KB, 49854 KB/s, 12 seconds passed
+... 88%, 612384 KB, 49855 KB/s, 12 seconds passed
+... 88%, 612416 KB, 49857 KB/s, 12 seconds passed
+... 88%, 612448 KB, 49858 KB/s, 12 seconds passed
+... 88%, 612480 KB, 49860 KB/s, 12 seconds passed
+... 88%, 612512 KB, 49861 KB/s, 12 seconds passed
+... 88%, 612544 KB, 49863 KB/s, 12 seconds passed
+... 88%, 612576 KB, 49864 KB/s, 12 seconds passed
+... 88%, 612608 KB, 49865 KB/s, 12 seconds passed
+... 88%, 612640 KB, 49867 KB/s, 12 seconds passed
+... 88%, 612672 KB, 49868 KB/s, 12 seconds passed
+... 88%, 612704 KB, 49869 KB/s, 12 seconds passed
+... 88%, 612736 KB, 49871 KB/s, 12 seconds passed
+... 88%, 612768 KB, 49872 KB/s, 12 seconds passed
+... 88%, 612800 KB, 49874 KB/s, 12 seconds passed
+... 88%, 612832 KB, 49876 KB/s, 12 seconds passed
+... 88%, 612864 KB, 49877 KB/s, 12 seconds passed
+... 88%, 612896 KB, 49879 KB/s, 12 seconds passed
+... 88%, 612928 KB, 49880 KB/s, 12 seconds passed
+... 88%, 612960 KB, 49882 KB/s, 12 seconds passed
+... 88%, 612992 KB, 49883 KB/s, 12 seconds passed
+... 88%, 613024 KB, 49885 KB/s, 12 seconds passed
+... 88%, 613056 KB, 49886 KB/s, 12 seconds passed
+... 88%, 613088 KB, 49888 KB/s, 12 seconds passed
+... 88%, 613120 KB, 49889 KB/s, 12 seconds passed
+... 88%, 613152 KB, 49891 KB/s, 12 seconds passed
+... 88%, 613184 KB, 49892 KB/s, 12 seconds passed
+... 88%, 613216 KB, 49893 KB/s, 12 seconds passed
+... 88%, 613248 KB, 49895 KB/s, 12 seconds passed
+... 88%, 613280 KB, 49896 KB/s, 12 seconds passed
+... 88%, 613312 KB, 49885 KB/s, 12 seconds passed
+... 88%, 613344 KB, 49886 KB/s, 12 seconds passed
+... 88%, 613376 KB, 49887 KB/s, 12 seconds passed
+... 88%, 613408 KB, 49889 KB/s, 12 seconds passed
+... 88%, 613440 KB, 49890 KB/s, 12 seconds passed
+... 88%, 613472 KB, 49892 KB/s, 12 seconds passed
+... 88%, 613504 KB, 49894 KB/s, 12 seconds passed
+... 88%, 613536 KB, 49895 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 88%, 613568 KB, 49754 KB/s, 12 seconds passed
+... 88%, 613600 KB, 49739 KB/s, 12 seconds passed
+... 88%, 613632 KB, 49739 KB/s, 12 seconds passed
+... 88%, 613664 KB, 49740 KB/s, 12 seconds passed
+... 88%, 613696 KB, 49742 KB/s, 12 seconds passed
+... 88%, 613728 KB, 49743 KB/s, 12 seconds passed
+... 88%, 613760 KB, 49744 KB/s, 12 seconds passed
+... 88%, 613792 KB, 49746 KB/s, 12 seconds passed
+... 88%, 613824 KB, 49747 KB/s, 12 seconds passed
+... 88%, 613856 KB, 49748 KB/s, 12 seconds passed
+... 88%, 613888 KB, 49750 KB/s, 12 seconds passed
+... 88%, 613920 KB, 49751 KB/s, 12 seconds passed
+... 88%, 613952 KB, 49752 KB/s, 12 seconds passed
+... 88%, 613984 KB, 49754 KB/s, 12 seconds passed
+... 88%, 614016 KB, 49755 KB/s, 12 seconds passed
+... 88%, 614048 KB, 49756 KB/s, 12 seconds passed
+... 88%, 614080 KB, 49758 KB/s, 12 seconds passed
+... 88%, 614112 KB, 49759 KB/s, 12 seconds passed
+... 88%, 614144 KB, 49760 KB/s, 12 seconds passed
+... 88%, 614176 KB, 49762 KB/s, 12 seconds passed
+... 88%, 614208 KB, 49763 KB/s, 12 seconds passed
+... 88%, 614240 KB, 49764 KB/s, 12 seconds passed
+... 88%, 614272 KB, 49766 KB/s, 12 seconds passed
+... 88%, 614304 KB, 49767 KB/s, 12 seconds passed
+... 88%, 614336 KB, 49769 KB/s, 12 seconds passed
+... 88%, 614368 KB, 49770 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 88%, 614400 KB, 49590 KB/s, 12 seconds passed
+... 88%, 614432 KB, 49589 KB/s, 12 seconds passed
+... 88%, 614464 KB, 49590 KB/s, 12 seconds passed
+... 88%, 614496 KB, 49592 KB/s, 12 seconds passed
+... 88%, 614528 KB, 49568 KB/s, 12 seconds passed
+... 88%, 614560 KB, 49569 KB/s, 12 seconds passed
+... 88%, 614592 KB, 49557 KB/s, 12 seconds passed
+... 88%, 614624 KB, 49558 KB/s, 12 seconds passed
+... 88%, 614656 KB, 49559 KB/s, 12 seconds passed
+... 88%, 614688 KB, 49560 KB/s, 12 seconds passed
+... 88%, 614720 KB, 49562 KB/s, 12 seconds passed
+... 88%, 614752 KB, 49563 KB/s, 12 seconds passed
+... 88%, 614784 KB, 49564 KB/s, 12 seconds passed
+... 88%, 614816 KB, 49566 KB/s, 12 seconds passed
+... 88%, 614848 KB, 49567 KB/s, 12 seconds passed
+... 88%, 614880 KB, 49568 KB/s, 12 seconds passed
+... 88%, 614912 KB, 49570 KB/s, 12 seconds passed
+... 88%, 614944 KB, 49571 KB/s, 12 seconds passed
+... 88%, 614976 KB, 49572 KB/s, 12 seconds passed
+... 88%, 615008 KB, 49574 KB/s, 12 seconds passed
+... 88%, 615040 KB, 49575 KB/s, 12 seconds passed
+... 88%, 615072 KB, 49577 KB/s, 12 seconds passed
+... 88%, 615104 KB, 49578 KB/s, 12 seconds passed
+... 88%, 615136 KB, 49579 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 88%, 615168 KB, 49580 KB/s, 12 seconds passed
+... 88%, 615200 KB, 49582 KB/s, 12 seconds passed
+... 88%, 615232 KB, 49583 KB/s, 12 seconds passed
+... 88%, 615264 KB, 49584 KB/s, 12 seconds passed
+... 88%, 615296 KB, 49585 KB/s, 12 seconds passed
+... 88%, 615328 KB, 49587 KB/s, 12 seconds passed
+... 88%, 615360 KB, 49588 KB/s, 12 seconds passed
+... 88%, 615392 KB, 49590 KB/s, 12 seconds passed
+... 88%, 615424 KB, 49591 KB/s, 12 seconds passed
+... 88%, 615456 KB, 49592 KB/s, 12 seconds passed
+... 88%, 615488 KB, 49594 KB/s, 12 seconds passed
+... 88%, 615520 KB, 49595 KB/s, 12 seconds passed
+... 88%, 615552 KB, 49597 KB/s, 12 seconds passed
+... 88%, 615584 KB, 49598 KB/s, 12 seconds passed
+... 88%, 615616 KB, 49600 KB/s, 12 seconds passed
+... 88%, 615648 KB, 49601 KB/s, 12 seconds passed
+... 88%, 615680 KB, 49603 KB/s, 12 seconds passed
+... 88%, 615712 KB, 49604 KB/s, 12 seconds passed
+... 88%, 615744 KB, 49606 KB/s, 12 seconds passed
+... 88%, 615776 KB, 49607 KB/s, 12 seconds passed
+... 88%, 615808 KB, 49609 KB/s, 12 seconds passed
+... 88%, 615840 KB, 49610 KB/s, 12 seconds passed
+... 88%, 615872 KB, 49612 KB/s, 12 seconds passed
+... 88%, 615904 KB, 49613 KB/s, 12 seconds passed
+... 88%, 615936 KB, 49615 KB/s, 12 seconds passed
+... 88%, 615968 KB, 49616 KB/s, 12 seconds passed
+... 88%, 616000 KB, 49618 KB/s, 12 seconds passed
+... 88%, 616032 KB, 49620 KB/s, 12 seconds passed
+... 88%, 616064 KB, 49621 KB/s, 12 seconds passed
+... 88%, 616096 KB, 49622 KB/s, 12 seconds passed
+... 88%, 616128 KB, 49624 KB/s, 12 seconds passed
+... 88%, 616160 KB, 49625 KB/s, 12 seconds passed
+... 88%, 616192 KB, 49627 KB/s, 12 seconds passed
+... 88%, 616224 KB, 49628 KB/s, 12 seconds passed
+... 88%, 616256 KB, 49630 KB/s, 12 seconds passed
+... 88%, 616288 KB, 49631 KB/s, 12 seconds passed
+... 88%, 616320 KB, 49633 KB/s, 12 seconds passed
+... 88%, 616352 KB, 49634 KB/s, 12 seconds passed
+... 88%, 616384 KB, 49636 KB/s, 12 seconds passed
+... 88%, 616416 KB, 49637 KB/s, 12 seconds passed
+... 88%, 616448 KB, 49639 KB/s, 12 seconds passed
+... 88%, 616480 KB, 49640 KB/s, 12 seconds passed
+... 88%, 616512 KB, 49642 KB/s, 12 seconds passed
+... 88%, 616544 KB, 49643 KB/s, 12 seconds passed
+... 88%, 616576 KB, 49645 KB/s, 12 seconds passed
+... 88%, 616608 KB, 49646 KB/s, 12 seconds passed
+... 88%, 616640 KB, 49648 KB/s, 12 seconds passed
+... 88%, 616672 KB, 49650 KB/s, 12 seconds passed
+... 88%, 616704 KB, 49652 KB/s, 12 seconds passed
+... 88%, 616736 KB, 49654 KB/s, 12 seconds passed
+... 88%, 616768 KB, 49656 KB/s, 12 seconds passed
+... 88%, 616800 KB, 49657 KB/s, 12 seconds passed
+... 88%, 616832 KB, 49659 KB/s, 12 seconds passed
+... 88%, 616864 KB, 49661 KB/s, 12 seconds passed
+... 88%, 616896 KB, 49662 KB/s, 12 seconds passed
+... 88%, 616928 KB, 49664 KB/s, 12 seconds passed
+... 88%, 616960 KB, 49665 KB/s, 12 seconds passed
+... 88%, 616992 KB, 49667 KB/s, 12 seconds passed
+... 88%, 617024 KB, 49668 KB/s, 12 seconds passed
+... 88%, 617056 KB, 49670 KB/s, 12 seconds passed
+... 88%, 617088 KB, 49671 KB/s, 12 seconds passed
+... 88%, 617120 KB, 49672 KB/s, 12 seconds passed
+... 88%, 617152 KB, 49674 KB/s, 12 seconds passed
+... 88%, 617184 KB, 49676 KB/s, 12 seconds passed
+... 88%, 617216 KB, 49677 KB/s, 12 seconds passed
+... 88%, 617248 KB, 49679 KB/s, 12 seconds passed
+... 88%, 617280 KB, 49680 KB/s, 12 seconds passed
+... 88%, 617312 KB, 49681 KB/s, 12 seconds passed
+... 88%, 617344 KB, 49683 KB/s, 12 seconds passed
+... 88%, 617376 KB, 49684 KB/s, 12 seconds passed
+... 88%, 617408 KB, 49686 KB/s, 12 seconds passed
+... 88%, 617440 KB, 49688 KB/s, 12 seconds passed
+... 88%, 617472 KB, 49689 KB/s, 12 seconds passed
+... 88%, 617504 KB, 49690 KB/s, 12 seconds passed
+... 88%, 617536 KB, 49691 KB/s, 12 seconds passed
+... 88%, 617568 KB, 49693 KB/s, 12 seconds passed
+... 88%, 617600 KB, 49695 KB/s, 12 seconds passed
+... 88%, 617632 KB, 49696 KB/s, 12 seconds passed
+... 88%, 617664 KB, 49697 KB/s, 12 seconds passed
+... 88%, 617696 KB, 49699 KB/s, 12 seconds passed
+... 88%, 617728 KB, 49700 KB/s, 12 seconds passed
+... 88%, 617760 KB, 49702 KB/s, 12 seconds passed
+... 88%, 617792 KB, 49703 KB/s, 12 seconds passed
+... 88%, 617824 KB, 49704 KB/s, 12 seconds passed
+... 88%, 617856 KB, 49706 KB/s, 12 seconds passed
+... 88%, 617888 KB, 49707 KB/s, 12 seconds passed
+... 88%, 617920 KB, 49709 KB/s, 12 seconds passed
+... 88%, 617952 KB, 49710 KB/s, 12 seconds passed
+... 88%, 617984 KB, 49712 KB/s, 12 seconds passed
+... 88%, 618016 KB, 49714 KB/s, 12 seconds passed
+... 88%, 618048 KB, 49715 KB/s, 12 seconds passed
+... 88%, 618080 KB, 49717 KB/s, 12 seconds passed
+... 88%, 618112 KB, 49718 KB/s, 12 seconds passed
+... 88%, 618144 KB, 49720 KB/s, 12 seconds passed
+... 88%, 618176 KB, 49721 KB/s, 12 seconds passed
+... 88%, 618208 KB, 49722 KB/s, 12 seconds passed
+... 88%, 618240 KB, 49723 KB/s, 12 seconds passed
+... 88%, 618272 KB, 49725 KB/s, 12 seconds passed
+... 88%, 618304 KB, 49724 KB/s, 12 seconds passed
+... 88%, 618336 KB, 49722 KB/s, 12 seconds passed
+... 88%, 618368 KB, 49723 KB/s, 12 seconds passed
+... 88%, 618400 KB, 49724 KB/s, 12 seconds passed
+... 88%, 618432 KB, 49726 KB/s, 12 seconds passed
+... 88%, 618464 KB, 49728 KB/s, 12 seconds passed
+... 88%, 618496 KB, 49723 KB/s, 12 seconds passed
+... 88%, 618528 KB, 49724 KB/s, 12 seconds passed
+... 88%, 618560 KB, 49725 KB/s, 12 seconds passed
+... 88%, 618592 KB, 49727 KB/s, 12 seconds passed
+... 88%, 618624 KB, 49728 KB/s, 12 seconds passed
+... 88%, 618656 KB, 49730 KB/s, 12 seconds passed
+... 88%, 618688 KB, 49731 KB/s, 12 seconds passed
+... 88%, 618720 KB, 49733 KB/s, 12 seconds passed
+... 88%, 618752 KB, 49734 KB/s, 12 seconds passed
+... 88%, 618784 KB, 49736 KB/s, 12 seconds passed
+... 88%, 618816 KB, 49737 KB/s, 12 seconds passed
+... 88%, 618848 KB, 49739 KB/s, 12 seconds passed
+... 88%, 618880 KB, 49741 KB/s, 12 seconds passed
+... 88%, 618912 KB, 49741 KB/s, 12 seconds passed
+... 88%, 618944 KB, 49743 KB/s, 12 seconds passed
+... 88%, 618976 KB, 49744 KB/s, 12 seconds passed
+... 89%, 619008 KB, 49746 KB/s, 12 seconds passed
+... 89%, 619040 KB, 49747 KB/s, 12 seconds passed
+... 89%, 619072 KB, 49749 KB/s, 12 seconds passed
+... 89%, 619104 KB, 49750 KB/s, 12 seconds passed
+... 89%, 619136 KB, 49752 KB/s, 12 seconds passed
+... 89%, 619168 KB, 49754 KB/s, 12 seconds passed
+... 89%, 619200 KB, 49755 KB/s, 12 seconds passed
+... 89%, 619232 KB, 49756 KB/s, 12 seconds passed
+... 89%, 619264 KB, 49757 KB/s, 12 seconds passed
+... 89%, 619296 KB, 49758 KB/s, 12 seconds passed
+... 89%, 619328 KB, 49760 KB/s, 12 seconds passed
+... 89%, 619360 KB, 49762 KB/s, 12 seconds passed
+... 89%, 619392 KB, 49763 KB/s, 12 seconds passed
+... 89%, 619424 KB, 49765 KB/s, 12 seconds passed
+... 89%, 619456 KB, 49766 KB/s, 12 seconds passed
+... 89%, 619488 KB, 49768 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 89%, 619520 KB, 49607 KB/s, 12 seconds passed
+... 89%, 619552 KB, 49608 KB/s, 12 seconds passed
+... 89%, 619584 KB, 49609 KB/s, 12 seconds passed
+... 89%, 619616 KB, 49611 KB/s, 12 seconds passed
+... 89%, 619648 KB, 49612 KB/s, 12 seconds passed
+... 89%, 619680 KB, 49613 KB/s, 12 seconds passed
+... 89%, 619712 KB, 49615 KB/s, 12 seconds passed
+... 89%, 619744 KB, 49616 KB/s, 12 seconds passed
+... 89%, 619776 KB, 49617 KB/s, 12 seconds passed
+... 89%, 619808 KB, 49619 KB/s, 12 seconds passed
+... 89%, 619840 KB, 49620 KB/s, 12 seconds passed
+... 89%, 619872 KB, 49621 KB/s, 12 seconds passed
+... 89%, 619904 KB, 49622 KB/s, 12 seconds passed
+... 89%, 619936 KB, 49624 KB/s, 12 seconds passed
+... 89%, 619968 KB, 49625 KB/s, 12 seconds passed
+... 89%, 620000 KB, 49627 KB/s, 12 seconds passed
+... 89%, 620032 KB, 49628 KB/s, 12 seconds passed
+... 89%, 620064 KB, 49629 KB/s, 12 seconds passed
+... 89%, 620096 KB, 49631 KB/s, 12 seconds passed
+... 89%, 620128 KB, 49632 KB/s, 12 seconds passed
+... 89%, 620160 KB, 49633 KB/s, 12 seconds passed
+... 89%, 620192 KB, 49635 KB/s, 12 seconds passed
+... 89%, 620224 KB, 49636 KB/s, 12 seconds passed
+... 89%, 620256 KB, 49637 KB/s, 12 seconds passed
+... 89%, 620288 KB, 49639 KB/s, 12 seconds passed
+... 89%, 620320 KB, 49640 KB/s, 12 seconds passed
+... 89%, 620352 KB, 49641 KB/s, 12 seconds passed
+... 89%, 620384 KB, 49642 KB/s, 12 seconds passed
+... 89%, 620416 KB, 49644 KB/s, 12 seconds passed
+... 89%, 620448 KB, 49645 KB/s, 12 seconds passed
+... 89%, 620480 KB, 49646 KB/s, 12 seconds passed
+... 89%, 620512 KB, 49648 KB/s, 12 seconds passed
+... 89%, 620544 KB, 49649 KB/s, 12 seconds passed
+... 89%, 620576 KB, 49650 KB/s, 12 seconds passed
+... 89%, 620608 KB, 49652 KB/s, 12 seconds passed
+... 89%, 620640 KB, 49653 KB/s, 12 seconds passed
+... 89%, 620672 KB, 49654 KB/s, 12 seconds passed
+... 89%, 620704 KB, 49656 KB/s, 12 seconds passed
+... 89%, 620736 KB, 49657 KB/s, 12 seconds passed
+... 89%, 620768 KB, 49658 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 89%, 620800 KB, 49621 KB/s, 12 seconds passed
+... 89%, 620832 KB, 49622 KB/s, 12 seconds passed
+... 89%, 620864 KB, 49623 KB/s, 12 seconds passed
+... 89%, 620896 KB, 49624 KB/s, 12 seconds passed
+... 89%, 620928 KB, 49626 KB/s, 12 seconds passed
+... 89%, 620960 KB, 49627 KB/s, 12 seconds passed
+... 89%, 620992 KB, 49628 KB/s, 12 seconds passed
+... 89%, 621024 KB, 49630 KB/s, 12 seconds passed
+... 89%, 621056 KB, 49631 KB/s, 12 seconds passed
+... 89%, 621088 KB, 49632 KB/s, 12 seconds passed
+... 89%, 621120 KB, 49634 KB/s, 12 seconds passed
+... 89%, 621152 KB, 49635 KB/s, 12 seconds passed
+... 89%, 621184 KB, 49636 KB/s, 12 seconds passed
+... 89%, 621216 KB, 49638 KB/s, 12 seconds passed
+... 89%, 621248 KB, 49639 KB/s, 12 seconds passed
+... 89%, 621280 KB, 49640 KB/s, 12 seconds passed
+... 89%, 621312 KB, 49641 KB/s, 12 seconds passed
+... 89%, 621344 KB, 49643 KB/s, 12 seconds passed
+... 89%, 621376 KB, 49644 KB/s, 12 seconds passed
+... 89%, 621408 KB, 49645 KB/s, 12 seconds passed
+... 89%, 621440 KB, 49647 KB/s, 12 seconds passed
+... 89%, 621472 KB, 49648 KB/s, 12 seconds passed
+... 89%, 621504 KB, 49649 KB/s, 12 seconds passed
+... 89%, 621536 KB, 49651 KB/s, 12 seconds passed
+... 89%, 621568 KB, 49652 KB/s, 12 seconds passed
+... 89%, 621600 KB, 49653 KB/s, 12 seconds passed
+... 89%, 621632 KB, 49655 KB/s, 12 seconds passed
+... 89%, 621664 KB, 49596 KB/s, 12 seconds passed
+... 89%, 621696 KB, 49597 KB/s, 12 seconds passed
+... 89%, 621728 KB, 49598 KB/s, 12 seconds passed
+... 89%, 621760 KB, 49599 KB/s, 12 seconds passed
+... 89%, 621792 KB, 49600 KB/s, 12 seconds passed
+... 89%, 621824 KB, 49602 KB/s, 12 seconds passed
+... 89%, 621856 KB, 49603 KB/s, 12 seconds passed
+... 89%, 621888 KB, 49604 KB/s, 12 seconds passed
+... 89%, 621920 KB, 49606 KB/s, 12 seconds passed
+... 89%, 621952 KB, 49607 KB/s, 12 seconds passed
+... 89%, 621984 KB, 49608 KB/s, 12 seconds passed
+... 89%, 622016 KB, 49609 KB/s, 12 seconds passed
+... 89%, 622048 KB, 49611 KB/s, 12 seconds passed
+... 89%, 622080 KB, 49612 KB/s, 12 seconds passed
+... 89%, 622112 KB, 49613 KB/s, 12 seconds passed
+... 89%, 622144 KB, 49615 KB/s, 12 seconds passed
+... 89%, 622176 KB, 49616 KB/s, 12 seconds passed
+... 89%, 622208 KB, 49617 KB/s, 12 seconds passed
+... 89%, 622240 KB, 49619 KB/s, 12 seconds passed
+... 89%, 622272 KB, 49620 KB/s, 12 seconds passed
+... 89%, 622304 KB, 49621 KB/s, 12 seconds passed
+... 89%, 622336 KB, 49623 KB/s, 12 seconds passed
+... 89%, 622368 KB, 49624 KB/s, 12 seconds passed
+... 89%, 622400 KB, 49625 KB/s, 12 seconds passed
+... 89%, 622432 KB, 49627 KB/s, 12 seconds passed
+... 89%, 622464 KB, 49628 KB/s, 12 seconds passed
+... 89%, 622496 KB, 49629 KB/s, 12 seconds passed
+... 89%, 622528 KB, 49630 KB/s, 12 seconds passed
+... 89%, 622560 KB, 49632 KB/s, 12 seconds passed
+... 89%, 622592 KB, 49633 KB/s, 12 seconds passed
+... 89%, 622624 KB, 49634 KB/s, 12 seconds passed
+... 89%, 622656 KB, 49636 KB/s, 12 seconds passed
+... 89%, 622688 KB, 49637 KB/s, 12 seconds passed
+... 89%, 622720 KB, 49638 KB/s, 12 seconds passed
+... 89%, 622752 KB, 49640 KB/s, 12 seconds passed
+... 89%, 622784 KB, 49641 KB/s, 12 seconds passed
+... 89%, 622816 KB, 49642 KB/s, 12 seconds passed
+... 89%, 622848 KB, 49644 KB/s, 12 seconds passed
+... 89%, 622880 KB, 49645 KB/s, 12 seconds passed
+... 89%, 622912 KB, 49646 KB/s, 12 seconds passed
+... 89%, 622944 KB, 49647 KB/s, 12 seconds passed
+... 89%, 622976 KB, 49649 KB/s, 12 seconds passed
+... 89%, 623008 KB, 49650 KB/s, 12 seconds passed
+... 89%, 623040 KB, 49651 KB/s, 12 seconds passed
+... 89%, 623072 KB, 49653 KB/s, 12 seconds passed
+... 89%, 623104 KB, 49654 KB/s, 12 seconds passed
+... 89%, 623136 KB, 49655 KB/s, 12 seconds passed
+... 89%, 623168 KB, 49657 KB/s, 12 seconds passed
+... 89%, 623200 KB, 49658 KB/s, 12 seconds passed
+... 89%, 623232 KB, 49659 KB/s, 12 seconds passed
+... 89%, 623264 KB, 49661 KB/s, 12 seconds passed
+... 89%, 623296 KB, 49662 KB/s, 12 seconds passed
+... 89%, 623328 KB, 49663 KB/s, 12 seconds passed
+... 89%, 623360 KB, 49665 KB/s, 12 seconds passed
+... 89%, 623392 KB, 49666 KB/s, 12 seconds passed
+... 89%, 623424 KB, 49667 KB/s, 12 seconds passed
+... 89%, 623456 KB, 49669 KB/s, 12 seconds passed
+... 89%, 623488 KB, 49670 KB/s, 12 seconds passed
+... 89%, 623520 KB, 49672 KB/s, 12 seconds passed
+... 89%, 623552 KB, 49674 KB/s, 12 seconds passed
+... 89%, 623584 KB, 49675 KB/s, 12 seconds passed
+... 89%, 623616 KB, 49677 KB/s, 12 seconds passed
+... 89%, 623648 KB, 49679 KB/s, 12 seconds passed
+... 89%, 623680 KB, 49681 KB/s, 12 seconds passed
+... 89%, 623712 KB, 49682 KB/s, 12 seconds passed
+... 89%, 623744 KB, 49684 KB/s, 12 seconds passed
+... 89%, 623776 KB, 49686 KB/s, 12 seconds passed
+... 89%, 623808 KB, 49688 KB/s, 12 seconds passed
+... 89%, 623840 KB, 49689 KB/s, 12 seconds passed
+... 89%, 623872 KB, 49691 KB/s, 12 seconds passed
+... 89%, 623904 KB, 49693 KB/s, 12 seconds passed
+... 89%, 623936 KB, 49694 KB/s, 12 seconds passed
+... 89%, 623968 KB, 49696 KB/s, 12 seconds passed
+... 89%, 624000 KB, 49698 KB/s, 12 seconds passed
+... 89%, 624032 KB, 49700 KB/s, 12 seconds passed
+... 89%, 624064 KB, 49701 KB/s, 12 seconds passed
+... 89%, 624096 KB, 49703 KB/s, 12 seconds passed
+... 89%, 624128 KB, 49705 KB/s, 12 seconds passed
+... 89%, 624160 KB, 49707 KB/s, 12 seconds passed
+... 89%, 624192 KB, 49708 KB/s, 12 seconds passed
+... 89%, 624224 KB, 49710 KB/s, 12 seconds passed
+... 89%, 624256 KB, 49712 KB/s, 12 seconds passed
+... 89%, 624288 KB, 49713 KB/s, 12 seconds passed
+... 89%, 624320 KB, 49715 KB/s, 12 seconds passed
+... 89%, 624352 KB, 49717 KB/s, 12 seconds passed
+... 89%, 624384 KB, 49719 KB/s, 12 seconds passed
+... 89%, 624416 KB, 49720 KB/s, 12 seconds passed
+... 89%, 624448 KB, 49722 KB/s, 12 seconds passed
+... 89%, 624480 KB, 49724 KB/s, 12 seconds passed
+... 89%, 624512 KB, 49725 KB/s, 12 seconds passed
+... 89%, 624544 KB, 49727 KB/s, 12 seconds passed
+... 89%, 624576 KB, 49728 KB/s, 12 seconds passed
+... 89%, 624608 KB, 49729 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 89%, 624640 KB, 49576 KB/s, 12 seconds passed
+... 89%, 624672 KB, 49575 KB/s, 12 seconds passed
+... 89%, 624704 KB, 49575 KB/s, 12 seconds passed
+... 89%, 624736 KB, 49577 KB/s, 12 seconds passed
+... 89%, 624768 KB, 49565 KB/s, 12 seconds passed
+... 89%, 624800 KB, 49565 KB/s, 12 seconds passed
+... 89%, 624832 KB, 49566 KB/s, 12 seconds passed
+... 89%, 624864 KB, 49568 KB/s, 12 seconds passed
+... 89%, 624896 KB, 49569 KB/s, 12 seconds passed
+... 89%, 624928 KB, 49570 KB/s, 12 seconds passed
+... 89%, 624960 KB, 49572 KB/s, 12 seconds passed
+... 89%, 624992 KB, 49573 KB/s, 12 seconds passed
+... 89%, 625024 KB, 49574 KB/s, 12 seconds passed
+... 89%, 625056 KB, 49575 KB/s, 12 seconds passed
+... 89%, 625088 KB, 49577 KB/s, 12 seconds passed
+... 89%, 625120 KB, 49578 KB/s, 12 seconds passed
+... 89%, 625152 KB, 49580 KB/s, 12 seconds passed
+... 89%, 625184 KB, 49581 KB/s, 12 seconds passed
+... 89%, 625216 KB, 49582 KB/s, 12 seconds passed
+... 89%, 625248 KB, 49583 KB/s, 12 seconds passed
+... 89%, 625280 KB, 49585 KB/s, 12 seconds passed
+... 89%, 625312 KB, 49586 KB/s, 12 seconds passed
+... 89%, 625344 KB, 49587 KB/s, 12 seconds passed
+... 89%, 625376 KB, 49589 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 89%, 625408 KB, 49578 KB/s, 12 seconds passed
+... 89%, 625440 KB, 49579 KB/s, 12 seconds passed
+... 89%, 625472 KB, 49580 KB/s, 12 seconds passed
+... 89%, 625504 KB, 49582 KB/s, 12 seconds passed
+... 89%, 625536 KB, 49583 KB/s, 12 seconds passed
+... 89%, 625568 KB, 49584 KB/s, 12 seconds passed
+... 89%, 625600 KB, 49586 KB/s, 12 seconds passed
+... 89%, 625632 KB, 49587 KB/s, 12 seconds passed
+... 89%, 625664 KB, 49588 KB/s, 12 seconds passed
+... 89%, 625696 KB, 49589 KB/s, 12 seconds passed
+... 89%, 625728 KB, 49591 KB/s, 12 seconds passed
+... 89%, 625760 KB, 49592 KB/s, 12 seconds passed
+... 89%, 625792 KB, 49593 KB/s, 12 seconds passed
+... 89%, 625824 KB, 49595 KB/s, 12 seconds passed
+... 89%, 625856 KB, 49596 KB/s, 12 seconds passed
+... 89%, 625888 KB, 49597 KB/s, 12 seconds passed
+... 89%, 625920 KB, 49599 KB/s, 12 seconds passed
+... 89%, 625952 KB, 49600 KB/s, 12 seconds passed
+... 90%, 625984 KB, 49601 KB/s, 12 seconds passed
+... 90%, 626016 KB, 49603 KB/s, 12 seconds passed
+... 90%, 626048 KB, 49604 KB/s, 12 seconds passed
+... 90%, 626080 KB, 49605 KB/s, 12 seconds passed
+... 90%, 626112 KB, 49607 KB/s, 12 seconds passed
+... 90%, 626144 KB, 49608 KB/s, 12 seconds passed
+... 90%, 626176 KB, 49609 KB/s, 12 seconds passed
+... 90%, 626208 KB, 49611 KB/s, 12 seconds passed
+... 90%, 626240 KB, 49612 KB/s, 12 seconds passed
+... 90%, 626272 KB, 49614 KB/s, 12 seconds passed
+... 90%, 626304 KB, 49615 KB/s, 12 seconds passed
+... 90%, 626336 KB, 49617 KB/s, 12 seconds passed
+... 90%, 626368 KB, 49618 KB/s, 12 seconds passed
+... 90%, 626400 KB, 49620 KB/s, 12 seconds passed
+... 90%, 626432 KB, 49621 KB/s, 12 seconds passed
+... 90%, 626464 KB, 49623 KB/s, 12 seconds passed
+... 90%, 626496 KB, 49624 KB/s, 12 seconds passed
+... 90%, 626528 KB, 49625 KB/s, 12 seconds passed
+... 90%, 626560 KB, 49627 KB/s, 12 seconds passed
+... 90%, 626592 KB, 49628 KB/s, 12 seconds passed
+... 90%, 626624 KB, 49630 KB/s, 12 seconds passed
+... 90%, 626656 KB, 49631 KB/s, 12 seconds passed
+... 90%, 626688 KB, 49633 KB/s, 12 seconds passed
+... 90%, 626720 KB, 49634 KB/s, 12 seconds passed
+... 90%, 626752 KB, 49636 KB/s, 12 seconds passed
+... 90%, 626784 KB, 49637 KB/s, 12 seconds passed
+... 90%, 626816 KB, 49639 KB/s, 12 seconds passed
+... 90%, 626848 KB, 49640 KB/s, 12 seconds passed
+... 90%, 626880 KB, 49642 KB/s, 12 seconds passed
+... 90%, 626912 KB, 49643 KB/s, 12 seconds passed
+... 90%, 626944 KB, 49645 KB/s, 12 seconds passed
+... 90%, 626976 KB, 49646 KB/s, 12 seconds passed
+... 90%, 627008 KB, 49648 KB/s, 12 seconds passed
+... 90%, 627040 KB, 49649 KB/s, 12 seconds passed
+... 90%, 627072 KB, 49651 KB/s, 12 seconds passed
+... 90%, 627104 KB, 49652 KB/s, 12 seconds passed
+... 90%, 627136 KB, 49654 KB/s, 12 seconds passed
+... 90%, 627168 KB, 49655 KB/s, 12 seconds passed
+... 90%, 627200 KB, 49657 KB/s, 12 seconds passed
+... 90%, 627232 KB, 49658 KB/s, 12 seconds passed
+... 90%, 627264 KB, 49660 KB/s, 12 seconds passed
+... 90%, 627296 KB, 49661 KB/s, 12 seconds passed
+... 90%, 627328 KB, 49663 KB/s, 12 seconds passed
+... 90%, 627360 KB, 49664 KB/s, 12 seconds passed
+... 90%, 627392 KB, 49666 KB/s, 12 seconds passed
+... 90%, 627424 KB, 49668 KB/s, 12 seconds passed
+... 90%, 627456 KB, 49669 KB/s, 12 seconds passed
+... 90%, 627488 KB, 49671 KB/s, 12 seconds passed
+... 90%, 627520 KB, 49673 KB/s, 12 seconds passed
+... 90%, 627552 KB, 49675 KB/s, 12 seconds passed
+... 90%, 627584 KB, 49676 KB/s, 12 seconds passed
+... 90%, 627616 KB, 49678 KB/s, 12 seconds passed
+... 90%, 627648 KB, 49679 KB/s, 12 seconds passed
+... 90%, 627680 KB, 49681 KB/s, 12 seconds passed
+... 90%, 627712 KB, 49682 KB/s, 12 seconds passed
+... 90%, 627744 KB, 49684 KB/s, 12 seconds passed
+... 90%, 627776 KB, 49685 KB/s, 12 seconds passed
+... 90%, 627808 KB, 49687 KB/s, 12 seconds passed
+... 90%, 627840 KB, 49688 KB/s, 12 seconds passed
+... 90%, 627872 KB, 49689 KB/s, 12 seconds passed
+... 90%, 627904 KB, 49691 KB/s, 12 seconds passed
+... 90%, 627936 KB, 49692 KB/s, 12 seconds passed
+... 90%, 627968 KB, 49694 KB/s, 12 seconds passed
+... 90%, 628000 KB, 49695 KB/s, 12 seconds passed
+... 90%, 628032 KB, 49696 KB/s, 12 seconds passed
+... 90%, 628064 KB, 49698 KB/s, 12 seconds passed
+... 90%, 628096 KB, 49699 KB/s, 12 seconds passed
+... 90%, 628128 KB, 49701 KB/s, 12 seconds passed
+... 90%, 628160 KB, 49702 KB/s, 12 seconds passed
+... 90%, 628192 KB, 49704 KB/s, 12 seconds passed
+... 90%, 628224 KB, 49705 KB/s, 12 seconds passed
+... 90%, 628256 KB, 49707 KB/s, 12 seconds passed
+... 90%, 628288 KB, 49708 KB/s, 12 seconds passed
+... 90%, 628320 KB, 49709 KB/s, 12 seconds passed
+... 90%, 628352 KB, 49711 KB/s, 12 seconds passed
+... 90%, 628384 KB, 49712 KB/s, 12 seconds passed
+... 90%, 628416 KB, 49713 KB/s, 12 seconds passed
+... 90%, 628448 KB, 49715 KB/s, 12 seconds passed
+... 90%, 628480 KB, 49716 KB/s, 12 seconds passed
+... 90%, 628512 KB, 49718 KB/s, 12 seconds passed
+... 90%, 628544 KB, 49719 KB/s, 12 seconds passed
+... 90%, 628576 KB, 49721 KB/s, 12 seconds passed
+... 90%, 628608 KB, 49722 KB/s, 12 seconds passed
+... 90%, 628640 KB, 49722 KB/s, 12 seconds passed
+... 90%, 628672 KB, 49721 KB/s, 12 seconds passed
+... 90%, 628704 KB, 49723 KB/s, 12 seconds passed
+... 90%, 628736 KB, 49724 KB/s, 12 seconds passed
+... 90%, 628768 KB, 49718 KB/s, 12 seconds passed
+... 90%, 628800 KB, 49719 KB/s, 12 seconds passed
+... 90%, 628832 KB, 49720 KB/s, 12 seconds passed
+... 90%, 628864 KB, 49722 KB/s, 12 seconds passed
+... 90%, 628896 KB, 49724 KB/s, 12 seconds passed
+... 90%, 628928 KB, 49725 KB/s, 12 seconds passed
+... 90%, 628960 KB, 49727 KB/s, 12 seconds passed
+... 90%, 628992 KB, 49728 KB/s, 12 seconds passed
+... 90%, 629024 KB, 49729 KB/s, 12 seconds passed
+... 90%, 629056 KB, 49731 KB/s, 12 seconds passed
+... 90%, 629088 KB, 49732 KB/s, 12 seconds passed
+... 90%, 629120 KB, 49734 KB/s, 12 seconds passed
+... 90%, 629152 KB, 49735 KB/s, 12 seconds passed
+... 90%, 629184 KB, 49737 KB/s, 12 seconds passed
+... 90%, 629216 KB, 49738 KB/s, 12 seconds passed
+... 90%, 629248 KB, 49740 KB/s, 12 seconds passed
+... 90%, 629280 KB, 49741 KB/s, 12 seconds passed
+... 90%, 629312 KB, 49742 KB/s, 12 seconds passed
+... 90%, 629344 KB, 49744 KB/s, 12 seconds passed
+... 90%, 629376 KB, 49745 KB/s, 12 seconds passed
+... 90%, 629408 KB, 49747 KB/s, 12 seconds passed
+... 90%, 629440 KB, 49748 KB/s, 12 seconds passed
+... 90%, 629472 KB, 49750 KB/s, 12 seconds passed
+... 90%, 629504 KB, 49751 KB/s, 12 seconds passed
+... 90%, 629536 KB, 49752 KB/s, 12 seconds passed
+... 90%, 629568 KB, 49754 KB/s, 12 seconds passed
+... 90%, 629600 KB, 49755 KB/s, 12 seconds passed
+... 90%, 629632 KB, 49757 KB/s, 12 seconds passed
+... 90%, 629664 KB, 49758 KB/s, 12 seconds passed
+... 90%, 629696 KB, 49759 KB/s, 12 seconds passed
+... 90%, 629728 KB, 49761 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 90%, 629760 KB, 49626 KB/s, 12 seconds passed
+... 90%, 629792 KB, 49627 KB/s, 12 seconds passed
+... 90%, 629824 KB, 49628 KB/s, 12 seconds passed
+... 90%, 629856 KB, 49630 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 90%, 629888 KB, 49498 KB/s, 12 seconds passed
+... 90%, 629920 KB, 49499 KB/s, 12 seconds passed
+... 90%, 629952 KB, 49500 KB/s, 12 seconds passed
+... 90%, 629984 KB, 49502 KB/s, 12 seconds passed
+... 90%, 630016 KB, 49503 KB/s, 12 seconds passed
+... 90%, 630048 KB, 49504 KB/s, 12 seconds passed
+... 90%, 630080 KB, 49505 KB/s, 12 seconds passed
+... 90%, 630112 KB, 49503 KB/s, 12 seconds passed
+... 90%, 630144 KB, 49504 KB/s, 12 seconds passed
+... 90%, 630176 KB, 49506 KB/s, 12 seconds passed
+... 90%, 630208 KB, 49507 KB/s, 12 seconds passed
+... 90%, 630240 KB, 49508 KB/s, 12 seconds passed
+... 90%, 630272 KB, 49509 KB/s, 12 seconds passed
+... 90%, 630304 KB, 49511 KB/s, 12 seconds passed
+... 90%, 630336 KB, 49512 KB/s, 12 seconds passed
+... 90%, 630368 KB, 49513 KB/s, 12 seconds passed
+... 90%, 630400 KB, 49515 KB/s, 12 seconds passed
+... 90%, 630432 KB, 49516 KB/s, 12 seconds passed
+... 90%, 630464 KB, 49517 KB/s, 12 seconds passed
+... 90%, 630496 KB, 49518 KB/s, 12 seconds passed
+... 90%, 630528 KB, 49520 KB/s, 12 seconds passed
+... 90%, 630560 KB, 49521 KB/s, 12 seconds passed
+... 90%, 630592 KB, 49522 KB/s, 12 seconds passed
+... 90%, 630624 KB, 49524 KB/s, 12 seconds passed
+... 90%, 630656 KB, 49525 KB/s, 12 seconds passed
+... 90%, 630688 KB, 49526 KB/s, 12 seconds passed
+... 90%, 630720 KB, 49528 KB/s, 12 seconds passed
+... 90%, 630752 KB, 49529 KB/s, 12 seconds passed
+... 90%, 630784 KB, 49530 KB/s, 12 seconds passed
+... 90%, 630816 KB, 49532 KB/s, 12 seconds passed
+... 90%, 630848 KB, 49533 KB/s, 12 seconds passed
+... 90%, 630880 KB, 49534 KB/s, 12 seconds passed
+... 90%, 630912 KB, 49536 KB/s, 12 seconds passed
+... 90%, 630944 KB, 49537 KB/s, 12 seconds passed
+... 90%, 630976 KB, 49538 KB/s, 12 seconds passed
+... 90%, 631008 KB, 49540 KB/s, 12 seconds passed
+... 90%, 631040 KB, 49541 KB/s, 12 seconds passed
+... 90%, 631072 KB, 49542 KB/s, 12 seconds passed
+... 90%, 631104 KB, 49544 KB/s, 12 seconds passed
+... 90%, 631136 KB, 49545 KB/s, 12 seconds passed
+... 90%, 631168 KB, 49546 KB/s, 12 seconds passed
+... 90%, 631200 KB, 49547 KB/s, 12 seconds passed
+... 90%, 631232 KB, 49549 KB/s, 12 seconds passed
+... 90%, 631264 KB, 49550 KB/s, 12 seconds passed
+... 90%, 631296 KB, 49551 KB/s, 12 seconds passed
+... 90%, 631328 KB, 49553 KB/s, 12 seconds passed
+... 90%, 631360 KB, 49554 KB/s, 12 seconds passed
+... 90%, 631392 KB, 49555 KB/s, 12 seconds passed
+... 90%, 631424 KB, 49557 KB/s, 12 seconds passed
+... 90%, 631456 KB, 49558 KB/s, 12 seconds passed
+... 90%, 631488 KB, 49559 KB/s, 12 seconds passed
+... 90%, 631520 KB, 49560 KB/s, 12 seconds passed
+... 90%, 631552 KB, 49562 KB/s, 12 seconds passed
+... 90%, 631584 KB, 49563 KB/s, 12 seconds passed
+... 90%, 631616 KB, 49564 KB/s, 12 seconds passed
+... 90%, 631648 KB, 49566 KB/s, 12 seconds passed
+... 90%, 631680 KB, 49567 KB/s, 12 seconds passed
+... 90%, 631712 KB, 49569 KB/s, 12 seconds passed
+... 90%, 631744 KB, 49571 KB/s, 12 seconds passed
+... 90%, 631776 KB, 49573 KB/s, 12 seconds passed
+... 90%, 631808 KB, 49574 KB/s, 12 seconds passed
+... 90%, 631840 KB, 49576 KB/s, 12 seconds passed
+... 90%, 631872 KB, 49578 KB/s, 12 seconds passed
+... 90%, 631904 KB, 49579 KB/s, 12 seconds passed
+... 90%, 631936 KB, 49581 KB/s, 12 seconds passed
+... 90%, 631968 KB, 49583 KB/s, 12 seconds passed
+... 90%, 632000 KB, 49584 KB/s, 12 seconds passed
+... 90%, 632032 KB, 49586 KB/s, 12 seconds passed
+... 90%, 632064 KB, 49588 KB/s, 12 seconds passed
+... 90%, 632096 KB, 49589 KB/s, 12 seconds passed
+... 90%, 632128 KB, 49591 KB/s, 12 seconds passed
+... 90%, 632160 KB, 49592 KB/s, 12 seconds passed
+... 90%, 632192 KB, 49594 KB/s, 12 seconds passed
+... 90%, 632224 KB, 49595 KB/s, 12 seconds passed
+... 90%, 632256 KB, 49597 KB/s, 12 seconds passed
+... 90%, 632288 KB, 49598 KB/s, 12 seconds passed
+... 90%, 632320 KB, 49599 KB/s, 12 seconds passed
+... 90%, 632352 KB, 49600 KB/s, 12 seconds passed
+... 90%, 632384 KB, 49601 KB/s, 12 seconds passed
+... 90%, 632416 KB, 49603 KB/s, 12 seconds passed
+... 90%, 632448 KB, 49604 KB/s, 12 seconds passed
+... 90%, 632480 KB, 49606 KB/s, 12 seconds passed
+... 90%, 632512 KB, 49608 KB/s, 12 seconds passed
+... 90%, 632544 KB, 49609 KB/s, 12 seconds passed
+... 90%, 632576 KB, 49611 KB/s, 12 seconds passed
+... 90%, 632608 KB, 49612 KB/s, 12 seconds passed
+... 90%, 632640 KB, 49614 KB/s, 12 seconds passed
+... 90%, 632672 KB, 49615 KB/s, 12 seconds passed
+... 90%, 632704 KB, 49616 KB/s, 12 seconds passed
+... 90%, 632736 KB, 49618 KB/s, 12 seconds passed
+... 90%, 632768 KB, 49619 KB/s, 12 seconds passed
+... 90%, 632800 KB, 49621 KB/s, 12 seconds passed
+... 90%, 632832 KB, 49622 KB/s, 12 seconds passed
+... 90%, 632864 KB, 49624 KB/s, 12 seconds passed
+... 90%, 632896 KB, 49625 KB/s, 12 seconds passed
+... 91%, 632928 KB, 49626 KB/s, 12 seconds passed
+... 91%, 632960 KB, 49628 KB/s, 12 seconds passed
+... 91%, 632992 KB, 49629 KB/s, 12 seconds passed
+... 91%, 633024 KB, 49630 KB/s, 12 seconds passed
+... 91%, 633056 KB, 49632 KB/s, 12 seconds passed
+... 91%, 633088 KB, 49633 KB/s, 12 seconds passed
+... 91%, 633120 KB, 49635 KB/s, 12 seconds passed
+... 91%, 633152 KB, 49636 KB/s, 12 seconds passed
+... 91%, 633184 KB, 49638 KB/s, 12 seconds passed
+... 91%, 633216 KB, 49639 KB/s, 12 seconds passed
+... 91%, 633248 KB, 49641 KB/s, 12 seconds passed
+... 91%, 633280 KB, 49642 KB/s, 12 seconds passed
+... 91%, 633312 KB, 49643 KB/s, 12 seconds passed
+... 91%, 633344 KB, 49645 KB/s, 12 seconds passed
+... 91%, 633376 KB, 49646 KB/s, 12 seconds passed
+... 91%, 633408 KB, 49648 KB/s, 12 seconds passed
+... 91%, 633440 KB, 49649 KB/s, 12 seconds passed
+... 91%, 633472 KB, 49650 KB/s, 12 seconds passed
+... 91%, 633504 KB, 49651 KB/s, 12 seconds passed
+... 91%, 633536 KB, 49653 KB/s, 12 seconds passed
+... 91%, 633568 KB, 49654 KB/s, 12 seconds passed
+... 91%, 633600 KB, 49656 KB/s, 12 seconds passed
+... 91%, 633632 KB, 49658 KB/s, 12 seconds passed
+... 91%, 633664 KB, 49659 KB/s, 12 seconds passed
+... 91%, 633696 KB, 49660 KB/s, 12 seconds passed
+... 91%, 633728 KB, 49662 KB/s, 12 seconds passed
+... 91%, 633760 KB, 49663 KB/s, 12 seconds passed
+... 91%, 633792 KB, 49665 KB/s, 12 seconds passed
+... 91%, 633824 KB, 49666 KB/s, 12 seconds passed
+... 91%, 633856 KB, 49668 KB/s, 12 seconds passed
+... 91%, 633888 KB, 49669 KB/s, 12 seconds passed
+... 91%, 633920 KB, 49670 KB/s, 12 seconds passed
+... 91%, 633952 KB, 49672 KB/s, 12 seconds passed
+... 91%, 633984 KB, 49673 KB/s, 12 seconds passed
+... 91%, 634016 KB, 49675 KB/s, 12 seconds passed
+... 91%, 634048 KB, 49676 KB/s, 12 seconds passed
+... 91%, 634080 KB, 49677 KB/s, 12 seconds passed
+... 91%, 634112 KB, 49679 KB/s, 12 seconds passed
+... 91%, 634144 KB, 49680 KB/s, 12 seconds passed
+... 91%, 634176 KB, 49680 KB/s, 12 seconds passed
+... 91%, 634208 KB, 49682 KB/s, 12 seconds passed
+... 91%, 634240 KB, 49683 KB/s, 12 seconds passed
+... 91%, 634272 KB, 49684 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 91%, 634304 KB, 49682 KB/s, 12 seconds passed
+... 91%, 634336 KB, 49680 KB/s, 12 seconds passed
+... 91%, 634368 KB, 49681 KB/s, 12 seconds passed
+... 91%, 634400 KB, 49683 KB/s, 12 seconds passed
+... 91%, 634432 KB, 49684 KB/s, 12 seconds passed
+... 91%, 634464 KB, 49686 KB/s, 12 seconds passed
+... 91%, 634496 KB, 49687 KB/s, 12 seconds passed
+... 91%, 634528 KB, 49688 KB/s, 12 seconds passed
+... 91%, 634560 KB, 49690 KB/s, 12 seconds passed
+... 91%, 634592 KB, 49691 KB/s, 12 seconds passed
+... 91%, 634624 KB, 49693 KB/s, 12 seconds passed
+... 91%, 634656 KB, 49694 KB/s, 12 seconds passed
+... 91%, 634688 KB, 49695 KB/s, 12 seconds passed
+... 91%, 634720 KB, 49697 KB/s, 12 seconds passed
+... 91%, 634752 KB, 49698 KB/s, 12 seconds passed
+... 91%, 634784 KB, 49700 KB/s, 12 seconds passed
+... 91%, 634816 KB, 49701 KB/s, 12 seconds passed
+... 91%, 634848 KB, 49703 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 91%, 634880 KB, 49489 KB/s, 12 seconds passed
+... 91%, 634912 KB, 49490 KB/s, 12 seconds passed
+... 91%, 634944 KB, 49491 KB/s, 12 seconds passed
+... 91%, 634976 KB, 49492 KB/s, 12 seconds passed
+... 91%, 635008 KB, 49489 KB/s, 12 seconds passed
+... 91%, 635040 KB, 49490 KB/s, 12 seconds passed
+... 91%, 635072 KB, 49491 KB/s, 12 seconds passed
+... 91%, 635104 KB, 49492 KB/s, 12 seconds passed
+... 91%, 635136 KB, 49494 KB/s, 12 seconds passed
+... 91%, 635168 KB, 49495 KB/s, 12 seconds passed
+... 91%, 635200 KB, 49496 KB/s, 12 seconds passed
+... 91%, 635232 KB, 49497 KB/s, 12 seconds passed
+... 91%, 635264 KB, 49499 KB/s, 12 seconds passed
+... 91%, 635296 KB, 49500 KB/s, 12 seconds passed
+... 91%, 635328 KB, 49501 KB/s, 12 seconds passed
+... 91%, 635360 KB, 49503 KB/s, 12 seconds passed
+... 91%, 635392 KB, 49504 KB/s, 12 seconds passed
+... 91%, 635424 KB, 49505 KB/s, 12 seconds passed
+... 91%, 635456 KB, 49489 KB/s, 12 seconds passed
+... 91%, 635488 KB, 49490 KB/s, 12 seconds passed
+... 91%, 635520 KB, 49491 KB/s, 12 seconds passed
+... 91%, 635552 KB, 49493 KB/s, 12 seconds passed
+... 91%, 635584 KB, 49494 KB/s, 12 seconds passed
+... 91%, 635616 KB, 49495 KB/s, 12 seconds passed
+... 91%, 635648 KB, 49496 KB/s, 12 seconds passed
+... 91%, 635680 KB, 49498 KB/s, 12 seconds passed
+... 91%, 635712 KB, 49499 KB/s, 12 seconds passed
+... 91%, 635744 KB, 49500 KB/s, 12 seconds passed
+... 91%, 635776 KB, 49501 KB/s, 12 seconds passed
+... 91%, 635808 KB, 49503 KB/s, 12 seconds passed
+... 91%, 635840 KB, 49504 KB/s, 12 seconds passed
+... 91%, 635872 KB, 49505 KB/s, 12 seconds passed
+... 91%, 635904 KB, 49507 KB/s, 12 seconds passed
+... 91%, 635936 KB, 49508 KB/s, 12 seconds passed
+... 91%, 635968 KB, 49509 KB/s, 12 seconds passed
+... 91%, 636000 KB, 49511 KB/s, 12 seconds passed
+... 91%, 636032 KB, 49512 KB/s, 12 seconds passed
+... 91%, 636064 KB, 49513 KB/s, 12 seconds passed
+... 91%, 636096 KB, 49515 KB/s, 12 seconds passed
+... 91%, 636128 KB, 49516 KB/s, 12 seconds passed
+... 91%, 636160 KB, 49517 KB/s, 12 seconds passed
+... 91%, 636192 KB, 49518 KB/s, 12 seconds passed
+... 91%, 636224 KB, 49520 KB/s, 12 seconds passed
+... 91%, 636256 KB, 49521 KB/s, 12 seconds passed
+... 91%, 636288 KB, 49522 KB/s, 12 seconds passed
+... 91%, 636320 KB, 49524 KB/s, 12 seconds passed
+... 91%, 636352 KB, 49525 KB/s, 12 seconds passed
+... 91%, 636384 KB, 49526 KB/s, 12 seconds passed
+... 91%, 636416 KB, 49528 KB/s, 12 seconds passed
+... 91%, 636448 KB, 49529 KB/s, 12 seconds passed
+... 91%, 636480 KB, 49530 KB/s, 12 seconds passed
+... 91%, 636512 KB, 49532 KB/s, 12 seconds passed
+... 91%, 636544 KB, 49533 KB/s, 12 seconds passed
+... 91%, 636576 KB, 49534 KB/s, 12 seconds passed
+... 91%, 636608 KB, 49536 KB/s, 12 seconds passed
+... 91%, 636640 KB, 49537 KB/s, 12 seconds passed
+... 91%, 636672 KB, 49538 KB/s, 12 seconds passed
+... 91%, 636704 KB, 49540 KB/s, 12 seconds passed
+... 91%, 636736 KB, 49541 KB/s, 12 seconds passed
+... 91%, 636768 KB, 49542 KB/s, 12 seconds passed
+... 91%, 636800 KB, 49544 KB/s, 12 seconds passed
+... 91%, 636832 KB, 49545 KB/s, 12 seconds passed
+... 91%, 636864 KB, 49546 KB/s, 12 seconds passed
+... 91%, 636896 KB, 49547 KB/s, 12 seconds passed
+... 91%, 636928 KB, 49549 KB/s, 12 seconds passed
+... 91%, 636960 KB, 49550 KB/s, 12 seconds passed
+... 91%, 636992 KB, 49552 KB/s, 12 seconds passed
+... 91%, 637024 KB, 49554 KB/s, 12 seconds passed
+... 91%, 637056 KB, 49555 KB/s, 12 seconds passed
+... 91%, 637088 KB, 49557 KB/s, 12 seconds passed
+... 91%, 637120 KB, 49559 KB/s, 12 seconds passed
+... 91%, 637152 KB, 49561 KB/s, 12 seconds passed
+... 91%, 637184 KB, 49562 KB/s, 12 seconds passed
+... 91%, 637216 KB, 49564 KB/s, 12 seconds passed
+... 91%, 637248 KB, 49566 KB/s, 12 seconds passed
+... 91%, 637280 KB, 49567 KB/s, 12 seconds passed
+... 91%, 637312 KB, 49569 KB/s, 12 seconds passed
+... 91%, 637344 KB, 49571 KB/s, 12 seconds passed
+... 91%, 637376 KB, 49573 KB/s, 12 seconds passed
+... 91%, 637408 KB, 49574 KB/s, 12 seconds passed
+... 91%, 637440 KB, 49576 KB/s, 12 seconds passed
+... 91%, 637472 KB, 49578 KB/s, 12 seconds passed
+... 91%, 637504 KB, 49579 KB/s, 12 seconds passed
+... 91%, 637536 KB, 49581 KB/s, 12 seconds passed
+... 91%, 637568 KB, 49583 KB/s, 12 seconds passed
+... 91%, 637600 KB, 49584 KB/s, 12 seconds passed
+... 91%, 637632 KB, 49586 KB/s, 12 seconds passed
+... 91%, 637664 KB, 49588 KB/s, 12 seconds passed
+... 91%, 637696 KB, 49589 KB/s, 12 seconds passed
+... 91%, 637728 KB, 49590 KB/s, 12 seconds passed
+... 91%, 637760 KB, 49592 KB/s, 12 seconds passed
+... 91%, 637792 KB, 49593 KB/s, 12 seconds passed
+... 91%, 637824 KB, 49594 KB/s, 12 seconds passed
+... 91%, 637856 KB, 49596 KB/s, 12 seconds passed
+... 91%, 637888 KB, 49598 KB/s, 12 seconds passed
+... 91%, 637920 KB, 49599 KB/s, 12 seconds passed
+... 91%, 637952 KB, 49600 KB/s, 12 seconds passed
+... 91%, 637984 KB, 49602 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 91%, 638016 KB, 49558 KB/s, 12 seconds passed
+... 91%, 638048 KB, 49559 KB/s, 12 seconds passed
+... 91%, 638080 KB, 49560 KB/s, 12 seconds passed
+... 91%, 638112 KB, 49561 KB/s, 12 seconds passed
+... 91%, 638144 KB, 49562 KB/s, 12 seconds passed
+... 91%, 638176 KB, 49564 KB/s, 12 seconds passed
+... 91%, 638208 KB, 49565 KB/s, 12 seconds passed
+... 91%, 638240 KB, 49561 KB/s, 12 seconds passed
+... 91%, 638272 KB, 49562 KB/s, 12 seconds passed
+... 91%, 638304 KB, 49563 KB/s, 12 seconds passed
+... 91%, 638336 KB, 49564 KB/s, 12 seconds passed
+... 91%, 638368 KB, 49565 KB/s, 12 seconds passed
+... 91%, 638400 KB, 49567 KB/s, 12 seconds passed
+... 91%, 638432 KB, 49568 KB/s, 12 seconds passed
+... 91%, 638464 KB, 49569 KB/s, 12 seconds passed
+... 91%, 638496 KB, 49571 KB/s, 12 seconds passed
+... 91%, 638528 KB, 49572 KB/s, 12 seconds passed
+... 91%, 638560 KB, 49573 KB/s, 12 seconds passed
+... 91%, 638592 KB, 49574 KB/s, 12 seconds passed
+... 91%, 638624 KB, 49576 KB/s, 12 seconds passed
+... 91%, 638656 KB, 49577 KB/s, 12 seconds passed
+... 91%, 638688 KB, 49578 KB/s, 12 seconds passed
+... 91%, 638720 KB, 49580 KB/s, 12 seconds passed
+... 91%, 638752 KB, 49581 KB/s, 12 seconds passed
+... 91%, 638784 KB, 49582 KB/s, 12 seconds passed
+... 91%, 638816 KB, 49584 KB/s, 12 seconds passed
+... 91%, 638848 KB, 49585 KB/s, 12 seconds passed
+... 91%, 638880 KB, 49586 KB/s, 12 seconds passed
+... 91%, 638912 KB, 49588 KB/s, 12 seconds passed
+... 91%, 638944 KB, 49589 KB/s, 12 seconds passed
+... 91%, 638976 KB, 49590 KB/s, 12 seconds passed
+... 91%, 639008 KB, 49592 KB/s, 12 seconds passed
+... 91%, 639040 KB, 49593 KB/s, 12 seconds passed
+... 91%, 639072 KB, 49594 KB/s, 12 seconds passed
+... 91%, 639104 KB, 49596 KB/s, 12 seconds passed
+... 91%, 639136 KB, 49597 KB/s, 12 seconds passed
+... 91%, 639168 KB, 49599 KB/s, 12 seconds passed
+... 91%, 639200 KB, 49601 KB/s, 12 seconds passed
+... 91%, 639232 KB, 49602 KB/s, 12 seconds passed
+... 91%, 639264 KB, 49603 KB/s, 12 seconds passed
+... 91%, 639296 KB, 49605 KB/s, 12 seconds passed
+... 91%, 639328 KB, 49606 KB/s, 12 seconds passed
+... 91%, 639360 KB, 49608 KB/s, 12 seconds passed
+... 91%, 639392 KB, 49609 KB/s, 12 seconds passed
+... 91%, 639424 KB, 49611 KB/s, 12 seconds passed
+... 91%, 639456 KB, 49613 KB/s, 12 seconds passed
+... 91%, 639488 KB, 49614 KB/s, 12 seconds passed
+... 91%, 639520 KB, 49616 KB/s, 12 seconds passed
+... 91%, 639552 KB, 49617 KB/s, 12 seconds passed
+... 91%, 639584 KB, 49619 KB/s, 12 seconds passed
+... 91%, 639616 KB, 49620 KB/s, 12 seconds passed
+... 91%, 639648 KB, 49622 KB/s, 12 seconds passed
+... 91%, 639680 KB, 49623 KB/s, 12 seconds passed
+... 91%, 639712 KB, 49625 KB/s, 12 seconds passed
+... 91%, 639744 KB, 49626 KB/s, 12 seconds passed
+... 91%, 639776 KB, 49628 KB/s, 12 seconds passed
+... 91%, 639808 KB, 49629 KB/s, 12 seconds passed
+... 91%, 639840 KB, 49631 KB/s, 12 seconds passed
+... 92%, 639872 KB, 49632 KB/s, 12 seconds passed
+... 92%, 639904 KB, 49634 KB/s, 12 seconds passed
+... 92%, 639936 KB, 49635 KB/s, 12 seconds passed
+... 92%, 639968 KB, 49637 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 92%, 640000 KB, 49505 KB/s, 12 seconds passed
+... 92%, 640032 KB, 49506 KB/s, 12 seconds passed
+... 92%, 640064 KB, 49503 KB/s, 12 seconds passed
+... 92%, 640096 KB, 49503 KB/s, 12 seconds passed
+... 92%, 640128 KB, 49504 KB/s, 12 seconds passed
+... 92%, 640160 KB, 49506 KB/s, 12 seconds passed
+... 92%, 640192 KB, 49507 KB/s, 12 seconds passed
+... 92%, 640224 KB, 49508 KB/s, 12 seconds passed
+... 92%, 640256 KB, 49509 KB/s, 12 seconds passed
+... 92%, 640288 KB, 49511 KB/s, 12 seconds passed
+... 92%, 640320 KB, 49512 KB/s, 12 seconds passed
+... 92%, 640352 KB, 49513 KB/s, 12 seconds passed
+... 92%, 640384 KB, 49515 KB/s, 12 seconds passed
+... 92%, 640416 KB, 49516 KB/s, 12 seconds passed
+... 92%, 640448 KB, 49517 KB/s, 12 seconds passed
+... 92%, 640480 KB, 49519 KB/s, 12 seconds passed
+... 92%, 640512 KB, 49520 KB/s, 12 seconds passed
+... 92%, 640544 KB, 49521 KB/s, 12 seconds passed
+... 92%, 640576 KB, 49522 KB/s, 12 seconds passed
+... 92%, 640608 KB, 49524 KB/s, 12 seconds passed
+... 92%, 640640 KB, 49518 KB/s, 12 seconds passed
+... 92%, 640672 KB, 49520 KB/s, 12 seconds passed
+... 92%, 640704 KB, 49521 KB/s, 12 seconds passed
+... 92%, 640736 KB, 49522 KB/s, 12 seconds passed
+... 92%, 640768 KB, 49524 KB/s, 12 seconds passed
+... 92%, 640800 KB, 49525 KB/s, 12 seconds passed
+... 92%, 640832 KB, 49526 KB/s, 12 seconds passed
+... 92%, 640864 KB, 49527 KB/s, 12 seconds passed
+... 92%, 640896 KB, 49529 KB/s, 12 seconds passed
+... 92%, 640928 KB, 49530 KB/s, 12 seconds passed
+... 92%, 640960 KB, 49531 KB/s, 12 seconds passed
+... 92%, 640992 KB, 49533 KB/s, 12 seconds passed
+... 92%, 641024 KB, 49534 KB/s, 12 seconds passed
+... 92%, 641056 KB, 49535 KB/s, 12 seconds passed
+... 92%, 641088 KB, 49537 KB/s, 12 seconds passed
+... 92%, 641120 KB, 49538 KB/s, 12 seconds passed
+... 92%, 641152 KB, 49539 KB/s, 12 seconds passed
+... 92%, 641184 KB, 49541 KB/s, 12 seconds passed
+... 92%, 641216 KB, 49542 KB/s, 12 seconds passed
+... 92%, 641248 KB, 49543 KB/s, 12 seconds passed
+... 92%, 641280 KB, 49544 KB/s, 12 seconds passed
+... 92%, 641312 KB, 49546 KB/s, 12 seconds passed
+... 92%, 641344 KB, 49547 KB/s, 12 seconds passed
+... 92%, 641376 KB, 49548 KB/s, 12 seconds passed
+... 92%, 641408 KB, 49549 KB/s, 12 seconds passed
+... 92%, 641440 KB, 49551 KB/s, 12 seconds passed
+... 92%, 641472 KB, 49552 KB/s, 12 seconds passed
+... 92%, 641504 KB, 49553 KB/s, 12 seconds passed
+... 92%, 641536 KB, 49555 KB/s, 12 seconds passed
+... 92%, 641568 KB, 49556 KB/s, 12 seconds passed
+... 92%, 641600 KB, 49558 KB/s, 12 seconds passed
+... 92%, 641632 KB, 49559 KB/s, 12 seconds passed
+... 92%, 641664 KB, 49561 KB/s, 12 seconds passed
+... 92%, 641696 KB, 49563 KB/s, 12 seconds passed
+... 92%, 641728 KB, 49564 KB/s, 12 seconds passed
+... 92%, 641760 KB, 49566 KB/s, 12 seconds passed
+... 92%, 641792 KB, 49567 KB/s, 12 seconds passed
+... 92%, 641824 KB, 49569 KB/s, 12 seconds passed
+... 92%, 641856 KB, 49570 KB/s, 12 seconds passed
+... 92%, 641888 KB, 49572 KB/s, 12 seconds passed
+... 92%, 641920 KB, 49574 KB/s, 12 seconds passed
+... 92%, 641952 KB, 49575 KB/s, 12 seconds passed
+... 92%, 641984 KB, 49577 KB/s, 12 seconds passed
+... 92%, 642016 KB, 49578 KB/s, 12 seconds passed
+... 92%, 642048 KB, 49580 KB/s, 12 seconds passed
+... 92%, 642080 KB, 49581 KB/s, 12 seconds passed
+... 92%, 642112 KB, 49583 KB/s, 12 seconds passed
+... 92%, 642144 KB, 49585 KB/s, 12 seconds passed
+... 92%, 642176 KB, 49586 KB/s, 12 seconds passed
+... 92%, 642208 KB, 49587 KB/s, 12 seconds passed
+... 92%, 642240 KB, 49588 KB/s, 12 seconds passed
+... 92%, 642272 KB, 49590 KB/s, 12 seconds passed
+... 92%, 642304 KB, 49592 KB/s, 12 seconds passed
+... 92%, 642336 KB, 49593 KB/s, 12 seconds passed
+... 92%, 642368 KB, 49594 KB/s, 12 seconds passed
+... 92%, 642400 KB, 49596 KB/s, 12 seconds passed
+... 92%, 642432 KB, 49597 KB/s, 12 seconds passed
+... 92%, 642464 KB, 49598 KB/s, 12 seconds passed
+... 92%, 642496 KB, 49600 KB/s, 12 seconds passed
+... 92%, 642528 KB, 49601 KB/s, 12 seconds passed
+... 92%, 642560 KB, 49603 KB/s, 12 seconds passed
+... 92%, 642592 KB, 49604 KB/s, 12 seconds passed
+... 92%, 642624 KB, 49606 KB/s, 12 seconds passed
+... 92%, 642656 KB, 49607 KB/s, 12 seconds passed
+... 92%, 642688 KB, 49609 KB/s, 12 seconds passed
+... 92%, 642720 KB, 49610 KB/s, 12 seconds passed
+... 92%, 642752 KB, 49611 KB/s, 12 seconds passed
+... 92%, 642784 KB, 49613 KB/s, 12 seconds passed
+... 92%, 642816 KB, 49614 KB/s, 12 seconds passed
+... 92%, 642848 KB, 49615 KB/s, 12 seconds passed
+... 92%, 642880 KB, 49616 KB/s, 12 seconds passed
+... 92%, 642912 KB, 49617 KB/s, 12 seconds passed
+... 92%, 642944 KB, 49619 KB/s, 12 seconds passed
+... 92%, 642976 KB, 49620 KB/s, 12 seconds passed
+... 92%, 643008 KB, 49622 KB/s, 12 seconds passed
+... 92%, 643040 KB, 49624 KB/s, 12 seconds passed
+... 92%, 643072 KB, 49625 KB/s, 12 seconds passed
+... 92%, 643104 KB, 49627 KB/s, 12 seconds passed
+... 92%, 643136 KB, 49628 KB/s, 12 seconds passed
+... 92%, 643168 KB, 49630 KB/s, 12 seconds passed
+... 92%, 643200 KB, 49631 KB/s, 12 seconds passed
+... 92%, 643232 KB, 49632 KB/s, 12 seconds passed
+... 92%, 643264 KB, 49634 KB/s, 12 seconds passed
+... 92%, 643296 KB, 49635 KB/s, 12 seconds passed
+... 92%, 643328 KB, 49636 KB/s, 12 seconds passed
+... 92%, 643360 KB, 49638 KB/s, 12 seconds passed
+... 92%, 643392 KB, 49639 KB/s, 12 seconds passed
+... 92%, 643424 KB, 49641 KB/s, 12 seconds passed
+... 92%, 643456 KB, 49642 KB/s, 12 seconds passed
+... 92%, 643488 KB, 49643 KB/s, 12 seconds passed
+... 92%, 643520 KB, 49645 KB/s, 12 seconds passed
+... 92%, 643552 KB, 49646 KB/s, 12 seconds passed
+... 92%, 643584 KB, 49648 KB/s, 12 seconds passed
+... 92%, 643616 KB, 49649 KB/s, 12 seconds passed
+... 92%, 643648 KB, 49650 KB/s, 12 seconds passed
+... 92%, 643680 KB, 49652 KB/s, 12 seconds passed
+... 92%, 643712 KB, 49653 KB/s, 12 seconds passed
+... 92%, 643744 KB, 49655 KB/s, 12 seconds passed
+... 92%, 643776 KB, 49656 KB/s, 12 seconds passed
+... 92%, 643808 KB, 49658 KB/s, 12 seconds passed
+... 92%, 643840 KB, 49659 KB/s, 12 seconds passed
+... 92%, 643872 KB, 49660 KB/s, 12 seconds passed
+... 92%, 643904 KB, 49662 KB/s, 12 seconds passed
+... 92%, 643936 KB, 49663 KB/s, 12 seconds passed
+... 92%, 643968 KB, 49664 KB/s, 12 seconds passed
+... 92%, 644000 KB, 49666 KB/s, 12 seconds passed
+... 92%, 644032 KB, 49667 KB/s, 12 seconds passed
+... 92%, 644064 KB, 49669 KB/s, 12 seconds passed
+... 92%, 644096 KB, 49670 KB/s, 12 seconds passed
+... 92%, 644128 KB, 49671 KB/s, 12 seconds passed
+... 92%, 644160 KB, 49673 KB/s, 12 seconds passed
+... 92%, 644192 KB, 49674 KB/s, 12 seconds passed
+... 92%, 644224 KB, 49676 KB/s, 12 seconds passed
+... 92%, 644256 KB, 49677 KB/s, 12 seconds passed
+... 92%, 644288 KB, 49679 KB/s, 12 seconds passed
+... 92%, 644320 KB, 49680 KB/s, 12 seconds passed
+... 92%, 644352 KB, 49681 KB/s, 12 seconds passed
+... 92%, 644384 KB, 49683 KB/s, 12 seconds passed
+... 92%, 644416 KB, 49684 KB/s, 12 seconds passed
+... 92%, 644448 KB, 49686 KB/s, 12 seconds passed
+... 92%, 644480 KB, 49687 KB/s, 12 seconds passed
+... 92%, 644512 KB, 49688 KB/s, 12 seconds passed
+... 92%, 644544 KB, 49690 KB/s, 12 seconds passed
+... 92%, 644576 KB, 49691 KB/s, 12 seconds passed
+... 92%, 644608 KB, 49693 KB/s, 12 seconds passed
+... 92%, 644640 KB, 49694 KB/s, 12 seconds passed
+... 92%, 644672 KB, 49695 KB/s, 12 seconds passed
+
+.. parsed-literal::
+
+    ... 92%, 644704 KB, 49697 KB/s, 12 seconds passed
+... 92%, 644736 KB, 49698 KB/s, 12 seconds passed
+... 92%, 644768 KB, 49700 KB/s, 12 seconds passed
+... 92%, 644800 KB, 49701 KB/s, 12 seconds passed
+... 92%, 644832 KB, 49702 KB/s, 12 seconds passed
+... 92%, 644864 KB, 49704 KB/s, 12 seconds passed
+... 92%, 644896 KB, 49705 KB/s, 12 seconds passed
+... 92%, 644928 KB, 49706 KB/s, 12 seconds passed
+... 92%, 644960 KB, 49708 KB/s, 12 seconds passed
+... 92%, 644992 KB, 49709 KB/s, 12 seconds passed
+... 92%, 645024 KB, 49711 KB/s, 12 seconds passed
+... 92%, 645056 KB, 49712 KB/s, 12 seconds passed
+... 92%, 645088 KB, 49713 KB/s, 12 seconds passed
+... 92%, 645120 KB, 49617 KB/s, 13 seconds passed
+... 92%, 645152 KB, 49618 KB/s, 13 seconds passed
+... 92%, 645184 KB, 49617 KB/s, 13 seconds passed
+... 92%, 645216 KB, 49619 KB/s, 13 seconds passed
+... 92%, 645248 KB, 49620 KB/s, 13 seconds passed
+... 92%, 645280 KB, 49621 KB/s, 13 seconds passed
+... 92%, 645312 KB, 49623 KB/s, 13 seconds passed
+... 92%, 645344 KB, 49624 KB/s, 13 seconds passed
+... 92%, 645376 KB, 49616 KB/s, 13 seconds passed
+... 92%, 645408 KB, 49618 KB/s, 13 seconds passed
+... 92%, 645440 KB, 49619 KB/s, 13 seconds passed
+... 92%, 645472 KB, 49620 KB/s, 13 seconds passed
+... 92%, 645504 KB, 49621 KB/s, 13 seconds passed
+... 92%, 645536 KB, 49623 KB/s, 13 seconds passed
+... 92%, 645568 KB, 49624 KB/s, 13 seconds passed
+... 92%, 645600 KB, 49625 KB/s, 13 seconds passed
+... 92%, 645632 KB, 49627 KB/s, 13 seconds passed
+... 92%, 645664 KB, 49628 KB/s, 13 seconds passed
+... 92%, 645696 KB, 49629 KB/s, 13 seconds passed
+... 92%, 645728 KB, 49630 KB/s, 13 seconds passed
+... 92%, 645760 KB, 49632 KB/s, 13 seconds passed
+... 92%, 645792 KB, 49633 KB/s, 13 seconds passed
+... 92%, 645824 KB, 49634 KB/s, 13 seconds passed
+... 92%, 645856 KB, 49636 KB/s, 13 seconds passed
+... 92%, 645888 KB, 49637 KB/s, 13 seconds passed
+... 92%, 645920 KB, 49638 KB/s, 13 seconds passed
+... 92%, 645952 KB, 49640 KB/s, 13 seconds passed
+... 92%, 645984 KB, 49641 KB/s, 13 seconds passed
+... 92%, 646016 KB, 49623 KB/s, 13 seconds passed
+... 92%, 646048 KB, 49625 KB/s, 13 seconds passed
+... 92%, 646080 KB, 49626 KB/s, 13 seconds passed
+... 92%, 646112 KB, 49627 KB/s, 13 seconds passed
+... 92%, 646144 KB, 49628 KB/s, 13 seconds passed
+... 92%, 646176 KB, 49630 KB/s, 13 seconds passed
+... 92%, 646208 KB, 49631 KB/s, 13 seconds passed
+... 92%, 646240 KB, 49632 KB/s, 13 seconds passed
+... 92%, 646272 KB, 49634 KB/s, 13 seconds passed
+... 92%, 646304 KB, 49635 KB/s, 13 seconds passed
+... 92%, 646336 KB, 49636 KB/s, 13 seconds passed
+... 92%, 646368 KB, 49637 KB/s, 13 seconds passed
+... 92%, 646400 KB, 49639 KB/s, 13 seconds passed
+... 92%, 646432 KB, 49640 KB/s, 13 seconds passed
+... 92%, 646464 KB, 49641 KB/s, 13 seconds passed
+... 92%, 646496 KB, 49643 KB/s, 13 seconds passed
+... 92%, 646528 KB, 49644 KB/s, 13 seconds passed
+... 92%, 646560 KB, 49645 KB/s, 13 seconds passed
+
+.. parsed-literal::
+
+    ... 92%, 646592 KB, 49647 KB/s, 13 seconds passed
+... 92%, 646624 KB, 49648 KB/s, 13 seconds passed
+... 92%, 646656 KB, 49649 KB/s, 13 seconds passed
+... 92%, 646688 KB, 49650 KB/s, 13 seconds passed
+... 92%, 646720 KB, 49652 KB/s, 13 seconds passed
+... 92%, 646752 KB, 49653 KB/s, 13 seconds passed
+... 92%, 646784 KB, 49654 KB/s, 13 seconds passed
+... 92%, 646816 KB, 49656 KB/s, 13 seconds passed
+... 93%, 646848 KB, 49657 KB/s, 13 seconds passed
+... 93%, 646880 KB, 49658 KB/s, 13 seconds passed
+... 93%, 646912 KB, 49660 KB/s, 13 seconds passed
+... 93%, 646944 KB, 49661 KB/s, 13 seconds passed
+... 93%, 646976 KB, 49662 KB/s, 13 seconds passed
+... 93%, 647008 KB, 49664 KB/s, 13 seconds passed
+... 93%, 647040 KB, 49665 KB/s, 13 seconds passed
+... 93%, 647072 KB, 49667 KB/s, 13 seconds passed
+... 93%, 647104 KB, 49668 KB/s, 13 seconds passed
+... 93%, 647136 KB, 49670 KB/s, 13 seconds passed
+... 93%, 647168 KB, 49671 KB/s, 13 seconds passed
+... 93%, 647200 KB, 49673 KB/s, 13 seconds passed
+... 93%, 647232 KB, 49674 KB/s, 13 seconds passed
+... 93%, 647264 KB, 49676 KB/s, 13 seconds passed
+... 93%, 647296 KB, 49677 KB/s, 13 seconds passed
+... 93%, 647328 KB, 49679 KB/s, 13 seconds passed
+... 93%, 647360 KB, 49680 KB/s, 13 seconds passed
+... 93%, 647392 KB, 49682 KB/s, 13 seconds passed
+... 93%, 647424 KB, 49684 KB/s, 13 seconds passed
+... 93%, 647456 KB, 49685 KB/s, 13 seconds passed
+... 93%, 647488 KB, 49686 KB/s, 13 seconds passed
+... 93%, 647520 KB, 49688 KB/s, 13 seconds passed
+... 93%, 647552 KB, 49689 KB/s, 13 seconds passed
+... 93%, 647584 KB, 49691 KB/s, 13 seconds passed
+... 93%, 647616 KB, 49692 KB/s, 13 seconds passed
+... 93%, 647648 KB, 49694 KB/s, 13 seconds passed
+... 93%, 647680 KB, 49695 KB/s, 13 seconds passed
+... 93%, 647712 KB, 49697 KB/s, 13 seconds passed
+... 93%, 647744 KB, 49698 KB/s, 13 seconds passed
+... 93%, 647776 KB, 49700 KB/s, 13 seconds passed
+... 93%, 647808 KB, 49701 KB/s, 13 seconds passed
+... 93%, 647840 KB, 49703 KB/s, 13 seconds passed
+... 93%, 647872 KB, 49704 KB/s, 13 seconds passed
+... 93%, 647904 KB, 49705 KB/s, 13 seconds passed
+... 93%, 647936 KB, 49707 KB/s, 13 seconds passed
+... 93%, 647968 KB, 49708 KB/s, 13 seconds passed
+... 93%, 648000 KB, 49710 KB/s, 13 seconds passed
+... 93%, 648032 KB, 49711 KB/s, 13 seconds passed
+... 93%, 648064 KB, 49712 KB/s, 13 seconds passed
+... 93%, 648096 KB, 49714 KB/s, 13 seconds passed
+... 93%, 648128 KB, 49715 KB/s, 13 seconds passed
+... 93%, 648160 KB, 49717 KB/s, 13 seconds passed
+... 93%, 648192 KB, 49718 KB/s, 13 seconds passed
+... 93%, 648224 KB, 49719 KB/s, 13 seconds passed
+... 93%, 648256 KB, 49721 KB/s, 13 seconds passed
+... 93%, 648288 KB, 49722 KB/s, 13 seconds passed
+... 93%, 648320 KB, 49723 KB/s, 13 seconds passed
+... 93%, 648352 KB, 49725 KB/s, 13 seconds passed
+... 93%, 648384 KB, 49699 KB/s, 13 seconds passed
+... 93%, 648416 KB, 49700 KB/s, 13 seconds passed
+... 93%, 648448 KB, 49701 KB/s, 13 seconds passed
+... 93%, 648480 KB, 49703 KB/s, 13 seconds passed
+... 93%, 648512 KB, 49704 KB/s, 13 seconds passed
+... 93%, 648544 KB, 49706 KB/s, 13 seconds passed
+... 93%, 648576 KB, 49707 KB/s, 13 seconds passed
+... 93%, 648608 KB, 49708 KB/s, 13 seconds passed
+... 93%, 648640 KB, 49710 KB/s, 13 seconds passed
+... 93%, 648672 KB, 49711 KB/s, 13 seconds passed
+... 93%, 648704 KB, 49712 KB/s, 13 seconds passed
+... 93%, 648736 KB, 49714 KB/s, 13 seconds passed
+... 93%, 648768 KB, 49715 KB/s, 13 seconds passed
+... 93%, 648800 KB, 49717 KB/s, 13 seconds passed
+... 93%, 648832 KB, 49718 KB/s, 13 seconds passed
+... 93%, 648864 KB, 49720 KB/s, 13 seconds passed
+... 93%, 648896 KB, 49721 KB/s, 13 seconds passed
+... 93%, 648928 KB, 49722 KB/s, 13 seconds passed
+... 93%, 648960 KB, 49724 KB/s, 13 seconds passed
+... 93%, 648992 KB, 49725 KB/s, 13 seconds passed
+... 93%, 649024 KB, 49726 KB/s, 13 seconds passed
+... 93%, 649056 KB, 49728 KB/s, 13 seconds passed
+... 93%, 649088 KB, 49729 KB/s, 13 seconds passed
+... 93%, 649120 KB, 49730 KB/s, 13 seconds passed
+... 93%, 649152 KB, 49732 KB/s, 13 seconds passed
+... 93%, 649184 KB, 49733 KB/s, 13 seconds passed
+... 93%, 649216 KB, 49735 KB/s, 13 seconds passed
+... 93%, 649248 KB, 49736 KB/s, 13 seconds passed
+... 93%, 649280 KB, 49737 KB/s, 13 seconds passed
+... 93%, 649312 KB, 49739 KB/s, 13 seconds passed
+... 93%, 649344 KB, 49740 KB/s, 13 seconds passed
+... 93%, 649376 KB, 49742 KB/s, 13 seconds passed
+... 93%, 649408 KB, 49743 KB/s, 13 seconds passed
+... 93%, 649440 KB, 49744 KB/s, 13 seconds passed
+... 93%, 649472 KB, 49746 KB/s, 13 seconds passed
+... 93%, 649504 KB, 49747 KB/s, 13 seconds passed
+... 93%, 649536 KB, 49749 KB/s, 13 seconds passed
+... 93%, 649568 KB, 49750 KB/s, 13 seconds passed
+... 93%, 649600 KB, 49751 KB/s, 13 seconds passed
+... 93%, 649632 KB, 49753 KB/s, 13 seconds passed
+... 93%, 649664 KB, 49754 KB/s, 13 seconds passed
+... 93%, 649696 KB, 49755 KB/s, 13 seconds passed
+... 93%, 649728 KB, 49757 KB/s, 13 seconds passed
+... 93%, 649760 KB, 49758 KB/s, 13 seconds passed
+... 93%, 649792 KB, 49760 KB/s, 13 seconds passed
+... 93%, 649824 KB, 49761 KB/s, 13 seconds passed
+... 93%, 649856 KB, 49762 KB/s, 13 seconds passed
+... 93%, 649888 KB, 49764 KB/s, 13 seconds passed
+... 93%, 649920 KB, 49765 KB/s, 13 seconds passed
+... 93%, 649952 KB, 49767 KB/s, 13 seconds passed
+... 93%, 649984 KB, 49768 KB/s, 13 seconds passed
+... 93%, 650016 KB, 49769 KB/s, 13 seconds passed
+... 93%, 650048 KB, 49771 KB/s, 13 seconds passed
+... 93%, 650080 KB, 49772 KB/s, 13 seconds passed
+... 93%, 650112 KB, 49773 KB/s, 13 seconds passed
+... 93%, 650144 KB, 49775 KB/s, 13 seconds passed
+... 93%, 650176 KB, 49776 KB/s, 13 seconds passed
+... 93%, 650208 KB, 49777 KB/s, 13 seconds passed
+
+.. parsed-literal::
+
+    ... 93%, 650240 KB, 49667 KB/s, 13 seconds passed
+... 93%, 650272 KB, 49668 KB/s, 13 seconds passed
+... 93%, 650304 KB, 49669 KB/s, 13 seconds passed
+... 93%, 650336 KB, 49670 KB/s, 13 seconds passed
+... 93%, 650368 KB, 49671 KB/s, 13 seconds passed
+... 93%, 650400 KB, 49673 KB/s, 13 seconds passed
+... 93%, 650432 KB, 49674 KB/s, 13 seconds passed
+... 93%, 650464 KB, 49675 KB/s, 13 seconds passed
+... 93%, 650496 KB, 49676 KB/s, 13 seconds passed
+... 93%, 650528 KB, 49678 KB/s, 13 seconds passed
+... 93%, 650560 KB, 49679 KB/s, 13 seconds passed
+... 93%, 650592 KB, 49680 KB/s, 13 seconds passed
+... 93%, 650624 KB, 49682 KB/s, 13 seconds passed
+... 93%, 650656 KB, 49683 KB/s, 13 seconds passed
+... 93%, 650688 KB, 49684 KB/s, 13 seconds passed
+... 93%, 650720 KB, 49685 KB/s, 13 seconds passed
+... 93%, 650752 KB, 49687 KB/s, 13 seconds passed
+... 93%, 650784 KB, 49688 KB/s, 13 seconds passed
+... 93%, 650816 KB, 49689 KB/s, 13 seconds passed
+... 93%, 650848 KB, 49690 KB/s, 13 seconds passed
+... 93%, 650880 KB, 49692 KB/s, 13 seconds passed
+... 93%, 650912 KB, 49693 KB/s, 13 seconds passed
+... 93%, 650944 KB, 49694 KB/s, 13 seconds passed
+... 93%, 650976 KB, 49696 KB/s, 13 seconds passed
+... 93%, 651008 KB, 49695 KB/s, 13 seconds passed
+... 93%, 651040 KB, 49696 KB/s, 13 seconds passed
+... 93%, 651072 KB, 49698 KB/s, 13 seconds passed
+... 93%, 651104 KB, 49699 KB/s, 13 seconds passed
+... 93%, 651136 KB, 49700 KB/s, 13 seconds passed
+... 93%, 651168 KB, 49702 KB/s, 13 seconds passed
+... 93%, 651200 KB, 49703 KB/s, 13 seconds passed
+... 93%, 651232 KB, 49704 KB/s, 13 seconds passed
+... 93%, 651264 KB, 49705 KB/s, 13 seconds passed
+... 93%, 651296 KB, 49707 KB/s, 13 seconds passed
+... 93%, 651328 KB, 49708 KB/s, 13 seconds passed
+... 93%, 651360 KB, 49709 KB/s, 13 seconds passed
+... 93%, 651392 KB, 49711 KB/s, 13 seconds passed
+... 93%, 651424 KB, 49712 KB/s, 13 seconds passed
+... 93%, 651456 KB, 49713 KB/s, 13 seconds passed
+... 93%, 651488 KB, 49714 KB/s, 13 seconds passed
+... 93%, 651520 KB, 49716 KB/s, 13 seconds passed
+... 93%, 651552 KB, 49717 KB/s, 13 seconds passed
+... 93%, 651584 KB, 49718 KB/s, 13 seconds passed
+... 93%, 651616 KB, 49720 KB/s, 13 seconds passed
+... 93%, 651648 KB, 49721 KB/s, 13 seconds passed
+... 93%, 651680 KB, 49722 KB/s, 13 seconds passed
+... 93%, 651712 KB, 49723 KB/s, 13 seconds passed
+... 93%, 651744 KB, 49725 KB/s, 13 seconds passed
+... 93%, 651776 KB, 49726 KB/s, 13 seconds passed
+... 93%, 651808 KB, 49727 KB/s, 13 seconds passed
+... 93%, 651840 KB, 49728 KB/s, 13 seconds passed
+... 93%, 651872 KB, 49730 KB/s, 13 seconds passed
+... 93%, 651904 KB, 49731 KB/s, 13 seconds passed
+... 93%, 651936 KB, 49733 KB/s, 13 seconds passed
+... 93%, 651968 KB, 49735 KB/s, 13 seconds passed
+... 93%, 652000 KB, 49736 KB/s, 13 seconds passed
+... 93%, 652032 KB, 49738 KB/s, 13 seconds passed
+... 93%, 652064 KB, 49740 KB/s, 13 seconds passed
+... 93%, 652096 KB, 49741 KB/s, 13 seconds passed
+... 93%, 652128 KB, 49743 KB/s, 13 seconds passed
+... 93%, 652160 KB, 49745 KB/s, 13 seconds passed
+... 93%, 652192 KB, 49746 KB/s, 13 seconds passed
+... 93%, 652224 KB, 49748 KB/s, 13 seconds passed
+... 93%, 652256 KB, 49750 KB/s, 13 seconds passed
+... 93%, 652288 KB, 49751 KB/s, 13 seconds passed
+... 93%, 652320 KB, 49752 KB/s, 13 seconds passed
+... 93%, 652352 KB, 49754 KB/s, 13 seconds passed
+... 93%, 652384 KB, 49755 KB/s, 13 seconds passed
+... 93%, 652416 KB, 49756 KB/s, 13 seconds passed
+... 93%, 652448 KB, 49758 KB/s, 13 seconds passed
+... 93%, 652480 KB, 49759 KB/s, 13 seconds passed
+... 93%, 652512 KB, 49761 KB/s, 13 seconds passed
+... 93%, 652544 KB, 49762 KB/s, 13 seconds passed
+... 93%, 652576 KB, 49764 KB/s, 13 seconds passed
+... 93%, 652608 KB, 49765 KB/s, 13 seconds passed
+... 93%, 652640 KB, 49766 KB/s, 13 seconds passed
+... 93%, 652672 KB, 49767 KB/s, 13 seconds passed
+... 93%, 652704 KB, 49769 KB/s, 13 seconds passed
+... 93%, 652736 KB, 49770 KB/s, 13 seconds passed
+... 93%, 652768 KB, 49772 KB/s, 13 seconds passed
+... 93%, 652800 KB, 49773 KB/s, 13 seconds passed
+... 93%, 652832 KB, 49774 KB/s, 13 seconds passed
+... 93%, 652864 KB, 49776 KB/s, 13 seconds passed
+... 93%, 652896 KB, 49777 KB/s, 13 seconds passed
+... 93%, 652928 KB, 49779 KB/s, 13 seconds passed
+... 93%, 652960 KB, 49780 KB/s, 13 seconds passed
+... 93%, 652992 KB, 49781 KB/s, 13 seconds passed
+... 93%, 653024 KB, 49782 KB/s, 13 seconds passed
+... 93%, 653056 KB, 49784 KB/s, 13 seconds passed
+... 93%, 653088 KB, 49785 KB/s, 13 seconds passed
+... 93%, 653120 KB, 49787 KB/s, 13 seconds passed
+... 93%, 653152 KB, 49788 KB/s, 13 seconds passed
+... 93%, 653184 KB, 49789 KB/s, 13 seconds passed
+... 93%, 653216 KB, 49791 KB/s, 13 seconds passed
+... 93%, 653248 KB, 49791 KB/s, 13 seconds passed
+... 93%, 653280 KB, 49793 KB/s, 13 seconds passed
+... 93%, 653312 KB, 49794 KB/s, 13 seconds passed
+... 93%, 653344 KB, 49796 KB/s, 13 seconds passed
+... 93%, 653376 KB, 49797 KB/s, 13 seconds passed
+... 93%, 653408 KB, 49799 KB/s, 13 seconds passed
+... 93%, 653440 KB, 49800 KB/s, 13 seconds passed
+... 93%, 653472 KB, 49802 KB/s, 13 seconds passed
+... 93%, 653504 KB, 49803 KB/s, 13 seconds passed
+... 93%, 653536 KB, 49805 KB/s, 13 seconds passed
+... 93%, 653568 KB, 49806 KB/s, 13 seconds passed
+... 93%, 653600 KB, 49807 KB/s, 13 seconds passed
+... 93%, 653632 KB, 49808 KB/s, 13 seconds passed
+... 93%, 653664 KB, 49809 KB/s, 13 seconds passed
+... 93%, 653696 KB, 49810 KB/s, 13 seconds passed
+... 93%, 653728 KB, 49812 KB/s, 13 seconds passed
+... 93%, 653760 KB, 49814 KB/s, 13 seconds passed
+... 94%, 653792 KB, 49815 KB/s, 13 seconds passed
+... 94%, 653824 KB, 49816 KB/s, 13 seconds passed
+... 94%, 653856 KB, 49818 KB/s, 13 seconds passed
+... 94%, 653888 KB, 49819 KB/s, 13 seconds passed
+... 94%, 653920 KB, 49821 KB/s, 13 seconds passed
+... 94%, 653952 KB, 49822 KB/s, 13 seconds passed
+... 94%, 653984 KB, 49824 KB/s, 13 seconds passed
+... 94%, 654016 KB, 49825 KB/s, 13 seconds passed
+
+.. parsed-literal::
+
+    ... 94%, 654048 KB, 49826 KB/s, 13 seconds passed
+... 94%, 654080 KB, 49828 KB/s, 13 seconds passed
+... 94%, 654112 KB, 49828 KB/s, 13 seconds passed
+... 94%, 654144 KB, 49830 KB/s, 13 seconds passed
+... 94%, 654176 KB, 49831 KB/s, 13 seconds passed
+... 94%, 654208 KB, 49833 KB/s, 13 seconds passed
+... 94%, 654240 KB, 49834 KB/s, 13 seconds passed
+... 94%, 654272 KB, 49836 KB/s, 13 seconds passed
+... 94%, 654304 KB, 49837 KB/s, 13 seconds passed
+... 94%, 654336 KB, 49839 KB/s, 13 seconds passed
+... 94%, 654368 KB, 49840 KB/s, 13 seconds passed
+... 94%, 654400 KB, 49842 KB/s, 13 seconds passed
+... 94%, 654432 KB, 49843 KB/s, 13 seconds passed
+... 94%, 654464 KB, 49844 KB/s, 13 seconds passed
+... 94%, 654496 KB, 49845 KB/s, 13 seconds passed
+... 94%, 654528 KB, 49847 KB/s, 13 seconds passed
+... 94%, 654560 KB, 49848 KB/s, 13 seconds passed
+... 94%, 654592 KB, 49850 KB/s, 13 seconds passed
+... 94%, 654624 KB, 49851 KB/s, 13 seconds passed
+... 94%, 654656 KB, 49852 KB/s, 13 seconds passed
+... 94%, 654688 KB, 49854 KB/s, 13 seconds passed
+... 94%, 654720 KB, 49855 KB/s, 13 seconds passed
+... 94%, 654752 KB, 49856 KB/s, 13 seconds passed
+... 94%, 654784 KB, 49857 KB/s, 13 seconds passed
+... 94%, 654816 KB, 49859 KB/s, 13 seconds passed
+... 94%, 654848 KB, 49860 KB/s, 13 seconds passed
+... 94%, 654880 KB, 49862 KB/s, 13 seconds passed
+... 94%, 654912 KB, 49863 KB/s, 13 seconds passed
+... 94%, 654944 KB, 49865 KB/s, 13 seconds passed
+... 94%, 654976 KB, 49866 KB/s, 13 seconds passed
+... 94%, 655008 KB, 49868 KB/s, 13 seconds passed
+... 94%, 655040 KB, 49869 KB/s, 13 seconds passed
+... 94%, 655072 KB, 49871 KB/s, 13 seconds passed
+... 94%, 655104 KB, 49871 KB/s, 13 seconds passed
+... 94%, 655136 KB, 49872 KB/s, 13 seconds passed
+... 94%, 655168 KB, 49874 KB/s, 13 seconds passed
+... 94%, 655200 KB, 49876 KB/s, 13 seconds passed
+... 94%, 655232 KB, 49877 KB/s, 13 seconds passed
+... 94%, 655264 KB, 49879 KB/s, 13 seconds passed
+... 94%, 655296 KB, 49880 KB/s, 13 seconds passed
+... 94%, 655328 KB, 49881 KB/s, 13 seconds passed
+... 94%, 655360 KB, 49788 KB/s, 13 seconds passed
+... 94%, 655392 KB, 49789 KB/s, 13 seconds passed
+... 94%, 655424 KB, 49790 KB/s, 13 seconds passed
+... 94%, 655456 KB, 49791 KB/s, 13 seconds passed
+... 94%, 655488 KB, 49793 KB/s, 13 seconds passed
+... 94%, 655520 KB, 49794 KB/s, 13 seconds passed
+... 94%, 655552 KB, 49795 KB/s, 13 seconds passed
+... 94%, 655584 KB, 49796 KB/s, 13 seconds passed
+... 94%, 655616 KB, 49797 KB/s, 13 seconds passed
+... 94%, 655648 KB, 49798 KB/s, 13 seconds passed
+... 94%, 655680 KB, 49798 KB/s, 13 seconds passed
+... 94%, 655712 KB, 49797 KB/s, 13 seconds passed
+... 94%, 655744 KB, 49798 KB/s, 13 seconds passed
+... 94%, 655776 KB, 49799 KB/s, 13 seconds passed
+... 94%, 655808 KB, 49800 KB/s, 13 seconds passed
+... 94%, 655840 KB, 49801 KB/s, 13 seconds passed
+... 94%, 655872 KB, 49803 KB/s, 13 seconds passed
+... 94%, 655904 KB, 49804 KB/s, 13 seconds passed
+... 94%, 655936 KB, 49805 KB/s, 13 seconds passed
+... 94%, 655968 KB, 49807 KB/s, 13 seconds passed
+... 94%, 656000 KB, 49808 KB/s, 13 seconds passed
+... 94%, 656032 KB, 49809 KB/s, 13 seconds passed
+... 94%, 656064 KB, 49810 KB/s, 13 seconds passed
+... 94%, 656096 KB, 49812 KB/s, 13 seconds passed
+... 94%, 656128 KB, 49813 KB/s, 13 seconds passed
+... 94%, 656160 KB, 49814 KB/s, 13 seconds passed
+... 94%, 656192 KB, 49816 KB/s, 13 seconds passed
+... 94%, 656224 KB, 49817 KB/s, 13 seconds passed
+... 94%, 656256 KB, 49818 KB/s, 13 seconds passed
+... 94%, 656288 KB, 49819 KB/s, 13 seconds passed
+... 94%, 656320 KB, 49821 KB/s, 13 seconds passed
+... 94%, 656352 KB, 49822 KB/s, 13 seconds passed
+... 94%, 656384 KB, 49823 KB/s, 13 seconds passed
+... 94%, 656416 KB, 49825 KB/s, 13 seconds passed
+... 94%, 656448 KB, 49826 KB/s, 13 seconds passed
+... 94%, 656480 KB, 49827 KB/s, 13 seconds passed
+... 94%, 656512 KB, 49828 KB/s, 13 seconds passed
+... 94%, 656544 KB, 49830 KB/s, 13 seconds passed
+... 94%, 656576 KB, 49831 KB/s, 13 seconds passed
+... 94%, 656608 KB, 49832 KB/s, 13 seconds passed
+... 94%, 656640 KB, 49833 KB/s, 13 seconds passed
+... 94%, 656672 KB, 49835 KB/s, 13 seconds passed
+... 94%, 656704 KB, 49836 KB/s, 13 seconds passed
+... 94%, 656736 KB, 49837 KB/s, 13 seconds passed
+
+.. parsed-literal::
+
+    ... 94%, 656768 KB, 49839 KB/s, 13 seconds passed
+... 94%, 656800 KB, 49840 KB/s, 13 seconds passed
+... 94%, 656832 KB, 49842 KB/s, 13 seconds passed
+... 94%, 656864 KB, 49843 KB/s, 13 seconds passed
+... 94%, 656896 KB, 49845 KB/s, 13 seconds passed
+... 94%, 656928 KB, 49847 KB/s, 13 seconds passed
+... 94%, 656960 KB, 49848 KB/s, 13 seconds passed
+... 94%, 656992 KB, 49850 KB/s, 13 seconds passed
+... 94%, 657024 KB, 49852 KB/s, 13 seconds passed
+... 94%, 657056 KB, 49853 KB/s, 13 seconds passed
+... 94%, 657088 KB, 49855 KB/s, 13 seconds passed
+... 94%, 657120 KB, 49856 KB/s, 13 seconds passed
+... 94%, 657152 KB, 49858 KB/s, 13 seconds passed
+... 94%, 657184 KB, 49859 KB/s, 13 seconds passed
+... 94%, 657216 KB, 49861 KB/s, 13 seconds passed
+... 94%, 657248 KB, 49862 KB/s, 13 seconds passed
+... 94%, 657280 KB, 49863 KB/s, 13 seconds passed
+... 94%, 657312 KB, 49865 KB/s, 13 seconds passed
+... 94%, 657344 KB, 49866 KB/s, 13 seconds passed
+... 94%, 657376 KB, 49868 KB/s, 13 seconds passed
+... 94%, 657408 KB, 49869 KB/s, 13 seconds passed
+... 94%, 657440 KB, 49871 KB/s, 13 seconds passed
+... 94%, 657472 KB, 49711 KB/s, 13 seconds passed
+
+.. parsed-literal::
+
+    ... 94%, 657504 KB, 49693 KB/s, 13 seconds passed
+... 94%, 657536 KB, 49694 KB/s, 13 seconds passed
+... 94%, 657568 KB, 49695 KB/s, 13 seconds passed
+... 94%, 657600 KB, 49696 KB/s, 13 seconds passed
+... 94%, 657632 KB, 49698 KB/s, 13 seconds passed
+... 94%, 657664 KB, 49699 KB/s, 13 seconds passed
+... 94%, 657696 KB, 49700 KB/s, 13 seconds passed
+... 94%, 657728 KB, 49701 KB/s, 13 seconds passed
+... 94%, 657760 KB, 49703 KB/s, 13 seconds passed
+... 94%, 657792 KB, 49704 KB/s, 13 seconds passed
+... 94%, 657824 KB, 49705 KB/s, 13 seconds passed
+... 94%, 657856 KB, 49706 KB/s, 13 seconds passed
+... 94%, 657888 KB, 49708 KB/s, 13 seconds passed
+... 94%, 657920 KB, 49709 KB/s, 13 seconds passed
+... 94%, 657952 KB, 49710 KB/s, 13 seconds passed
+... 94%, 657984 KB, 49711 KB/s, 13 seconds passed
+... 94%, 658016 KB, 49712 KB/s, 13 seconds passed
+... 94%, 658048 KB, 49714 KB/s, 13 seconds passed
+... 94%, 658080 KB, 49715 KB/s, 13 seconds passed
+... 94%, 658112 KB, 49716 KB/s, 13 seconds passed
+... 94%, 658144 KB, 49718 KB/s, 13 seconds passed
+... 94%, 658176 KB, 49719 KB/s, 13 seconds passed
+... 94%, 658208 KB, 49720 KB/s, 13 seconds passed
+... 94%, 658240 KB, 49721 KB/s, 13 seconds passed
+... 94%, 658272 KB, 49723 KB/s, 13 seconds passed
+... 94%, 658304 KB, 49724 KB/s, 13 seconds passed
+... 94%, 658336 KB, 49725 KB/s, 13 seconds passed
+... 94%, 658368 KB, 49726 KB/s, 13 seconds passed
+... 94%, 658400 KB, 49728 KB/s, 13 seconds passed
+... 94%, 658432 KB, 49729 KB/s, 13 seconds passed
+... 94%, 658464 KB, 49730 KB/s, 13 seconds passed
+... 94%, 658496 KB, 49732 KB/s, 13 seconds passed
+... 94%, 658528 KB, 49733 KB/s, 13 seconds passed
+... 94%, 658560 KB, 49734 KB/s, 13 seconds passed
+... 94%, 658592 KB, 49736 KB/s, 13 seconds passed
+... 94%, 658624 KB, 49737 KB/s, 13 seconds passed
+... 94%, 658656 KB, 49739 KB/s, 13 seconds passed
+... 94%, 658688 KB, 49740 KB/s, 13 seconds passed
+... 94%, 658720 KB, 49741 KB/s, 13 seconds passed
+... 94%, 658752 KB, 49742 KB/s, 13 seconds passed
+... 94%, 658784 KB, 49744 KB/s, 13 seconds passed
+... 94%, 658816 KB, 49745 KB/s, 13 seconds passed
+... 94%, 658848 KB, 49746 KB/s, 13 seconds passed
+... 94%, 658880 KB, 49748 KB/s, 13 seconds passed
+... 94%, 658912 KB, 49749 KB/s, 13 seconds passed
+... 94%, 658944 KB, 49751 KB/s, 13 seconds passed
+... 94%, 658976 KB, 49752 KB/s, 13 seconds passed
+... 94%, 659008 KB, 49753 KB/s, 13 seconds passed
+... 94%, 659040 KB, 49755 KB/s, 13 seconds passed
+... 94%, 659072 KB, 49756 KB/s, 13 seconds passed
+... 94%, 659104 KB, 49757 KB/s, 13 seconds passed
+... 94%, 659136 KB, 49759 KB/s, 13 seconds passed
+... 94%, 659168 KB, 49760 KB/s, 13 seconds passed
+... 94%, 659200 KB, 49761 KB/s, 13 seconds passed
+... 94%, 659232 KB, 49763 KB/s, 13 seconds passed
+... 94%, 659264 KB, 49764 KB/s, 13 seconds passed
+... 94%, 659296 KB, 49765 KB/s, 13 seconds passed
+... 94%, 659328 KB, 49767 KB/s, 13 seconds passed
+... 94%, 659360 KB, 49768 KB/s, 13 seconds passed
+... 94%, 659392 KB, 49769 KB/s, 13 seconds passed
+... 94%, 659424 KB, 49771 KB/s, 13 seconds passed
+... 94%, 659456 KB, 49772 KB/s, 13 seconds passed
+... 94%, 659488 KB, 49774 KB/s, 13 seconds passed
+... 94%, 659520 KB, 49776 KB/s, 13 seconds passed
+... 94%, 659552 KB, 49777 KB/s, 13 seconds passed
+... 94%, 659584 KB, 49779 KB/s, 13 seconds passed
+... 94%, 659616 KB, 49781 KB/s, 13 seconds passed
+... 94%, 659648 KB, 49779 KB/s, 13 seconds passed
+... 94%, 659680 KB, 49780 KB/s, 13 seconds passed
+... 94%, 659712 KB, 49782 KB/s, 13 seconds passed
+... 94%, 659744 KB, 49784 KB/s, 13 seconds passed
+... 94%, 659776 KB, 49785 KB/s, 13 seconds passed
+... 94%, 659808 KB, 49786 KB/s, 13 seconds passed
+... 94%, 659840 KB, 49788 KB/s, 13 seconds passed
+... 94%, 659872 KB, 49789 KB/s, 13 seconds passed
+... 94%, 659904 KB, 49790 KB/s, 13 seconds passed
+... 94%, 659936 KB, 49792 KB/s, 13 seconds passed
+... 94%, 659968 KB, 49793 KB/s, 13 seconds passed
+... 94%, 660000 KB, 49795 KB/s, 13 seconds passed
+... 94%, 660032 KB, 49796 KB/s, 13 seconds passed
+... 94%, 660064 KB, 49797 KB/s, 13 seconds passed
+... 94%, 660096 KB, 49799 KB/s, 13 seconds passed
+... 94%, 660128 KB, 49800 KB/s, 13 seconds passed
+... 94%, 660160 KB, 49801 KB/s, 13 seconds passed
+... 94%, 660192 KB, 49803 KB/s, 13 seconds passed
+... 94%, 660224 KB, 49804 KB/s, 13 seconds passed
+... 94%, 660256 KB, 49805 KB/s, 13 seconds passed
+... 94%, 660288 KB, 49807 KB/s, 13 seconds passed
+... 94%, 660320 KB, 49808 KB/s, 13 seconds passed
+... 94%, 660352 KB, 49810 KB/s, 13 seconds passed
+... 94%, 660384 KB, 49811 KB/s, 13 seconds passed
+... 94%, 660416 KB, 49813 KB/s, 13 seconds passed
+... 94%, 660448 KB, 49813 KB/s, 13 seconds passed
+
+.. parsed-literal::
+
+    ... 94%, 660480 KB, 49584 KB/s, 13 seconds passed
+... 94%, 660512 KB, 49585 KB/s, 13 seconds passed
+... 94%, 660544 KB, 49586 KB/s, 13 seconds passed
+... 94%, 660576 KB, 49587 KB/s, 13 seconds passed
+... 94%, 660608 KB, 49587 KB/s, 13 seconds passed
+... 94%, 660640 KB, 49588 KB/s, 13 seconds passed
+... 94%, 660672 KB, 49589 KB/s, 13 seconds passed
+... 94%, 660704 KB, 49590 KB/s, 13 seconds passed
+... 95%, 660736 KB, 49591 KB/s, 13 seconds passed
+... 95%, 660768 KB, 49593 KB/s, 13 seconds passed
+... 95%, 660800 KB, 49594 KB/s, 13 seconds passed
+... 95%, 660832 KB, 49595 KB/s, 13 seconds passed
+... 95%, 660864 KB, 49596 KB/s, 13 seconds passed
+... 95%, 660896 KB, 49598 KB/s, 13 seconds passed
+... 95%, 660928 KB, 49599 KB/s, 13 seconds passed
+... 95%, 660960 KB, 49600 KB/s, 13 seconds passed
+... 95%, 660992 KB, 49601 KB/s, 13 seconds passed
+... 95%, 661024 KB, 49602 KB/s, 13 seconds passed
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+... 95%, 661120 KB, 49604 KB/s, 13 seconds passed
+... 95%, 661152 KB, 49605 KB/s, 13 seconds passed
+... 95%, 661184 KB, 49606 KB/s, 13 seconds passed
+... 95%, 661216 KB, 49608 KB/s, 13 seconds passed
+... 95%, 661248 KB, 49606 KB/s, 13 seconds passed
+... 95%, 661280 KB, 49607 KB/s, 13 seconds passed
+... 95%, 661312 KB, 49608 KB/s, 13 seconds passed
+... 95%, 661344 KB, 49610 KB/s, 13 seconds passed
+... 95%, 661376 KB, 49610 KB/s, 13 seconds passed
+... 95%, 661408 KB, 49612 KB/s, 13 seconds passed
+
+.. parsed-literal::
+
+    ... 95%, 661440 KB, 49613 KB/s, 13 seconds passed
+... 95%, 661472 KB, 49614 KB/s, 13 seconds passed
+... 95%, 661504 KB, 49616 KB/s, 13 seconds passed
+... 95%, 661536 KB, 49617 KB/s, 13 seconds passed
+... 95%, 661568 KB, 49618 KB/s, 13 seconds passed
+... 95%, 661600 KB, 49620 KB/s, 13 seconds passed
+... 95%, 661632 KB, 49621 KB/s, 13 seconds passed
+... 95%, 661664 KB, 49622 KB/s, 13 seconds passed
+... 95%, 661696 KB, 49620 KB/s, 13 seconds passed
+... 95%, 661728 KB, 49621 KB/s, 13 seconds passed
+... 95%, 661760 KB, 49622 KB/s, 13 seconds passed
+... 95%, 661792 KB, 49624 KB/s, 13 seconds passed
+... 95%, 661824 KB, 49625 KB/s, 13 seconds passed
+... 95%, 661856 KB, 49626 KB/s, 13 seconds passed
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+... 95%, 661920 KB, 49625 KB/s, 13 seconds passed
+... 95%, 661952 KB, 49627 KB/s, 13 seconds passed
+... 95%, 661984 KB, 49628 KB/s, 13 seconds passed
+... 95%, 662016 KB, 49629 KB/s, 13 seconds passed
+... 95%, 662048 KB, 49630 KB/s, 13 seconds passed
+... 95%, 662080 KB, 49626 KB/s, 13 seconds passed
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+... 95%, 662144 KB, 49629 KB/s, 13 seconds passed
+... 95%, 662176 KB, 49631 KB/s, 13 seconds passed
+... 95%, 662208 KB, 49632 KB/s, 13 seconds passed
+... 95%, 662240 KB, 49634 KB/s, 13 seconds passed
+... 95%, 662272 KB, 49635 KB/s, 13 seconds passed
+... 95%, 662304 KB, 49636 KB/s, 13 seconds passed
+... 95%, 662336 KB, 49638 KB/s, 13 seconds passed
+... 95%, 662368 KB, 49639 KB/s, 13 seconds passed
+... 95%, 662400 KB, 49640 KB/s, 13 seconds passed
+... 95%, 662432 KB, 49642 KB/s, 13 seconds passed
+... 95%, 662464 KB, 49643 KB/s, 13 seconds passed
+... 95%, 662496 KB, 49645 KB/s, 13 seconds passed
+... 95%, 662528 KB, 49646 KB/s, 13 seconds passed
+... 95%, 662560 KB, 49647 KB/s, 13 seconds passed
+... 95%, 662592 KB, 49649 KB/s, 13 seconds passed
+... 95%, 662624 KB, 49650 KB/s, 13 seconds passed
+... 95%, 662656 KB, 49651 KB/s, 13 seconds passed
+... 95%, 662688 KB, 49653 KB/s, 13 seconds passed
+... 95%, 662720 KB, 49654 KB/s, 13 seconds passed
+... 95%, 662752 KB, 49655 KB/s, 13 seconds passed
+... 95%, 662784 KB, 49656 KB/s, 13 seconds passed
+... 95%, 662816 KB, 49658 KB/s, 13 seconds passed
+... 95%, 662848 KB, 49659 KB/s, 13 seconds passed
+... 95%, 662880 KB, 49661 KB/s, 13 seconds passed
+... 95%, 662912 KB, 49662 KB/s, 13 seconds passed
+... 95%, 662944 KB, 49663 KB/s, 13 seconds passed
+... 95%, 662976 KB, 49664 KB/s, 13 seconds passed
+... 95%, 663008 KB, 49665 KB/s, 13 seconds passed
+... 95%, 663040 KB, 49667 KB/s, 13 seconds passed
+... 95%, 663072 KB, 49668 KB/s, 13 seconds passed
+... 95%, 663104 KB, 49670 KB/s, 13 seconds passed
+... 95%, 663136 KB, 49671 KB/s, 13 seconds passed
+... 95%, 663168 KB, 49673 KB/s, 13 seconds passed
+... 95%, 663200 KB, 49674 KB/s, 13 seconds passed
+... 95%, 663232 KB, 49676 KB/s, 13 seconds passed
+... 95%, 663264 KB, 49677 KB/s, 13 seconds passed
+... 95%, 663296 KB, 49679 KB/s, 13 seconds passed
+... 95%, 663328 KB, 49680 KB/s, 13 seconds passed
+... 95%, 663360 KB, 49681 KB/s, 13 seconds passed
+... 95%, 663392 KB, 49683 KB/s, 13 seconds passed
+... 95%, 663424 KB, 49684 KB/s, 13 seconds passed
+... 95%, 663456 KB, 49685 KB/s, 13 seconds passed
+... 95%, 663488 KB, 49687 KB/s, 13 seconds passed
+... 95%, 663520 KB, 49688 KB/s, 13 seconds passed
+... 95%, 663552 KB, 49690 KB/s, 13 seconds passed
+... 95%, 663584 KB, 49691 KB/s, 13 seconds passed
+... 95%, 663616 KB, 49692 KB/s, 13 seconds passed
+... 95%, 663648 KB, 49693 KB/s, 13 seconds passed
+... 95%, 663680 KB, 49695 KB/s, 13 seconds passed
+... 95%, 663712 KB, 49696 KB/s, 13 seconds passed
+... 95%, 663744 KB, 49697 KB/s, 13 seconds passed
+... 95%, 663776 KB, 49699 KB/s, 13 seconds passed
+... 95%, 663808 KB, 49700 KB/s, 13 seconds passed
+... 95%, 663840 KB, 49702 KB/s, 13 seconds passed
+... 95%, 663872 KB, 49703 KB/s, 13 seconds passed
+... 95%, 663904 KB, 49704 KB/s, 13 seconds passed
+... 95%, 663936 KB, 49705 KB/s, 13 seconds passed
+... 95%, 663968 KB, 49707 KB/s, 13 seconds passed
+... 95%, 664000 KB, 49708 KB/s, 13 seconds passed
+... 95%, 664032 KB, 49710 KB/s, 13 seconds passed
+... 95%, 664064 KB, 49711 KB/s, 13 seconds passed
+... 95%, 664096 KB, 49713 KB/s, 13 seconds passed
+... 95%, 664128 KB, 49714 KB/s, 13 seconds passed
+... 95%, 664160 KB, 49715 KB/s, 13 seconds passed
+... 95%, 664192 KB, 49716 KB/s, 13 seconds passed
+... 95%, 664224 KB, 49718 KB/s, 13 seconds passed
+... 95%, 664256 KB, 49719 KB/s, 13 seconds passed
+... 95%, 664288 KB, 49721 KB/s, 13 seconds passed
+... 95%, 664320 KB, 49722 KB/s, 13 seconds passed
+... 95%, 664352 KB, 49723 KB/s, 13 seconds passed
+... 95%, 664384 KB, 49724 KB/s, 13 seconds passed
+... 95%, 664416 KB, 49726 KB/s, 13 seconds passed
+... 95%, 664448 KB, 49728 KB/s, 13 seconds passed
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+... 95%, 664512 KB, 49730 KB/s, 13 seconds passed
+... 95%, 664544 KB, 49732 KB/s, 13 seconds passed
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+... 95%, 664608 KB, 49734 KB/s, 13 seconds passed
+... 95%, 664640 KB, 49735 KB/s, 13 seconds passed
+... 95%, 664672 KB, 49736 KB/s, 13 seconds passed
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+... 95%, 664736 KB, 49738 KB/s, 13 seconds passed
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+... 95%, 664800 KB, 49742 KB/s, 13 seconds passed
+... 95%, 664832 KB, 49744 KB/s, 13 seconds passed
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+... 95%, 664992 KB, 49750 KB/s, 13 seconds passed
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+... 95%, 665152 KB, 49757 KB/s, 13 seconds passed
+... 95%, 665184 KB, 49759 KB/s, 13 seconds passed
+... 95%, 665216 KB, 49759 KB/s, 13 seconds passed
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+... 95%, 665280 KB, 49762 KB/s, 13 seconds passed
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+... 95%, 665344 KB, 49765 KB/s, 13 seconds passed
+... 95%, 665376 KB, 49766 KB/s, 13 seconds passed
+... 95%, 665408 KB, 49768 KB/s, 13 seconds passed
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+... 95%, 665472 KB, 49771 KB/s, 13 seconds passed
+... 95%, 665504 KB, 49772 KB/s, 13 seconds passed
+... 95%, 665536 KB, 49773 KB/s, 13 seconds passed
+... 95%, 665568 KB, 49775 KB/s, 13 seconds passed
+
+.. parsed-literal::
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+... 95%, 667392 KB, 49558 KB/s, 13 seconds passed
+... 95%, 667424 KB, 49560 KB/s, 13 seconds passed
+... 95%, 667456 KB, 49561 KB/s, 13 seconds passed
+... 95%, 667488 KB, 49562 KB/s, 13 seconds passed
+... 95%, 667520 KB, 49564 KB/s, 13 seconds passed
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+... 95%, 667584 KB, 49566 KB/s, 13 seconds passed
+... 95%, 667616 KB, 49568 KB/s, 13 seconds passed
+... 95%, 667648 KB, 49569 KB/s, 13 seconds passed
+... 95%, 667680 KB, 49570 KB/s, 13 seconds passed
+... 96%, 667712 KB, 49572 KB/s, 13 seconds passed
+... 96%, 667744 KB, 49573 KB/s, 13 seconds passed
+... 96%, 667776 KB, 49574 KB/s, 13 seconds passed
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+... 96%, 667840 KB, 49577 KB/s, 13 seconds passed
+... 96%, 667872 KB, 49579 KB/s, 13 seconds passed
+... 96%, 667904 KB, 49580 KB/s, 13 seconds passed
+... 96%, 667936 KB, 49581 KB/s, 13 seconds passed
+... 96%, 667968 KB, 49583 KB/s, 13 seconds passed
+... 96%, 668000 KB, 49584 KB/s, 13 seconds passed
+... 96%, 668032 KB, 49585 KB/s, 13 seconds passed
+... 96%, 668064 KB, 49587 KB/s, 13 seconds passed
+... 96%, 668096 KB, 49588 KB/s, 13 seconds passed
+... 96%, 668128 KB, 49589 KB/s, 13 seconds passed
+... 96%, 668160 KB, 49591 KB/s, 13 seconds passed
+... 96%, 668192 KB, 49592 KB/s, 13 seconds passed
+... 96%, 668224 KB, 49593 KB/s, 13 seconds passed
+... 96%, 668256 KB, 49595 KB/s, 13 seconds passed
+... 96%, 668288 KB, 49596 KB/s, 13 seconds passed
+... 96%, 668320 KB, 49597 KB/s, 13 seconds passed
+... 96%, 668352 KB, 49598 KB/s, 13 seconds passed
+... 96%, 668384 KB, 49599 KB/s, 13 seconds passed
+... 96%, 668416 KB, 49601 KB/s, 13 seconds passed
+... 96%, 668448 KB, 49603 KB/s, 13 seconds passed
+... 96%, 668480 KB, 49604 KB/s, 13 seconds passed
+... 96%, 668512 KB, 49605 KB/s, 13 seconds passed
+... 96%, 668544 KB, 49606 KB/s, 13 seconds passed
+... 96%, 668576 KB, 49608 KB/s, 13 seconds passed
+... 96%, 668608 KB, 49609 KB/s, 13 seconds passed
+... 96%, 668640 KB, 49611 KB/s, 13 seconds passed
+... 96%, 668672 KB, 49612 KB/s, 13 seconds passed
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+... 96%, 668736 KB, 49615 KB/s, 13 seconds passed
+... 96%, 668768 KB, 49616 KB/s, 13 seconds passed
+... 96%, 668800 KB, 49618 KB/s, 13 seconds passed
+... 96%, 668832 KB, 49619 KB/s, 13 seconds passed
+... 96%, 668864 KB, 49620 KB/s, 13 seconds passed
+... 96%, 668896 KB, 49622 KB/s, 13 seconds passed
+... 96%, 668928 KB, 49623 KB/s, 13 seconds passed
+... 96%, 668960 KB, 49624 KB/s, 13 seconds passed
+... 96%, 668992 KB, 49626 KB/s, 13 seconds passed
+... 96%, 669024 KB, 49627 KB/s, 13 seconds passed
+... 96%, 669056 KB, 49628 KB/s, 13 seconds passed
+... 96%, 669088 KB, 49630 KB/s, 13 seconds passed
+... 96%, 669120 KB, 49631 KB/s, 13 seconds passed
+... 96%, 669152 KB, 49633 KB/s, 13 seconds passed
+... 96%, 669184 KB, 49634 KB/s, 13 seconds passed
+... 96%, 669216 KB, 49635 KB/s, 13 seconds passed
+... 96%, 669248 KB, 49636 KB/s, 13 seconds passed
+... 96%, 669280 KB, 49638 KB/s, 13 seconds passed
+... 96%, 669312 KB, 49639 KB/s, 13 seconds passed
+... 96%, 669344 KB, 49641 KB/s, 13 seconds passed
+... 96%, 669376 KB, 49642 KB/s, 13 seconds passed
+... 96%, 669408 KB, 49643 KB/s, 13 seconds passed
+... 96%, 669440 KB, 49645 KB/s, 13 seconds passed
+... 96%, 669472 KB, 49646 KB/s, 13 seconds passed
+... 96%, 669504 KB, 49647 KB/s, 13 seconds passed
+
+.. parsed-literal::
+
+    ... 96%, 669536 KB, 49645 KB/s, 13 seconds passed
+... 96%, 669568 KB, 49646 KB/s, 13 seconds passed
+... 96%, 669600 KB, 49628 KB/s, 13 seconds passed
+... 96%, 669632 KB, 49629 KB/s, 13 seconds passed
+... 96%, 669664 KB, 49628 KB/s, 13 seconds passed
+... 96%, 669696 KB, 49629 KB/s, 13 seconds passed
+... 96%, 669728 KB, 49630 KB/s, 13 seconds passed
+... 96%, 669760 KB, 49632 KB/s, 13 seconds passed
+... 96%, 669792 KB, 49633 KB/s, 13 seconds passed
+... 96%, 669824 KB, 49635 KB/s, 13 seconds passed
+... 96%, 669856 KB, 49636 KB/s, 13 seconds passed
+... 96%, 669888 KB, 49638 KB/s, 13 seconds passed
+... 96%, 669920 KB, 49639 KB/s, 13 seconds passed
+... 96%, 669952 KB, 49640 KB/s, 13 seconds passed
+... 96%, 669984 KB, 49642 KB/s, 13 seconds passed
+... 96%, 670016 KB, 49643 KB/s, 13 seconds passed
+... 96%, 670048 KB, 49644 KB/s, 13 seconds passed
+... 96%, 670080 KB, 49646 KB/s, 13 seconds passed
+... 96%, 670112 KB, 49647 KB/s, 13 seconds passed
+... 96%, 670144 KB, 49648 KB/s, 13 seconds passed
+... 96%, 670176 KB, 49650 KB/s, 13 seconds passed
+... 96%, 670208 KB, 49651 KB/s, 13 seconds passed
+... 96%, 670240 KB, 49652 KB/s, 13 seconds passed
+... 96%, 670272 KB, 49654 KB/s, 13 seconds passed
+... 96%, 670304 KB, 49655 KB/s, 13 seconds passed
+... 96%, 670336 KB, 49656 KB/s, 13 seconds passed
+... 96%, 670368 KB, 49658 KB/s, 13 seconds passed
+... 96%, 670400 KB, 49659 KB/s, 13 seconds passed
+... 96%, 670432 KB, 49660 KB/s, 13 seconds passed
+... 96%, 670464 KB, 49662 KB/s, 13 seconds passed
+... 96%, 670496 KB, 49663 KB/s, 13 seconds passed
+... 96%, 670528 KB, 49664 KB/s, 13 seconds passed
+... 96%, 670560 KB, 49666 KB/s, 13 seconds passed
+... 96%, 670592 KB, 49667 KB/s, 13 seconds passed
+... 96%, 670624 KB, 49668 KB/s, 13 seconds passed
+... 96%, 670656 KB, 49670 KB/s, 13 seconds passed
+... 96%, 670688 KB, 49671 KB/s, 13 seconds passed
+
+.. parsed-literal::
+
+    ... 96%, 670720 KB, 49498 KB/s, 13 seconds passed
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+... 96%, 670784 KB, 49500 KB/s, 13 seconds passed
+... 96%, 670816 KB, 49501 KB/s, 13 seconds passed
+... 96%, 670848 KB, 49500 KB/s, 13 seconds passed
+... 96%, 670880 KB, 49496 KB/s, 13 seconds passed
+... 96%, 670912 KB, 49497 KB/s, 13 seconds passed
+... 96%, 670944 KB, 49498 KB/s, 13 seconds passed
+... 96%, 670976 KB, 49500 KB/s, 13 seconds passed
+... 96%, 671008 KB, 49501 KB/s, 13 seconds passed
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+... 96%, 671072 KB, 49500 KB/s, 13 seconds passed
+... 96%, 671104 KB, 49501 KB/s, 13 seconds passed
+... 96%, 671136 KB, 49502 KB/s, 13 seconds passed
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+... 96%, 671200 KB, 49505 KB/s, 13 seconds passed
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+... 96%, 671296 KB, 49505 KB/s, 13 seconds passed
+... 96%, 671328 KB, 49506 KB/s, 13 seconds passed
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+... 96%, 671392 KB, 49508 KB/s, 13 seconds passed
+... 96%, 671424 KB, 49509 KB/s, 13 seconds passed
+... 96%, 671456 KB, 49511 KB/s, 13 seconds passed
+... 96%, 671488 KB, 49512 KB/s, 13 seconds passed
+... 96%, 671520 KB, 49513 KB/s, 13 seconds passed
+... 96%, 671552 KB, 49514 KB/s, 13 seconds passed
+... 96%, 671584 KB, 49514 KB/s, 13 seconds passed
+... 96%, 671616 KB, 49515 KB/s, 13 seconds passed
+... 96%, 671648 KB, 49517 KB/s, 13 seconds passed
+... 96%, 671680 KB, 49516 KB/s, 13 seconds passed
+... 96%, 671712 KB, 49518 KB/s, 13 seconds passed
+... 96%, 671744 KB, 49519 KB/s, 13 seconds passed
+... 96%, 671776 KB, 49520 KB/s, 13 seconds passed
+... 96%, 671808 KB, 49521 KB/s, 13 seconds passed
+... 96%, 671840 KB, 49522 KB/s, 13 seconds passed
+... 96%, 671872 KB, 49524 KB/s, 13 seconds passed
+... 96%, 671904 KB, 49525 KB/s, 13 seconds passed
+... 96%, 671936 KB, 49526 KB/s, 13 seconds passed
+... 96%, 671968 KB, 49527 KB/s, 13 seconds passed
+... 96%, 672000 KB, 49528 KB/s, 13 seconds passed
+... 96%, 672032 KB, 49529 KB/s, 13 seconds passed
+... 96%, 672064 KB, 49531 KB/s, 13 seconds passed
+... 96%, 672096 KB, 49532 KB/s, 13 seconds passed
+... 96%, 672128 KB, 49533 KB/s, 13 seconds passed
+... 96%, 672160 KB, 49533 KB/s, 13 seconds passed
+... 96%, 672192 KB, 49535 KB/s, 13 seconds passed
+... 96%, 672224 KB, 49536 KB/s, 13 seconds passed
+... 96%, 672256 KB, 49537 KB/s, 13 seconds passed
+... 96%, 672288 KB, 49538 KB/s, 13 seconds passed
+... 96%, 672320 KB, 49539 KB/s, 13 seconds passed
+... 96%, 672352 KB, 49541 KB/s, 13 seconds passed
+... 96%, 672384 KB, 49542 KB/s, 13 seconds passed
+... 96%, 672416 KB, 49543 KB/s, 13 seconds passed
+... 96%, 672448 KB, 49544 KB/s, 13 seconds passed
+... 96%, 672480 KB, 49545 KB/s, 13 seconds passed
+... 96%, 672512 KB, 49547 KB/s, 13 seconds passed
+... 96%, 672544 KB, 49548 KB/s, 13 seconds passed
+... 96%, 672576 KB, 49549 KB/s, 13 seconds passed
+... 96%, 672608 KB, 49550 KB/s, 13 seconds passed
+... 96%, 672640 KB, 49551 KB/s, 13 seconds passed
+... 96%, 672672 KB, 49553 KB/s, 13 seconds passed
+... 96%, 672704 KB, 49554 KB/s, 13 seconds passed
+... 96%, 672736 KB, 49555 KB/s, 13 seconds passed
+... 96%, 672768 KB, 49557 KB/s, 13 seconds passed
+... 96%, 672800 KB, 49558 KB/s, 13 seconds passed
+... 96%, 672832 KB, 49559 KB/s, 13 seconds passed
+... 96%, 672864 KB, 49561 KB/s, 13 seconds passed
+... 96%, 672896 KB, 49562 KB/s, 13 seconds passed
+... 96%, 672928 KB, 49563 KB/s, 13 seconds passed
+... 96%, 672960 KB, 49565 KB/s, 13 seconds passed
+... 96%, 672992 KB, 49566 KB/s, 13 seconds passed
+... 96%, 673024 KB, 49567 KB/s, 13 seconds passed
+... 96%, 673056 KB, 49568 KB/s, 13 seconds passed
+... 96%, 673088 KB, 49569 KB/s, 13 seconds passed
+... 96%, 673120 KB, 49571 KB/s, 13 seconds passed
+... 96%, 673152 KB, 49572 KB/s, 13 seconds passed
+... 96%, 673184 KB, 49573 KB/s, 13 seconds passed
+... 96%, 673216 KB, 49574 KB/s, 13 seconds passed
+... 96%, 673248 KB, 49576 KB/s, 13 seconds passed
+... 96%, 673280 KB, 49577 KB/s, 13 seconds passed
+... 96%, 673312 KB, 49579 KB/s, 13 seconds passed
+... 96%, 673344 KB, 49579 KB/s, 13 seconds passed
+... 96%, 673376 KB, 49581 KB/s, 13 seconds passed
+... 96%, 673408 KB, 49582 KB/s, 13 seconds passed
+... 96%, 673440 KB, 49583 KB/s, 13 seconds passed
+... 96%, 673472 KB, 49584 KB/s, 13 seconds passed
+... 96%, 673504 KB, 49586 KB/s, 13 seconds passed
+... 96%, 673536 KB, 49587 KB/s, 13 seconds passed
+... 96%, 673568 KB, 49588 KB/s, 13 seconds passed
+... 96%, 673600 KB, 49590 KB/s, 13 seconds passed
+... 96%, 673632 KB, 49591 KB/s, 13 seconds passed
+... 96%, 673664 KB, 49592 KB/s, 13 seconds passed
+... 96%, 673696 KB, 49594 KB/s, 13 seconds passed
+... 96%, 673728 KB, 49595 KB/s, 13 seconds passed
+... 96%, 673760 KB, 49596 KB/s, 13 seconds passed
+... 96%, 673792 KB, 49597 KB/s, 13 seconds passed
+... 96%, 673824 KB, 49599 KB/s, 13 seconds passed
+... 96%, 673856 KB, 49600 KB/s, 13 seconds passed
+... 96%, 673888 KB, 49601 KB/s, 13 seconds passed
+... 96%, 673920 KB, 49603 KB/s, 13 seconds passed
+... 96%, 673952 KB, 49604 KB/s, 13 seconds passed
+... 96%, 673984 KB, 49605 KB/s, 13 seconds passed
+... 96%, 674016 KB, 49607 KB/s, 13 seconds passed
+... 96%, 674048 KB, 49608 KB/s, 13 seconds passed
+... 96%, 674080 KB, 49609 KB/s, 13 seconds passed
+... 96%, 674112 KB, 49611 KB/s, 13 seconds passed
+... 96%, 674144 KB, 49612 KB/s, 13 seconds passed
+
+.. parsed-literal::
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+... 96%, 674240 KB, 49616 KB/s, 13 seconds passed
+... 96%, 674272 KB, 49617 KB/s, 13 seconds passed
+... 96%, 674304 KB, 49618 KB/s, 13 seconds passed
+... 96%, 674336 KB, 49620 KB/s, 13 seconds passed
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+... 96%, 674400 KB, 49623 KB/s, 13 seconds passed
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+... 96%, 674496 KB, 49627 KB/s, 13 seconds passed
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+... 96%, 674592 KB, 49631 KB/s, 13 seconds passed
+... 96%, 674624 KB, 49632 KB/s, 13 seconds passed
+... 97%, 674656 KB, 49633 KB/s, 13 seconds passed
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+... 97%, 674976 KB, 49606 KB/s, 13 seconds passed
+... 97%, 675008 KB, 49608 KB/s, 13 seconds passed
+... 97%, 675040 KB, 49609 KB/s, 13 seconds passed
+... 97%, 675072 KB, 49610 KB/s, 13 seconds passed
+... 97%, 675104 KB, 49612 KB/s, 13 seconds passed
+... 97%, 675136 KB, 49613 KB/s, 13 seconds passed
+... 97%, 675168 KB, 49614 KB/s, 13 seconds passed
+... 97%, 675200 KB, 49616 KB/s, 13 seconds passed
+... 97%, 675232 KB, 49617 KB/s, 13 seconds passed
+... 97%, 675264 KB, 49618 KB/s, 13 seconds passed
+... 97%, 675296 KB, 49620 KB/s, 13 seconds passed
+... 97%, 675328 KB, 49621 KB/s, 13 seconds passed
+... 97%, 675360 KB, 49622 KB/s, 13 seconds passed
+... 97%, 675392 KB, 49624 KB/s, 13 seconds passed
+... 97%, 675424 KB, 49625 KB/s, 13 seconds passed
+... 97%, 675456 KB, 49626 KB/s, 13 seconds passed
+... 97%, 675488 KB, 49628 KB/s, 13 seconds passed
+... 97%, 675520 KB, 49629 KB/s, 13 seconds passed
+... 97%, 675552 KB, 49630 KB/s, 13 seconds passed
+... 97%, 675584 KB, 49632 KB/s, 13 seconds passed
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+... 97%, 675648 KB, 49634 KB/s, 13 seconds passed
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+... 97%, 675744 KB, 49638 KB/s, 13 seconds passed
+... 97%, 675776 KB, 49640 KB/s, 13 seconds passed
+... 97%, 675808 KB, 49641 KB/s, 13 seconds passed
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+... 97%, 675936 KB, 49586 KB/s, 13 seconds passed
+... 97%, 675968 KB, 49585 KB/s, 13 seconds passed
+... 97%, 676000 KB, 49587 KB/s, 13 seconds passed
+... 97%, 676032 KB, 49588 KB/s, 13 seconds passed
+... 97%, 676064 KB, 49589 KB/s, 13 seconds passed
+... 97%, 676096 KB, 49590 KB/s, 13 seconds passed
+... 97%, 676128 KB, 49591 KB/s, 13 seconds passed
+... 97%, 676160 KB, 49593 KB/s, 13 seconds passed
+... 97%, 676192 KB, 49594 KB/s, 13 seconds passed
+... 97%, 676224 KB, 49595 KB/s, 13 seconds passed
+... 97%, 676256 KB, 49596 KB/s, 13 seconds passed
+... 97%, 676288 KB, 49598 KB/s, 13 seconds passed
+... 97%, 676320 KB, 49599 KB/s, 13 seconds passed
+... 97%, 676352 KB, 49600 KB/s, 13 seconds passed
+... 97%, 676384 KB, 49601 KB/s, 13 seconds passed
+... 97%, 676416 KB, 49602 KB/s, 13 seconds passed
+... 97%, 676448 KB, 49604 KB/s, 13 seconds passed
+... 97%, 676480 KB, 49605 KB/s, 13 seconds passed
+... 97%, 676512 KB, 49606 KB/s, 13 seconds passed
+... 97%, 676544 KB, 49607 KB/s, 13 seconds passed
+... 97%, 676576 KB, 49609 KB/s, 13 seconds passed
+... 97%, 676608 KB, 49610 KB/s, 13 seconds passed
+... 97%, 676640 KB, 49611 KB/s, 13 seconds passed
+... 97%, 676672 KB, 49612 KB/s, 13 seconds passed
+... 97%, 676704 KB, 49613 KB/s, 13 seconds passed
+
+.. parsed-literal::
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+... 97%, 676992 KB, 49625 KB/s, 13 seconds passed
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+... 97%, 677088 KB, 49628 KB/s, 13 seconds passed
+... 97%, 677120 KB, 49629 KB/s, 13 seconds passed
+... 97%, 677152 KB, 49631 KB/s, 13 seconds passed
+... 97%, 677184 KB, 49632 KB/s, 13 seconds passed
+... 97%, 677216 KB, 49634 KB/s, 13 seconds passed
+... 97%, 677248 KB, 49635 KB/s, 13 seconds passed
+... 97%, 677280 KB, 49637 KB/s, 13 seconds passed
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+... 97%, 677376 KB, 49642 KB/s, 13 seconds passed
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+... 97%, 677728 KB, 49659 KB/s, 13 seconds passed
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+... 97%, 678016 KB, 49671 KB/s, 13 seconds passed
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+... 97%, 678144 KB, 49675 KB/s, 13 seconds passed
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+... 97%, 678208 KB, 49678 KB/s, 13 seconds passed
+... 97%, 678240 KB, 49680 KB/s, 13 seconds passed
+... 97%, 678272 KB, 49681 KB/s, 13 seconds passed
+... 97%, 678304 KB, 49683 KB/s, 13 seconds passed
+... 97%, 678336 KB, 49684 KB/s, 13 seconds passed
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+... 97%, 678400 KB, 49687 KB/s, 13 seconds passed
+... 97%, 678432 KB, 49688 KB/s, 13 seconds passed
+... 97%, 678464 KB, 49689 KB/s, 13 seconds passed
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+... 97%, 678528 KB, 49692 KB/s, 13 seconds passed
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+... 97%, 678720 KB, 49700 KB/s, 13 seconds passed
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+... 97%, 678784 KB, 49702 KB/s, 13 seconds passed
+... 97%, 678816 KB, 49704 KB/s, 13 seconds passed
+... 97%, 678848 KB, 49705 KB/s, 13 seconds passed
+... 97%, 678880 KB, 49706 KB/s, 13 seconds passed
+... 97%, 678912 KB, 49708 KB/s, 13 seconds passed
+... 97%, 678944 KB, 49709 KB/s, 13 seconds passed
+... 97%, 678976 KB, 49711 KB/s, 13 seconds passed
+... 97%, 679008 KB, 49712 KB/s, 13 seconds passed
+... 97%, 679040 KB, 49713 KB/s, 13 seconds passed
+... 97%, 679072 KB, 49714 KB/s, 13 seconds passed
+... 97%, 679104 KB, 49716 KB/s, 13 seconds passed
+... 97%, 679136 KB, 49717 KB/s, 13 seconds passed
+... 97%, 679168 KB, 49719 KB/s, 13 seconds passed
+... 97%, 679200 KB, 49720 KB/s, 13 seconds passed
+... 97%, 679232 KB, 49721 KB/s, 13 seconds passed
+... 97%, 679264 KB, 49656 KB/s, 13 seconds passed
+... 97%, 679296 KB, 49656 KB/s, 13 seconds passed
+... 97%, 679328 KB, 49658 KB/s, 13 seconds passed
+... 97%, 679360 KB, 49659 KB/s, 13 seconds passed
+... 97%, 679392 KB, 49660 KB/s, 13 seconds passed
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+... 97%, 679520 KB, 49665 KB/s, 13 seconds passed
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+... 97%, 679840 KB, 49677 KB/s, 13 seconds passed
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+... 97%, 679904 KB, 49680 KB/s, 13 seconds passed
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+... 97%, 680032 KB, 49685 KB/s, 13 seconds passed
+... 97%, 680064 KB, 49686 KB/s, 13 seconds passed
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+... 97%, 680192 KB, 49691 KB/s, 13 seconds passed
+... 97%, 680224 KB, 49692 KB/s, 13 seconds passed
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+... 97%, 680352 KB, 49697 KB/s, 13 seconds passed
+... 97%, 680384 KB, 49698 KB/s, 13 seconds passed
+... 97%, 680416 KB, 49699 KB/s, 13 seconds passed
+
+.. parsed-literal::
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+... 97%, 680512 KB, 49703 KB/s, 13 seconds passed
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+... 97%, 680832 KB, 49716 KB/s, 13 seconds passed
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+... 97%, 680960 KB, 49672 KB/s, 13 seconds passed
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+... 97%, 681280 KB, 49655 KB/s, 13 seconds passed
+... 97%, 681312 KB, 49656 KB/s, 13 seconds passed
+... 97%, 681344 KB, 49658 KB/s, 13 seconds passed
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+... 97%, 681408 KB, 49660 KB/s, 13 seconds passed
+... 97%, 681440 KB, 49661 KB/s, 13 seconds passed
+... 97%, 681472 KB, 49662 KB/s, 13 seconds passed
+... 97%, 681504 KB, 49664 KB/s, 13 seconds passed
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+... 98%, 681600 KB, 49667 KB/s, 13 seconds passed
+... 98%, 681632 KB, 49668 KB/s, 13 seconds passed
+... 98%, 681664 KB, 49670 KB/s, 13 seconds passed
+... 98%, 681696 KB, 49671 KB/s, 13 seconds passed
+... 98%, 681728 KB, 49672 KB/s, 13 seconds passed
+... 98%, 681760 KB, 49673 KB/s, 13 seconds passed
+... 98%, 681792 KB, 49674 KB/s, 13 seconds passed
+... 98%, 681824 KB, 49676 KB/s, 13 seconds passed
+... 98%, 681856 KB, 49677 KB/s, 13 seconds passed
+... 98%, 681888 KB, 49678 KB/s, 13 seconds passed
+... 98%, 681920 KB, 49679 KB/s, 13 seconds passed
+... 98%, 681952 KB, 49681 KB/s, 13 seconds passed
+... 98%, 681984 KB, 49682 KB/s, 13 seconds passed
+... 98%, 682016 KB, 49683 KB/s, 13 seconds passed
+... 98%, 682048 KB, 49684 KB/s, 13 seconds passed
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+... 98%, 682112 KB, 49687 KB/s, 13 seconds passed
+... 98%, 682144 KB, 49688 KB/s, 13 seconds passed
+... 98%, 682176 KB, 49689 KB/s, 13 seconds passed
+... 98%, 682208 KB, 49690 KB/s, 13 seconds passed
+... 98%, 682240 KB, 49691 KB/s, 13 seconds passed
+... 98%, 682272 KB, 49693 KB/s, 13 seconds passed
+... 98%, 682304 KB, 49694 KB/s, 13 seconds passed
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+... 98%, 682432 KB, 49699 KB/s, 13 seconds passed
+... 98%, 682464 KB, 49700 KB/s, 13 seconds passed
+... 98%, 682496 KB, 49701 KB/s, 13 seconds passed
+... 98%, 682528 KB, 49702 KB/s, 13 seconds passed
+... 98%, 682560 KB, 49704 KB/s, 13 seconds passed
+... 98%, 682592 KB, 49705 KB/s, 13 seconds passed
+... 98%, 682624 KB, 49706 KB/s, 13 seconds passed
+... 98%, 682656 KB, 49707 KB/s, 13 seconds passed
+... 98%, 682688 KB, 49709 KB/s, 13 seconds passed
+... 98%, 682720 KB, 49710 KB/s, 13 seconds passed
+... 98%, 682752 KB, 49711 KB/s, 13 seconds passed
+... 98%, 682784 KB, 49713 KB/s, 13 seconds passed
+... 98%, 682816 KB, 49714 KB/s, 13 seconds passed
+... 98%, 682848 KB, 49716 KB/s, 13 seconds passed
+... 98%, 682880 KB, 49717 KB/s, 13 seconds passed
+... 98%, 682912 KB, 49719 KB/s, 13 seconds passed
+... 98%, 682944 KB, 49721 KB/s, 13 seconds passed
+... 98%, 682976 KB, 49722 KB/s, 13 seconds passed
+... 98%, 683008 KB, 49724 KB/s, 13 seconds passed
+... 98%, 683040 KB, 49725 KB/s, 13 seconds passed
+... 98%, 683072 KB, 49727 KB/s, 13 seconds passed
+... 98%, 683104 KB, 49728 KB/s, 13 seconds passed
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+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
+
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+... 99%, 693280 KB, 49889 KB/s, 13 seconds passed
+
+.. parsed-literal::
+
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+... 100%, 695506 KB, 49961 KB/s, 13 seconds passed
+
+
+
+.. parsed-literal::
+
+
+    ========== Downloading model/public/text-recognition-resnet-fc/vedastr/addict-2.4.0-py3-none-any.whl
+
+
+.. parsed-literal::
+
+    ... 100%, 3 KB, 15323 KB/s, 0 seconds passed
+
+
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/heads/__init__.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/__init__.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/sequences/__init__.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/component.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/feature_extractors/decoders/__init__.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/feature_extractors/decoders/bricks/__init__.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/feature_extractors/encoders/backbones/__init__.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/feature_extractors/encoders/enhance_modules/__init__.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/utils/__init__.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/utils/__init__.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/utils/config.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/utils/config.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/utils/config.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/utils/config.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/utils/config.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/feature_extractors/encoders/backbones/resnet.py
+    ========== Replacing text in model/public/text-recognition-resnet-fc/vedastr/models/bodies/feature_extractors/encoders/backbones/resnet.py
+
+
+.. parsed-literal::
+
+    ========== Unpacking model/public/text-recognition-resnet-fc/vedastr/addict-2.4.0-py3-none-any.whl
+
+
+
+
+Finished downloading horizontal-text-detection-0001,
+text-recognition-resnet-fc.
+
+
+.. code:: ipython3
+
+    ### The text-recognition-resnet-fc model consists of many files. All filenames are printed in
+    ### the output of Model Downloader. Uncomment the next two lines to show this output.
+
+    # for line in download_result:
+    #    print(line)
+
+Convert Models
+--------------
+
+
+
+The downloaded detection model is an Intel model, which is already in
+OpenVINO Intermediate Representation (OpenVINO IR) format. The text
+recognition model is a public model which needs to be converted to
+OpenVINO IR. Since this model was downloaded from Open Model Zoo, use
+Model Converter to convert the model to OpenVINO IR format.
+
+The output of Model Converter will be displayed. When the conversion is
+successful, the last lines of output will include
+``[ SUCCESS ] Generated IR version 11 model.``
+
+.. code:: ipython3
+
+    convert_command = f"omz_converter --name {recognition_model} --precisions {precision} --download_dir {model_dir} --output_dir {model_dir}"
+    display(Markdown(f"Convert command: `{convert_command}`"))
+    display(Markdown(f"Converting {recognition_model}..."))
+    ! $convert_command
+
+
+
+Convert command:
+``omz_converter --name text-recognition-resnet-fc --precisions FP16 --download_dir model --output_dir model``
+
+
+
+Converting text-recognition-resnet-fc…
+
+
+.. parsed-literal::
+
+    ========== Converting text-recognition-resnet-fc to ONNX
+    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/text-recognition-resnet-fc --model-path=model/public/text-recognition-resnet-fc --model-name=get_model --import-module=model '--model-param=file_config=r"model/public/text-recognition-resnet-fc/vedastr/configs/resnet_fc.py"' '--model-param=weights=r"model/public/text-recognition-resnet-fc/vedastr/ckpt/resnet_fc.pth"' --input-shape=1,1,32,100 --input-names=input --output-names=output --output-file=model/public/text-recognition-resnet-fc/resnet_fc.onnx
+
+
+
+.. parsed-literal::
+
+    ONNX check passed successfully.
+
+
+.. parsed-literal::
+
+
+    ========== Converting text-recognition-resnet-fc to IR (FP16)
+    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/text-recognition-resnet-fc/FP16 --model_name=text-recognition-resnet-fc --input=input '--mean_values=input[127.5]' '--scale_values=input[127.5]' --output=output --input_model=model/public/text-recognition-resnet-fc/resnet_fc.onnx '--layout=input(NCHW)' '--input_shape=[1, 1, 32, 100]' --compress_to_fp16=True
+
+
+
+.. parsed-literal::
+
+    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
+    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
+
+
+.. parsed-literal::
+
+    [ SUCCESS ] Generated IR version 11 model.
+    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/optical-character-recognition/model/public/text-recognition-resnet-fc/FP16/text-recognition-resnet-fc.xml
+    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/optical-character-recognition/model/public/text-recognition-resnet-fc/FP16/text-recognition-resnet-fc.bin
+
+
+
+Select inference device
+-----------------------
+
+
+
+select device from dropdown list for running inference using OpenVINO
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+Object Detection
+----------------
+
+
+
+Load a detection model, load an image, do inference and get the
+detection inference result.
+
+Load a Detection Model
+~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    detection_model = core.read_model(model=detection_model_path, weights=detection_model_path.with_suffix(".bin"))
+    detection_compiled_model = core.compile_model(model=detection_model, device_name=device.value)
+
+    detection_input_layer = detection_compiled_model.input(0)
+
+Load an Image
+~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    # The `image_file` variable can point to a URL or a local image.
+    image_file = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg"
+
+    image = load_image(image_file)
+
+    # N,C,H,W = batch size, number of channels, height, width.
+    N, C, H, W = detection_input_layer.shape
+
+    # Resize the image to meet network expected input sizes.
+    resized_image = cv2.resize(image, (W, H))
+
+    # Reshape to the network input shape.
+    input_image = np.expand_dims(resized_image.transpose(2, 0, 1), 0)
+
+    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB));
+
+
+
+.. image:: optical-character-recognition-with-output_files/optical-character-recognition-with-output_16_0.png
+
+
+Do Inference
+~~~~~~~~~~~~
+
+
+
+Text boxes are detected in the images and returned as blobs of data in
+the shape of ``[100, 5]``. Each description of detection has the
+``[x_min, y_min, x_max, y_max, conf]`` format.
+
+.. code:: ipython3
+
+    output_key = detection_compiled_model.output("boxes")
+    boxes = detection_compiled_model([input_image])[output_key]
+
+    # Remove zero only boxes.
+    boxes = boxes[~np.all(boxes == 0, axis=1)]
+
+Get Detection Results
+~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    def multiply_by_ratio(ratio_x, ratio_y, box):
+        return [max(shape * ratio_y, 10) if idx % 2 else shape * ratio_x for idx, shape in enumerate(box[:-1])]
+
+
+    def run_preprocesing_on_crop(crop, net_shape):
+        temp_img = cv2.resize(crop, net_shape)
+        temp_img = temp_img.reshape((1,) * 2 + temp_img.shape)
+        return temp_img
+
+
+    def convert_result_to_image(bgr_image, resized_image, boxes, threshold=0.3, conf_labels=True):
+        # Define colors for boxes and descriptions.
+        colors = {"red": (255, 0, 0), "green": (0, 255, 0), "white": (255, 255, 255)}
+
+        # Fetch image shapes to calculate a ratio.
+        (real_y, real_x), (resized_y, resized_x) = image.shape[:2], resized_image.shape[:2]
+        ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
+
+        # Convert the base image from BGR to RGB format.
+        rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
+
+        # Iterate through non-zero boxes.
+        for box, annotation in boxes:
+            # Pick a confidence factor from the last place in an array.
+            conf = box[-1]
+            if conf > threshold:
+                # Convert float to int and multiply position of each box by x and y ratio.
+                (x_min, y_min, x_max, y_max) = map(int, multiply_by_ratio(ratio_x, ratio_y, box))
+
+                # Draw a box based on the position. Parameters in the `rectangle` function are: image, start_point, end_point, color, thickness.
+                cv2.rectangle(rgb_image, (x_min, y_min), (x_max, y_max), colors["green"], 3)
+
+                # Add a text to an image based on the position and confidence. Parameters in the `putText` function are: image, text, bottomleft_corner_textfield, font, font_scale, color, thickness, line_type
+                if conf_labels:
+                    # Create a background box based on annotation length.
+                    (text_w, text_h), _ = cv2.getTextSize(f"{annotation}", cv2.FONT_HERSHEY_TRIPLEX, 0.8, 1)
+                    image_copy = rgb_image.copy()
+                    cv2.rectangle(
+                        image_copy,
+                        (x_min, y_min - text_h - 10),
+                        (x_min + text_w, y_min - 10),
+                        colors["white"],
+                        -1,
+                    )
+                    # Add weighted image copy with white boxes under a text.
+                    cv2.addWeighted(image_copy, 0.4, rgb_image, 0.6, 0, rgb_image)
+                    cv2.putText(
+                        rgb_image,
+                        f"{annotation}",
+                        (x_min, y_min - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX,
+                        0.8,
+                        colors["red"],
+                        1,
+                        cv2.LINE_AA,
+                    )
+
+        return rgb_image
+
+Text Recognition
+----------------
+
+
+
+Load the text recognition model and do inference on the detected boxes
+from the detection model.
+
+Load Text Recognition Model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    recognition_model = core.read_model(model=recognition_model_path, weights=recognition_model_path.with_suffix(".bin"))
+
+    recognition_compiled_model = core.compile_model(model=recognition_model, device_name=device.value)
+
+    recognition_output_layer = recognition_compiled_model.output(0)
+    recognition_input_layer = recognition_compiled_model.input(0)
+
+    # Get the height and width of the input layer.
+    _, _, H, W = recognition_input_layer.shape
+
+Do Inference
+~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    # Calculate scale for image resizing.
+    (real_y, real_x), (resized_y, resized_x) = image.shape[:2], resized_image.shape[:2]
+    ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
+
+    # Convert the image to grayscale for the text recognition model.
+    grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Get a dictionary to encode output, based on the model documentation.
+    letters = "~0123456789abcdefghijklmnopqrstuvwxyz"
+
+    # Prepare an empty list for annotations.
+    annotations = list()
+    cropped_images = list()
+    # fig, ax = plt.subplots(len(boxes), 1, figsize=(5,15), sharex=True, sharey=True)
+    # Get annotations for each crop, based on boxes given by the detection model.
+    for i, crop in enumerate(boxes):
+        # Get coordinates on corners of a crop.
+        (x_min, y_min, x_max, y_max) = map(int, multiply_by_ratio(ratio_x, ratio_y, crop))
+        image_crop = run_preprocesing_on_crop(grayscale_image[y_min:y_max, x_min:x_max], (W, H))
+
+        # Run inference with the recognition model.
+        result = recognition_compiled_model([image_crop])[recognition_output_layer]
+
+        # Squeeze the output to remove unnecessary dimension.
+        recognition_results_test = np.squeeze(result)
+
+        # Read an annotation based on probabilities from the output layer.
+        annotation = list()
+        for letter in recognition_results_test:
+            parsed_letter = letters[letter.argmax()]
+
+            # Returning 0 index from `argmax` signalizes an end of a string.
+            if parsed_letter == letters[0]:
+                break
+            annotation.append(parsed_letter)
+        annotations.append("".join(annotation))
+        cropped_image = Image.fromarray(image[y_min:y_max, x_min:x_max])
+        cropped_images.append(cropped_image)
+
+    boxes_with_annotations = list(zip(boxes, annotations))
+
+Show Results
+------------
+
+
+
+Show Detected Text Boxes and OCR Results for the Image
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Visualize the result by drawing boxes around recognized text and showing
+the OCR result from the text recognition model.
+
+.. code:: ipython3
+
+    plt.figure(figsize=(12, 12))
+    plt.imshow(convert_result_to_image(image, resized_image, boxes_with_annotations, conf_labels=True));
+
+
+
+.. image:: optical-character-recognition-with-output_files/optical-character-recognition-with-output_26_0.png
+
+
+Show the OCR Result per Bounding Box
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Depending on the image, the OCR result may not be readable in the image
+with boxes, as displayed in the cell above. Use the code below to
+display the extracted boxes and the OCR result per box.
+
+.. code:: ipython3
+
+    for cropped_image, annotation in zip(cropped_images, annotations):
+        display(cropped_image, Markdown("".join(annotation)))
+
+
+
+.. image:: optical-character-recognition-with-output_files/optical-character-recognition-with-output_28_0.png
+
+
+
+building
+
+
+
+.. image:: optical-character-recognition-with-output_files/optical-character-recognition-with-output_28_2.png
+
+
+
+noyce
+
+
+
+.. image:: optical-character-recognition-with-output_files/optical-character-recognition-with-output_28_4.png
+
+
+
+2200
+
+
+
+.. image:: optical-character-recognition-with-output_files/optical-character-recognition-with-output_28_6.png
+
+
+
+n
+
+
+
+.. image:: optical-character-recognition-with-output_files/optical-character-recognition-with-output_28_8.png
+
+
+
+center
+
+
+
+.. image:: optical-character-recognition-with-output_files/optical-character-recognition-with-output_28_10.png
+
+
+
+robert
+
+
+Print Annotations in Plain Text Format
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Print annotations for detected text based on their position in the input
+image, starting from the upper left corner.
+
+.. code:: ipython3
+
+    [annotation for _, annotation in sorted(zip(boxes, annotations), key=lambda x: x[0][0] ** 2 + x[0][1] ** 2)]
+
+
+
+
+.. parsed-literal::
+
+    ['robert', 'n', 'noyce', 'building', '2200', 'center']
+
+
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rename from docs/notebooks/208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_28_8.jpg
rename to docs/notebooks/optical-character-recognition-with-output_files/optical-character-recognition-with-output_28_8.jpg
diff --git a/docs/notebooks/208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_28_8.png b/docs/notebooks/optical-character-recognition-with-output_files/optical-character-recognition-with-output_28_8.png
similarity index 100%
rename from docs/notebooks/208-optical-character-recognition-with-output_files/208-optical-character-recognition-with-output_28_8.png
rename to docs/notebooks/optical-character-recognition-with-output_files/optical-character-recognition-with-output_28_8.png
diff --git a/docs/notebooks/118-optimize-preprocessing-with-output.rst b/docs/notebooks/optimize-preprocessing-with-output.rst
similarity index 83%
rename from docs/notebooks/118-optimize-preprocessing-with-output.rst
rename to docs/notebooks/optimize-preprocessing-with-output.rst
index b4e72347e48..0d3f7771dc7 100644
--- a/docs/notebooks/118-optimize-preprocessing-with-output.rst
+++ b/docs/notebooks/optimize-preprocessing-with-output.rst
@@ -70,8 +70,56 @@ Settings
 
 .. code:: ipython3
 
+    import platform
+    
     # Install openvino package
-    %pip install -q "openvino>=2023.1.0" tensorflow opencv-python matplotlib
+    %pip install -q "openvino>=2023.1.0" opencv-python tqdm
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+    
+    %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version <= '3.8'"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -87,8 +135,12 @@ Imports
 .. code:: ipython3
 
     import time
+    import os
     from pathlib import Path
     
+    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+    os.environ["TF_USE_LEGACY_KERAS"] = "1"
+    
     import cv2
     import matplotlib.pyplot as plt
     import numpy as np
@@ -96,26 +148,15 @@ Imports
     import tensorflow as tf
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
-
-.. parsed-literal::
-
-    2024-03-12 22:47:40.396566: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:47:40.431134: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-
-
-.. parsed-literal::
-
-    2024-03-12 22:47:40.948008: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
 Setup image and device
 ~~~~~~~~~~~~~~~~~~~~~~
 
@@ -126,7 +167,7 @@ Setup image and device
     # Download the image from the openvino_notebooks storage
     image_path = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
-        directory="data"
+        directory="data",
     )
     image_path = str(image_path)
 
@@ -144,8 +185,8 @@ Setup image and device
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -194,12 +235,8 @@ and save it to the disk.
 
 .. parsed-literal::
 
-    2024-03-12 22:47:44.291989: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-03-12 22:47:44.292027: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
-    2024-03-12 22:47:44.292031: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
-    2024-03-12 22:47:44.292168: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
-    2024-03-12 22:47:44.292184: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
-    2024-03-12 22:47:44.292187: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-04-18 00:18:31.032726: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-04-18 00:18:31.032904: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 .. parsed-literal::
@@ -239,7 +276,7 @@ Check the original parameters of image
 .. code:: ipython3
 
     image = cv2.imread(image_path)
-    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB));
+    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
     print(f"The original shape of the image is {image.shape}")
     print(f"The original data type of the image is {image.dtype}")
 
@@ -251,7 +288,7 @@ Check the original parameters of image
 
 
 
-.. image:: 118-optimize-preprocessing-with-output_files/118-optimize-preprocessing-with-output_14_1.png
+.. image:: optimize-preprocessing-with-output_files/optimize-preprocessing-with-output_14_1.png
 
 
 Setup preprocessing steps with Preprocessing API and perform inference
@@ -298,9 +335,8 @@ The options for preprocessing are not required.
     if ir_path.exists():
         ppp_model = core.read_model(model=ir_path)
         print(f"Model in OpenVINO format already exists: {ir_path}")
-    else: 
-        ppp_model = ov.convert_model(model_path,
-                                     input=[1,299,299,3])
+    else:
+        ppp_model = ov.convert_model(model_path, input=[1, 299, 299, 3])
         ov.save_model(ppp_model, str(ir_path))
 
 Create ``PrePostProcessor`` Object
@@ -351,16 +387,14 @@ for mean/scale normalization.
 .. code:: ipython3
 
     # setup formant of data
-    ppp.input().tensor().set_element_type(ov.Type.u8)\
-                        .set_spatial_dynamic_shape()\
-                        .set_layout(ov.Layout('NHWC'))
+    ppp.input().tensor().set_element_type(ov.Type.u8).set_spatial_dynamic_shape().set_layout(ov.Layout("NHWC"))
 
 
 
 
 .. parsed-literal::
 
-    
+    
 
 
 
@@ -379,7 +413,7 @@ may be specified is input data
     input_layer_ir = next(iter(ppp_model.inputs))
     print(f"The input shape of the model is {input_layer_ir.shape}")
     
-    ppp.input().model().set_layout(ov.Layout('NHWC'))
+    ppp.input().model().set_layout(ov.Layout("NHWC"))
 
 
 .. parsed-literal::
@@ -391,7 +425,7 @@ may be specified is input data
 
 .. parsed-literal::
 
-    
+    
 
 
 
@@ -422,17 +456,14 @@ then such conversion will be added explicitly.
 
     from openvino.preprocess import ResizeAlgorithm
     
-    ppp.input().preprocess().convert_element_type(ov.Type.f32) \
-                            .resize(ResizeAlgorithm.RESIZE_LINEAR)\
-                            .mean([127.5,127.5,127.5])\
-                            .scale([127.5,127.5,127.5])
+    ppp.input().preprocess().convert_element_type(ov.Type.f32).resize(ResizeAlgorithm.RESIZE_LINEAR).mean([127.5, 127.5, 127.5]).scale([127.5, 127.5, 127.5])
 
 
 
 
 .. parsed-literal::
 
-    
+    
 
 
 
@@ -447,7 +478,7 @@ configuration for debugging purposes.
 
 .. code:: ipython3
 
-    print(f'Dump preprocessor: {ppp}')
+    print(f"Dump preprocessor: {ppp}")
     model_with_preprocess = ppp.build()
 
 
@@ -511,9 +542,9 @@ Load image and fit it to model input
     
         # N, H, W, C = batch size, height, width, number of channels
         N, H, W, C = input_layer_ir.shape
-        
+    
         # load  image, image will be resized to model input size and converted to RGB
-        img = tf.keras.preprocessing.image.load_img(image_path, target_size=(H, W), color_mode='rgb')
+        img = tf.keras.preprocessing.image.load_img(image_path, target_size=(H, W), color_mode="rgb")
     
         x = tf.keras.preprocessing.image.img_to_array(img)
         x = np.expand_dims(x, axis=0)
@@ -575,10 +606,10 @@ Compare results on one image
     # Convert the inference result to a class name.
     imagenet_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/datasets/imagenet/imagenet_2012.txt",
-        directory="data"
+        directory="data",
     )
     imagenet_classes = imagenet_filename.read_text().splitlines()
-    imagenet_classes = ['background'] + imagenet_classes
+    imagenet_classes = ["background"] + imagenet_classes
     
     # get result for inference with preprocessing api
     print("Result of inference with Preprocessing API:")
@@ -636,25 +667,20 @@ Compare performance
     
         return time_ir, num_images
     
+    
     time_ir, num_images = check_performance(compiled_model, manual_image_preprocessing)
-    print(
-        f"IR model in OpenVINO Runtime/CPU with manual image preprocessing: {time_ir/num_images:.4f} "
-        f"seconds per image, FPS: {num_images/time_ir:.2f}"
-    )
+    print(f"IR model in OpenVINO Runtime/CPU with manual image preprocessing: {time_ir/num_images:.4f} " f"seconds per image, FPS: {num_images/time_ir:.2f}")
     
     time_ir, num_images = check_performance(compiled_model_with_preprocess_api, prepare_image_api_preprocess)
-    print(
-        f"IR model in OpenVINO Runtime/CPU with preprocessing API: {time_ir/num_images:.4f} "
-        f"seconds per image, FPS: {num_images/time_ir:.2f}"
-    )
+    print(f"IR model in OpenVINO Runtime/CPU with preprocessing API: {time_ir/num_images:.4f} " f"seconds per image, FPS: {num_images/time_ir:.2f}")
 
 
 .. parsed-literal::
 
-    IR model in OpenVINO Runtime/CPU with manual image preprocessing: 0.0153 seconds per image, FPS: 65.44
+    IR model in OpenVINO Runtime/CPU with manual image preprocessing: 0.0153 seconds per image, FPS: 65.42
 
 
 .. parsed-literal::
 
-    IR model in OpenVINO Runtime/CPU with preprocessing API: 0.0183 seconds per image, FPS: 54.74
+    IR model in OpenVINO Runtime/CPU with preprocessing API: 0.0184 seconds per image, FPS: 54.27
 
diff --git a/docs/notebooks/optimize-preprocessing-with-output_files/optimize-preprocessing-with-output_14_1.png b/docs/notebooks/optimize-preprocessing-with-output_files/optimize-preprocessing-with-output_14_1.png
new file mode 100644
index 00000000000..cca7858e3bc
--- /dev/null
+++ b/docs/notebooks/optimize-preprocessing-with-output_files/optimize-preprocessing-with-output_14_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbd7b81cc8e7a73ea9bcb8be0c0575134f50b6af8f7de23ee9feed645a4cf66c
+size 387941
diff --git a/docs/notebooks/405-paddle-ocr-webcam-with-output.rst b/docs/notebooks/paddle-ocr-webcam-with-output.rst
similarity index 82%
rename from docs/notebooks/405-paddle-ocr-webcam-with-output.rst
rename to docs/notebooks/paddle-ocr-webcam-with-output.rst
index 18dca6fe2d1..d7e64f3b5ad 100644
--- a/docs/notebooks/405-paddle-ocr-webcam-with-output.rst
+++ b/docs/notebooks/paddle-ocr-webcam-with-output.rst
@@ -53,34 +53,19 @@ Table of contents:
 
     %pip install -q "openvino>=2023.1.0"
     %pip install -q "paddlepaddle>=2.5.1"
-    %pip install -q "pyclipper>=1.2.1" "shapely>=1.7.1"
+    %pip install -q "pyclipper>=1.2.1" "shapely>=1.7.1" tqdm
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -93,7 +78,6 @@ Imports
 
 .. code:: ipython3
 
-    import sys
     import cv2
     import numpy as np
     import paddle
@@ -112,17 +96,15 @@ Imports
 
     # Import local modules
     
-    utils_file_path = Path('../utils/notebook_utils.py')
-    notebook_directory_path = Path('.')
+    if not Path("./notebook_utils.py").exists():
+        # Fetch `notebook_utils` module
+        import requests
     
-    if not utils_file_path.exists():
-        !git clone --depth 1 https://github.com/igor-davidyuk/openvino_notebooks.git -b moving_data_to_cloud openvino_notebooks
-        utils_file_path = Path('./openvino_notebooks/notebooks/utils/notebook_utils.py')
-        notebook_directory_path = Path('./openvino_notebooks/notebooks/405-paddle-ocr-webcam/')
-    
-    sys.path.append(str(utils_file_path.parent))
-    sys.path.append(str(notebook_directory_path))
+        r = requests.get(
+            url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+        )
     
+        open("notebook_utils.py", "w").write(r.text)
     import notebook_utils as utils
     import pre_post_processing as processing
 
@@ -141,8 +123,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -175,6 +157,7 @@ files to load to CPU/GPU.
 
     # Define the function to download text detection and recognition models from PaddleOCR resources.
     
+    
     def run_model_download(model_url: str, model_file_path: Path) -> None:
         """
         Download pre-trained models from PaddleOCR resources
@@ -184,7 +167,7 @@ files to load to CPU/GPU.
             model_file_path: file path to store the downloaded model
         """
         archive_path = model_file_path.absolute().parent.parent / model_url.split("/")[-1]
-        if model_file_path.is_file(): 
+        if model_file_path.is_file():
             print("Model already exists")
         else:
             # Download the model from the server, and untar it.
@@ -194,7 +177,6 @@ files to load to CPU/GPU.
             utils.download_file(model_url, archive_path.name, archive_path.parent)
             print("Model Downloaded")
     
-    
             file = tarfile.open(archive_path)
             res = file.extractall(archive_path.parent)
             file.close()
@@ -226,7 +208,7 @@ Download the Model for Text **Detection**
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/405-padd…
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/paddle-o…
 
 
 .. parsed-literal::
@@ -272,7 +254,7 @@ Download the Model for Text **Recognition**
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/405-padd…
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/paddle-o…
 
 
 .. parsed-literal::
@@ -331,7 +313,7 @@ with Chinese text) for easy batching in inference.
         Preprocess input image for text detection
     
         Parameters:
-            input_image: input image 
+            input_image: input image
             size: value for the image to be resized for text detection model
         """
         img = cv2.resize(input_image, (size, size))
@@ -352,7 +334,7 @@ with Chinese text) for easy batching in inference.
         Resize input image for text recognition
     
         Parameters:
-            img: bounding box image from text detection 
+            img: bounding box image from text detection
             max_wh_ratio: value for the resizing for text recognition model
         """
         rec_image_shape = [3, 48, 320]
@@ -368,7 +350,7 @@ with Chinese text) for easy batching in inference.
         else:
             resized_w = int(math.ceil(imgH * ratio))
         resized_image = cv2.resize(img, (resized_w, imgH))
-        resized_image = resized_image.astype('float32')
+        resized_image = resized_image.astype("float32")
         resized_image = resized_image.transpose((2, 0, 1)) / 255
         resized_image -= 0.5
         resized_image /= 0.5
@@ -382,22 +364,22 @@ with Chinese text) for easy batching in inference.
         Preprocessing of the detected bounding boxes for text recognition
     
         Parameters:
-            dt_boxes: detected bounding boxes from text detection 
-            frame: original input frame 
+            dt_boxes: detected bounding boxes from text detection
+            frame: original input frame
         """
         ori_im = frame.copy()
-        img_crop_list = [] 
+        img_crop_list = []
         for bno in range(len(dt_boxes)):
             tmp_box = copy.deepcopy(dt_boxes[bno])
             img_crop = processing.get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
-            
+    
         img_num = len(img_crop_list)
         # Calculate the aspect ratio of all text bars.
         width_list = []
         for img in img_crop_list:
             width_list.append(img.shape[1] / float(img.shape[0]))
-        
+    
         # Sorting can speed up the recognition process.
         indices = np.argsort(np.array(width_list))
         return img_crop_list, img_num, indices
@@ -408,7 +390,7 @@ with Chinese text) for easy batching in inference.
         Batch for text recognition
     
         Parameters:
-            img_crop_list: processed detected bounding box images 
+            img_crop_list: processed detected bounding box images
             img_num: number of bounding boxes from text detection
             indices: sorting for bounding boxes to speed up text recognition
             beg_img_no: the beginning number of bounding boxes for each batch of text recognition inference
@@ -442,20 +424,20 @@ Postprocessing Image for Text Detection
         Postprocess the results from text detection into bounding boxes
     
         Parameters:
-            frame: input image 
+            frame: input image
             det_results: inference results from text detection model
-        """   
+        """
         ori_im = frame.copy()
-        data = {'image': frame}
+        data = {"image": frame}
         data_resize = processing.DetResizeForTest(data)
         data_list = []
-        keep_keys = ['image', 'shape']
+        keep_keys = ["image", "shape"]
         for key in keep_keys:
             data_list.append(data_resize[key])
         img, shape_list = data_list
     
-        shape_list = np.expand_dims(shape_list, axis=0) 
-        pred = det_results[0]    
+        shape_list = np.expand_dims(shape_list, axis=0)
+        pred = det_results[0]
         if isinstance(pred, paddle.Tensor):
             pred = pred.numpy()
         segmentation = pred > 0.3
@@ -465,10 +447,10 @@ Postprocessing Image for Text Detection
             src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
             mask = segmentation[batch_index]
             boxes, scores = processing.boxes_from_bitmap(pred[batch_index], mask, src_w, src_h)
-            boxes_batch.append({'points': boxes})
+            boxes_batch.append({"points": boxes})
         post_result = boxes_batch
-        dt_boxes = post_result[0]['points']
-        dt_boxes = processing.filter_tag_det_res(dt_boxes, ori_im.shape)    
+        dt_boxes = post_result[0]["points"]
+        dt_boxes = processing.filter_tag_det_res(dt_boxes, ori_im.shape)
         return dt_boxes
 
 Main Processing Function for PaddleOCR
@@ -489,12 +471,12 @@ video file. See the list of procedures below:
 
     # Download font and a character dictionary for printing OCR results.
     font_path = utils.download_file(
-        url='https://raw.githubusercontent.com/Halfish/lstm-ctc-ocr/master/fonts/simfang.ttf',
-        directory='fonts'
+        url="https://raw.githubusercontent.com/Halfish/lstm-ctc-ocr/master/fonts/simfang.ttf",
+        directory="fonts",
     )
     character_dictionary_path = utils.download_file(
-        url='https://raw.githubusercontent.com/WenmuZhou/PytorchOCR/master/torchocr/datasets/alphabets/ppocr_keys_v1.txt',
-        directory='fonts'
+        url="https://raw.githubusercontent.com/WenmuZhou/PytorchOCR/master/torchocr/datasets/alphabets/ppocr_keys_v1.txt",
+        directory="fonts",
     )
 
 
@@ -521,10 +503,10 @@ video file. See the list of procedures below:
         4. Visualize the results.
     
         Parameters:
-            source: The webcam number to feed the video stream with primary webcam set to "0", or the video path.  
+            source: The webcam number to feed the video stream with primary webcam set to "0", or the video path.
             flip: To be used by VideoPlayer function for flipping capture image.
             use_popup: False for showing encoded frames over this notebook, True for creating a popup window.
-            skip_first_frames: Number of frames to skip at the beginning of the video. 
+            skip_first_frames: Number of frames to skip at the beginning of the video.
         """
         # Create a video player to play with target fps.
         player = None
@@ -546,11 +528,16 @@ video file. See the list of procedures below:
                 # If the frame is larger than full HD, reduce size to improve the performance.
                 scale = 1280 / max(frame.shape)
                 if scale < 1:
-                    frame = cv2.resize(src=frame, dsize=None, fx=scale, fy=scale,
-                                       interpolation=cv2.INTER_AREA)
+                    frame = cv2.resize(
+                        src=frame,
+                        dsize=None,
+                        fx=scale,
+                        fy=scale,
+                        interpolation=cv2.INTER_AREA,
+                    )
                 # Preprocess the image for text detection.
                 test_image = image_preprocess(frame, 640)
-                    
+    
                 # Measure processing time for text detection.
                 start_time = time.time()
                 # Perform the inference step.
@@ -567,55 +554,53 @@ video file. See the list of procedures below:
                 processing_time_det = np.mean(processing_times) * 1000
     
                 # Preprocess detection results for recognition.
-                dt_boxes = processing.sorted_boxes(dt_boxes)  
+                dt_boxes = processing.sorted_boxes(dt_boxes)
                 batch_num = 6
                 img_crop_list, img_num, indices = prep_for_rec(dt_boxes, frame)
-                
+    
                 # For storing recognition results, include two parts:
-                # txts are the recognized text results, scores are the recognition confidence level. 
-                rec_res = [['', 0.0]] * img_num
-                txts = [] 
+                # txts are the recognized text results, scores are the recognition confidence level.
+                rec_res = [["", 0.0]] * img_num
+                txts = []
                 scores = []
     
                 for beg_img_no in range(0, img_num, batch_num):
-    
                     # Recognition starts from here.
-                    norm_img_batch = batch_text_box(
-                        img_crop_list, img_num, indices, beg_img_no, batch_num)
+                    norm_img_batch = batch_text_box(img_crop_list, img_num, indices, beg_img_no, batch_num)
     
-                    # Run inference for text recognition. 
+                    # Run inference for text recognition.
                     rec_results = rec_compiled_model([norm_img_batch])[rec_output_layer]
     
                     # Postprocessing recognition results.
                     postprocess_op = processing.build_post_process(processing.postprocess_params)
                     rec_result = postprocess_op(rec_results)
                     for rno in range(len(rec_result)):
-                        rec_res[indices[beg_img_no + rno]] = rec_result[rno]   
+                        rec_res[indices[beg_img_no + rno]] = rec_result[rno]
                     if rec_res:
-                        txts = [rec_res[i][0] for i in range(len(rec_res))] 
+                        txts = [rec_res[i][0] for i in range(len(rec_res))]
                         scores = [rec_res[i][1] for i in range(len(rec_res))]
-                                       
+    
                 image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
                 boxes = dt_boxes
                 # Draw text recognition results beside the image.
-                draw_img = processing.draw_ocr_box_txt(
-                    image,
-                    boxes,
-                    txts,
-                    scores,
-                    drop_score=0.5,
-                    font_path=str(font_path)
-                )
+                draw_img = processing.draw_ocr_box_txt(image, boxes, txts, scores, drop_score=0.5, font_path=str(font_path))
     
                 # Visualize the PaddleOCR results.
                 f_height, f_width = draw_img.shape[:2]
                 fps = 1000 / processing_time_det
-                cv2.putText(img=draw_img, text=f"Inference time: {processing_time_det:.1f}ms ({fps:.1f} FPS)", 
-                            org=(20, 40),fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=f_width / 1000,
-                            color=(0, 0, 255), thickness=1, lineType=cv2.LINE_AA)
-                
+                cv2.putText(
+                    img=draw_img,
+                    text=f"Inference time: {processing_time_det:.1f}ms ({fps:.1f} FPS)",
+                    org=(20, 40),
+                    fontFace=cv2.FONT_HERSHEY_COMPLEX,
+                    fontScale=f_width / 1000,
+                    color=(0, 0, 255),
+                    thickness=1,
+                    lineType=cv2.LINE_AA,
+                )
+    
                 # Use this workaround if there is flickering.
-                if use_popup: 
+                if use_popup:
                     draw_img = cv2.cvtColor(draw_img, cv2.COLOR_RGB2BGR)
                     cv2.imshow(winname=title, mat=draw_img)
                     key = cv2.waitKey(1)
@@ -625,14 +610,13 @@ video file. See the list of procedures below:
                 else:
                     # Encode numpy array to jpg.
                     draw_img = cv2.cvtColor(draw_img, cv2.COLOR_RGB2BGR)
-                    _, encoded_img = cv2.imencode(ext=".jpg", img=draw_img,
-                                                  params=[cv2.IMWRITE_JPEG_QUALITY, 100])
+                    _, encoded_img = cv2.imencode(ext=".jpg", img=draw_img, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                     # Create an IPython image.
                     i = display.Image(data=encoded_img)
                     # Display the image in this notebook.
                     display.clear_output(wait=True)
                     display.display(i)
-                
+    
         # ctrl-c
         except KeyboardInterrupt:
             print("Interrupted")
@@ -675,13 +659,13 @@ Run live PaddleOCR:
     cam_id = 0
     video_file = "https://raw.githubusercontent.com/yoyowz/classification/master/images/test.mp4"
     
-    source = cam_id if USE_WEBCAM else video_file 
+    source = cam_id if USE_WEBCAM else video_file
     
     run_paddle_ocr(source, flip=False, use_popup=False)
 
 
 
-.. image:: 405-paddle-ocr-webcam-with-output_files/405-paddle-ocr-webcam-with-output_30_0.png
+.. image:: paddle-ocr-webcam-with-output_files/paddle-ocr-webcam-with-output_30_0.png
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/paddle-ocr-webcam-with-output_files/paddle-ocr-webcam-with-output_30_0.png b/docs/notebooks/paddle-ocr-webcam-with-output_files/paddle-ocr-webcam-with-output_30_0.png
new file mode 100644
index 00000000000..feb90485f72
--- /dev/null
+++ b/docs/notebooks/paddle-ocr-webcam-with-output_files/paddle-ocr-webcam-with-output_30_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2cb42d35d1d0e83e8c8005245770a1d8db1f27c738632246f217aa9b1b80b8fa
+size 598187
diff --git a/docs/notebooks/103-paddle-to-openvino-classification-with-output.rst b/docs/notebooks/paddle-to-openvino-classification-with-output.rst
similarity index 82%
rename from docs/notebooks/103-paddle-to-openvino-classification-with-output.rst
rename to docs/notebooks/paddle-to-openvino-classification-with-output.rst
index 22f761783e8..4fa17d2a633 100644
--- a/docs/notebooks/103-paddle-to-openvino-classification-with-output.rst
+++ b/docs/notebooks/paddle-to-openvino-classification-with-output.rst
@@ -51,8 +51,8 @@ Imports
         %pip install -q "paddlepaddle>=2.5.1,<2.6.0"
     else:
         %pip install -q "paddlepaddle>=2.5.1"
-    %pip install -q paddleclas --no-deps
-    %pip install -q "prettytable" "ujson" "visualdl>=2.2.0" "faiss-cpu>=1.7.1"
+    %pip install -q "paddleclas>=2.5.2" --no-deps
+    %pip install -q "prettytable" "ujson" "visualdl>=2.5.3" "faiss-cpu>=1.7.1" Pillow tqdm
     # Install openvino package
     %pip install -q "openvino>=2023.1.0"
 
@@ -70,9 +70,9 @@ Imports
 .. parsed-literal::
 
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    paddleclas 2.5.1 requires easydict, which is not installed.
-    paddleclas 2.5.1 requires faiss-cpu==1.7.1.post2, but you have faiss-cpu 1.8.0 which is incompatible.
-    paddleclas 2.5.1 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.
+    paddleclas 2.5.2 requires easydict, which is not installed.
+    paddleclas 2.5.2 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.
+    paddleclas 2.5.2 requires opencv-python==4.6.0.66, but you have opencv-python 4.9.0.80 which is incompatible.
     
 
 .. parsed-literal::
@@ -87,14 +87,14 @@ Imports
 
 .. code:: ipython3
 
-    if (platform.system() == "Linux"):
+    if platform.system() == "Linux":
         !wget http://nz2.archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2.19_amd64.deb
         !sudo dpkg -i libssl1.1_1.1.1f-1ubuntu2.19_amd64.deb
 
 
 .. parsed-literal::
 
-    --2024-03-12 22:20:41--  http://nz2.archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2.19_amd64.deb
+    --2024-04-18 00:20:15--  http://nz2.archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2.19_amd64.deb
     Resolving proxy-dmz.intel.com (proxy-dmz.intel.com)... 10.241.208.166
     Connecting to proxy-dmz.intel.com (proxy-dmz.intel.com)|10.241.208.166|:911... connected.
     Proxy request sent, awaiting response... 
@@ -102,7 +102,7 @@ Imports
 .. parsed-literal::
 
     404 Not Found
-    2024-03-12 22:20:41 ERROR 404: Not Found.
+    2024-04-18 00:20:16 ERROR 404: Not Found.
     
 
 
@@ -124,23 +124,25 @@ Imports
     from PIL import Image
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file
 
 
 .. parsed-literal::
 
-    2024-03-12 22:20:43 INFO: Loading faiss with AVX512 support.
+    2024-04-18 00:20:17 INFO: Loading faiss with AVX512 support.
 
 
 .. parsed-literal::
 
-    2024-03-12 22:20:43 INFO: Successfully loaded faiss with AVX512 support.
+    2024-04-18 00:20:17 INFO: Successfully loaded faiss with AVX512 support.
 
 
 Settings
@@ -166,7 +168,7 @@ PaddleHub. This may take a while.
     # Download the image from the openvino_notebooks storage
     img = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_close.png",
-        directory="data"
+        directory="data",
     )
     
     IMAGE_FILENAME = img.as_posix()
@@ -175,12 +177,12 @@ PaddleHub. This may take a while.
     MODEL_DIR = Path("model")
     if not MODEL_DIR.exists():
         MODEL_DIR.mkdir()
-    MODEL_URL = 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/{}_infer.tar'.format(MODEL_NAME)
+    MODEL_URL = "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/{}_infer.tar".format(MODEL_NAME)
     download_file(MODEL_URL, directory=MODEL_DIR)
-    file = tarfile.open(MODEL_DIR / '{}_infer.tar'.format(MODEL_NAME))
+    file = tarfile.open(MODEL_DIR / "{}_infer.tar".format(MODEL_NAME))
     res = file.extractall(MODEL_DIR)
     if not res:
-        print(f"Model Extracted to \"./{MODEL_DIR}\".")
+        print(f'Model Extracted to "./{MODEL_DIR}".')
     else:
         print("Error Extracting the model. Please check the network.")
 
@@ -212,10 +214,10 @@ inference on that image, and then show the top three prediction results.
 
 .. code:: ipython3
 
-    classifier = PaddleClas(inference_model_dir=MODEL_DIR / '{}_infer'.format(MODEL_NAME))
+    classifier = PaddleClas(inference_model_dir=MODEL_DIR / "{}_infer".format(MODEL_NAME))
     result = next(classifier.predict(IMAGE_FILENAME))
-    class_names = result[0]['label_names']
-    scores = result[0]['scores']
+    class_names = result[0]["label_names"]
+    scores = result[0]["scores"]
     image = Image.open(IMAGE_FILENAME)
     plt.imshow(image)
     for class_name, softmax_probability in zip(class_names, scores):
@@ -224,7 +226,7 @@ inference on that image, and then show the top three prediction results.
 
 .. parsed-literal::
 
-    [2024/03/12 22:21:11] ppcls WARNING: The current running environment does not support the use of GPU. CPU has been used instead.
+    [2024/04/18 00:20:47] ppcls WARNING: The current running environment does not support the use of GPU. CPU has been used instead.
 
 
 .. parsed-literal::
@@ -237,7 +239,7 @@ inference on that image, and then show the top three prediction results.
 
 
 
-.. image:: 103-paddle-to-openvino-classification-with-output_files/103-paddle-to-openvino-classification-with-output_8_2.png
+.. image:: paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_8_2.png
 
 
 ``classifier.predict()`` takes an image file name, reads the image,
@@ -294,7 +296,7 @@ clipping values.
 
 .. parsed-literal::
 
-    2024-03-12 22:21:12 WARNING: Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+    2024-04-18 00:20:48 WARNING: Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
 
 
 .. parsed-literal::
@@ -306,12 +308,12 @@ clipping values.
 
 .. parsed-literal::
 
-    
+    
 
 
 
 
-.. image:: 103-paddle-to-openvino-classification-with-output_files/103-paddle-to-openvino-classification-with-output_15_3.png
+.. image:: paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_15_3.png
 
 
 To decode the labels predicted by the model to names of classes, we need
@@ -322,7 +324,7 @@ OpenVINO model.
 
 .. code:: ipython3
 
-    class_id_map_file = classifier.get_config()['PostProcess']['Topk']['class_id_map_file']
+    class_id_map_file = classifier.get_config()["PostProcess"]["Topk"]["class_id_map_file"]
     class_id_map = {}
     with open(class_id_map_file, "r") as fin:
         lines = fin.readlines()
@@ -346,7 +348,7 @@ for more information about the Model Conversion API.
 
 .. code:: ipython3
 
-    model_xml = Path(MODEL_NAME).with_suffix('.xml')
+    model_xml = Path(MODEL_NAME).with_suffix(".xml")
     if not model_xml.exists():
         ov_model = ov.convert_model("model/MobileNetV3_large_x1_0_infer/inference.pdmodel")
         ov.save_model(ov_model, str(model_xml))
@@ -367,8 +369,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -391,8 +393,7 @@ Show Inference on OpenVINO Model
 Load the IR model, get model information, load the image, do inference,
 convert the inference to a meaningful result, and show the output. See
 the `OpenVINO Runtime API
-Notebook <002-openvino-api-with-output.html>`__ for more
-information.
+Notebook `__ for more information.
 
 .. code:: ipython3
 
@@ -430,7 +431,7 @@ information.
 
 
 
-.. image:: 103-paddle-to-openvino-classification-with-output_files/103-paddle-to-openvino-classification-with-output_23_1.png
+.. image:: paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_23_1.png
 
 
 Timing and Comparison
@@ -475,13 +476,10 @@ Note that many optimizations are possible to improve the performance.
         result = next(classifier.predict(np.array(image)))
     end = time.perf_counter()
     time_ir = end - start
-    print(
-        f"PaddlePaddle model on CPU: {time_ir/num_images:.4f} "
-        f"seconds per image, FPS: {num_images/time_ir:.2f}\n"
-    )
+    print(f"PaddlePaddle model on CPU: {time_ir/num_images:.4f} " f"seconds per image, FPS: {num_images/time_ir:.2f}\n")
     print("PaddlePaddle result:")
-    class_names = result[0]['label_names']
-    scores = result[0]['scores']
+    class_names = result[0]["label_names"]
+    scores = result[0]["scores"]
     for class_name, softmax_probability in zip(class_names, scores):
         print(f"{class_name}, {softmax_probability:.5f}")
     plt.imshow(image);
@@ -489,7 +487,7 @@ Note that many optimizations are possible to improve the performance.
 
 .. parsed-literal::
 
-    PaddlePaddle model on CPU: 0.0074 seconds per image, FPS: 134.43
+    PaddlePaddle model on CPU: 0.0075 seconds per image, FPS: 133.90
     
     PaddlePaddle result:
     Labrador retriever, 0.75138
@@ -500,7 +498,7 @@ Note that many optimizations are possible to improve the performance.
 
 
 
-.. image:: 103-paddle-to-openvino-classification-with-output_files/103-paddle-to-openvino-classification-with-output_27_1.png
+.. image:: paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_27_1.png
 
 
 Select inference device
@@ -540,10 +538,7 @@ select device from dropdown list for running inference using OpenVINO
     end = time.perf_counter()
     time_ir = end - start
     
-    print(
-        f"OpenVINO IR model in OpenVINO Runtime ({device.value}): {time_ir/num_images:.4f} "
-        f"seconds per image, FPS: {num_images/time_ir:.2f}"
-    )
+    print(f"OpenVINO IR model in OpenVINO Runtime ({device.value}): {time_ir/num_images:.4f} " f"seconds per image, FPS: {num_images/time_ir:.2f}")
     print()
     print("OpenVINO result:")
     for index, softmax_probability in zip(top_indices, top_softmax):
@@ -553,7 +548,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    OpenVINO IR model in OpenVINO Runtime (AUTO): 0.0028 seconds per image, FPS: 353.76
+    OpenVINO IR model in OpenVINO Runtime (AUTO): 0.0029 seconds per image, FPS: 343.70
     
     OpenVINO result:
     Labrador retriever, 0.74909
@@ -564,7 +559,7 @@ select device from dropdown list for running inference using OpenVINO
 
 
 
-.. image:: 103-paddle-to-openvino-classification-with-output_files/103-paddle-to-openvino-classification-with-output_30_1.png
+.. image:: paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_30_1.png
 
 
 References
diff --git a/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_15_3.png b/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_15_3.png
new file mode 100644
index 00000000000..97c14460591
--- /dev/null
+++ b/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_15_3.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba922b89ca992098fd516d86f4d0c97858a8264664f9a49d431978b790a9135f
+size 120883
diff --git a/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_23_1.png b/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_23_1.png
new file mode 100644
index 00000000000..74feaaeb12e
--- /dev/null
+++ b/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_23_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b77eb48f499b17e5306d574b90a5d123ab82440225c034a20256a0ce6378cba
+size 224886
diff --git a/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_27_1.png b/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_27_1.png
new file mode 100644
index 00000000000..74feaaeb12e
--- /dev/null
+++ b/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_27_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b77eb48f499b17e5306d574b90a5d123ab82440225c034a20256a0ce6378cba
+size 224886
diff --git a/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_30_1.png b/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_30_1.png
new file mode 100644
index 00000000000..74feaaeb12e
--- /dev/null
+++ b/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_30_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b77eb48f499b17e5306d574b90a5d123ab82440225c034a20256a0ce6378cba
+size 224886
diff --git a/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_8_2.png b/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_8_2.png
new file mode 100644
index 00000000000..74feaaeb12e
--- /dev/null
+++ b/docs/notebooks/paddle-to-openvino-classification-with-output_files/paddle-to-openvino-classification-with-output_8_2.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b77eb48f499b17e5306d574b90a5d123ab82440225c034a20256a0ce6378cba
+size 224886
diff --git a/docs/notebooks/272-paint-by-example-with-output.rst b/docs/notebooks/paint-by-example-with-output.rst
similarity index 79%
rename from docs/notebooks/272-paint-by-example-with-output.rst
rename to docs/notebooks/paint-by-example-with-output.rst
index 1063036bef5..c3e240f6b63 100644
--- a/docs/notebooks/272-paint-by-example-with-output.rst
+++ b/docs/notebooks/paint-by-example-with-output.rst
@@ -25,7 +25,7 @@ Table of contents:
 Stable Diffusion in Diffusers library
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-To work with Stable Diffusion,
+ To work with Stable Diffusion,
 we will use the Hugging Face
 `Diffusers `__ library. To
 experiment with in-painting we can use Diffusers which exposes the
@@ -37,17 +37,17 @@ The code below demonstrates how to create
 ``stable-diffusion-2-inpainting``. To create the drawing tool we will
 install Gradio for handling user interaction.
 
-This is the overall flow of the application: |Flow Diagram|
+This is the overall flow of the application:
 
-This is the detailed flowchart for the pipeline: |pipeline-flowchart|
+.. figure:: https://user-images.githubusercontent.com/103226580/236954918-f364b227-293c-4f78-a9bf-9dcebcb1034a.png
+   :alt: Flow Diagram
 
-.. |Flow Diagram| image:: https://user-images.githubusercontent.com/103226580/236954918-f364b227-293c-4f78-a9bf-9dcebcb1034a.png
-.. |pipeline-flowchart| image:: https://github.com/openvinotoolkit/openvino_notebooks/assets/103226580/cde2d5c4-2540-4a45-ad9c-339f7a69459d
+   Flow Diagram
 
 .. code:: ipython3
 
-    %pip install -q torch torchvision --extra-index-url "https://download.pytorch.org/whl/cpu"
-    %pip install -q "diffusers>=0.25.0" "peft<=0.6.2" "openvino>=2023.2.0" "transformers>=4.25.1" ipywidgets opencv_python pillow "nncf>=2.7.0" "gradio==3.44.1"
+    %pip install -q "torch>=2.1" torchvision --extra-index-url "https://download.pytorch.org/whl/cpu"
+    %pip install -q "diffusers>=0.25.0" "peft==0.6.2" "openvino>=2023.2.0" "transformers>=4.25.1" ipywidgets opencv-python pillow "nncf>=2.7.0" "gradio==3.44.1" tqdm
 
 
 .. parsed-literal::
@@ -95,34 +95,59 @@ Download default images.
 .. code:: ipython3
 
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file
     
-    download_file("https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377210-edc98e97-0e43-4796-b771-dacd074c39ea.png", "0.png", "data/image")
+    download_file(
+        "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377210-edc98e97-0e43-4796-b771-dacd074c39ea.png",
+        "0.png",
+        "data/image",
+    )
     
-    download_file("https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377233-b2c2d902-d379-415a-8183-5bdd37c52429.png", "1.png", "data/image")
+    download_file(
+        "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377233-b2c2d902-d379-415a-8183-5bdd37c52429.png",
+        "1.png",
+        "data/image",
+    )
     
-    download_file("https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377248-da1db61e-3521-4cdb-85c8-1386d360ce22.png", "2.png", "data/image")
+    download_file(
+        "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377248-da1db61e-3521-4cdb-85c8-1386d360ce22.png",
+        "2.png",
+        "data/image",
+    )
     
-    download_file("https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377279-fa496f17-e850-4351-87c5-2552dfbc4633.jpg", "bird.jpg", "data/reference")
+    download_file(
+        "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377279-fa496f17-e850-4351-87c5-2552dfbc4633.jpg",
+        "bird.jpg",
+        "data/reference",
+    )
     
-    download_file("https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377298-06a25ff2-84d8-4d46-95cd-8c25efa690d8.jpg", "car.jpg", "data/reference")
+    download_file(
+        "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377298-06a25ff2-84d8-4d46-95cd-8c25efa690d8.jpg",
+        "car.jpg",
+        "data/reference",
+    )
     
-    download_file("https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377318-8841a801-1933-4523-a433-7d2fb64c47e6.jpg", "dog.jpg", "data/reference")
-
+    download_file(
+        "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377318-8841a801-1933-4523-a433-7d2fb64c47e6.jpg",
+        "dog.jpg",
+        "data/reference",
+    )
 
 Convert models to OpenVINO Intermediate representation (IR) format
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 
 
-Adapted from `236 Stable Diffusion v2 Infinite Zoom
-notebook <236-stable-diffusion-v2-with-output.html>`__
+Adapted from `Stable Diffusion v2 Infinite Zoom
+notebook `__
 
 .. code:: ipython3
 
@@ -149,16 +174,17 @@ Functions to convert to OpenVINO IR format
         torch.jit._state._clear_class_state()
     
     
-    def convert_image_encoder(image_encoder: torch.nn.Module, ir_path:Path):
+    def convert_image_encoder(image_encoder: torch.nn.Module, ir_path: Path):
         """
-        Convert Image Encoder model to IR. 
+        Convert Image Encoder model to IR.
         Function accepts pipeline, prepares example inputs for conversion
-        Parameters: 
+        Parameters:
             image_encoder (torch.nn.Module): image encoder PyTorch model
             ir_path (Path): File for storing model
         Returns:
             None
         """
+    
         class ImageEncoderWrapper(torch.nn.Module):
             def __init__(self, image_encoder):
                 super().__init__()
@@ -171,27 +197,29 @@ Functions to convert to OpenVINO IR format
         if not ir_path.exists():
             image_encoder = ImageEncoderWrapper(image_encoder)
             image_encoder.eval()
-            input_ids = torch.randn((1,3,224,224))
+            input_ids = torch.randn((1, 3, 224, 224))
             # switch model to inference mode
     
             # disable gradients calculation for reducing memory consumption
             with torch.no_grad():
-                ov_model = ov.convert_model(
-                    image_encoder,
-                    example_input=input_ids,
-                    input=([1,3,224,224],)
-                )
+                ov_model = ov.convert_model(image_encoder, example_input=input_ids, input=([1, 3, 224, 224],))
                 ov.save_model(ov_model, ir_path)
                 del ov_model
                 cleanup_torchscript_cache()
-            print('Image Encoder successfully converted to IR')
+            print("Image Encoder successfully converted to IR")
     
-            
-    def convert_unet(unet:torch.nn.Module, ir_path:Path, num_channels:int = 4, width:int = 64, height:int = 64):
+    
+    def convert_unet(
+        unet: torch.nn.Module,
+        ir_path: Path,
+        num_channels: int = 4,
+        width: int = 64,
+        height: int = 64,
+    ):
         """
-        Convert Unet model to IR format. 
-        Function accepts pipeline, prepares example inputs for conversion 
-        Parameters: 
+        Convert Unet model to IR format.
+        Function accepts pipeline, prepares example inputs for conversion
+        Parameters:
             unet (torch.nn.Module): UNet PyTorch model
             ir_path (Path): File for storing model
             num_channels (int, optional, 4): number of input channels
@@ -200,10 +228,7 @@ Functions to convert to OpenVINO IR format
         Returns:
             None
         """
-        dtype_mapping = {
-            torch.float32: ov.Type.f32,
-            torch.float64: ov.Type.f64
-        }
+        dtype_mapping = {torch.float32: ov.Type.f32, torch.float64: ov.Type.f64}
         if not ir_path.exists():
             # prepare inputs
             encoder_hidden_state = torch.ones((2, 1, 768))
@@ -219,23 +244,19 @@ Functions to convert to OpenVINO IR format
                 input_info.append((shape, element_type))
     
             with torch.no_grad():
-                ov_model = ov.convert_model(
-                    unet, 
-                    example_input=dummy_inputs,
-                    input=input_info
-                )
+                ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
                 ov.save_model(ov_model, ir_path)
                 del ov_model
                 cleanup_torchscript_cache()
-            print('U-Net successfully converted to IR')
+            print("U-Net successfully converted to IR")
     
     
-    def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path, width:int = 512, height:int = 512):
+    def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path, width: int = 512, height: int = 512):
         """
-        Convert VAE model to IR format. 
-        Function accepts VAE model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion, 
-        Parameters: 
+        Convert VAE model to IR format.
+        Function accepts VAE model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion,
+        Parameters:
             vae (torch.nn.Module): VAE PyTorch model
             ir_path (Path): File for storing model
             width (int, optional, 512): input width
@@ -243,6 +264,7 @@ Functions to convert to OpenVINO IR format
         Returns:
             None
         """
+    
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -257,26 +279,27 @@ Functions to convert to OpenVINO IR format
             vae_encoder.eval()
             image = torch.zeros((1, 3, width, height))
             with torch.no_grad():
-                ov_model = ov.convert_model(vae_encoder, example_input=image, input=([1,3, width, height],))
+                ov_model = ov.convert_model(vae_encoder, example_input=image, input=([1, 3, width, height],))
             ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
-            print('VAE encoder successfully converted to IR')
+            print("VAE encoder successfully converted to IR")
     
     
-    def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path, width:int = 64, height:int = 64):
+    def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path, width: int = 64, height: int = 64):
         """
-        Convert VAE decoder model to IR format. 
-        Function accepts VAE model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion, 
-        Parameters: 
-            vae (torch.nn.Module): VAE model 
+        Convert VAE decoder model to IR format.
+        Function accepts VAE model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion,
+        Parameters:
+            vae (torch.nn.Module): VAE model
             ir_path (Path): File for storing model
             width (int, optional, 64): input width
             height (int, optional, 64): input height
         Returns:
             None
         """
+    
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -296,7 +319,7 @@ Functions to convert to OpenVINO IR format
             ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
-            print('VAE decoder successfully converted to ')
+            print("VAE decoder successfully converted to ")
 
 Do the conversion of the in-painting model:
 
@@ -316,7 +339,7 @@ Do the conversion of the Unet model
 
 .. code:: ipython3
 
-    UNET_OV_PATH_INPAINT = sd2_inpainting_model_dir / 'unet.xml'
+    UNET_OV_PATH_INPAINT = sd2_inpainting_model_dir / "unet.xml"
     if not UNET_OV_PATH_INPAINT.exists():
         convert_unet(unet_inpaint, UNET_OV_PATH_INPAINT, num_channels=9, width=64, height=64)
         del unet_inpaint
@@ -330,14 +353,14 @@ Do the conversion of the VAE Encoder model
 
 .. code:: ipython3
 
-    VAE_ENCODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / 'vae_encoder.xml'
+    VAE_ENCODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "vae_encoder.xml"
     
     if not VAE_ENCODER_OV_PATH_INPAINT.exists():
         convert_vae_encoder(vae_inpaint, VAE_ENCODER_OV_PATH_INPAINT, 512, 512)
     else:
         print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH_INPAINT}")
     
-    VAE_DECODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / 'vae_decoder.xml'
+    VAE_DECODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "vae_decoder.xml"
     if not VAE_DECODER_OV_PATH_INPAINT.exists():
         convert_vae_decoder(vae_inpaint, VAE_DECODER_OV_PATH_INPAINT, 64, 64)
     else:
@@ -353,12 +376,19 @@ Prepare Inference pipeline
 
 Function to prepare the mask and masked image.
 
-Adapted from `236 Stable Diffusion v2 Infinite Zoom
-notebook <236-stable-diffusion-v2-with-output.html>`__
+Adapted from `Stable Diffusion v2 Infinite Zoom
+notebook `__
 
 The main difference is that instead of encoding a text prompt it will
 now encode an image as the prompt.
 
+This is the detailed flowchart for the pipeline:
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/103226580/cde2d5c4-2540-4a45-ad9c-339f7a69459d
+   :alt: pipeline-flowchart
+
+   pipeline-flowchart
+
 .. code:: ipython3
 
     import inspect
@@ -372,7 +402,7 @@ now encode an image as the prompt.
     from openvino.runtime import Model
     
     
-    def prepare_mask_and_masked_image(image:PIL.Image.Image, mask:PIL.Image.Image):
+    def prepare_mask_and_masked_image(image: PIL.Image.Image, mask: PIL.Image.Image):
         """
         Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
         converted to ``np.array`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
@@ -496,11 +526,7 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             masked_image_latents = 0.18215 * masked_image_latents
     
             mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask
-            masked_image_latents = (
-                np.concatenate([masked_image_latents] * 2)
-                if do_classifier_free_guidance
-                else masked_image_latents
-            )
+            masked_image_latents = np.concatenate([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
             return mask, masked_image_latents
     
         def __call__(
@@ -556,9 +582,7 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             # prepare mask
             mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
             # set timesteps
-            accepts_offset = "offset" in set(
-                inspect.signature(self.scheduler.set_timesteps).parameters.keys()
-            )
+            accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
             extra_set_kwargs = {}
             if accepts_offset:
                 extra_set_kwargs["offset"] = 1
@@ -579,34 +603,22 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
             # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
             # and should be between [0, 1]
-            accepts_eta = "eta" in set(
-                inspect.signature(self.scheduler.step).parameters.keys()
-            )
+            accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
             extra_step_kwargs = {}
             if accepts_eta:
                 extra_step_kwargs["eta"] = eta
     
             for t in self.progress_bar(timesteps):
                 # expand the latents if we are doing classifier free guidance
-                latent_model_input = (
-                    np.concatenate([latents] * 2)
-                    if do_classifier_free_guidance
-                    else latents
-                )
+                latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                latent_model_input = np.concatenate(
-                    [latent_model_input, masked_image_latents, mask], axis=1
-                )
+                latent_model_input = np.concatenate([latent_model_input, masked_image_latents, mask], axis=1)
                 # predict the noise residual
-                noise_pred = self.unet(
-                    [latent_model_input, np.array(t, dtype=np.float32), image_embeddings]
-                )[self._unet_output]
+                noise_pred = self.unet([latent_model_input, np.array(t, dtype=np.float32), image_embeddings])[self._unet_output]
                 # perform guidance
                 if do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
-                    noise_pred = noise_pred_uncond + guidance_scale * (
-                        noise_pred_text - noise_pred_uncond
-                    )
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(
@@ -621,7 +633,7 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             image = self.postprocess_image(image, meta, output_type)
             return {"sample": image}
     
-        def _encode_image(self, image:PIL.Image.Image, do_classifier_free_guidance:bool = True):
+        def _encode_image(self, image: PIL.Image.Image, do_classifier_free_guidance: bool = True):
             """
             Encodes the image into image encoder hidden states.
     
@@ -632,7 +644,7 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
                 image_embeddings (np.ndarray): image encoder hidden states
             """
             processed_image = self.image_processor(image)
-            processed_image = processed_image['pixel_values'][0]
+            processed_image = processed_image["pixel_values"][0]
             processed_image = np.expand_dims(processed_image, axis=0)
     
             output = self.image_encoder(processed_image)
@@ -643,10 +655,10 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
     
             return image_embeddings
     
-        def prepare_latents(self, latent_timestep:torch.Tensor = None):
+        def prepare_latents(self, latent_timestep: torch.Tensor = None):
             """
             Function for getting initial latents for starting generation
-            
+    
             Parameters:
                 latent_timestep (torch.Tensor, *optional*, None):
                     Predicted by scheduler initial step for image generation, required for latent image mixing with nosie
@@ -661,11 +673,11 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
                 noise = noise * self.scheduler.sigmas[0].numpy()
             return noise, {}
     
-        def postprocess_image(self, image:np.ndarray, meta:Dict, output_type:str = "pil"):
+        def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
             """
-            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required), 
+            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
             normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-            
+    
             Parameters:
                 image (np.ndarray):
                     Generated image
@@ -691,25 +703,23 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
                 image = self.numpy_to_pil(image)
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [img.resize((orig_width, orig_height),
-                                        PIL.Image.Resampling.LANCZOS) for img in image]
+                    image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
             else:
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [cv2.resize(img, (orig_width, orig_width))
-                             for img in image]
+                    image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
             return image
     
-        def get_timesteps(self, num_inference_steps:int, strength:float):
+        def get_timesteps(self, num_inference_steps: int, strength: float):
             """
             Helper function for getting scheduler timesteps for generation
             In case of image-to-image generation, it updates number of steps according to strength
-            
+    
             Parameters:
                num_inference_steps (int):
                   number of inference steps for generation
                strength (float):
-                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. 
+                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
                    Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.
             """
             # get the original timestep using init_timestep
@@ -718,7 +728,7 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             t_start = max(num_inference_steps - init_timestep, 0)
             timesteps = self.scheduler.timesteps[t_start:]
     
-            return timesteps, num_inference_steps - t_start 
+            return timesteps, num_inference_steps - t_start
 
 Select inference device
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -736,8 +746,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -769,12 +779,11 @@ This can take a while to run.
     
     
     def get_ov_pipeline():
-    
         image_encoder_inpaint = core.compile_model(IMAGE_ENCODER_OV_PATH_INPAINT, device.value)
         unet_model_inpaint = core.compile_model(UNET_OV_PATH_INPAINT, device.value)
         vae_decoder_inpaint = core.compile_model(VAE_DECODER_OV_PATH_INPAINT, device.value, ov_config)
         vae_encoder_inpaint = core.compile_model(VAE_ENCODER_OV_PATH_INPAINT, device.value, ov_config)
-        
+    
         ov_pipe_inpaint = OVStableDiffusionInpaintingPipeline(
             image_processor=extractor,
             image_encoder=image_encoder_inpaint,
@@ -828,7 +837,7 @@ improve model inference speed.
     
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -848,8 +857,11 @@ Let’s load ``skip magic`` extension to skip quantization if
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     if to_quantize.value and "GPU" in device.value:
         to_quantize.value = False
@@ -877,9 +889,21 @@ to create a calibration dataset.
         return PIL.Image.open(BytesIO(response.content)).convert("RGB")
     
     
-    example1 = ['https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/image/example_1.png?raw=true', 'https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/mask/example_1.png?raw=true', 'https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/reference/example_1.jpg?raw=true']
-    example2 = ['https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/image/example_2.png?raw=true', 'https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/mask/example_2.png?raw=true', 'https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/reference/example_2.jpg?raw=true']
-    example3 = ['https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/image/example_3.png?raw=true', 'https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/mask/example_3.png?raw=true', 'https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/reference/example_3.jpg?raw=true']
+    example1 = [
+        "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/image/example_1.png?raw=true",
+        "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/mask/example_1.png?raw=true",
+        "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/reference/example_1.jpg?raw=true",
+    ]
+    example2 = [
+        "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/image/example_2.png?raw=true",
+        "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/mask/example_2.png?raw=true",
+        "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/reference/example_2.jpg?raw=true",
+    ]
+    example3 = [
+        "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/image/example_3.png?raw=true",
+        "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/mask/example_3.png?raw=true",
+        "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/reference/example_3.jpg?raw=true",
+    ]
     examples = [example1, example2, example3]
     
     
@@ -1157,7 +1181,7 @@ Quantized pipeline:
 
 
 
-.. image:: 272-paint-by-example-with-output_files/272-paint-by-example-with-output_41_0.png
+.. image:: paint-by-example-with-output_files/paint-by-example-with-output_41_0.png
 
 
 .. code:: ipython3
@@ -1200,19 +1224,19 @@ can choose both, FP16 and INT8.
 
 .. code:: ipython3
 
-    available_models = ['FP16']
+    available_models = ["FP16"]
     
     if UNET_INT8_OV_PATH.exists():
-        available_models.append('INT8')
+        available_models.append("INT8")
     
     model_to_use = widgets.Select(
         options=available_models,
-        value='FP16',
-        description='Select model:',
+        value="FP16",
+        description="Select model:",
         disabled=False,
     )
     
-    model_to_use   
+    model_to_use
 
 
 
@@ -1225,7 +1249,7 @@ can choose both, FP16 and INT8.
 
 .. code:: ipython3
 
-    if 'INT8' == model_to_use.value:
+    if "INT8" == model_to_use.value:
         chosen_pipeline = int8_ov_pipe_inpaint or get_quantized_pipeline()
         ov_pipe_inpaint = None
     else:
@@ -1248,25 +1272,25 @@ and push “Paint!”
     
     def predict(input_dict, reference, seed, steps):
         """
-            This function runs when the 'paint' button is pressed. It takes 3 input images. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required), 
-            normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-            
-            Parameters:
-                input_dict (Dict):
-                    Contains two images in a dictionary
-                        'image' is the image that will be painted on
-                        'mask' is the black/white image specifying where to paint (white) and not to paint (black)
-                image (PIL.Image.Image):
-                    Reference image that will be used by the model to know what to paint in the specified area
-                seed (int):
-                    Used to initialize the random number generator state
-                steps (int):
-                    The number of denoising steps to run during inference. Low = fast/low quality, High = slow/higher quality
-                use_quantize_model (bool):
-                    Use fp16 or int8 model
-            Returns:
-                image (PIL.Image.Image):
-                    Postprocessed images
+        This function runs when the 'paint' button is pressed. It takes 3 input images. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
+        normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
+    
+        Parameters:
+            input_dict (Dict):
+                Contains two images in a dictionary
+                    'image' is the image that will be painted on
+                    'mask' is the black/white image specifying where to paint (white) and not to paint (black)
+            image (PIL.Image.Image):
+                Reference image that will be used by the model to know what to paint in the specified area
+            seed (int):
+                Used to initialize the random number generator state
+            steps (int):
+                The number of denoising steps to run during inference. Low = fast/low quality, High = slow/higher quality
+            use_quantize_model (bool):
+                Use fp16 or int8 model
+        Returns:
+            image (PIL.Image.Image):
+                Postprocessed images
         """
         width, height = input_dict["image"].size
     
@@ -1280,8 +1304,8 @@ and push “Paint!”
             height = 512
             width = int((width / factor) / 8.0) * 8
     
-        init_image = input_dict["image"].convert("RGB").resize((width,height))
-        mask = input_dict["mask"].convert("RGB").resize((width,height))
+        init_image = input_dict["image"].convert("RGB").resize((width, height))
+        mask = input_dict["mask"].convert("RGB").resize((width, height))
     
         # If the image is not a 512x512 square then crop
         if width > height:
@@ -1295,13 +1319,13 @@ and push “Paint!”
         else:
             input_image = init_image
     
-        if not os.path.exists('output'):
-            os.mkdir('output')
-        input_image.save('output/init.png')
-        mask.save('output/mask.png')
-        reference.save('output/ref.png')
+        if not os.path.exists("output"):
+            os.mkdir("output")
+        input_image.save("output/init.png")
+        mask.save("output/mask.png")
+        reference.save("output/ref.png")
     
-        mask = [mask]  
+        mask = [mask]
     
         result = chosen_pipeline(
             image=input_image,
@@ -1309,22 +1333,24 @@ and push “Paint!”
             reference_image=reference,
             seed=seed,
             num_inference_steps=steps,
-        )["sample"][0]
+        )[
+            "sample"
+        ][0]
     
         out_dir = Path("output")
         out_dir.mkdir(exist_ok=True)
-        result.save('output/result.png')
+        result.save("output/result.png")
     
         return result
     
     
     example = {}
-    title = f'# {model_to_use.value} pipeline'
-    ref_dir = 'data/reference'
-    image_dir = 'data/image'
-    ref_list = [os.path.join(ref_dir,file) for file in os.listdir(ref_dir) if file.endswith(".jpg")]
+    title = f"# {model_to_use.value} pipeline"
+    ref_dir = "data/reference"
+    image_dir = "data/image"
+    ref_list = [os.path.join(ref_dir, file) for file in os.listdir(ref_dir) if file.endswith(".jpg")]
     ref_list.sort()
-    image_list = [os.path.join(image_dir,file) for file in os.listdir(image_dir) if file.endswith(".png")]
+    image_list = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith(".png")]
     image_list.sort()
     
     
@@ -1334,24 +1360,56 @@ and push “Paint!”
         with gr.Group():
             with gr.Row():
                 with gr.Column():
-                    image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Source Image")
-                    reference = gr.Image(source='upload', elem_id="image_upload", type="pil", label="Reference Image")
+                    image = gr.Image(
+                        source="upload",
+                        tool="sketch",
+                        elem_id="image_upload",
+                        type="pil",
+                        label="Source Image",
+                    )
+                    reference = gr.Image(
+                        source="upload",
+                        elem_id="image_upload",
+                        type="pil",
+                        label="Reference Image",
+                    )
     
                 with gr.Column():
                     image_out = gr.Image(label="Output", elem_id="output-img")
-                    steps = gr.Slider(label="Steps", value=15, minimum=2, maximum=75, step=1, interactive=True)
-                    seed = gr.Slider(0, 10000, label='Seed (0 = random)', value=0, step=1)
+                    steps = gr.Slider(
+                        label="Steps",
+                        value=15,
+                        minimum=2,
+                        maximum=75,
+                        step=1,
+                        interactive=True,
+                    )
+                    seed = gr.Slider(0, 10000, label="Seed (0 = random)", value=0, step=1)
     
                     with gr.Row(elem_id="prompt-container"):
                         btn = gr.Button("Paint!")
-                           
+    
             with gr.Row():
                 with gr.Column():
-                    gr.Examples(image_list, inputs=[image], label="Examples - Source Image", examples_per_page=12)
+                    gr.Examples(
+                        image_list,
+                        inputs=[image],
+                        label="Examples - Source Image",
+                        examples_per_page=12,
+                    )
                 with gr.Column():
-                    gr.Examples(ref_list, inputs=[reference], label="Examples - Reference Image", examples_per_page=12)
-            
-            btn.click(fn=predict, inputs=[image, reference, seed, steps], outputs=[image_out],)
+                    gr.Examples(
+                        ref_list,
+                        inputs=[reference],
+                        label="Examples - Reference Image",
+                        examples_per_page=12,
+                    )
+    
+            btn.click(
+                fn=predict,
+                inputs=[image, reference, seed, steps],
+                outputs=[image_out],
+            )
     
     # Launching the Gradio app
     try:
diff --git a/docs/notebooks/272-paint-by-example-with-output_files/272-paint-by-example-with-output_41_0.png b/docs/notebooks/paint-by-example-with-output_files/paint-by-example-with-output_41_0.png
similarity index 100%
rename from docs/notebooks/272-paint-by-example-with-output_files/272-paint-by-example-with-output_41_0.png
rename to docs/notebooks/paint-by-example-with-output_files/paint-by-example-with-output_41_0.png
diff --git a/docs/notebooks/407-person-tracking-with-output.rst b/docs/notebooks/person-tracking-with-output.rst
similarity index 57%
rename from docs/notebooks/407-person-tracking-with-output.rst
rename to docs/notebooks/person-tracking-with-output.rst
index 74652b31ee1..f1c4f8f82f5 100644
--- a/docs/notebooks/407-person-tracking-with-output.rst
+++ b/docs/notebooks/person-tracking-with-output.rst
@@ -120,7 +120,7 @@ Table of contents:
     import platform
     
     %pip install -q "openvino-dev>=2024.0.0"
-    %pip install -q opencv-python requests scipy
+    %pip install -q opencv-python requests scipy tqdm
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -128,31 +128,16 @@ Table of contents:
         %pip install -q "matplotlib>=3.4,<3.7"
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -167,7 +152,6 @@ Imports
 
     import collections
     from pathlib import Path
-    import sys
     import time
     
     import numpy as np
@@ -180,21 +164,26 @@ Imports
 
     # Import local modules
     
-    utils_file_path = Path('../utils/notebook_utils.py')
-    notebook_directory_path = Path('.')
+    if not Path("./notebook_utils.py").exists():
+        # Fetch `notebook_utils` module
+        import requests
     
-    if not utils_file_path.exists():
-        !git clone --depth 1 https://github.com/igor-davidyuk/openvino_notebooks.git -b moving_data_to_cloud openvino_notebooks
-        utils_file_path = Path('./openvino_notebooks/notebooks/utils/notebook_utils.py')
-        notebook_directory_path = Path('./openvino_notebooks/notebooks/407-person-tracking-webcam/')
+        r = requests.get(
+            url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+        )
     
-    sys.path.append(str(utils_file_path.parent))
-    sys.path.append(str(notebook_directory_path))
+        open("notebook_utils.py", "w").write(r.text)
     
     import notebook_utils as utils
     from deepsort_utils.tracker import Tracker
     from deepsort_utils.nn_matching import NearestNeighborDistanceMetric
-    from deepsort_utils.detection import Detection, compute_color_for_labels, xywh_to_xyxy, xywh_to_tlwh, tlwh_to_xyxy
+    from deepsort_utils.detection import (
+        Detection,
+        compute_color_for_labels,
+        xywh_to_xyxy,
+        xywh_to_tlwh,
+        tlwh_to_xyxy,
+    )
 
 Download the Model
 ------------------
@@ -238,11 +227,9 @@ replace the name of the model in the code below.
     # The name of the model from Open Model Zoo
     detection_model_name = "person-detection-0202"
     
-    download_command = f"omz_downloader " \
-                       f"--name {detection_model_name} " \
-                       f"--precisions {precision} " \
-                       f"--output_dir {base_model_dir} " \
-                       f"--cache_dir {base_model_dir}"
+    download_command = (
+        f"omz_downloader " f"--name {detection_model_name} " f"--precisions {precision} " f"--output_dir {base_model_dir} " f"--cache_dir {base_model_dir}"
+    )
     ! $download_command
     
     detection_model_path = f"model/intel/{detection_model_name}/{precision}/{detection_model_name}.xml"
@@ -250,11 +237,9 @@ replace the name of the model in the code below.
     
     reidentification_model_name = "person-reidentification-retail-0287"
     
-    download_command = f"omz_downloader " \
-                       f"--name {reidentification_model_name} " \
-                       f"--precisions {precision} " \
-                       f"--output_dir {base_model_dir} " \
-                       f"--cache_dir {base_model_dir}"
+    download_command = (
+        f"omz_downloader " f"--name {reidentification_model_name} " f"--precisions {precision} " f"--output_dir {base_model_dir} " f"--cache_dir {base_model_dir}"
+    )
     ! $download_command
     
     reidentification_model_path = f"model/intel/{reidentification_model_name}/{precision}/{reidentification_model_name}.xml"
@@ -269,20 +254,17 @@ replace the name of the model in the code below.
 
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+    ... 12%, 32 KB, 900 KB/s, 0 seconds passed
 
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     ========== Downloading model/intel/person-detection-0202/FP16/person-detection-0202.bin
@@ -290,129 +272,129 @@ replace the name of the model in the code below.
 
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@@ -426,127 +408,127 @@ replace the name of the model in the code below.
 
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     ========== Downloading model/intel/person-reidentification-retail-0287/FP16/person-reidentification-retail-0287.xml
@@ -554,31 +536,34 @@ replace the name of the model in the code below.
 
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+
+.. parsed-literal::
+
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     ========== Downloading model/intel/person-reidentification-retail-0287/FP16/person-reidentification-retail-0287.bin
@@ -586,52 +571,52 @@ replace the name of the model in the code below.
 
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+... 88%, 1024 KB, 6176 KB/s, 0 seconds passed
+... 91%, 1056 KB, 6354 KB/s, 0 seconds passed
+... 94%, 1088 KB, 6531 KB/s, 0 seconds passed
+... 97%, 1120 KB, 6708 KB/s, 0 seconds passed
+... 99%, 1152 KB, 6886 KB/s, 0 seconds passed
+... 100%, 1153 KB, 6882 KB/s, 0 seconds passed
 
     
 
@@ -665,10 +650,11 @@ performance, but slightly longer startup time).
         This class represents a OpenVINO model object.
     
         """
+    
         def __init__(self, model_path, batchsize=1, device="AUTO"):
             """
             Initialize the model object
-            
+    
             Parameters
             ----------
             model_path: path of inference model
@@ -691,7 +677,7 @@ performance, but slightly longer startup time).
         def predict(self, input):
             """
             Run inference
-            
+    
             Parameters
             ----------
             input: array of input data
@@ -712,8 +698,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -750,7 +736,7 @@ network’s original output and visualize it.
     def preprocess(frame, height, width):
         """
         Preprocess a single image
-        
+    
         Parameters
         ----------
         frame: input frame
@@ -766,24 +752,21 @@ network’s original output and visualize it.
     def batch_preprocess(img_crops, height, width):
         """
         Preprocess batched images
-        
+    
         Parameters
         ----------
         img_crops: batched input images
         height: height of model input data
         width: width of model input data
         """
-        img_batch = np.concatenate([
-            preprocess(img, height, width)
-            for img in img_crops
-        ], axis=0)
+        img_batch = np.concatenate([preprocess(img, height, width) for img in img_crops], axis=0)
         return img_batch
     
     
     def process_results(h, w, results, thresh=0.5):
         """
         postprocess detection results
-        
+    
         Parameters
         ----------
         h, w: original height and width of input image
@@ -801,7 +784,12 @@ network’s original output and visualize it.
             if score > thresh:
                 # Create a box with pixels coordinates from the box with normalized coordinates [0,1].
                 boxes.append(
-                    [(xmin + xmax) / 2 * w, (ymin + ymax) / 2 * h, (xmax - xmin) * w, (ymax - ymin) * h]
+                    [
+                        (xmin + xmax) / 2 * w,
+                        (ymin + ymax) / 2 * h,
+                        (xmax - xmin) * w,
+                        (ymax - ymin) * h,
+                    ]
                 )
                 labels.append(int(label))
                 scores.append(float(score))
@@ -816,7 +804,7 @@ network’s original output and visualize it.
     def draw_boxes(img, bbox, identities=None):
         """
         Draw bounding box in original image
-        
+    
         Parameters
         ----------
         img: original image
@@ -828,11 +816,10 @@ network’s original output and visualize it.
             # box text and bar
             id = int(identities[i]) if identities is not None else 0
             color = compute_color_for_labels(id)
-            label = '{}{:d}'.format("", id)
+            label = "{}{:d}".format("", id)
             t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
             cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
-            cv2.rectangle(
-                img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1)
+            cv2.rectangle(img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1)
             cv2.putText(
                 img,
                 label,
@@ -840,7 +827,7 @@ network’s original output and visualize it.
                 cv2.FONT_HERSHEY_PLAIN,
                 1.6,
                 [255, 255, 255],
-                2
+                2,
             )
         return img
     
@@ -848,7 +835,7 @@ network’s original output and visualize it.
     def cosin_metric(x1, x2):
         """
         Calculate the consin distance of two vector
-        
+    
         Parameters
         ----------
         x1, x2: input vectors
@@ -871,9 +858,9 @@ Visualize data
 
 .. code:: ipython3
 
-    base_file_link = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/person_'
-    image_indices = ['1_1.png', '1_2.png', '2_1.png']
-    image_paths = [utils.download_file(base_file_link + image_index, directory='data') for image_index in image_indices]
+    base_file_link = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/person_"
+    image_indices = ["1_1.png", "1_2.png", "2_1.png"]
+    image_paths = [utils.download_file(base_file_link + image_index, directory="data") for image_index in image_indices]
     image1, image2, image3 = [cv2.cvtColor(cv2.imread(str(image_path)), cv2.COLOR_BGR2RGB) for image_path in image_paths]
     
     # Define titles with images.
@@ -884,7 +871,7 @@ Visualize data
     
     # Fill the subplot.
     for ax, (name, image) in zip(axs, data.items()):
-        ax.axis('off')
+        ax.axis("off")
         ax.set_title(name)
         ax.imshow(image)
     
@@ -911,7 +898,7 @@ Visualize data
 
 
 
-.. image:: 407-person-tracking-with-output_files/407-person-tracking-with-output_17_3.png
+.. image:: person-tracking-with-output_files/person-tracking-with-output_17_3.png
 
 
 Compare two persons
@@ -928,9 +915,9 @@ Compare two persons
     features = extractor.predict(img_batch)
     sim = cosin_metric(features[0], features[1])
     if sim >= 1 - MAX_COSINE_DISTANCE:
-        print(f'Same person (confidence: {sim})')
+        print(f"Same person (confidence: {sim})")
     else:
-        print(f'Different person (confidence: {sim})')
+        print(f"Different person (confidence: {sim})")
 
 
 .. parsed-literal::
@@ -959,24 +946,26 @@ video file.
     
         Parameters:
         ----------
-            source: The webcam number to feed the video stream with primary webcam set to "0", or the video path.  
+            source: The webcam number to feed the video stream with primary webcam set to "0", or the video path.
             flip: To be used by VideoPlayer function for flipping capture image.
             use_popup: False for showing encoded frames over this notebook, True for creating a popup window.
-            skip_first_frames: Number of frames to skip at the beginning of the video. 
+            skip_first_frames: Number of frames to skip at the beginning of the video.
         """
         player = None
         try:
             # Create a video player to play with target fps.
             player = utils.VideoPlayer(
-                source=source, size=(700, 450), flip=flip, fps=24, skip_first_frames=skip_first_frames
+                source=source,
+                size=(700, 450),
+                flip=flip,
+                fps=24,
+                skip_first_frames=skip_first_frames,
             )
             # Start capturing.
             player.start()
             if use_popup:
                 title = "Press ESC to Exit"
-                cv2.namedWindow(
-                    winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE
-                )
+                cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
     
             processing_times = collections.deque()
             while True:
@@ -1007,7 +996,7 @@ video file.
     
                 # Get poses from detection results.
                 bbox_xywh, score, label = process_results(h, w, results=output)
-                
+    
                 img_crops = []
                 for box in bbox_xywh:
                     x1, y1, x2, y2 = xywh_to_xyxy(box, h, w)
@@ -1024,12 +1013,9 @@ video file.
     
                 # Wrap the detection and reidentification results together
                 bbox_tlwh = xywh_to_tlwh(bbox_xywh)
-                detections = [
-                    Detection(bbox_tlwh[i], features[i])
-                    for i in range(features.shape[0])
-                ]
+                detections = [Detection(bbox_tlwh[i], features[i]) for i in range(features.shape[0])]
     
-                # predict the position of tracking target 
+                # predict the position of tracking target
                 tracker.predict()
     
                 # update tracker
@@ -1064,7 +1050,7 @@ video file.
                     thickness=1,
                     lineType=cv2.LINE_AA,
                 )
-                
+    
                 if use_popup:
                     cv2.imshow(winname=title, mat=frame)
                     key = cv2.waitKey(1)
@@ -1073,9 +1059,7 @@ video file.
                         break
                 else:
                     # Encode numpy array to jpg.
-                    _, encoded_img = cv2.imencode(
-                        ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100]
-                    )
+                    _, encoded_img = cv2.imencode(ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                     # Create an IPython image.
                     i = display.Image(data=encoded_img)
                     # Display the image in this notebook.
@@ -1112,15 +1096,8 @@ object
 
     NN_BUDGET = 100
     MAX_COSINE_DISTANCE = 0.6  # threshold of matching object
-    metric = NearestNeighborDistanceMetric(
-        "cosine", MAX_COSINE_DISTANCE, NN_BUDGET
-    )
-    tracker = Tracker(
-        metric,
-        max_iou_distance=0.7,
-        max_age=70,
-        n_init=3
-    )
+    metric = NearestNeighborDistanceMetric("cosine", MAX_COSINE_DISTANCE, NN_BUDGET)
+    tracker = Tracker(metric, max_iou_distance=0.7, max_age=70, n_init=3)
 
 Run Live Person Tracking
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1144,14 +1121,14 @@ will work.
     USE_WEBCAM = False
     
     cam_id = 0
-    video_file = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4'
+    video_file = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4"
     source = cam_id if USE_WEBCAM else video_file
     
     run_person_tracking(source=source, flip=USE_WEBCAM, use_popup=False)
 
 
 
-.. image:: 407-person-tracking-with-output_files/407-person-tracking-with-output_25_0.png
+.. image:: person-tracking-with-output_files/person-tracking-with-output_25_0.png
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/person-tracking-with-output_files/person-tracking-with-output_17_3.png b/docs/notebooks/person-tracking-with-output_files/person-tracking-with-output_17_3.png
new file mode 100644
index 00000000000..20280b15f5d
--- /dev/null
+++ b/docs/notebooks/person-tracking-with-output_files/person-tracking-with-output_17_3.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3983e7e27f73b5e03e02cfb02950ce8aef26d9d6a19a7376c51a6f0b00913732
+size 106259
diff --git a/docs/notebooks/person-tracking-with-output_files/person-tracking-with-output_25_0.png b/docs/notebooks/person-tracking-with-output_files/person-tracking-with-output_25_0.png
new file mode 100644
index 00000000000..8e400dc9b25
--- /dev/null
+++ b/docs/notebooks/person-tracking-with-output_files/person-tracking-with-output_25_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:284342ed56c46142b62f464ec44136a63122ef644ad8397d705c1d8d9f3e81cd
+size 218352
diff --git a/docs/notebooks/260-pix2struct-docvqa-with-output.rst b/docs/notebooks/pix2struct-docvqa-with-output.rst
similarity index 95%
rename from docs/notebooks/260-pix2struct-docvqa-with-output.rst
rename to docs/notebooks/pix2struct-docvqa-with-output.rst
index 46ee3232f36..8a4f622859c 100644
--- a/docs/notebooks/260-pix2struct-docvqa-with-output.rst
+++ b/docs/notebooks/pix2struct-docvqa-with-output.rst
@@ -99,8 +99,8 @@ documentation `__.
 
 .. code:: ipython3
 
-    %pip install -q torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-    %pip install -q "git+https://github.com/huggingface/optimum-intel.git" "openvino>=2023.1.0" "transformers>=4.33.0" "peft==0.6.2" onnx gradio --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "torch>=2.1" torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "git+https://github.com/huggingface/optimum-intel.git" "openvino>=2023.1.0" "transformers>=4.33.0" "peft==0.6.2" onnx "gradio>=4.19" --extra-index-url https://download.pytorch.org/whl/cpu
 
 Download and Convert Model
 --------------------------
@@ -138,7 +138,7 @@ applicable for other models from pix2struct family.
     from optimum.intel.openvino import OVModelForPix2Struct
     
     model_id = "google/pix2struct-docvqa-base"
-    model_dir = Path(model_id.split('/')[-1])
+    model_dir = Path(model_id.split("/")[-1])
     
     if not model_dir.exists():
         ov_model = OVModelForPix2Struct.from_pretrained(model_id, export=True, compile=False)
@@ -180,8 +180,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=[d for d in core.available_devices if "GPU" not in d] + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -245,6 +245,7 @@ documentation =2023.1.0"
+    %pip install -q "openvino>=2023.1.0" opencv-python tqdm
 
 
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -57,7 +52,6 @@ Imports
 .. code:: ipython3
 
     import collections
-    import sys
     import time
     from pathlib import Path
     
@@ -69,7 +63,14 @@ Imports
     
     from decoder import OpenPoseDecoder
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     import notebook_utils as utils
 
 The model
@@ -105,8 +106,12 @@ precision in the code below.
     
     if not model_path.exists():
         model_url_dir = f"https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/3/{model_name}/{precision}/"
-        utils.download_file(model_url_dir + model_name + '.xml', model_path.name, model_path.parent)
-        utils.download_file(model_url_dir + model_name + '.bin', model_path.with_suffix('.bin').name, model_path.parent)
+        utils.download_file(model_url_dir + model_name + ".xml", model_path.name, model_path.parent)
+        utils.download_file(
+            model_url_dir + model_name + ".bin",
+            model_path.with_suffix(".bin").name,
+            model_path.parent,
+        )
 
 
 
@@ -143,8 +148,8 @@ using OpenVINO.
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -258,7 +263,7 @@ factor.
         A_w = as_strided(
             A,
             shape=output_shape + kernel_size,
-            strides=(stride * A.strides[0], stride * A.strides[1]) + A.strides
+            strides=(stride * A.strides[0], stride * A.strides[1]) + A.strides,
         )
         A_w = A_w.reshape(-1, *kernel_size)
     
@@ -278,15 +283,16 @@ factor.
     def process_results(img, pafs, heatmaps):
         # This processing comes from
         # https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/models/open_pose.py
-        pooled_heatmaps = np.array(
-            [[pool2d(h, kernel_size=3, stride=1, padding=1, pool_mode="max") for h in heatmaps[0]]]
-        )
+        pooled_heatmaps = np.array([[pool2d(h, kernel_size=3, stride=1, padding=1, pool_mode="max") for h in heatmaps[0]]])
         nms_heatmaps = heatmap_nms(heatmaps, pooled_heatmaps)
     
         # Decode poses.
         poses, scores = decoder(heatmaps, nms_heatmaps, pafs)
         output_shape = list(compiled_model.output(index=0).partial_shape)
-        output_scale = img.shape[1] / output_shape[3].get_length(), img.shape[0] / output_shape[2].get_length()
+        output_scale = (
+            img.shape[1] / output_shape[3].get_length(),
+            img.shape[0] / output_shape[2].get_length(),
+        )
         # Multiply coordinates by a scaling factor.
         poses[:, :, :2] *= output_scale
         return poses, scores
@@ -304,12 +310,47 @@ from Open Model Zoo.
 
 .. code:: ipython3
 
-    colors = ((255, 0, 0), (255, 0, 255), (170, 0, 255), (255, 0, 85), (255, 0, 170), (85, 255, 0),
-              (255, 170, 0), (0, 255, 0), (255, 255, 0), (0, 255, 85), (170, 255, 0), (0, 85, 255),
-              (0, 255, 170), (0, 0, 255), (0, 255, 255), (85, 0, 255), (0, 170, 255))
+    colors = (
+        (255, 0, 0),
+        (255, 0, 255),
+        (170, 0, 255),
+        (255, 0, 85),
+        (255, 0, 170),
+        (85, 255, 0),
+        (255, 170, 0),
+        (0, 255, 0),
+        (255, 255, 0),
+        (0, 255, 85),
+        (170, 255, 0),
+        (0, 85, 255),
+        (0, 255, 170),
+        (0, 0, 255),
+        (0, 255, 255),
+        (85, 0, 255),
+        (0, 170, 255),
+    )
     
-    default_skeleton = ((15, 13), (13, 11), (16, 14), (14, 12), (11, 12), (5, 11), (6, 12), (5, 6), (5, 7),
-                        (6, 8), (7, 9), (8, 10), (1, 2), (0, 1), (0, 2), (1, 3), (2, 4), (3, 5), (4, 6))
+    default_skeleton = (
+        (15, 13),
+        (13, 11),
+        (16, 14),
+        (14, 12),
+        (11, 12),
+        (5, 11),
+        (6, 12),
+        (5, 6),
+        (5, 7),
+        (6, 8),
+        (7, 9),
+        (8, 10),
+        (1, 2),
+        (0, 1),
+        (0, 2),
+        (1, 3),
+        (2, 4),
+        (3, 5),
+        (4, 6),
+    )
     
     
     def draw_poses(img, poses, point_score_threshold, skeleton=default_skeleton):
@@ -327,7 +368,13 @@ from Open Model Zoo.
             # Draw limbs.
             for i, j in skeleton:
                 if points_scores[i] > point_score_threshold and points_scores[j] > point_score_threshold:
-                    cv2.line(img_limbs, tuple(points[i]), tuple(points[j]), color=colors[j], thickness=4)
+                    cv2.line(
+                        img_limbs,
+                        tuple(points[i]),
+                        tuple(points[j]),
+                        color=colors[j],
+                        thickness=4,
+                    )
         cv2.addWeighted(img, 0.4, img_limbs, 0.6, 0, dst=img)
         return img
 
@@ -372,7 +419,7 @@ file.
                 # (see https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/human-pose-estimation-0001)
                 input_img = cv2.resize(frame, (width, height), interpolation=cv2.INTER_AREA)
                 # Create a batch of images (size = 1).
-                input_img = input_img.transpose((2,0,1))[np.newaxis, ...]
+                input_img = input_img.transpose((2, 0, 1))[np.newaxis, ...]
     
                 # Measure processing time.
                 start_time = time.time()
@@ -397,8 +444,16 @@ file.
                 # mean processing time [ms]
                 processing_time = np.mean(processing_times) * 1000
                 fps = 1000 / processing_time
-                cv2.putText(frame, f"Inference time: {processing_time:.1f}ms ({fps:.1f} FPS)", (20, 40),
-                            cv2.FONT_HERSHEY_COMPLEX, f_width / 1000, (0, 0, 255), 1, cv2.LINE_AA)
+                cv2.putText(
+                    frame,
+                    f"Inference time: {processing_time:.1f}ms ({fps:.1f} FPS)",
+                    (20, 40),
+                    cv2.FONT_HERSHEY_COMPLEX,
+                    f_width / 1000,
+                    (0, 0, 255),
+                    1,
+                    cv2.LINE_AA,
+                )
     
                 # Use this workaround if there is flickering.
                 if use_popup:
@@ -470,7 +525,7 @@ Run the pose estimation:
 
 
 
-.. image:: 402-pose-estimation-with-output_files/402-pose-estimation-with-output_20_0.png
+.. image:: pose-estimation-with-output_files/pose-estimation-with-output_20_0.png
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/pose-estimation-with-output_files/pose-estimation-with-output_20_0.png b/docs/notebooks/pose-estimation-with-output_files/pose-estimation-with-output_20_0.png
new file mode 100644
index 00000000000..2614140c287
--- /dev/null
+++ b/docs/notebooks/pose-estimation-with-output_files/pose-estimation-with-output_20_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc169ae2255a627b398a42161a5ebafb0d22d4a644bfc15cfa8b050471910991
+size 108138
diff --git a/docs/notebooks/212-pyannote-speaker-diarization-with-output.rst b/docs/notebooks/pyannote-speaker-diarization-with-output.rst
similarity index 99%
rename from docs/notebooks/212-pyannote-speaker-diarization-with-output.rst
rename to docs/notebooks/pyannote-speaker-diarization-with-output.rst
index 466d5daa0d4..aeee17d8f1f 100644
--- a/docs/notebooks/212-pyannote-speaker-diarization-with-output.rst
+++ b/docs/notebooks/pyannote-speaker-diarization-with-output.rst
@@ -59,9 +59,14 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install  -q "librosa>=0.8.1" "matplotlib<3.8" "ruamel.yaml>=0.17.8,<0.17.29" --extra-index-url https://download.pytorch.org/whl/cpu torch torchvision torchaudio git+https://github.com/eaidova/pyannote-audio.git@hub0.10 openvino>=2023.1.0
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  "librosa>=0.8.1" "matplotlib<3.8" "ruamel.yaml>=0.17.8,<0.17.29" "torch>=2.1" tqdm torchvision torchaudio "git+https://github.com/eaidova/pyannote-audio.git@hub0.10" "openvino>=2023.1.0"
 
 
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
+
 .. parsed-literal::
 
     DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
@@ -71,11 +76,8 @@ Prerequisites
 
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
     googleapis-common-protos 1.63.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
-    onnx 1.15.0 requires protobuf>=3.20.2, but you have protobuf 3.20.1 which is incompatible.
-    paddlepaddle 2.6.0 requires protobuf>=3.20.2; platform_system != "Windows", but you have protobuf 3.20.1 which is incompatible.
-    ppgan 2.1.0 requires imageio==2.9.0, but you have imageio 2.34.0 which is incompatible.
-    ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.
-    ppgan 2.1.0 requires opencv-python<=4.6.0.66, but you have opencv-python 4.9.0.80 which is incompatible.
+    onnx 1.16.0 requires protobuf>=3.20.2, but you have protobuf 3.20.1 which is incompatible.
+    paddlepaddle 2.6.1 requires protobuf>=3.20.2; platform_system != "Windows", but you have protobuf 3.20.1 which is incompatible.
     tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.
     tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.
     
@@ -163,6 +165,25 @@ hub `__.
     
     pipeline = Pipeline.from_pretrained("philschmid/pyannote-speaker-diarization-endpoint")
 
+
+.. parsed-literal::
+
+    2024-04-18 00:26:15.129545: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:26:15.165135: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+
+
+.. parsed-literal::
+
+    2024-04-18 00:26:15.736692: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/pyannote/audio/core/io.py:42: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
+      torchaudio.set_audio_backend("soundfile")
+
+
 Load test audio file
 --------------------
 
@@ -170,17 +191,22 @@ Load test audio file
 
 .. code:: ipython3
 
-    import sys
+    # Fetch `notebook_utils` module
+    import requests
     
-    sys.path.append("../utils")
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     
     from notebook_utils import download_file
     
     test_data_url = "https://github.com/pyannote/pyannote-audio/raw/develop/tutorials/assets/sample.wav"
     
-    sample_file = 'sample.wav'
-    download_file(test_data_url, 'sample.wav')
-    AUDIO_FILE = {'uri': sample_file.replace('.wav', ''), 'audio': sample_file}
+    sample_file = "sample.wav"
+    download_file(test_data_url, "sample.wav")
+    AUDIO_FILE = {"uri": sample_file.replace(".wav", ""), "audio": sample_file}
 
 
 
@@ -218,7 +244,7 @@ Load test audio file
 
 
 
-.. image:: 212-pyannote-speaker-diarization-with-output_files/212-pyannote-speaker-diarization-with-output_9_1.png
+.. image:: pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_9_1.png
 
 
 Run inference pipeline
@@ -245,7 +271,7 @@ pipeline
 
 .. parsed-literal::
 
-    Diarization pipeline took 15.71 s
+    Diarization pipeline took 16.70 s
 
 
 The result of running the pipeline can be represented as a diagram
@@ -258,7 +284,7 @@ indicating when each person speaks.
 
 
 
-.. image:: 212-pyannote-speaker-diarization-with-output_files/212-pyannote-speaker-diarization-with-output_14_0.png
+.. image:: pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_14_0.png
 
 
 
@@ -311,7 +337,14 @@ with ``ov.save_model``.
     
     if not ov_speaker_segmentation_path.exists():
         onnx_path = ov_speaker_segmentation_path.with_suffix(".onnx")
-        torch.onnx.export(pipeline._segmentation.model, torch.zeros((1, 1, 80000)), onnx_path, input_names=["chunks"], output_names=["outputs"], dynamic_axes={"chunks": {0: "batch_size", 2: "wave_len"}})
+        torch.onnx.export(
+            pipeline._segmentation.model,
+            torch.zeros((1, 1, 80000)),
+            onnx_path,
+            input_names=["chunks"],
+            output_names=["outputs"],
+            dynamic_axes={"chunks": {0: "batch_size", 2: "wave_len"}},
+        )
         ov_speaker_segmentation = ov.convert_model(onnx_path)
         ov.save_model(ov_speaker_segmentation, str(ov_speaker_segmentation_path))
         print(f"Model successfully converted to IR and saved to {ov_speaker_segmentation_path}")
@@ -338,8 +371,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -373,7 +406,7 @@ Replace segmentation model with OpenVINO
     
     
     def infer_segm(chunks: torch.Tensor) -> np.ndarray:
-        """ 
+        """
         Inference speaker segmentation mode using OpenVINO
         Parameters:
             chunks (torch.Tensor) input audio chunks
@@ -397,6 +430,17 @@ Run speaker diarization with OpenVINO
     diarization = pipeline(AUDIO_FILE)
     end = time.perf_counter()
 
+
+.. parsed-literal::
+
+    Model is not converging.  Current: 16444.281598619917 is not greater than 16444.330820454463. Delta is -0.04922183454618789
+
+
+.. parsed-literal::
+
+    Model is not converging.  Current: 16444.281598619917 is not greater than 16444.330820454463. Delta is -0.04922183454618789
+
+
 .. code:: ipython3
 
     print(f"Diarization pipeline took {end - start:.2f} s")
@@ -404,7 +448,7 @@ Run speaker diarization with OpenVINO
 
 .. parsed-literal::
 
-    Diarization pipeline took 15.14 s
+    Diarization pipeline took 16.17 s
 
 
 .. code:: ipython3
@@ -414,7 +458,7 @@ Run speaker diarization with OpenVINO
 
 
 
-.. image:: 212-pyannote-speaker-diarization-with-output_files/212-pyannote-speaker-diarization-with-output_27_0.png
+.. image:: pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_27_0.png
 
 
 
diff --git a/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_14_0.png b/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_14_0.png
new file mode 100644
index 00000000000..b50ecb8ca64
--- /dev/null
+++ b/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_14_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:74e686795f39e2a8378fb62b0f66f6088da0a9f49bcbda2ec6ba306c0708675a
+size 7969
diff --git a/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_27_0.png b/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_27_0.png
new file mode 100644
index 00000000000..b50ecb8ca64
--- /dev/null
+++ b/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_27_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:74e686795f39e2a8378fb62b0f66f6088da0a9f49bcbda2ec6ba306c0708675a
+size 7969
diff --git a/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_9_1.png b/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_9_1.png
new file mode 100644
index 00000000000..a5788ec84d8
--- /dev/null
+++ b/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_9_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e2a9dcbd10f859efd95f9473627e87779603b6982365c0fdf588dab6f3e862eb
+size 43095
diff --git a/docs/notebooks/102-pytorch-onnx-to-openvino-with-output.rst b/docs/notebooks/pytorch-onnx-to-openvino-with-output.rst
similarity index 82%
rename from docs/notebooks/102-pytorch-onnx-to-openvino-with-output.rst
rename to docs/notebooks/pytorch-onnx-to-openvino-with-output.rst
index d40df85f070..470ad982c3a 100644
--- a/docs/notebooks/102-pytorch-onnx-to-openvino-with-output.rst
+++ b/docs/notebooks/pytorch-onnx-to-openvino-with-output.rst
@@ -69,9 +69,24 @@ Table of contents:
 .. code:: ipython3
 
     # Install openvino package
-    %pip install -q "openvino>=2023.1.0" onnx
+    %pip install -q "openvino>=2023.1.0" onnx torch torchvision opencv-python tqdm --extra-index-url https://download.pytorch.org/whl/cpu
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    googleapis-common-protos 1.63.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 5.26.1 which is incompatible.
+    pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 5.26.1 which is incompatible.
+    tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.26.1 which is incompatible.
+    tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 5.26.1 which is incompatible.
+    tf2onnx 1.16.1 requires protobuf~=3.20, but you have protobuf 5.26.1 which is incompatible.
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -97,16 +112,27 @@ Imports
     import numpy as np
     import openvino as ov
     import torch
-    from torchvision.models.segmentation import lraspp_mobilenet_v3_large, LRASPP_MobileNet_V3_Large_Weights
-    
-    # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    from torchvision.models.segmentation import (
+        lraspp_mobilenet_v3_large,
+        LRASPP_MobileNet_V3_Large_Weights,
     )
     
-    from notebook_utils import segmentation_map_to_image, viz_result_image, SegmentationMap, Label, download_file
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
+    
+    from notebook_utils import (
+        segmentation_map_to_image,
+        viz_result_image,
+        SegmentationMap,
+        Label,
+        download_file,
+    )
 
 Settings
 ~~~~~~~~
@@ -127,7 +153,7 @@ transforms function, the model is pre-trained on images with a height of
     weights_path = Path(BASE_MODEL_NAME + ".pt")
     
     # Paths where ONNX and OpenVINO IR models will be stored.
-    onnx_path = weights_path.with_suffix('.onnx')
+    onnx_path = weights_path.with_suffix(".onnx")
     if not onnx_path.parent.exists():
         onnx_path.parent.mkdir()
     ir_path = onnx_path.with_suffix(".xml")
@@ -156,12 +182,16 @@ have not downloaded the model before.
 
 .. code:: ipython3
 
-    print("Downloading the LRASPP MobileNetV3 model (if it has not been downloaded already)...") 
-    download_file(LRASPP_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1.url, filename=weights_path.name, directory=weights_path.parent)
+    print("Downloading the LRASPP MobileNetV3 model (if it has not been downloaded already)...")
+    download_file(
+        LRASPP_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1.url,
+        filename=weights_path.name,
+        directory=weights_path.parent,
+    )
     # create model object
     model = lraspp_mobilenet_v3_large()
     # read state dict, use map_location argument to avoid a situation where weights are saved in cuda (which may not be unavailable on the system)
-    state_dict = torch.load(weights_path, map_location='cpu')
+    state_dict = torch.load(weights_path, map_location="cpu")
     # load state dict to model
     model.load_state_dict(state_dict)
     # switch model from training to inference mode
@@ -292,7 +322,7 @@ Images need to be normalized before propagating through the network.
     # Download the image from the openvino_notebooks storage
     image_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
-        directory="data"
+        directory="data",
     )
     
     image = cv2.cvtColor(cv2.imread(str(image_filename)), cv2.COLOR_BGR2RGB)
@@ -348,8 +378,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -400,7 +430,7 @@ be applied to each label for more convenient visualization.
         Label(index=17, color=(128, 64, 0), name="sheep"),
         Label(index=18, color=(0, 192, 0), name="sofa"),
         Label(index=19, color=(128, 192, 0), name="train"),
-        Label(index=20, color=(0, 64, 128), name="tv monitor")
+        Label(index=20, color=(0, 64, 128), name="tv monitor"),
     ]
     VOCLabels = SegmentationMap(voc_labels)
     
@@ -415,7 +445,7 @@ be applied to each label for more convenient visualization.
 
 
 
-.. image:: 102-pytorch-onnx-to-openvino-with-output_files/102-pytorch-onnx-to-openvino-with-output_22_0.png
+.. image:: pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_22_0.png
 
 
 
@@ -469,7 +499,7 @@ select device from dropdown list for running inference using OpenVINO
 
 
 
-.. image:: 102-pytorch-onnx-to-openvino-with-output_files/102-pytorch-onnx-to-openvino-with-output_27_0.png
+.. image:: pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_27_0.png
 
 
 
@@ -487,7 +517,7 @@ looks the same as the output on the ONNX/OpenVINO IR models.
     with torch.no_grad():
         result_torch = model(torch.as_tensor(normalized_input_image).float())
     
-    result_mask_torch = torch.argmax(result_torch['out'], dim=1).squeeze(0).numpy().astype(np.uint8)
+    result_mask_torch = torch.argmax(result_torch["out"], dim=1).squeeze(0).numpy().astype(np.uint8)
     viz_result_image(
         image,
         segmentation_map_to_image(result=result_mask_torch, colormap=VOCLabels.get_colormap()),
@@ -497,7 +527,7 @@ looks the same as the output on the ONNX/OpenVINO IR models.
 
 
 
-.. image:: 102-pytorch-onnx-to-openvino-with-output_files/102-pytorch-onnx-to-openvino-with-output_29_0.png
+.. image:: pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_29_0.png
 
 
 
@@ -523,10 +553,7 @@ performance.
             model(torch.as_tensor(input_image).float())
         end = time.perf_counter()
         time_torch = end - start
-    print(
-        f"PyTorch model on CPU: {time_torch/num_images:.3f} seconds per image, "
-        f"FPS: {num_images/time_torch:.2f}"
-    )
+    print(f"PyTorch model on CPU: {time_torch/num_images:.3f} seconds per image, " f"FPS: {num_images/time_torch:.2f}")
     
     compiled_model_onnx = core.compile_model(model=model_onnx, device_name=device.value)
     start = time.perf_counter()
@@ -534,10 +561,7 @@ performance.
         compiled_model_onnx([normalized_input_image])
     end = time.perf_counter()
     time_onnx = end - start
-    print(
-        f"ONNX model in OpenVINO Runtime/{device.value}: {time_onnx/num_images:.3f} "
-        f"seconds per image, FPS: {num_images/time_onnx:.2f}"
-    )
+    print(f"ONNX model in OpenVINO Runtime/{device.value}: {time_onnx/num_images:.3f} " f"seconds per image, FPS: {num_images/time_onnx:.2f}")
     
     compiled_model_ir = core.compile_model(model=model_ir, device_name=device.value)
     start = time.perf_counter()
@@ -545,26 +569,22 @@ performance.
         compiled_model_ir([input_image])
     end = time.perf_counter()
     time_ir = end - start
-    print(
-        f"OpenVINO IR model in OpenVINO Runtime/{device.value}: {time_ir/num_images:.3f} "
-        f"seconds per image, FPS: {num_images/time_ir:.2f}"
-    )
-
-
-
-.. parsed-literal::
-
-    PyTorch model on CPU: 0.039 seconds per image, FPS: 25.55
+    print(f"OpenVINO IR model in OpenVINO Runtime/{device.value}: {time_ir/num_images:.3f} " f"seconds per image, FPS: {num_images/time_ir:.2f}")
 
 
 .. parsed-literal::
 
-    ONNX model in OpenVINO Runtime/AUTO: 0.018 seconds per image, FPS: 55.42
+    PyTorch model on CPU: 0.040 seconds per image, FPS: 25.21
 
 
 .. parsed-literal::
 
-    OpenVINO IR model in OpenVINO Runtime/AUTO: 0.019 seconds per image, FPS: 52.62
+    ONNX model in OpenVINO Runtime/AUTO: 0.018 seconds per image, FPS: 54.74
+
+
+.. parsed-literal::
+
+    OpenVINO IR model in OpenVINO Runtime/AUTO: 0.028 seconds per image, FPS: 35.55
 
 
 **Show Device Information**
diff --git a/docs/notebooks/pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_22_0.png b/docs/notebooks/pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_22_0.png
new file mode 100644
index 00000000000..500477edbb4
--- /dev/null
+++ b/docs/notebooks/pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_22_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ea549103d4c18c4db782b5ec2feb51be9b0e937d3f0a8bd73bd159b15c0252d
+size 465692
diff --git a/docs/notebooks/pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_27_0.png b/docs/notebooks/pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_27_0.png
new file mode 100644
index 00000000000..75d41a14576
--- /dev/null
+++ b/docs/notebooks/pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_27_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebc49057d92cdb740e63d35ca0794072574d5a6117ff18b118bec8964d25bcff
+size 465695
diff --git a/docs/notebooks/pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_29_0.png b/docs/notebooks/pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_29_0.png
new file mode 100644
index 00000000000..500477edbb4
--- /dev/null
+++ b/docs/notebooks/pytorch-onnx-to-openvino-with-output_files/pytorch-onnx-to-openvino-with-output_29_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ea549103d4c18c4db782b5ec2feb51be9b0e937d3f0a8bd73bd159b15c0252d
+size 465692
diff --git a/docs/notebooks/112-pytorch-post-training-quantization-nncf-with-output.rst b/docs/notebooks/pytorch-post-training-quantization-nncf-with-output.rst
similarity index 80%
rename from docs/notebooks/112-pytorch-post-training-quantization-nncf-with-output.rst
rename to docs/notebooks/pytorch-post-training-quantization-nncf-with-output.rst
index b47b31b66a2..7849e520edb 100644
--- a/docs/notebooks/112-pytorch-post-training-quantization-nncf-with-output.rst
+++ b/docs/notebooks/pytorch-post-training-quantization-nncf-with-output.rst
@@ -58,15 +58,25 @@ Preparations
 .. code:: ipython3
 
     # Install openvino package
-    %pip install -q "openvino>=2024.0.0" torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2024.0.0" torch torchvision tqdm --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "nncf>=2.9.0"
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -80,7 +90,6 @@ Imports
 .. code:: ipython3
 
     import os
-    import sys
     import time
     import zipfile
     from pathlib import Path
@@ -94,7 +103,14 @@ Imports
     from torchvision.models import resnet50
     import torchvision.transforms as transforms
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 
@@ -146,7 +162,7 @@ Settings
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/112-pytorch-post-training-quantization-nncf/model/resnet50_fp32.pth')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/pytorch-post-training-quantization-nncf/model/resnet50_fp32.pth')
 
 
 
@@ -291,7 +307,10 @@ Validation function
     from openvino.runtime.ie_api import CompiledModel
     
     
-    def validate(val_loader: torch.utils.data.DataLoader, model: Union[torch.nn.Module, CompiledModel]):
+    def validate(
+        val_loader: torch.utils.data.DataLoader,
+        model: Union[torch.nn.Module, CompiledModel],
+    ):
         """Compute the metrics using data from val_loader for the model"""
         batch_time = AverageMeter("Time", ":3.3f")
         top1 = AverageMeter("Acc@1", ":2.2f")
@@ -330,9 +349,7 @@ Validation function
                 if i % print_frequency == 0:
                     progress.display(i)
     
-            print(
-                " * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f} Total time: {total_time:.3f}".format(top1=top1, top5=top5, total_time=end - start_time)
-            )
+            print(" * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f} Total time: {total_time:.3f}".format(top1=top1, top5=top5, total_time=end - start_time))
         return top1.avg
 
 Create and load original uncompressed model
@@ -376,9 +393,7 @@ Create train and validation DataLoaders
         """Creates train dataloader that is used for quantization initialization and validation dataloader for computing the model accruacy"""
         train_dir = DATASET_DIR / "train"
         val_dir = DATASET_DIR / "val" / "images"
-        normalize = transforms.Normalize(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
+        normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
         train_dataset = ImageFolder(
             train_dir,
             transforms.Compose(
@@ -391,9 +406,7 @@ Create train and validation DataLoaders
         )
         val_dataset = ImageFolder(
             val_dir,
-            transforms.Compose(
-                [transforms.Resize(IMAGE_SIZE), transforms.ToTensor(), normalize]
-            ),
+            transforms.Compose([transforms.Resize(IMAGE_SIZE), transforms.ToTensor(), normalize]),
         )
     
         train_loader = torch.utils.data.DataLoader(
@@ -439,47 +452,47 @@ I. Evaluate the loaded model
 
 .. parsed-literal::
 
-    Test: [ 0/79]	Time 0.266 (0.266)	Acc@1 81.25 (81.25)	Acc@5 92.19 (92.19)
+    Test: [ 0/79]	Time 0.261 (0.261)	Acc@1 81.25 (81.25)	Acc@5 92.19 (92.19)
 
 
 .. parsed-literal::
 
-    Test: [10/79]	Time 0.237 (0.240)	Acc@1 56.25 (66.97)	Acc@5 86.72 (87.50)
+    Test: [10/79]	Time 0.234 (0.238)	Acc@1 56.25 (66.97)	Acc@5 86.72 (87.50)
 
 
 .. parsed-literal::
 
-    Test: [20/79]	Time 0.226 (0.238)	Acc@1 67.97 (64.29)	Acc@5 85.16 (87.35)
+    Test: [20/79]	Time 0.267 (0.239)	Acc@1 67.97 (64.29)	Acc@5 85.16 (87.35)
 
 
 .. parsed-literal::
 
-    Test: [30/79]	Time 0.289 (0.240)	Acc@1 53.12 (62.37)	Acc@5 77.34 (85.33)
+    Test: [30/79]	Time 0.259 (0.239)	Acc@1 53.12 (62.37)	Acc@5 77.34 (85.33)
 
 
 .. parsed-literal::
 
-    Test: [40/79]	Time 0.239 (0.239)	Acc@1 67.19 (60.86)	Acc@5 90.62 (84.51)
+    Test: [40/79]	Time 0.237 (0.239)	Acc@1 67.19 (60.86)	Acc@5 90.62 (84.51)
 
 
 .. parsed-literal::
 
-    Test: [50/79]	Time 0.238 (0.239)	Acc@1 60.16 (60.80)	Acc@5 88.28 (84.42)
+    Test: [50/79]	Time 0.236 (0.240)	Acc@1 60.16 (60.80)	Acc@5 88.28 (84.42)
 
 
 .. parsed-literal::
 
-    Test: [60/79]	Time 0.239 (0.239)	Acc@1 66.41 (60.46)	Acc@5 86.72 (83.79)
+    Test: [60/79]	Time 0.248 (0.240)	Acc@1 66.41 (60.46)	Acc@5 86.72 (83.79)
 
 
 .. parsed-literal::
 
-    Test: [70/79]	Time 0.237 (0.239)	Acc@1 52.34 (60.21)	Acc@5 80.47 (83.33)
+    Test: [70/79]	Time 0.248 (0.242)	Acc@1 52.34 (60.21)	Acc@5 80.47 (83.33)
 
 
 .. parsed-literal::
 
-     * Acc@1 60.740 Acc@5 83.960 Total time: 18.642
+     * Acc@1 60.740 Acc@5 83.960 Total time: 18.845
     Test accuracy of FP32 model: 60.740
 
 
@@ -522,24 +535,19 @@ Guide =2024.0.0" "torch" "torchvision"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  "openvino>=2024.0.0" "torch" "torchvision" "tqdm"
     %pip install -q "nncf>=2.9.0"
 
 
@@ -98,7 +98,6 @@ models will be stored.
 
 .. code:: ipython3
 
-    import sys
     import time
     import warnings  # To disable warnings on export model
     import zipfile
@@ -118,7 +117,14 @@ models will be stored.
     import openvino as ov
     from torch.jit import TracerWarning
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
     
     torch.manual_seed(0)
@@ -161,7 +167,7 @@ models will be stored.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/302-pytorch-quantization-aware-training/model/resnet18_fp32.pth')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/pytorch-quantization-aware-training/model/resnet18_fp32.pth')
 
 
 
@@ -184,13 +190,14 @@ Download Tiny ImageNet dataset
         zip_ref.extractall(path=data_dir)
         zip_ref.close()
     
+    
     def prepare_tiny_imagenet_200(dataset_dir: Path):
         # Format validation set the same way as train set is formatted.
-        val_data_dir = dataset_dir / 'val'
-        val_annotations_file = val_data_dir / 'val_annotations.txt'
-        with open(val_annotations_file, 'r') as f:
-            val_annotation_data = map(lambda line: line.split('\t')[:2], f.readlines())
-        val_images_dir = val_data_dir / 'images'
+        val_data_dir = dataset_dir / "val"
+        val_annotations_file = val_data_dir / "val_annotations.txt"
+        with open(val_annotations_file, "r") as f:
+            val_annotation_data = map(lambda line: line.split("\t")[:2], f.readlines())
+        val_images_dir = val_data_dir / "images"
         for image_filename, image_label in val_annotation_data:
             from_image_filepath = val_images_dir / image_filename
             to_image_dir = val_data_dir / image_label
@@ -200,7 +207,7 @@ Download Tiny ImageNet dataset
             from_image_filepath.rename(to_image_filepath)
         val_annotations_file.unlink()
         val_images_dir.rmdir()
-        
+    
     
     DATASET_DIR = DATA_DIR / "tiny-imagenet-200"
     if not DATASET_DIR.exists():
@@ -248,7 +255,9 @@ Train Function
         top1 = AverageMeter("Acc@1", ":2.2f")
         top5 = AverageMeter("Acc@5", ":2.2f")
         progress = ProgressMeter(
-            len(train_loader), [batch_time, losses, top1, top5], prefix="Epoch:[{}]".format(epoch)
+            len(train_loader),
+            [batch_time, losses, top1, top5],
+            prefix="Epoch:[{}]".format(epoch),
         )
     
         # Switch to train mode.
@@ -446,12 +455,15 @@ section at the top of this notebook.
     )
     
     train_loader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True, sampler=None
+        train_dataset,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=0,
+        pin_memory=True,
+        sampler=None,
     )
     
-    val_loader = torch.utils.data.DataLoader(
-        val_dataset, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True
-    )
+    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True)
     
     # Define loss function (criterion) and optimizer.
     criterion = nn.CrossEntropyLoss().to(device)
@@ -460,9 +472,9 @@ section at the top of this notebook.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.
       warnings.warn(msg)
 
 
@@ -539,14 +551,14 @@ modifications.
 
     import nncf
     
+    
     def transform_fn(data_item):
         return data_item[0]
     
+    
     # Creating separate dataloader with batch size = 1
     # as dataloaders with batches > 1 is not supported yet.
-    quantization_loader = torch.utils.data.DataLoader(
-        val_dataset, batch_size=1, shuffle=False, num_workers=0, pin_memory=True
-    )
+    quantization_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=0, pin_memory=True)
     
     quantization_dataset = nncf.Dataset(quantization_loader, transform_fn)
 
@@ -572,24 +584,19 @@ about supported parameters can be found on this
 
 .. parsed-literal::
 
-    2024-03-13 01:05:17.144827: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 01:05:17.180554: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 00:33:36.062903: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:33:36.099858: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 01:05:17.770875: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:33:36.657984: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
 
-    WARNING:nncf:NNCF provides best results with torch==2.1.2, while current torch version is 1.13.1+cpu. If you encounter issues, consider switching to torch==2.1.2
-
-
-.. parsed-literal::
-
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
+    WARNING:nncf:NNCF provides best results with torch==2.1.2, while current torch version is 2.2.2+cpu. If you encounter issues, consider switching to torch==2.1.2
 
 
 
@@ -657,42 +664,42 @@ demonstrated here.
 
 .. parsed-literal::
 
-    Test: [ 0/79]	Time 0.179 (0.179)	Loss 1.005 (1.005)	Acc@1 78.91 (78.91)	Acc@5 88.28 (88.28)
+    Test: [ 0/79]	Time 0.183 (0.183)	Loss 1.005 (1.005)	Acc@1 78.91 (78.91)	Acc@5 88.28 (88.28)
 
 
 .. parsed-literal::
 
-    Test: [10/79]	Time 0.158 (0.160)	Loss 1.992 (1.625)	Acc@1 44.53 (60.37)	Acc@5 79.69 (83.66)
+    Test: [10/79]	Time 0.144 (0.153)	Loss 1.992 (1.625)	Acc@1 44.53 (60.37)	Acc@5 79.69 (83.66)
 
 
 .. parsed-literal::
 
-    Test: [20/79]	Time 0.160 (0.161)	Loss 1.814 (1.705)	Acc@1 60.94 (58.04)	Acc@5 80.47 (82.66)
+    Test: [20/79]	Time 0.145 (0.149)	Loss 1.814 (1.705)	Acc@1 60.94 (58.04)	Acc@5 80.47 (82.66)
 
 
 .. parsed-literal::
 
-    Test: [30/79]	Time 0.157 (0.160)	Loss 2.287 (1.795)	Acc@1 50.78 (56.48)	Acc@5 68.75 (80.97)
+    Test: [30/79]	Time 0.146 (0.148)	Loss 2.287 (1.795)	Acc@1 50.78 (56.48)	Acc@5 68.75 (80.97)
 
 
 .. parsed-literal::
 
-    Test: [40/79]	Time 0.155 (0.160)	Loss 1.615 (1.832)	Acc@1 60.94 (55.43)	Acc@5 82.81 (80.43)
+    Test: [40/79]	Time 0.146 (0.147)	Loss 1.615 (1.832)	Acc@1 60.94 (55.43)	Acc@5 82.81 (80.43)
 
 
 .. parsed-literal::
 
-    Test: [50/79]	Time 0.156 (0.160)	Loss 1.952 (1.833)	Acc@1 57.03 (55.51)	Acc@5 75.00 (80.16)
+    Test: [50/79]	Time 0.144 (0.147)	Loss 1.952 (1.833)	Acc@1 57.03 (55.51)	Acc@5 75.00 (80.16)
 
 
 .. parsed-literal::
 
-    Test: [60/79]	Time 0.160 (0.159)	Loss 1.794 (1.856)	Acc@1 57.03 (55.16)	Acc@5 84.38 (79.84)
+    Test: [60/79]	Time 0.143 (0.146)	Loss 1.794 (1.856)	Acc@1 57.03 (55.16)	Acc@5 84.38 (79.84)
 
 
 .. parsed-literal::
 
-    Test: [70/79]	Time 0.156 (0.159)	Loss 2.371 (1.889)	Acc@1 46.88 (54.68)	Acc@5 74.22 (79.14)
+    Test: [70/79]	Time 0.144 (0.146)	Loss 2.371 (1.889)	Acc@1 46.88 (54.68)	Acc@5 74.22 (79.14)
 
 
 .. parsed-literal::
@@ -729,129 +736,129 @@ training pipeline are required. Here is a simple example.
 
 .. parsed-literal::
 
-    Epoch:[0][  0/782]	Time 0.403 (0.403)	Loss 0.917 (0.917)	Acc@1 76.56 (76.56)	Acc@5 93.75 (93.75)
+    Epoch:[0][  0/782]	Time 0.410 (0.410)	Loss 0.917 (0.917)	Acc@1 76.56 (76.56)	Acc@5 93.75 (93.75)
 
 
 .. parsed-literal::
 
-    Epoch:[0][ 50/782]	Time 0.377 (0.398)	Loss 0.625 (0.812)	Acc@1 85.94 (80.22)	Acc@5 96.88 (93.95)
+    Epoch:[0][ 50/782]	Time 0.363 (0.366)	Loss 0.625 (0.812)	Acc@1 87.50 (80.27)	Acc@5 96.88 (93.92)
 
 
 .. parsed-literal::
 
-    Epoch:[0][100/782]	Time 0.379 (0.393)	Loss 0.761 (0.807)	Acc@1 78.91 (80.37)	Acc@5 94.53 (94.14)
+    Epoch:[0][100/782]	Time 0.361 (0.366)	Loss 0.764 (0.807)	Acc@1 79.69 (80.37)	Acc@5 94.53 (94.17)
 
 
 .. parsed-literal::
 
-    Epoch:[0][150/782]	Time 0.364 (0.392)	Loss 0.864 (0.799)	Acc@1 82.81 (80.54)	Acc@5 92.97 (94.22)
+    Epoch:[0][150/782]	Time 0.363 (0.366)	Loss 0.863 (0.799)	Acc@1 82.81 (80.53)	Acc@5 92.97 (94.25)
 
 
 .. parsed-literal::
 
-    Epoch:[0][200/782]	Time 0.412 (0.391)	Loss 0.581 (0.787)	Acc@1 85.94 (80.84)	Acc@5 96.88 (94.33)
+    Epoch:[0][200/782]	Time 0.365 (0.367)	Loss 0.581 (0.787)	Acc@1 85.16 (80.80)	Acc@5 97.66 (94.34)
 
 
 .. parsed-literal::
 
-    Epoch:[0][250/782]	Time 0.403 (0.391)	Loss 0.724 (0.782)	Acc@1 83.59 (80.92)	Acc@5 93.75 (94.43)
+    Epoch:[0][250/782]	Time 0.363 (0.367)	Loss 0.722 (0.782)	Acc@1 82.81 (80.88)	Acc@5 93.75 (94.42)
 
 
 .. parsed-literal::
 
-    Epoch:[0][300/782]	Time 0.379 (0.391)	Loss 0.738 (0.777)	Acc@1 78.91 (81.06)	Acc@5 93.75 (94.41)
+    Epoch:[0][300/782]	Time 0.365 (0.367)	Loss 0.737 (0.777)	Acc@1 78.91 (81.01)	Acc@5 93.75 (94.41)
 
 
 .. parsed-literal::
 
-    Epoch:[0][350/782]	Time 0.383 (0.390)	Loss 0.819 (0.767)	Acc@1 79.69 (81.31)	Acc@5 92.97 (94.54)
+    Epoch:[0][350/782]	Time 0.366 (0.367)	Loss 0.819 (0.767)	Acc@1 80.47 (81.29)	Acc@5 92.97 (94.53)
 
 
 .. parsed-literal::
 
-    Epoch:[0][400/782]	Time 0.372 (0.390)	Loss 0.783 (0.766)	Acc@1 80.47 (81.36)	Acc@5 94.53 (94.54)
+    Epoch:[0][400/782]	Time 0.368 (0.366)	Loss 0.787 (0.767)	Acc@1 80.47 (81.35)	Acc@5 94.53 (94.53)
 
 
 .. parsed-literal::
 
-    Epoch:[0][450/782]	Time 0.409 (0.391)	Loss 0.732 (0.763)	Acc@1 82.03 (81.49)	Acc@5 96.88 (94.55)
+    Epoch:[0][450/782]	Time 0.364 (0.366)	Loss 0.726 (0.763)	Acc@1 82.03 (81.48)	Acc@5 96.88 (94.55)
 
 
 .. parsed-literal::
 
-    Epoch:[0][500/782]	Time 0.381 (0.391)	Loss 0.726 (0.760)	Acc@1 82.03 (81.56)	Acc@5 95.31 (94.59)
+    Epoch:[0][500/782]	Time 0.365 (0.366)	Loss 0.727 (0.760)	Acc@1 82.03 (81.54)	Acc@5 94.53 (94.58)
 
 
 .. parsed-literal::
 
-    Epoch:[0][550/782]	Time 0.379 (0.390)	Loss 0.774 (0.758)	Acc@1 83.59 (81.60)	Acc@5 95.31 (94.60)
+    Epoch:[0][550/782]	Time 0.365 (0.366)	Loss 0.781 (0.758)	Acc@1 82.81 (81.58)	Acc@5 95.31 (94.59)
 
 
 .. parsed-literal::
 
-    Epoch:[0][600/782]	Time 0.389 (0.390)	Loss 0.721 (0.756)	Acc@1 80.47 (81.65)	Acc@5 97.66 (94.60)
+    Epoch:[0][600/782]	Time 0.362 (0.366)	Loss 0.721 (0.756)	Acc@1 80.47 (81.63)	Acc@5 97.66 (94.61)
 
 
 .. parsed-literal::
 
-    Epoch:[0][650/782]	Time 0.431 (0.390)	Loss 0.919 (0.755)	Acc@1 75.78 (81.66)	Acc@5 92.97 (94.62)
+    Epoch:[0][650/782]	Time 0.365 (0.366)	Loss 0.922 (0.755)	Acc@1 76.56 (81.64)	Acc@5 92.97 (94.63)
 
 
 .. parsed-literal::
 
-    Epoch:[0][700/782]	Time 0.411 (0.390)	Loss 0.645 (0.753)	Acc@1 85.16 (81.70)	Acc@5 93.75 (94.62)
+    Epoch:[0][700/782]	Time 0.366 (0.366)	Loss 0.651 (0.753)	Acc@1 83.59 (81.68)	Acc@5 92.97 (94.63)
 
 
 .. parsed-literal::
 
-    Epoch:[0][750/782]	Time 0.376 (0.390)	Loss 0.780 (0.750)	Acc@1 81.25 (81.72)	Acc@5 93.75 (94.65)
+    Epoch:[0][750/782]	Time 0.369 (0.366)	Loss 0.781 (0.751)	Acc@1 80.47 (81.70)	Acc@5 95.31 (94.66)
 
 
 .. parsed-literal::
 
-    Test: [ 0/79]	Time 0.164 (0.164)	Loss 1.095 (1.095)	Acc@1 72.66 (72.66)	Acc@5 85.94 (85.94)
+    Test: [ 0/79]	Time 0.146 (0.146)	Loss 1.092 (1.092)	Acc@1 73.44 (73.44)	Acc@5 86.72 (86.72)
 
 
 .. parsed-literal::
 
-    Test: [10/79]	Time 0.139 (0.142)	Loss 1.832 (1.524)	Acc@1 50.00 (62.78)	Acc@5 82.03 (83.95)
+    Test: [10/79]	Time 0.145 (0.147)	Loss 1.826 (1.522)	Acc@1 49.22 (62.78)	Acc@5 81.25 (84.23)
 
 
 .. parsed-literal::
 
-    Test: [20/79]	Time 0.137 (0.140)	Loss 1.535 (1.596)	Acc@1 65.62 (60.53)	Acc@5 82.03 (83.71)
+    Test: [20/79]	Time 0.147 (0.147)	Loss 1.531 (1.594)	Acc@1 64.84 (60.83)	Acc@5 82.03 (83.85)
 
 
 .. parsed-literal::
 
-    Test: [30/79]	Time 0.140 (0.141)	Loss 2.056 (1.691)	Acc@1 56.25 (59.12)	Acc@5 71.09 (82.13)
+    Test: [30/79]	Time 0.148 (0.147)	Loss 2.059 (1.690)	Acc@1 57.03 (59.22)	Acc@5 71.09 (82.26)
 
 
 .. parsed-literal::
 
-    Test: [40/79]	Time 0.143 (0.140)	Loss 1.517 (1.745)	Acc@1 64.06 (57.81)	Acc@5 85.16 (81.36)
+    Test: [40/79]	Time 0.147 (0.147)	Loss 1.516 (1.744)	Acc@1 64.06 (57.91)	Acc@5 85.16 (81.46)
 
 
 .. parsed-literal::
 
-    Test: [50/79]	Time 0.140 (0.140)	Loss 1.911 (1.752)	Acc@1 53.91 (57.61)	Acc@5 77.34 (80.99)
+    Test: [50/79]	Time 0.147 (0.147)	Loss 1.922 (1.750)	Acc@1 53.12 (57.69)	Acc@5 76.56 (81.14)
 
 
 .. parsed-literal::
 
-    Test: [60/79]	Time 0.138 (0.140)	Loss 1.587 (1.787)	Acc@1 66.41 (57.04)	Acc@5 85.16 (80.57)
+    Test: [60/79]	Time 0.146 (0.147)	Loss 1.594 (1.785)	Acc@1 65.62 (57.17)	Acc@5 84.38 (80.60)
 
 
 .. parsed-literal::
 
-    Test: [70/79]	Time 0.141 (0.140)	Loss 2.455 (1.813)	Acc@1 45.31 (56.64)	Acc@5 75.00 (80.16)
+    Test: [70/79]	Time 0.141 (0.147)	Loss 2.460 (1.811)	Acc@1 46.09 (56.75)	Acc@5 74.22 (80.08)
 
 
 .. parsed-literal::
 
-     * Acc@1 57.020 Acc@5 80.770
-    Accuracy of tuned INT8 model: 57.020
-    Accuracy drop of tuned INT8 model over pre-trained FP32 model: -1.500
+     * Acc@1 57.180 Acc@5 80.680
+    Accuracy of tuned INT8 model: 57.180
+    Accuracy drop of tuned INT8 model over pre-trained FP32 model: -1.660
 
 
 Export INT8 Model to OpenVINO IR
@@ -910,8 +917,8 @@ throughput (frames per second) values.
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices,
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
     
@@ -929,15 +936,15 @@ throughput (frames per second) values.
 .. code:: ipython3
 
     def parse_benchmark_output(benchmark_output):
-        parsed_output = [line for line in benchmark_output if 'FPS' in line]
-        print(*parsed_output, sep='\n')
+        parsed_output = [line for line in benchmark_output if "FPS" in line]
+        print(*parsed_output, sep="\n")
     
     
-    print('Benchmark FP32 model (IR)')
+    print("Benchmark FP32 model (IR)")
     benchmark_output = ! benchmark_app -m $fp32_ir_path -d $device.value -api async -t 15
     parse_benchmark_output(benchmark_output)
     
-    print('Benchmark INT8 model (IR)')
+    print("Benchmark INT8 model (IR)")
     benchmark_output = ! benchmark_app -m $int8_ir_path -d $device.value -api async -t 15
     parse_benchmark_output(benchmark_output)
 
@@ -949,13 +956,13 @@ throughput (frames per second) values.
 
 .. parsed-literal::
 
-    [ INFO ] Throughput:   2946.75 FPS
+    [ INFO ] Throughput:   2928.21 FPS
     Benchmark INT8 model (IR)
 
 
 .. parsed-literal::
 
-    [ INFO ] Throughput:   11933.05 FPS
+    [ INFO ] Throughput:   11678.74 FPS
 
 
 Show Device Information for reference.
diff --git a/docs/notebooks/102-pytorch-to-openvino-with-output.rst b/docs/notebooks/pytorch-to-openvino-with-output.rst
similarity index 91%
rename from docs/notebooks/102-pytorch-to-openvino-with-output.rst
rename to docs/notebooks/pytorch-to-openvino-with-output.rst
index 2b8cf8e3426..12c29bb7635 100644
--- a/docs/notebooks/102-pytorch-to-openvino-with-output.rst
+++ b/docs/notebooks/pytorch-to-openvino-with-output.rst
@@ -7,7 +7,7 @@ Starting from OpenVINO 2023.0 release, OpenVINO supports direct PyTorch
 model conversion without an intermediate step to convert them into ONNX
 format. In order, if you try to use the lower OpenVINO version or prefer
 to use ONNX, please check this
-`tutorial <102-pytorch-to-openvino-with-output.html>`__.
+`tutorial `__.
 
 In this tutorial, we will use the
 `RegNetY_800MF `__ model from
@@ -84,9 +84,14 @@ Install notebook dependencies
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0" scipy
+    %pip install -q "openvino>=2023.1.0" scipy Pillow torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -148,7 +153,7 @@ enum ``RegNet_Y_800MF_Weights.DEFAULT``.
     # create model topology and load weights
     model = torchvision.models.regnet_y_800mf(weights=weights)
     
-    # switch model to inference mode 
+    # switch model to inference mode
     model.eval();
 
 Prepare Input Data
@@ -194,8 +199,9 @@ can be reused later.
     # Perform model inference on input tensor
     result = model(input_tensor)
     
+    
     # Postprocessing function for getting results in the same way for both PyTorch model inference and OpenVINO
-    def postprocess_result(output_tensor:np.ndarray, top_k:int = 5):
+    def postprocess_result(output_tensor: np.ndarray, top_k: int = 5):
         """
         Posprocess model results. This function applied sofrmax on output tensor and returns specified top_k number of labels with highest probability
         Parameters:
@@ -210,6 +216,7 @@ can be reused later.
         topk_scores = softmaxed_scores[topk_labels]
         return topk_labels, topk_scores
     
+    
     # Postprocess results
     top_labels, top_scores = postprocess_result(result.detach().numpy())
     
@@ -221,7 +228,7 @@ can be reused later.
 
 
 
-.. image:: 102-pytorch-to-openvino-with-output_files/102-pytorch-to-openvino-with-output_11_0.png
+.. image:: pytorch-to-openvino-with-output_files/pytorch-to-openvino-with-output_11_0.png
 
 
 .. parsed-literal::
@@ -248,7 +255,7 @@ Benchmark PyTorch Model Inference
 
 .. parsed-literal::
 
-    16.1 ms ± 389 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+    16.6 ms ± 93.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
 
 Convert PyTorch Model to OpenVINO Intermediate Representation
@@ -323,8 +330,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -381,7 +388,7 @@ Run OpenVINO Model Inference
 
 
 
-.. image:: 102-pytorch-to-openvino-with-output_files/102-pytorch-to-openvino-with-output_20_0.png
+.. image:: pytorch-to-openvino-with-output_files/pytorch-to-openvino-with-output_20_0.png
 
 
 .. parsed-literal::
@@ -407,7 +414,7 @@ Benchmark OpenVINO Model Inference
 
 .. parsed-literal::
 
-    3.13 ms ± 17.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+    3.17 ms ± 17.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
 
 Convert PyTorch Model with Static Input Shape
@@ -420,12 +427,12 @@ you want to convert the model with static shapes, you can explicitly
 specify it during conversion using the ``input_shape`` parameter or
 reshape the model into the desired shape after conversion. For the model
 reshaping example please check the following
-`tutorial <002-openvino-api-with-output.html>`__.
+`tutorial `__.
 
 .. code:: ipython3
 
     # Convert model to openvino.runtime.Model object
-    ov_model = ov.convert_model(model, input=[[1,3,224,224]])
+    ov_model = ov.convert_model(model, input=[[1, 3, 224, 224]])
     # Save openvino.runtime.Model object on disk
     ov.save_model(ov_model, MODEL_DIR / f"{MODEL_NAME}_static.xml")
     ov_model
@@ -511,7 +518,7 @@ Run OpenVINO Model Inference with Static Input Shape
 
 
 
-.. image:: 102-pytorch-to-openvino-with-output_files/102-pytorch-to-openvino-with-output_31_0.png
+.. image:: pytorch-to-openvino-with-output_files/pytorch-to-openvino-with-output_31_0.png
 
 
 .. parsed-literal::
@@ -537,7 +544,7 @@ Benchmark OpenVINO Model Inference with Static Input Shape
 
 .. parsed-literal::
 
-    2.87 ms ± 22 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+    2.88 ms ± 28 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
 
 Convert TorchScript Model to OpenVINO Intermediate Representation
@@ -606,7 +613,7 @@ Reference `__
+`tutorial `__
 
 Table of contents:
 ^^^^^^^^^^^^^^^^^^
@@ -70,7 +70,7 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q accelerate diffusers transformers torch gradio qrcode opencv-python "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q accelerate diffusers transformers "torch>=2.1" "gradio>=4.19" qrcode opencv-python "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "openvino>=2023.1.0" "nncf>=2.7.0"
 
 Instantiating Generation Pipeline
@@ -104,9 +104,7 @@ controlnet model and ``stable-diffusion-v1-5``:
         ControlNetModel,
     )
     
-    controlnet = ControlNetModel.from_pretrained(
-        "monster-labs/control_v1p_sd15_qrcode_monster"
-    )
+    controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster")
     
     pipe = StableDiffusionControlNetPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5",
@@ -140,6 +138,7 @@ The pipeline consists of four important parts:
     import openvino as ov
     import torch
     
+    
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -168,13 +167,13 @@ blocks, which serves additional context for the UNet model.
 
 .. code:: ipython3
 
-    controlnet_ir_path = Path('./controlnet.xml')
+    controlnet_ir_path = Path("./controlnet.xml")
     
     controlnet_inputs = {
         "sample": torch.randn((2, 4, 96, 96)),
         "timestep": torch.tensor(1),
-        "encoder_hidden_states": torch.randn((2,77,768)),
-        "controlnet_cond": torch.randn((2,3,768,768))
+        "encoder_hidden_states": torch.randn((2, 77, 768)),
+        "controlnet_cond": torch.randn((2, 3, 768, 768)),
     }
     
     with torch.no_grad():
@@ -188,13 +187,12 @@ blocks, which serves additional context for the UNet model.
         del ov_model
         del pipe.controlnet, controlnet
         cleanup_torchscript_cache()
-        print('ControlNet successfully converted to IR')
+        print("ControlNet successfully converted to IR")
     else:
         del pipe.controlnet, controlnet
         print(f"ControlNet will be loaded from {controlnet_ir_path}")
 
 
-
 .. parsed-literal::
 
     ControlNet will be loaded from controlnet.xml
@@ -220,7 +218,7 @@ hidden states.
 
 .. code:: ipython3
 
-    text_encoder_ir_path = Path('./text_encoder.xml')
+    text_encoder_ir_path = Path("./text_encoder.xml")
     
     if not text_encoder_ir_path.exists():
         pipe.text_encoder.eval()
@@ -233,7 +231,7 @@ hidden states.
         del ov_model
         del pipe.text_encoder
         cleanup_torchscript_cache()
-        print('Text Encoder successfully converted to IR')
+        print("Text Encoder successfully converted to IR")
     else:
         del pipe.text_encoder
         print(f"Text Encoder will be loaded from {controlnet_ir_path}")
@@ -257,15 +255,16 @@ ControlNet.
 
     from typing import Tuple
     
-    unet_ir_path = Path('./unet.xml')
+    unet_ir_path = Path("./unet.xml")
     
     dtype_mapping = {
         torch.float32: ov.Type.f32,
         torch.float64: ov.Type.f64,
         torch.int32: ov.Type.i32,
-        torch.int64: ov.Type.i64
+        torch.int64: ov.Type.i64,
     }
     
+    
     def flattenize_inputs(inputs):
         flatten_inputs = []
         for input_data in inputs:
@@ -280,13 +279,13 @@ ControlNet.
     
     class UnetWrapper(torch.nn.Module):
         def __init__(
-            self, 
-            unet, 
-            sample_dtype=torch.float32, 
-            timestep_dtype=torch.int64, 
-            encoder_hidden_states=torch.float32, 
-            down_block_additional_residuals=torch.float32, 
-            mid_block_additional_residual=torch.float32
+            self,
+            unet,
+            sample_dtype=torch.float32,
+            timestep_dtype=torch.int64,
+            encoder_hidden_states=torch.float32,
+            down_block_additional_residuals=torch.float32,
+            mid_block_additional_residual=torch.float32,
         ):
             super().__init__()
             self.unet = unet
@@ -297,12 +296,12 @@ ControlNet.
             self.mid_block_additional_residual_dtype = mid_block_additional_residual
     
         def forward(
-            self, 
-            sample:torch.Tensor, 
-            timestep:torch.Tensor, 
-            encoder_hidden_states:torch.Tensor, 
-            down_block_additional_residuals:Tuple[torch.Tensor],  
-            mid_block_additional_residual:torch.Tensor
+            self,
+            sample: torch.Tensor,
+            timestep: torch.Tensor,
+            encoder_hidden_states: torch.Tensor,
+            down_block_additional_residuals: Tuple[torch.Tensor],
+            mid_block_additional_residual: torch.Tensor,
         ):
             sample.to(self.sample_dtype)
             timestep.to(self.timestep_dtype)
@@ -310,11 +309,11 @@ ControlNet.
             down_block_additional_residuals = [res.to(self.down_block_additional_residuals_dtype) for res in down_block_additional_residuals]
             mid_block_additional_residual.to(self.mid_block_additional_residual_dtype)
             return self.unet(
-                sample, 
-                timestep, 
-                encoder_hidden_states, 
-                down_block_additional_residuals=down_block_additional_residuals, 
-                mid_block_additional_residual=mid_block_additional_residual
+                sample,
+                timestep,
+                encoder_hidden_states,
+                down_block_additional_residuals=down_block_additional_residuals,
+                mid_block_additional_residual=mid_block_additional_residual,
             )
     
     
@@ -322,9 +321,9 @@ ControlNet.
     unet_inputs = {
         "sample": torch.randn((2, 4, 96, 96)),
         "timestep": torch.tensor(1),
-        "encoder_hidden_states": torch.randn((2,77,768)),
+        "encoder_hidden_states": torch.randn((2, 77, 768)),
         "down_block_additional_residuals": down_block_res_samples,
-        "mid_block_additional_residual": mid_block_res_sample
+        "mid_block_additional_residual": mid_block_res_sample,
     }
     
     if not unet_ir_path.exists():
@@ -336,13 +335,13 @@ ControlNet.
             input_tensor.get_node().set_partial_shape(ov.PartialShape(input_data.shape))
             input_tensor.get_node().set_element_type(dtype_mapping[input_data.dtype])
         ov_model.validate_nodes_and_infer_types()
-            
+    
         ov.save_model(ov_model, unet_ir_path)
         del ov_model
         cleanup_torchscript_cache()
         del pipe.unet
         gc.collect()
-        print('Unet successfully converted to IR')
+        print("Unet successfully converted to IR")
     else:
         del pipe.unet
         print(f"Unet will be loaded from {unet_ir_path}")
@@ -371,11 +370,11 @@ process are converted back into images using the VAE decoder. During
 inference, we will see that we **only need the VAE decoder**. You can
 find instructions on how to convert the encoder part in a stable
 diffusion
-`notebook <225-stable-diffusion-text-to-image-with-output.html>`__.
+`notebook `__.
 
 .. code:: ipython3
 
-    vae_ir_path = Path('./vae.xml')
+    vae_ir_path = Path("./vae.xml")
     
     
     class VAEDecoderWrapper(torch.nn.Module):
@@ -387,6 +386,7 @@ diffusion
         def forward(self, latents):
             return self.vae.decode(latents)
     
+    
     if not vae_ir_path.exists():
         vae_decoder = VAEDecoderWrapper(pipe.vae)
         latents = torch.zeros((1, 4, 96, 96))
@@ -398,7 +398,7 @@ diffusion
         del ov_model
         del pipe.vae
         cleanup_torchscript_cache()
-        print('VAE decoder successfully converted to IR')
+        print("VAE decoder successfully converted to IR")
     else:
         del pipe.vae
         print(f"VAE decoder will be loaded from {vae_ir_path}")
@@ -504,11 +504,11 @@ on OpenVINO.
     import numpy as np
     
     
-    def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
+    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
-        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio 
+        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-        
+    
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -528,7 +528,7 @@ on OpenVINO.
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-        
+    
         Parameters:
           image (Image.Image): input image
         Returns:
@@ -554,7 +554,7 @@ on OpenVINO.
     ):
         """
         Helper function for generation random values tensor with given shape and data type
-        
+    
         Parameters:
           shape (Union[Tuple, List]): shape for filling random values
           dtype (np.dtype, *optiona*, np.float32): data type for result
@@ -570,6 +570,7 @@ on OpenVINO.
         """
         OpenVINO inference pipeline for Stable Diffusion with ControlNet guidence
         """
+    
         def __init__(
             self,
             tokenizer: CLIPTokenizer,
@@ -579,7 +580,7 @@ on OpenVINO.
             text_encoder: ov.Model,
             unet: ov.Model,
             vae_decoder: ov.Model,
-            device:str = "AUTO"
+            device: str = "AUTO",
         ):
             super().__init__()
             self.tokenizer = tokenizer
@@ -588,10 +589,18 @@ on OpenVINO.
             self.load_models(core, device, controlnet, text_encoder, unet, vae_decoder)
             self.set_progress_bar_config(disable=True)
     
-        def load_models(self, core: ov.Core, device: str, controlnet:ov.Model, text_encoder: ov.Model, unet: ov.Model, vae_decoder: ov.Model):
+        def load_models(
+            self,
+            core: ov.Core,
+            device: str,
+            controlnet: ov.Model,
+            text_encoder: ov.Model,
+            unet: ov.Model,
+            vae_decoder: ov.Model,
+        ):
             """
             Function for loading models on device using OpenVINO
-            
+    
             Parameters:
               core (Core): OpenVINO runtime Core class instance
               device (str): inference device
@@ -650,7 +659,7 @@ on OpenVINO.
                     [PIL](https://pillow.readthedocs.io/en/stable/): `Image.Image` or `np.array`.
             Returns:
                 image ([List[Union[np.ndarray, Image.Image]]): generaited images
-                
+    
             """
     
             # 1. Define call parameters
@@ -691,8 +700,7 @@ on OpenVINO.
                     # Expand the latents if we are doing classifier free guidance.
                     # The latents are expanded 3 times because for pix2pix the guidance\
                     # is applied for both the text and the input image.
-                    latent_model_input = np.concatenate(
-                        [latents] * 2) if do_classifier_free_guidance else latents
+                    latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
     
                     result = self.controlnet([latent_model_input, t, text_embeddings, image])
@@ -721,12 +729,17 @@ on OpenVINO.
                 image = self.numpy_to_pil(image)
                 image = [img.resize((orig_width, orig_height), Image.Resampling.LANCZOS) for img in image]
             else:
-                image = [cv2.resize(img, (orig_width, orig_width))
-                         for img in image]
+                image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
     
             return image
     
-        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True, negative_prompt:Union[str, List[str]] = None):
+        def _encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+            negative_prompt: Union[str, List[str]] = None,
+        ):
             """
             Encodes the prompt into text encoder hidden states.
     
@@ -750,16 +763,13 @@ on OpenVINO.
             )
             text_input_ids = text_inputs.input_ids
     
-            text_embeddings = self.text_encoder(
-                text_input_ids)[self.text_encoder_out]
+            text_embeddings = self.text_encoder(text_input_ids)[self.text_encoder_out]
     
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(
-                    text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(
-                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
+                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
+                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
     
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
@@ -793,11 +803,19 @@ on OpenVINO.
     
             return text_embeddings
     
-        def prepare_latents(self, batch_size:int, num_channels_latents:int, height:int, width:int, dtype:np.dtype = np.float32, latents:np.ndarray = None):
+        def prepare_latents(
+            self,
+            batch_size: int,
+            num_channels_latents: int,
+            height: int,
+            width: int,
+            dtype: np.dtype = np.float32,
+            latents: np.ndarray = None,
+        ):
             """
-            Preparing noise to image generation. If initial latents are not provided, they will be generated randomly, 
+            Preparing noise to image generation. If initial latents are not provided, they will be generated randomly,
             then prepared latents scaled by the standard deviation required by the scheduler
-            
+    
             Parameters:
                batch_size (int): input batch size
                num_channels_latents (int): number of channels for noise generation
@@ -808,7 +826,12 @@ on OpenVINO.
             Returns:
                latents (np.ndarray): scaled initial noise for diffusion
             """
-            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            shape = (
+                batch_size,
+                num_channels_latents,
+                height // self.vae_scale_factor,
+                width // self.vae_scale_factor,
+            )
             if latents is None:
                 latents = randn_tensor(shape, dtype=dtype)
             else:
@@ -818,10 +841,10 @@ on OpenVINO.
             latents = latents * np.array(self.scheduler.init_noise_sigma)
             return latents
     
-        def decode_latents(self, latents:np.array, pad:Tuple[int]):
+        def decode_latents(self, latents: np.array, pad: Tuple[int]):
             """
             Decode predicted image from latent space using VAE Decoder and unpad image result
-            
+    
             Parameters:
                latents (np.ndarray): image encoded in diffusion latent space
                pad (Tuple[int]): each side padding sizes obtained on preprocessing step
@@ -843,6 +866,7 @@ on OpenVINO.
 
     import qrcode
     
+    
     def create_code(content: str):
         """Creates QR codes with provided content."""
         qr = qrcode.QRCode(
@@ -862,11 +886,10 @@ on OpenVINO.
         h = (h + 255 + offset_min) // 256 * 256
         if w > 1024:
             raise RuntimeError("QR code is too large, please use a shorter content")
-        bg = Image.new('L', (w, h), 128)
+        bg = Image.new("L", (w, h), 128)
     
         # align on 16px grid
-        coords = ((w - img.size[0]) // 2 // 16 * 16,
-                  (h - img.size[1]) // 2 // 16 * 16)
+        coords = ((w - img.size[0]) // 2 // 16 * 16, (h - img.size[1]) // 2 // 16 * 16)
         bg.paste(img, coords)
         return bg
 
@@ -875,10 +898,19 @@ on OpenVINO.
     from transformers import CLIPTokenizer
     from diffusers import EulerAncestralDiscreteScheduler
     
-    tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
     scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
     
-    ov_pipe = OVContrlNetStableDiffusionPipeline(tokenizer, scheduler, core, controlnet_ir_path, text_encoder_ir_path, unet_ir_path, vae_ir_path, device=device.value)
+    ov_pipe = OVContrlNetStableDiffusionPipeline(
+        tokenizer,
+        scheduler,
+        core,
+        controlnet_ir_path,
+        text_encoder_ir_path,
+        unet_ir_path,
+        vae_ir_path,
+        device=device.value,
+    )
 
 Now, let’s see model in action
 
@@ -893,7 +925,7 @@ Now, let’s see model in action
         negative_prompt="blurry unreal occluded",
         num_inference_steps=25,
         guidance_scale=7.7,
-        controlnet_conditioning_scale=1.4
+        controlnet_conditioning_scale=1.4,
     )[0]
     
     image
@@ -907,7 +939,7 @@ Now, let’s see model in action
 
 
 
-.. image:: 264-qrcode-monster-with-output_files/264-qrcode-monster-with-output_22_1.png
+.. image:: qrcode-monster-with-output_files/qrcode-monster-with-output_22_1.png
 
 
 
@@ -945,7 +977,7 @@ improve model inference speed.
     is_gpu_device = "GPU" in device.value
     to_quantize = widgets.Checkbox(
         value=not is_gpu_device,
-        description='Quantization',
+        description="Quantization",
         disabled=is_gpu_device,
     )
     
@@ -965,8 +997,13 @@ Let’s load ``skip magic`` extension to skip quantization if
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     int8_pipe = None
     
@@ -1218,7 +1255,7 @@ pipelines.
 
 
 
-.. image:: 264-qrcode-monster-with-output_files/264-qrcode-monster-with-output_39_0.png
+.. image:: qrcode-monster-with-output_files/qrcode-monster-with-output_39_0.png
 
 
 Compare model file sizes
@@ -1337,7 +1374,7 @@ launch the interactive demo.
     
     use_quantized_model = widgets.Checkbox(
         value=True if quantized_model_present else False,
-        description='Use quantized model',
+        description="Use quantized model",
         disabled=not quantized_model_present,
     )
     
@@ -1358,6 +1395,7 @@ launch the interactive demo.
     
     pipeline = int8_pipe if use_quantized_model.value else ov_pipe
     
+    
     def _generate(
         qr_code_content: str,
         prompt: str,
@@ -1372,12 +1410,15 @@ launch the interactive demo.
             np.random.seed(int(seed))
         qrcode_image = create_code(qr_code_content)
         return pipeline(
-            prompt, qrcode_image, negative_prompt=negative_prompt,
+            prompt,
+            qrcode_image,
+            negative_prompt=negative_prompt,
             num_inference_steps=int(num_inference_steps),
             guidance_scale=guidance_scale,
-            controlnet_conditioning_scale=controlnet_conditioning_scale
+            controlnet_conditioning_scale=controlnet_conditioning_scale,
         )[0]
     
+    
     demo = gr.Interface(
         _generate,
         inputs=[
@@ -1390,7 +1431,7 @@ launch the interactive demo.
                 step=1,
                 value=42,
                 label="Seed",
-                info="Seed for the random number generator"
+                info="Seed for the random number generator",
             ),
             gr.Slider(
                 minimum=0.0,
@@ -1398,7 +1439,7 @@ launch the interactive demo.
                 step=0.25,
                 value=7,
                 label="Guidance Scale",
-                info="Controls the amount of guidance the text prompt guides the image generation"
+                info="Controls the amount of guidance the text prompt guides the image generation",
             ),
             gr.Slider(
                 minimum=0.5,
@@ -1409,19 +1450,20 @@ launch the interactive demo.
                 info="""Controls the readability/creativity of the QR code.
                 High values: The generated QR code will be more readable.
                 Low values: The generated QR code will be more creative.
-                """
+                """,
             ),
-            gr.Slider(label="Steps", step=1, value=5, minimum=1, maximum=50)
-        ],
-        outputs=[
-            "image"
+            gr.Slider(label="Steps", step=1, value=5, minimum=1, maximum=50),
         ],
+        outputs=["image"],
         examples=[
             [
                 "Hi OpenVINO",
                 "cozy town on snowy mountain slope 8k",
                 "blurry unreal occluded",
-                42, 7.7, 1.4, 25
+                42,
+                7.7,
+                1.4,
+                25,
             ],
         ],
     )
diff --git a/docs/notebooks/264-qrcode-monster-with-output_files/264-qrcode-monster-with-output_22_1.jpg b/docs/notebooks/qrcode-monster-with-output_files/qrcode-monster-with-output_22_1.jpg
similarity index 100%
rename from docs/notebooks/264-qrcode-monster-with-output_files/264-qrcode-monster-with-output_22_1.jpg
rename to docs/notebooks/qrcode-monster-with-output_files/qrcode-monster-with-output_22_1.jpg
diff --git a/docs/notebooks/264-qrcode-monster-with-output_files/264-qrcode-monster-with-output_22_1.png b/docs/notebooks/qrcode-monster-with-output_files/qrcode-monster-with-output_22_1.png
similarity index 100%
rename from docs/notebooks/264-qrcode-monster-with-output_files/264-qrcode-monster-with-output_22_1.png
rename to docs/notebooks/qrcode-monster-with-output_files/qrcode-monster-with-output_22_1.png
diff --git a/docs/notebooks/264-qrcode-monster-with-output_files/264-qrcode-monster-with-output_39_0.png b/docs/notebooks/qrcode-monster-with-output_files/qrcode-monster-with-output_39_0.png
similarity index 100%
rename from docs/notebooks/264-qrcode-monster-with-output_files/264-qrcode-monster-with-output_39_0.png
rename to docs/notebooks/qrcode-monster-with-output_files/qrcode-monster-with-output_39_0.png
diff --git a/docs/notebooks/213-question-answering-with-output.rst b/docs/notebooks/question-answering-with-output.rst
similarity index 86%
rename from docs/notebooks/213-question-answering-with-output.rst
rename to docs/notebooks/question-answering-with-output.rst
index 231666636dc..e568322bf3f 100644
--- a/docs/notebooks/213-question-answering-with-output.rst
+++ b/docs/notebooks/question-answering-with-output.rst
@@ -33,7 +33,7 @@ Table of contents:
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" tqdm
 
 
 .. parsed-literal::
@@ -54,12 +54,14 @@ Imports
 .. code:: ipython3
 
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file
 
 .. code:: ipython3
@@ -74,14 +76,14 @@ Imports
     
     
     download_file(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/213-question-answering/html_reader.py',
-        filename='html_reader.py'
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/question-answering/html_reader.py",
+        filename="html_reader.py",
     )
     import html_reader as reader
     
     download_file(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/213-question-answering/tokens_bert.py',
-        filename='tokens_bert.py'
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/question-answering/tokens_bert.py",
+        filename="tokens_bert.py",
     )
     import tokens_bert as tokens
 
@@ -89,13 +91,13 @@ Imports
 
 .. parsed-literal::
 
-    html_reader.py:   0%|          | 0.00/635 [00:00=0.16.1" "transformers>=4.33.0"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu Pillow scipy "torch>=2.1" torchaudio "diffusers>=0.16.1" "transformers>=4.33.0"
     %pip install -q "git+https://github.com/huggingface/optimum-intel.git" onnx "gradio>=3.34.0" "openvino>=2023.1.0"
 
 Stable Diffusion pipeline in Optimum Intel
@@ -119,7 +119,7 @@ combined during inference into the pipeline:
 -  The VAE decoder
 
 More details about the Stable Diffusion pipeline can be found in
-`stable-diffusion <225-stable-diffusion-text-to-image-with-output.html>`__
+`stable-diffusion `__
 notebook.
 
 For running the Stable Diffusion model with Optimum Intel, we should use
@@ -147,14 +147,14 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    from openvino.runtime import Core
+    import openvino as ov
     
-    core = Core()
+    core = ov.Core()
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -242,7 +242,7 @@ from a spectrogram image using Griffin-Lim Algorithm.
     def wav_bytes_from_spectrogram_image(image: Image.Image) -> Tuple[io.BytesIO, float]:
         """
         Reconstruct a WAV audio clip from a spectrogram image. Also returns the duration in seconds.
-        
+    
         Parameters:
           image (Image.Image): generated spectrogram image
         Returns:
@@ -292,12 +292,10 @@ from a spectrogram image using Griffin-Lim Algorithm.
         return wav_bytes, duration_s
     
     
-    def spectrogram_from_image(
-        image: Image.Image, max_volume: float = 50, power_for_image: float = 0.25
-    ) -> np.ndarray:
+    def spectrogram_from_image(image: Image.Image, max_volume: float = 50, power_for_image: float = 0.25) -> np.ndarray:
         """
         Compute a spectrogram magnitude array from a spectrogram image.
-        
+    
         Parameters:
           image (image.Image): input image
           max_volume (float, *optional*, 50): max volume for spectrogram magnitude
@@ -402,10 +400,11 @@ reconstructed audio.
     pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
     pipe.compile()
     
-    def generate(prompt:str, negative_prompt:str = "") -> Tuple[Image.Image, str]:
+    
+    def generate(prompt: str, negative_prompt: str = "") -> Tuple[Image.Image, str]:
         """
         function for generation audio from text prompt
-        
+    
         Parameters:
           prompt (str): input prompt for generation.
           negative_prompt (str): negative prompt for generation, contains undesired concepts for generation, which should be avoided. Can be empty.
@@ -471,13 +470,14 @@ without the other. More explanation of how it works can be found in this
 
 
 
-.. image:: 241-riffusion-text-to-music-with-output_files/241-riffusion-text-to-music-with-output_14_0.png
+.. image:: riffusion-text-to-music-with-output_files/riffusion-text-to-music-with-output_14_0.png
 
 
 
 .. code:: ipython3
 
     import IPython.display as ipd
+    
     ipd.Audio(wav_path)
 
 
@@ -502,9 +502,8 @@ Interactive demo
 .. code:: ipython3
 
     import gradio as gr
-    from openvino.runtime import Core
     
-    available_devices = Core().available_devices + ["AUTO"]
+    available_devices = core.available_devices + ["AUTO"]
     
     examples = [
         "acoustic folk violin jam",
@@ -516,13 +515,14 @@ Interactive demo
         "ibiza at 3am",
         "k-pop boy group",
         "laughing",
-        "water drops"
+        "water drops",
     ]
     
-    def select_device(device_str:str, current_text:str = "", progress:gr.Progress = gr.Progress()):
+    
+    def select_device(device_str: str, current_text: str = "", progress: gr.Progress = gr.Progress()):
         """
         Helper function for uploading model on the device.
-        
+    
         Parameters:
           device_str (str): Device name.
           current_text (str): Current content of user instruction field (used only for backup purposes, temporally replacing it on the progress bar during model loading).
@@ -533,31 +533,32 @@ Interactive demo
         if device_str != pipe._device:
             pipe.clear_requests()
             pipe.to(device_str)
-            
+    
             for i in progress.tqdm(range(1), desc=f"Model loading on {device_str}"):
                 pipe.compile()
         return current_text
     
+    
     with gr.Blocks() as demo:
-        
         with gr.Column():
-            gr.Markdown(
-                "# Riffusion music generation with OpenVINO\n"
-                " Describe a musical prompt, generate music by getting a spectrogram image and sound."
-            )
-            
+            gr.Markdown("# Riffusion music generation with OpenVINO\n" " Describe a musical prompt, generate music by getting a spectrogram image and sound.")
+    
             prompt_input = gr.Textbox(placeholder="", label="Musical prompt")
             negative_prompt = gr.Textbox(label="Negative prompt")
             device = gr.Dropdown(choices=available_devices, value=DEVICE, label="Device")
-                
+    
             send_btn = gr.Button(value="Get a new spectrogram!")
             gr.Examples(examples, prompt_input, examples_per_page=15)
-                
+    
         with gr.Column():
-            sound_output = gr.Audio(type='filepath', label="spectrogram sound")
+            sound_output = gr.Audio(type="filepath", label="spectrogram sound")
             spectrogram_output = gr.Image(label="spectrogram image result", height=256)
-        
-        send_btn.click(generate, inputs=[prompt_input, negative_prompt], outputs=[spectrogram_output, sound_output])
+    
+        send_btn.click(
+            generate,
+            inputs=[prompt_input, negative_prompt],
+            outputs=[spectrogram_output, sound_output],
+        )
         device.change(select_device, [device, prompt_input], [prompt_input])
     
     if __name__ == "__main__":
diff --git a/docs/notebooks/241-riffusion-text-to-music-with-output_files/241-riffusion-text-to-music-with-output_14_0.jpg b/docs/notebooks/riffusion-text-to-music-with-output_files/riffusion-text-to-music-with-output_14_0.jpg
similarity index 100%
rename from docs/notebooks/241-riffusion-text-to-music-with-output_files/241-riffusion-text-to-music-with-output_14_0.jpg
rename to docs/notebooks/riffusion-text-to-music-with-output_files/riffusion-text-to-music-with-output_14_0.jpg
diff --git a/docs/notebooks/241-riffusion-text-to-music-with-output_files/241-riffusion-text-to-music-with-output_14_0.png b/docs/notebooks/riffusion-text-to-music-with-output_files/riffusion-text-to-music-with-output_14_0.png
similarity index 100%
rename from docs/notebooks/241-riffusion-text-to-music-with-output_files/241-riffusion-text-to-music-with-output_14_0.png
rename to docs/notebooks/riffusion-text-to-music-with-output_files/riffusion-text-to-music-with-output_14_0.png
diff --git a/docs/notebooks/rmbg-background-removal-with-output.rst b/docs/notebooks/rmbg-background-removal-with-output.rst
new file mode 100644
index 00000000000..f71222b4d0d
--- /dev/null
+++ b/docs/notebooks/rmbg-background-removal-with-output.rst
@@ -0,0 +1,349 @@
+Background removal with RMBG v1.4 and OpenVINO
+==============================================
+
+Background matting is the process of accurately estimating the
+foreground object in images and videos. It is a very important technique
+in image and video editing applications, particularly in film production
+for creating visual effects. In case of image segmentation, we segment
+the image into foreground and background by labeling the pixels. Image
+segmentation generates a binary image, in which a pixel either belongs
+to foreground or background. However, Image Matting is different from
+the image segmentation, wherein some pixels may belong to foreground as
+well as background, such pixels are called partial or mixed pixels. In
+order to fully separate the foreground from the background in an image,
+accurate estimation of the alpha values for partial or mixed pixels is
+necessary.
+
+RMBG v1.4 is background removal model, designed to effectively separate
+foreground from background in a range of categories and image types.
+This model has been trained on a carefully selected dataset, which
+includes: general stock images, e-commerce, gaming, and advertising
+content, making it suitable for commercial use cases powering enterprise
+content creation at scale. The accuracy, efficiency, and versatility
+currently rival leading source-available models.
+
+More details about model can be found in `model
+card `__.
+
+In this tutorial we consider how to convert and run this model using
+OpenVINO. #### Table of contents:
+
+-  `Prerequisites <#prerequisites>`__
+-  `Load PyTorch model <#load-pytorch-model>`__
+-  `Run PyTorch model inference <#run-pytorch-model-inference>`__
+-  `Convert Model to OpenVINO Intermediate Representation
+   format <#convert-model-to-openvino-intermediate-representation-format>`__
+-  `Run OpenVINO model inference <#run-openvino-model-inference>`__
+-  `Interactive demo <#interactive-demo>`__
+
+Prerequisites
+-------------
+
+
+
+install required dependencies
+
+.. code:: ipython3
+
+    %pip install -q torch torchvision pillow huggingface_hub "openvino>=2024.0.0" matplotlib "gradio>=4.15" "transformers>=4.39.1" tqdm --extra-index-url https://download.pytorch.org/whl/cpu
+
+
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Download model code from HuggingFace hub
+
+.. code:: ipython3
+
+    from huggingface_hub import hf_hub_download
+    from pathlib import Path
+    
+    repo_id = "briaai/RMBG-1.4"
+    
+    download_files = ["utilities.py", "example_input.jpg"]
+    
+    for file_for_downloading in download_files:
+        if not Path(file_for_downloading).exists():
+            hf_hub_download(repo_id=repo_id, filename=file_for_downloading, local_dir=".")
+
+Load PyTorch model
+------------------
+
+
+
+For loading model using PyTorch, we should use
+``AutoModelForImageSegmentation.from_pretrained`` method. Model weights
+will be downloaded automatically during first model usage. Please, note,
+it may take some time.
+
+.. code:: ipython3
+
+    from transformers import AutoModelForImageSegmentation
+    
+    net = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4", trust_remote_code=True)
+
+Run PyTorch model inference
+---------------------------
+
+
+
+``preprocess_image`` function is responsible for preparing input data in
+model-specific format. ``postprocess_image`` function is responsible for
+postprocessing model output. After postprocessing, generated background
+mask can be inserted into original image as alpha-channel.
+
+.. code:: ipython3
+
+    import torch
+    from PIL import Image
+    from utilities import preprocess_image, postprocess_image
+    import numpy as np
+    from matplotlib import pyplot as plt
+    
+    
+    def visualize_result(orig_img: Image, mask: Image, result_img: Image):
+        """
+        Helper for results visualization
+    
+        parameters:
+           orig_img (Image): input image
+           mask (Image): background mask
+           result_img (Image) output image
+        returns:
+          plt.Figure: plot with 3 images for visualization
+        """
+        titles = ["Original", "Background Mask", "Without background"]
+        im_w, im_h = orig_img.size
+        is_horizontal = im_h <= im_w
+        figsize = (20, 20)
+        num_images = 3
+        fig, axs = plt.subplots(
+            num_images if is_horizontal else 1,
+            1 if is_horizontal else num_images,
+            figsize=figsize,
+            sharex="all",
+            sharey="all",
+        )
+        fig.patch.set_facecolor("white")
+        list_axes = list(axs.flat)
+        for a in list_axes:
+            a.set_xticklabels([])
+            a.set_yticklabels([])
+            a.get_xaxis().set_visible(False)
+            a.get_yaxis().set_visible(False)
+            a.grid(False)
+        list_axes[0].imshow(np.array(orig_img))
+        list_axes[1].imshow(np.array(mask), cmap="gray")
+        list_axes[0].set_title(titles[0], fontsize=15)
+        list_axes[1].set_title(titles[1], fontsize=15)
+        list_axes[2].imshow(np.array(result_img))
+        list_axes[2].set_title(titles[2], fontsize=15)
+    
+        fig.subplots_adjust(wspace=0.01 if is_horizontal else 0.00, hspace=0.01 if is_horizontal else 0.1)
+        fig.tight_layout()
+        return fig
+    
+    
+    im_path = "./example_input.jpg"
+    
+    # prepare input
+    model_input_size = [1024, 1024]
+    orig_im = np.array(Image.open(im_path))
+    orig_im_size = orig_im.shape[0:2]
+    image = preprocess_image(orig_im, model_input_size)
+    
+    # inference
+    result = net(image)
+    
+    # post process
+    result_image = postprocess_image(result[0][0], orig_im_size)
+    
+    # save result
+    pil_im = Image.fromarray(result_image)
+    no_bg_image = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
+    orig_image = Image.open(im_path)
+    no_bg_image.paste(orig_image, mask=pil_im)
+    no_bg_image.save("example_image_no_bg.png")
+    
+    visualize_result(orig_image, pil_im, no_bg_image);
+
+
+
+.. image:: rmbg-background-removal-with-output_files/rmbg-background-removal-with-output_8_0.png
+
+
+Convert Model to OpenVINO Intermediate Representation format
+------------------------------------------------------------
+
+
+
+OpenVINO supports PyTorch models via conversion to OpenVINO Intermediate
+Representation (IR). `OpenVINO model conversion
+API `__
+should be used for these purposes. ``ov.convert_model`` function accepts
+original PyTorch model instance and example input for tracing and
+returns ``ov.Model`` representing this model in OpenVINO framework.
+Converted model can be used for saving on disk using ``ov.save_model``
+function or directly loading on device using ``core.complie_model``.
+
+.. code:: ipython3
+
+    import openvino as ov
+    
+    ov_model_path = Path("rmbg-1.4.xml")
+    
+    if not ov_model_path.exists():
+        ov_model = ov.convert_model(net, example_input=image, input=[1, 3, *model_input_size])
+        ov.save_model(ov_model, ov_model_path)
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+      warnings.warn(
+
+
+Run OpenVINO model inference
+----------------------------
+
+
+
+After finishing conversion, we can compile converted model and run it
+using OpenVINO on specified device. For selection inference device,
+please use dropdown list below:
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    core = ov.Core()
+    
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+Let’s run model on the same image that we used before for launching
+PyTorch model. OpenVINO model input and output is fully compatible with
+original pre- and postprocessing steps, it means that we can reuse them.
+
+.. code:: ipython3
+
+    ov_compiled_model = core.compile_model(ov_model_path, device.value)
+    
+    result = ov_compiled_model(image)[0]
+    
+    # post process
+    result_image = postprocess_image(torch.from_numpy(result), orig_im_size)
+    
+    # save result
+    pil_im = Image.fromarray(result_image)
+    no_bg_image = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
+    orig_image = Image.open(im_path)
+    no_bg_image.paste(orig_image, mask=pil_im)
+    no_bg_image.save("example_image_no_bg.png")
+    
+    visualize_result(orig_image, pil_im, no_bg_image);
+
+
+
+.. image:: rmbg-background-removal-with-output_files/rmbg-background-removal-with-output_14_0.png
+
+
+Interactive demo
+----------------
+
+
+
+.. code:: ipython3
+
+    import gradio as gr
+    
+    
+    title = "# RMBG background removal with OpenVINO"
+    
+    
+    def get_background_mask(model, image):
+        return model(image)[0]
+    
+    
+    with gr.Blocks() as demo:
+        gr.Markdown(title)
+    
+        with gr.Row():
+            input_image = gr.Image(label="Input Image", type="numpy")
+            background_image = gr.Image(label="Background removal Image")
+        submit = gr.Button("Submit")
+    
+        def on_submit(image):
+            original_image = image.copy()
+    
+            h, w = image.shape[:2]
+            image = preprocess_image(original_image, model_input_size)
+    
+            mask = get_background_mask(ov_compiled_model, image)
+            result_image = postprocess_image(torch.from_numpy(mask), (h, w))
+            pil_im = Image.fromarray(result_image)
+            orig_img = Image.fromarray(original_image)
+            no_bg_image = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
+            no_bg_image.paste(orig_img, mask=pil_im)
+    
+            return no_bg_image
+    
+        submit.click(on_submit, inputs=[input_image], outputs=[background_image])
+        examples = gr.Examples(
+            examples=["./example_input.jpg"],
+            inputs=[input_image],
+            outputs=[background_image],
+            fn=on_submit,
+            cache_examples=False,
+        )
+    
+    
+    if __name__ == "__main__":
+        try:
+            demo.launch(debug=False)
+        except Exception:
+            demo.launch(share=True, debug=False)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+    
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
diff --git a/docs/notebooks/rmbg-background-removal-with-output_files/rmbg-background-removal-with-output_14_0.png b/docs/notebooks/rmbg-background-removal-with-output_files/rmbg-background-removal-with-output_14_0.png
new file mode 100644
index 00000000000..3da45ed8e62
--- /dev/null
+++ b/docs/notebooks/rmbg-background-removal-with-output_files/rmbg-background-removal-with-output_14_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20f75a8ea279af55ef5ce7921d7dc2f60432273d62f1d05f55d8c902a7e852aa
+size 1082425
diff --git a/docs/notebooks/rmbg-background-removal-with-output_files/rmbg-background-removal-with-output_8_0.png b/docs/notebooks/rmbg-background-removal-with-output_files/rmbg-background-removal-with-output_8_0.png
new file mode 100644
index 00000000000..99b7f2b5254
--- /dev/null
+++ b/docs/notebooks/rmbg-background-removal-with-output_files/rmbg-background-removal-with-output_8_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49abbdabfb1a9e50c8d1685c82e99c14e24b535e0a9e2338f513d7640432e267
+size 1082450
diff --git a/docs/notebooks/271-sdxl-turbo-with-output.rst b/docs/notebooks/sdxl-turbo-with-output.rst
similarity index 87%
rename from docs/notebooks/271-sdxl-turbo-with-output.rst
rename to docs/notebooks/sdxl-turbo-with-output.rst
index 4ef2d128905..011a0be7721 100644
--- a/docs/notebooks/271-sdxl-turbo-with-output.rst
+++ b/docs/notebooks/sdxl-turbo-with-output.rst
@@ -16,9 +16,8 @@ More details about model can be found in `Stability AI blog
 post `__.
 
 Previously, we already discussed how to launch Stable Diffusion XL model
-using OpenVINO in the following
-`notebook <248-stable-diffusion-xl-with-output.html>`__, in this tutorial we will
-focus on the
+using OpenVINO in the following `notebook <../stable-diffusion-xl>`__,
+in this tutorial we will focus on the
 `SDXL-turbo `__ version.
 Additionally, to improve image decoding speed, we will use `Tiny
 Autoencoder `__, which is useful
@@ -62,7 +61,7 @@ Prerequisites
 .. code:: ipython3
 
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
-    torch transformers diffusers "git+https://github.com/huggingface/optimum-intel.git" gradio "peft==0.6.2" "openvino>=2023.3.0"
+    "torch>=2.1" transformers diffusers "git+https://github.com/huggingface/optimum-intel.git" "gradio>=4.19" "peft==0.6.2" "openvino>=2023.3.0"
 
 Convert model to OpenVINO format
 --------------------------------
@@ -121,6 +120,7 @@ back to image format.
     from diffusers import AutoencoderTiny
     import gc
     
+    
     class VAEEncoder(torch.nn.Module):
         def __init__(self, vae):
             super().__init__()
@@ -128,7 +128,8 @@ back to image format.
     
         def forward(self, sample):
             return self.vae.encode(sample)
-        
+    
+    
     class VAEDecoder(torch.nn.Module):
         def __init__(self, vae):
             super().__init__()
@@ -137,18 +138,19 @@ back to image format.
         def forward(self, latent_sample):
             return self.vae.decode(latent_sample)
     
+    
     def convert_tiny_vae(model_id, output_path):
         tiny_vae = AutoencoderTiny.from_pretrained(model_id)
         tiny_vae.eval()
         vae_encoder = VAEEncoder(tiny_vae)
-        ov_model = ov.convert_model(vae_encoder, example_input=torch.zeros((1,3,512,512)))
+        ov_model = ov.convert_model(vae_encoder, example_input=torch.zeros((1, 3, 512, 512)))
         ov.save_model(ov_model, output_path / "vae_encoder/openvino_model.xml")
         tiny_vae.save_config(output_path / "vae_encoder")
         vae_decoder = VAEDecoder(tiny_vae)
-        ov_model = ov.convert_model(vae_decoder, example_input=torch.zeros((1,4,64,64)))
+        ov_model = ov.convert_model(vae_decoder, example_input=torch.zeros((1, 4, 64, 64)))
         ov.save_model(ov_model, output_path / "vae_decoder/openvino_model.xml")
-        tiny_vae.save_config(output_path / "vae_decoder")    
-        
+        tiny_vae.save_config(output_path / "vae_decoder")
+    
     
     if not skip_convert_model:
         !optimum-cli export openvino --model $sdxl_model_id --task stable-diffusion-xl $model_dir --fp16
@@ -180,8 +182,8 @@ Select inference device for text-to-image generation
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -199,6 +201,7 @@ Select inference device for text-to-image generation
 .. code:: ipython3
 
     from optimum.intel.openvino import OVStableDiffusionXLPipeline
+    
     text2image_pipe = OVStableDiffusionXLPipeline.from_pretrained(model_dir, device=device.value)
 
 
@@ -231,7 +234,14 @@ disabled using ``guidance_scale = 0``
     import numpy as np
     
     prompt = "cute cat"
-    image = text2image_pipe(prompt, num_inference_steps=1, height=512, width=512, guidance_scale=0.0, generator=np.random.RandomState(987)).images[0]
+    image = text2image_pipe(
+        prompt,
+        num_inference_steps=1,
+        height=512,
+        width=512,
+        guidance_scale=0.0,
+        generator=np.random.RandomState(987),
+    ).images[0]
     image.save("cat.png")
     image
 
@@ -244,7 +254,7 @@ disabled using ``guidance_scale = 0``
 
 
 
-.. image:: 271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_11_1.png
+.. image:: sdxl-turbo-with-output_files/sdxl-turbo-with-output_11_1.png
 
 
 
@@ -302,7 +312,14 @@ finally, we get 0.5 \* 2.0 = 1 step in our pipeline.
 
 .. code:: ipython3
 
-    photo_image = image2image_pipe(photo_prompt, image=image, num_inference_steps=2, generator=np.random.RandomState(511), guidance_scale=0.0, strength=0.5).images[0]
+    photo_image = image2image_pipe(
+        photo_prompt,
+        image=image,
+        num_inference_steps=2,
+        generator=np.random.RandomState(511),
+        guidance_scale=0.0,
+        strength=0.5,
+    ).images[0]
     photo_image.save("cat_tie.png")
     photo_image
 
@@ -315,7 +332,7 @@ finally, we get 0.5 \* 2.0 = 1 step in our pipeline.
 
 
 
-.. image:: 271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_17_1.png
+.. image:: sdxl-turbo-with-output_files/sdxl-turbo-with-output_17_1.png
 
 
 
@@ -357,7 +374,7 @@ improve model inference speed.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -374,8 +391,13 @@ improve model inference speed.
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     int8_pipe = None
     
@@ -398,11 +420,12 @@ model inputs for calibration we should customize ``CompiledModel``.
 
     UNET_INT8_OV_PATH = model_dir / "optimized_unet" / "openvino_model.xml"
     
+    
     def disable_progress_bar(pipeline, disable=True):
         if not hasattr(pipeline, "_progress_bar_config"):
-            pipeline._progress_bar_config = {'disable': disable}
+            pipeline._progress_bar_config = {"disable": disable}
         else:
-            pipeline._progress_bar_config['disable'] = disable
+            pipeline._progress_bar_config["disable"] = disable
 
 .. code:: ipython3
 
@@ -541,7 +564,7 @@ data.
 
 
 
-.. image:: 271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_29_3.png
+.. image:: sdxl-turbo-with-output_files/sdxl-turbo-with-output_29_3.png
 
 
 .. code:: ipython3
@@ -577,7 +600,7 @@ data.
 
 
 
-.. image:: 271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_30_3.png
+.. image:: sdxl-turbo-with-output_files/sdxl-turbo-with-output_30_3.png
 
 
 Compare UNet file size
@@ -701,7 +724,7 @@ launch the interactive demo.
     
     use_quantized_model = widgets.Checkbox(
         value=True if quantized_model_present else False,
-        description='Use quantized model',
+        description="Use quantized model",
         disabled=False,
     )
     
@@ -729,7 +752,14 @@ launch the interactive demo.
     
     
     def generate_from_text(text, seed, num_steps, height, width):
-        result = text2image_pipe(text, num_inference_steps=num_steps, guidance_scale=0.0, generator=np.random.RandomState(seed), height=height, width=width).images[0]
+        result = text2image_pipe(
+            text,
+            num_inference_steps=num_steps,
+            guidance_scale=0.0,
+            generator=np.random.RandomState(seed),
+            height=height,
+            width=width,
+        ).images[0]
         return result
     
     
@@ -742,15 +772,38 @@ launch the interactive demo.
                 height_input = gr.Slider(label="Height", value=512, minimum=256, maximum=1024, step=32)
                 width_input = gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=32)
                 btn = gr.Button()
-            out = gr.Image(label="Result (Quantized)" if use_quantized_model.value else "Result (Original)", type="pil", width=512)
-            btn.click(generate_from_text, [positive_input, seed_input, steps_input, height_input, width_input], out)
-            gr.Examples([
-                ["cute cat", 999], 
-                ["underwater world coral reef, colorful jellyfish, 35mm, cinematic lighting, shallow depth of field,  ultra quality, masterpiece, realistic", 89],
-                ["a photo realistic happy white poodle dog ​​playing in the grass, extremely detailed, high res, 8k, masterpiece, dynamic angle", 1569],
-                ["Astronaut on Mars watching sunset, best quality, cinematic effects,", 65245],
-                ["Black and white street photography of a rainy night in New York, reflections on wet pavement", 48199]
-            ], [positive_input, seed_input])
+            out = gr.Image(
+                label=("Result (Quantized)" if use_quantized_model.value else "Result (Original)"),
+                type="pil",
+                width=512,
+            )
+            btn.click(
+                generate_from_text,
+                [positive_input, seed_input, steps_input, height_input, width_input],
+                out,
+            )
+            gr.Examples(
+                [
+                    ["cute cat", 999],
+                    [
+                        "underwater world coral reef, colorful jellyfish, 35mm, cinematic lighting, shallow depth of field,  ultra quality, masterpiece, realistic",
+                        89,
+                    ],
+                    [
+                        "a photo realistic happy white poodle dog ​​playing in the grass, extremely detailed, high res, 8k, masterpiece, dynamic angle",
+                        1569,
+                    ],
+                    [
+                        "Astronaut on Mars watching sunset, best quality, cinematic effects,",
+                        65245,
+                    ],
+                    [
+                        "Black and white street photography of a rainy night in New York, reflections on wet pavement",
+                        48199,
+                    ],
+                ],
+                [positive_input, seed_input],
+            )
     
     # if you are launching remotely, specify server_name and server_port
     # demo.launch(server_name='your server name', server_port='server port in int')
diff --git a/docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_11_1.jpg b/docs/notebooks/sdxl-turbo-with-output_files/sdxl-turbo-with-output_11_1.jpg
similarity index 100%
rename from docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_11_1.jpg
rename to docs/notebooks/sdxl-turbo-with-output_files/sdxl-turbo-with-output_11_1.jpg
diff --git a/docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_11_1.png b/docs/notebooks/sdxl-turbo-with-output_files/sdxl-turbo-with-output_11_1.png
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rename from docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_11_1.png
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diff --git a/docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_17_1.png b/docs/notebooks/sdxl-turbo-with-output_files/sdxl-turbo-with-output_17_1.png
similarity index 100%
rename from docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_17_1.png
rename to docs/notebooks/sdxl-turbo-with-output_files/sdxl-turbo-with-output_17_1.png
diff --git a/docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_29_3.jpg b/docs/notebooks/sdxl-turbo-with-output_files/sdxl-turbo-with-output_29_3.jpg
similarity index 100%
rename from docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_29_3.jpg
rename to docs/notebooks/sdxl-turbo-with-output_files/sdxl-turbo-with-output_29_3.jpg
diff --git a/docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_29_3.png b/docs/notebooks/sdxl-turbo-with-output_files/sdxl-turbo-with-output_29_3.png
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rename from docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_29_3.png
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diff --git a/docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_30_3.jpg b/docs/notebooks/sdxl-turbo-with-output_files/sdxl-turbo-with-output_30_3.jpg
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diff --git a/docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_30_3.png b/docs/notebooks/sdxl-turbo-with-output_files/sdxl-turbo-with-output_30_3.png
similarity index 100%
rename from docs/notebooks/271-sdxl-turbo-with-output_files/271-sdxl-turbo-with-output_30_3.png
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diff --git a/docs/notebooks/237-segment-anything-with-output.rst b/docs/notebooks/segment-anything-with-output.rst
similarity index 90%
rename from docs/notebooks/237-segment-anything-with-output.rst
rename to docs/notebooks/segment-anything-with-output.rst
index 6b1d7eb8121..23b4ddbc0bc 100644
--- a/docs/notebooks/237-segment-anything-with-output.rst
+++ b/docs/notebooks/segment-anything-with-output.rst
@@ -141,7 +141,14 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q "segment_anything" "gradio>=4.13" "openvino>=2023.1.0" "nncf>=2.7.0" "torch>=2.1" "torchvision>=0.16"  --extra-index-url https://download.pytorch.org/whl/cpu
+    import platform
+    
+    %pip install -q "segment_anything" "gradio>=4.13" "openvino>=2023.1.0" "nncf>=2.7.0" "torch>=2.1" "torchvision>=0.16" Pillow opencv-python tqdm  --extra-index-url https://download.pytorch.org/whl/cpu
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
 
 Convert model to OpenVINO Intermediate Representation
 -----------------------------------------------------
@@ -163,9 +170,14 @@ model type below to a SAM model checkpoint, then load the model using
 
 .. code:: ipython3
 
-    import sys
+    # Fetch `notebook_utils` module
+    import requests
     
-    sys.path.append("../utils")
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
     
     checkpoint = "sam_vit_b_01ec64.pth"
@@ -184,7 +196,7 @@ model type below to a SAM model checkpoint, then load the model using
 
 .. parsed-literal::
 
-    PosixPath('/home/ea/work/openvino_notebooks/notebooks/237-segment-anything/sam_vit_b_01ec64.pth')
+    PosixPath('/home/ea/work/openvino_notebooks/notebooks/segment-anything/sam_vit_b_01ec64.pth')
 
 
 
@@ -225,7 +237,11 @@ embeddings, tensor with shape ``1x256x64x64``
             warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
             warnings.filterwarnings("ignore", category=UserWarning)
     
-            ov_encoder_model = ov.convert_model(sam.image_encoder, example_input=torch.zeros(1,3,1024,1024), input=([1,3,1024,1024],))
+            ov_encoder_model = ov.convert_model(
+                sam.image_encoder,
+                example_input=torch.zeros(1, 3, 1024, 1024),
+                input=([1, 3, 1024, 1024],),
+            )
         ov.save_model(ov_encoder_model, ov_encoder_path)
     else:
         ov_encoder_model = core.read_model(ov_encoder_path)
@@ -236,8 +252,8 @@ embeddings, tensor with shape ``1x256x64x64``
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -295,6 +311,7 @@ Model outputs:
 
     from typing import Tuple
     
+    
     class SamExportableModel(torch.nn.Module):
         def __init__(
             self,
@@ -319,14 +336,10 @@ Model outputs:
             point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)
     
             point_embedding = point_embedding * (point_labels != -1).to(torch.float32)
-            point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (
-                point_labels == -1
-            ).to(torch.float32)
+            point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (point_labels == -1).to(torch.float32)
     
             for i in range(self.model.prompt_encoder.num_point_embeddings):
-                point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[
-                    i
-                ].weight * (point_labels == i).to(torch.float32)
+                point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[i].weight * (point_labels == i).to(torch.float32)
     
             return point_embedding
     
@@ -343,14 +356,10 @@ Model outputs:
             )
             return masks
     
-        def select_masks(
-            self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
-        ) -> Tuple[torch.Tensor, torch.Tensor]:
+        def select_masks(self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int) -> Tuple[torch.Tensor, torch.Tensor]:
             # Determine if we should return the multiclick mask or not from the number of points.
             # The reweighting is used to avoid control flow.
-            score_reweight = torch.tensor(
-                [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]
-            ).to(iou_preds.device)
+            score_reweight = torch.tensor([[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]).to(iou_preds.device)
             score = iou_preds + (num_points - 2.5) * score_reweight
             best_idx = torch.argmax(score, dim=1)
             masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
@@ -382,9 +391,7 @@ Model outputs:
             )
     
             if self.use_stability_score:
-                scores = calculate_stability_score(
-                    masks, self.model.mask_threshold, self.stability_score_offset
-                )
+                scores = calculate_stability_score(masks, self.model.mask_threshold, self.stability_score_offset)
     
             if self.return_single_mask:
                 masks, scores = self.select_masks(masks, scores, point_coords.shape[1])
@@ -392,14 +399,13 @@ Model outputs:
             upscaled_masks = self.mask_postprocessing(masks)
     
             if self.return_extra_metrics:
-                stability_scores = calculate_stability_score(
-                    upscaled_masks, self.model.mask_threshold, self.stability_score_offset
-                )
+                stability_scores = calculate_stability_score(upscaled_masks, self.model.mask_threshold, self.stability_score_offset)
                 areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
                 return upscaled_masks, scores, stability_scores, areas, masks
     
             return upscaled_masks, scores
     
+    
     ov_model_path = Path("sam_mask_predictor.xml")
     if not ov_model_path.exists():
         exportable_model = SamExportableModel(sam, return_single_mask=True)
@@ -452,7 +458,7 @@ Example Image
     import matplotlib.pyplot as plt
     
     download_file("https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg")
-    image = cv2.imread('truck.jpg')
+    image = cv2.imread("truck.jpg")
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
 
@@ -463,14 +469,14 @@ Example Image
 
 .. code:: ipython3
 
-    plt.figure(figsize=(10,10))
+    plt.figure(figsize=(10, 10))
     plt.imshow(image)
-    plt.axis('off')
+    plt.axis("off")
     plt.show()
 
 
 
-.. image:: 237-segment-anything-with-output_files/237-segment-anything-with-output_21_0.png
+.. image:: segment-anything-with-output_files/segment-anything-with-output_21_0.png
 
 
 Preprocessing and visualization utilities
@@ -495,7 +501,8 @@ These steps are applicable to all available models
 
     from copy import deepcopy
     from typing import Tuple
-    from torchvision.transforms.functional import resize, to_pil_image 
+    from torchvision.transforms.functional import resize, to_pil_image
+    
     
     class ResizeLongestSide:
         """
@@ -520,9 +527,7 @@ These steps are applicable to all available models
             original image size in (H, W) format.
             """
             old_h, old_w = original_size
-            new_h, new_w = self.get_preprocess_shape(
-                original_size[0], original_size[1], self.target_length
-            )
+            new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
             coords = deepcopy(coords).astype(float)
             coords[..., 0] = coords[..., 0] * (new_w / old_w)
             coords[..., 1] = coords[..., 1] * (new_h / old_h)
@@ -553,7 +558,11 @@ These steps are applicable to all available models
     
     def preprocess_image(image: np.ndarray):
         resized_image = resizer.apply_image(image)
-        resized_image = (resized_image.astype(np.float32) - [123.675, 116.28, 103.53]) / [58.395, 57.12, 57.375]
+        resized_image = (resized_image.astype(np.float32) - [123.675, 116.28, 103.53]) / [
+            58.395,
+            57.12,
+            57.375,
+        ]
         resized_image = np.expand_dims(np.transpose(resized_image, (2, 0, 1)).astype(np.float32), 0)
     
         # Pad
@@ -566,7 +575,7 @@ These steps are applicable to all available models
     
     def postprocess_masks(masks: np.ndarray, orig_size):
         size_before_pad = resizer.get_preprocess_shape(orig_size[0], orig_size[1], masks.shape[-1])
-        masks = masks[..., :int(size_before_pad[0]), :int(size_before_pad[1])]
+        masks = masks[..., : int(size_before_pad[0]), : int(size_before_pad[1])]
         masks = torch.nn.functional.interpolate(torch.from_numpy(masks), size=orig_size, mode="bilinear", align_corners=False).numpy()
         return masks
 
@@ -578,18 +587,34 @@ These steps are applicable to all available models
         mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
         ax.imshow(mask_image)
     
-        
+    
     def show_points(coords, labels, ax, marker_size=375):
         pos_points = coords[labels == 1]
         neg_points = coords[labels == 0]
-        ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
-        ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)   
+        ax.scatter(
+            pos_points[:, 0],
+            pos_points[:, 1],
+            color="green",
+            marker="*",
+            s=marker_size,
+            edgecolor="white",
+            linewidth=1.25,
+        )
+        ax.scatter(
+            neg_points[:, 0],
+            neg_points[:, 1],
+            color="red",
+            marker="*",
+            s=marker_size,
+            edgecolor="white",
+            linewidth=1.25,
+        )
+    
     
-        
     def show_box(box, ax):
         x0, y0 = box[0], box[1]
         w, h = box[2] - box[0], box[3] - box[1]
-        ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2))  
+        ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor="green", facecolor=(0, 0, 0, 0), lw=2))
 
 Image encoding
 ~~~~~~~~~~~~~~
@@ -623,15 +648,15 @@ location on the image below.
     input_point = np.array([[500, 375]])
     input_label = np.array([1])
     
-    plt.figure(figsize=(10,10))
+    plt.figure(figsize=(10, 10))
     plt.imshow(image)
     show_points(input_point, input_label, plt.gca())
-    plt.axis('off')
-    plt.show() 
+    plt.axis("off")
+    plt.show()
 
 
 
-.. image:: 237-segment-anything-with-output_files/237-segment-anything-with-output_28_0.png
+.. image:: segment-anything-with-output_files/segment-anything-with-output_28_0.png
 
 
 Add a batch index, concatenate a padding point, and transform it to
@@ -666,16 +691,16 @@ object).
 
 .. code:: ipython3
 
-    plt.figure(figsize=(10,10))
+    plt.figure(figsize=(10, 10))
     plt.imshow(image)
     show_mask(masks, plt.gca())
     show_points(input_point, input_label, plt.gca())
-    plt.axis('off')
-    plt.show() 
+    plt.axis("off")
+    plt.show()
 
 
 
-.. image:: 237-segment-anything-with-output_files/237-segment-anything-with-output_35_0.png
+.. image:: segment-anything-with-output_files/segment-anything-with-output_35_0.png
 
 
 Example with multiple points
@@ -695,15 +720,15 @@ Now, prompt for model looks like represented on this image:
 
 .. code:: ipython3
 
-    plt.figure(figsize=(10,10))
+    plt.figure(figsize=(10, 10))
     plt.imshow(image)
     show_points(input_point, input_label, plt.gca())
-    plt.axis('off')
-    plt.show() 
+    plt.axis("off")
+    plt.show()
 
 
 
-.. image:: 237-segment-anything-with-output_files/237-segment-anything-with-output_39_0.png
+.. image:: segment-anything-with-output_files/segment-anything-with-output_39_0.png
 
 
 Transform the points as in the previous example.
@@ -733,16 +758,16 @@ Package inputs, then predict and threshold the mask.
 
 .. code:: ipython3
 
-    plt.figure(figsize=(10,10))
+    plt.figure(figsize=(10, 10))
     plt.imshow(image)
     show_mask(masks, plt.gca())
     show_points(input_point, input_label, plt.gca())
-    plt.axis('off')
-    plt.show() 
+    plt.axis("off")
+    plt.show()
 
 
 
-.. image:: 237-segment-anything-with-output_files/237-segment-anything-with-output_44_0.png
+.. image:: segment-anything-with-output_files/segment-anything-with-output_44_0.png
 
 
 Great! Looks like now, predicted mask cover whole truck.
@@ -769,12 +794,12 @@ point should be excluded from mask.
     plt.imshow(image)
     show_box(input_box, plt.gca())
     show_points(input_point, input_label, plt.gca())
-    plt.axis('off')
+    plt.axis("off")
     plt.show()
 
 
 
-.. image:: 237-segment-anything-with-output_files/237-segment-anything-with-output_48_0.png
+.. image:: segment-anything-with-output_files/segment-anything-with-output_48_0.png
 
 
 Add a batch index, concatenate a box and point inputs, add the
@@ -784,7 +809,7 @@ padding point since the input includes a box input.
 .. code:: ipython3
 
     box_coords = input_box.reshape(2, 2)
-    box_labels = np.array([2,3])
+    box_labels = np.array([2, 3])
     
     coord = np.concatenate([input_point, box_coords], axis=0)[None, :, :]
     label = np.concatenate([input_label, box_labels], axis=0)[None, :].astype(np.float32)
@@ -814,12 +839,12 @@ Package inputs, then predict and threshold the mask.
     show_mask(masks[0], plt.gca())
     show_box(input_box, plt.gca())
     show_points(input_point, input_label, plt.gca())
-    plt.axis('off')
+    plt.axis("off")
     plt.show()
 
 
 
-.. image:: 237-segment-anything-with-output_files/237-segment-anything-with-output_53_0.png
+.. image:: segment-anything-with-output_files/segment-anything-with-output_53_0.png
 
 
 Interactive segmentation
@@ -835,13 +860,14 @@ point.
 
     import gradio as gr
     
+    
     class Segmenter:
         def __init__(self, ov_encoder, ov_predictor):
             self.encoder = ov_encoder
             self.predictor = ov_predictor
             self._img_embeddings = None
     
-        def set_image(self, img:np.ndarray):
+        def set_image(self, img: np.ndarray):
             if self._img_embeddings is not None:
                 del self._img_embeddings
             preprocessed_image = preprocess_image(img)
@@ -852,7 +878,7 @@ point.
     
         def get_mask(self, points, img):
             coord = np.array(points)
-            coord = np.concatenate([coord, np.array([[0,0]])], axis=0)
+            coord = np.concatenate([coord, np.array([[0, 0]])], axis=0)
             coord = coord[None, :, :]
             label = np.concatenate([np.ones(len(points)), np.array([-1])], axis=0)[None, :].astype(np.float32)
             coord = resizer.apply_coords(coord, img.shape[:2]).astype(np.float32)
@@ -867,20 +893,21 @@ point.
             results = self.predictor(inputs)
             masks = results[ov_predictor.output(0)]
             masks = postprocess_masks(masks, img.shape[:-1])
-            
+    
             masks = masks > 0.0
             mask = masks[0]
             mask = np.transpose(mask, (1, 2, 0))
             return mask
-            
+    
+    
     segmenter = Segmenter(ov_encoder, ov_predictor)
-            
-            
+    
+    
     with gr.Blocks() as demo:
         with gr.Row():
             input_img = gr.Image(label="Input", type="numpy", height=480, width=480)
             output_img = gr.Image(label="Selected Segment", type="numpy", height=480, width=480)
-        
+    
         def on_image_change(img):
             segmenter.set_image(img)
             return img
@@ -900,7 +927,7 @@ point.
                 out = cv2.addWeighted(out.astype(np.float32), 0.7, mask_image.astype(np.float32), 0.3, 0.0)
             out = out.astype(np.uint8)
             return out
-        
+    
         input_img.select(get_select_coords, [input_img], output_img)
         input_img.upload(on_image_change, [input_img], [input_img])
     
@@ -942,21 +969,21 @@ postprocessing masks to remove small disconnected regions and holes.
 .. code:: ipython3
 
     from segment_anything.utils.amg import (
-        MaskData, 
-        generate_crop_boxes, 
-        uncrop_boxes_xyxy, 
-        uncrop_masks, 
-        uncrop_points, 
-        calculate_stability_score, 
-        rle_to_mask, 
-        batched_mask_to_box, 
-        mask_to_rle_pytorch, 
+        MaskData,
+        generate_crop_boxes,
+        uncrop_boxes_xyxy,
+        uncrop_masks,
+        uncrop_points,
+        calculate_stability_score,
+        rle_to_mask,
+        batched_mask_to_box,
+        mask_to_rle_pytorch,
         is_box_near_crop_edge,
         batch_iterator,
         remove_small_regions,
         build_all_layer_point_grids,
         box_xyxy_to_xywh,
-        area_from_rle
+        area_from_rle,
     )
     from torchvision.ops.boxes import batched_nms, box_area
     from typing import Tuple, List, Dict, Any
@@ -972,7 +999,7 @@ postprocessing masks to remove small disconnected regions and holes.
         iou_thresh,
         mask_threshold,
         stability_score_offset,
-        stability_score_thresh
+        stability_score_thresh,
     ) -> MaskData:
         orig_h, orig_w = orig_size
     
@@ -1005,9 +1032,7 @@ postprocessing masks to remove small disconnected regions and holes.
             data.filter(keep_mask)
     
         # Calculate stability score
-        data["stability_score"] = calculate_stability_score(
-            data["masks"], mask_threshold, stability_score_offset
-        )
+        data["stability_score"] = calculate_stability_score(data["masks"], mask_threshold, stability_score_offset)
         if stability_score_thresh > 0.0:
             keep_mask = data["stability_score"] >= stability_score_thresh
             data.filter(keep_mask)
@@ -1036,8 +1061,8 @@ postprocessing masks to remove small disconnected regions and holes.
         crop_box: List[int],
         crop_layer_idx: int,
         orig_size: Tuple[int, ...],
-        box_nms_thresh:float = 0.7,
-        mask_threshold:float = 0.0,
+        box_nms_thresh: float = 0.7,
+        mask_threshold: float = 0.0,
         points_per_batch: int = 64,
         pred_iou_thresh: float = 0.88,
         stability_score_thresh: float = 0.95,
@@ -1057,7 +1082,17 @@ postprocessing masks to remove small disconnected regions and holes.
         # Generate masks for this crop in batches
         data = MaskData()
         for (points,) in batch_iterator(points_per_batch, points_for_image):
-            batch_data = process_batch(crop_embeddings, points, cropped_im_size, crop_box, orig_size, pred_iou_thresh, mask_threshold, stability_score_offset, stability_score_thresh)
+            batch_data = process_batch(
+                crop_embeddings,
+                points,
+                cropped_im_size,
+                crop_box,
+                orig_size,
+                pred_iou_thresh,
+                mask_threshold,
+                stability_score_offset,
+                stability_score_thresh,
+            )
             data.cat(batch_data)
             del batch_data
     
@@ -1081,9 +1116,7 @@ postprocessing masks to remove small disconnected regions and holes.
 
     def generate_masks(image: np.ndarray, point_grids, crop_n_layers, crop_overlap_ratio, crop_nms_thresh) -> MaskData:
         orig_size = image.shape[:2]
-        crop_boxes, layer_idxs = generate_crop_boxes(
-            orig_size, crop_n_layers, crop_overlap_ratio
-        )
+        crop_boxes, layer_idxs = generate_crop_boxes(orig_size, crop_n_layers, crop_overlap_ratio)
     
         # Iterate over image crops
         data = MaskData()
@@ -1167,7 +1200,14 @@ smaller objects, and post-processing can remove stray pixels and holes
 .. code:: ipython3
 
     def automatic_mask_generation(
-        image: np.ndarray, min_mask_region_area: int = 0, points_per_side: int = 32, crop_n_layers: int = 0, crop_n_points_downscale_factor: int = 1, crop_overlap_ratio: float = 512 / 1500, box_nms_thresh: float = 0.7, crop_nms_thresh: float = 0.7
+        image: np.ndarray,
+        min_mask_region_area: int = 0,
+        points_per_side: int = 32,
+        crop_n_layers: int = 0,
+        crop_n_points_downscale_factor: int = 1,
+        crop_overlap_ratio: float = 512 / 1500,
+        box_nms_thresh: float = 0.7,
+        crop_nms_thresh: float = 0.7,
     ) -> List[Dict[str, Any]]:
         """
         Generates masks for the given image.
@@ -1197,8 +1237,7 @@ smaller objects, and post-processing can remove stray pixels and holes
             crop_n_layers,
             crop_n_points_downscale_factor,
         )
-        mask_data = generate_masks(
-            image, point_grids, crop_n_layers, crop_overlap_ratio, crop_nms_thresh)
+        mask_data = generate_masks(image, point_grids, crop_n_layers, crop_overlap_ratio, crop_nms_thresh)
     
         # Filter small disconnected regions and holes in masks
         if min_mask_region_area > 0:
@@ -1208,8 +1247,7 @@ smaller objects, and post-processing can remove stray pixels and holes
                 max(box_nms_thresh, crop_nms_thresh),
             )
     
-        mask_data["segmentations"] = [
-            rle_to_mask(rle) for rle in mask_data["rles"]]
+        mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
     
         # Write mask records
         curr_anns = []
@@ -1260,11 +1298,12 @@ is a dictionary containing various data about the mask. These keys are:
 
     from tqdm.notebook import tqdm
     
+    
     def draw_anns(image, anns):
         if len(anns) == 0:
             return
         segments_image = image.copy()
-        sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
+        sorted_anns = sorted(anns, key=(lambda x: x["area"]), reverse=True)
         for ann in tqdm(sorted_anns):
             mask = ann["segmentation"]
             mask_color = np.random.randint(0, 255, size=(1, 1, 3)).astype(np.uint8)
@@ -1289,7 +1328,7 @@ is a dictionary containing various data about the mask. These keys are:
 
 
 
-.. image:: 237-segment-anything-with-output_files/237-segment-anything-with-output_68_1.png
+.. image:: segment-anything-with-output_files/segment-anything-with-output_68_1.png
 
 
 
@@ -1327,12 +1366,12 @@ the label files.
     from zipfile import ZipFile
     
     DATA_URL = "https://ultralytics.com/assets/coco128.zip"
-    OUT_DIR = Path('.')
+    OUT_DIR = Path(".")
     
     download_file(DATA_URL, directory=OUT_DIR, show_progress=True)
     
     if not (OUT_DIR / "coco128/images/train2017").exists():
-        with ZipFile('coco128.zip' , "r") as zip_ref:
+        with ZipFile("coco128.zip", "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
 
 
@@ -1349,6 +1388,7 @@ calibration dataset. For PyTorch, we can pass an instance of the
 
     import torch.utils.data as data
     
+    
     class COCOLoader(data.Dataset):
         def __init__(self, images_path):
             self.images = list(Path(images_path).iterdir())
@@ -1358,11 +1398,12 @@ calibration dataset. For PyTorch, we can pass an instance of the
             image = cv2.imread(str(image_path))
             image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
             return image
-        
+    
         def __len__(self):
             return len(self.images)
-        
-    coco_dataset = COCOLoader(OUT_DIR / 'coco128/images/train2017')
+    
+    
+    coco_dataset = COCOLoader(OUT_DIR / "coco128/images/train2017")
     calibration_loader = torch.utils.data.DataLoader(coco_dataset)
 
 The transformation function is a function that takes a sample from the
@@ -1372,6 +1413,7 @@ dataset and returns data that can be passed to the model for inference.
 
     import nncf
     
+    
     def transform_fn(image_data):
         """
         Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
@@ -1384,6 +1426,7 @@ dataset and returns data that can be passed to the model for inference.
         processed_image = preprocess_image(np.squeeze(image))
         return processed_image
     
+    
     calibration_dataset = nncf.Dataset(calibration_loader, transform_fn)
 
 
@@ -1418,12 +1461,13 @@ activations.
 
 .. code:: ipython3
 
-    
     model = core.read_model(ov_encoder_path)
-    quantized_model = nncf.quantize(model,
-                                    calibration_dataset,
-                                    model_type=nncf.parameters.ModelType.TRANSFORMER,
-                                    subset_size=128)
+    quantized_model = nncf.quantize(
+        model,
+        calibration_dataset,
+        model_type=nncf.parameters.ModelType.TRANSFORMER,
+        subset_size=128,
+    )
     print("model quantization finished")
 
 
@@ -1493,16 +1537,16 @@ We can reuse the previous code to validate the output of ``INT8`` model.
     masks = results[ov_predictor.output(0)]
     masks = postprocess_masks(masks, image.shape[:-1])
     masks = masks > 0.0
-    plt.figure(figsize=(10,10))
+    plt.figure(figsize=(10, 10))
     plt.imshow(image)
     show_mask(masks, plt.gca())
     show_points(input_point, input_label, plt.gca())
-    plt.axis('off')
-    plt.show() 
+    plt.axis("off")
+    plt.show()
 
 
 
-.. image:: 237-segment-anything-with-output_files/237-segment-anything-with-output_80_0.png
+.. image:: segment-anything-with-output_files/segment-anything-with-output_80_0.png
 
 
 Run ``INT8`` model in automatic mask generation mode
@@ -1524,14 +1568,14 @@ Run ``INT8`` model in automatic mask generation mode
 
 
 
-.. image:: 237-segment-anything-with-output_files/237-segment-anything-with-output_82_1.png
+.. image:: segment-anything-with-output_files/segment-anything-with-output_82_1.png
 
 
 
 Compare Performance of the Original and Quantized Models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Finally, use the OpenVINO
+ Finally, use the OpenVINO
 `Benchmark
 Tool `__
 to measure the inference performance of the ``FP32`` and ``INT8``
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_21_0.png b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_21_0.png
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_21_0.png
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_21_0.png
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_28_0.png b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_28_0.png
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_28_0.png
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_28_0.png
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_35_0.png b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_35_0.png
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_35_0.png
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_35_0.png
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_39_0.png b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_39_0.png
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_39_0.png
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_39_0.png
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_44_0.png b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_44_0.png
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_44_0.png
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_44_0.png
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_48_0.png b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_48_0.png
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_48_0.png
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_48_0.png
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_53_0.png b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_53_0.png
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_53_0.png
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_53_0.png
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_68_1.jpg b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_68_1.jpg
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_68_1.jpg
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_68_1.jpg
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_68_1.png b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_68_1.png
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_68_1.png
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_68_1.png
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_80_0.png b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_80_0.png
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_80_0.png
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_80_0.png
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_82_1.jpg b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_82_1.jpg
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_82_1.jpg
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_82_1.jpg
diff --git a/docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_82_1.png b/docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_82_1.png
similarity index 100%
rename from docs/notebooks/237-segment-anything-with-output_files/237-segment-anything-with-output_82_1.png
rename to docs/notebooks/segment-anything-with-output_files/segment-anything-with-output_82_1.png
diff --git a/docs/notebooks/204-segmenter-semantic-segmentation-with-output.rst b/docs/notebooks/segmenter-semantic-segmentation-with-output.rst
similarity index 68%
rename from docs/notebooks/204-segmenter-semantic-segmentation-with-output.rst
rename to docs/notebooks/segmenter-semantic-segmentation-with-output.rst
index b45f1514f9f..36f20d1123d 100644
--- a/docs/notebooks/204-segmenter-semantic-segmentation-with-output.rst
+++ b/docs/notebooks/segmenter-semantic-segmentation-with-output.rst
@@ -119,10 +119,7 @@ Receiving objects:   6% (17/268)
 Receiving objects:   7% (19/268)
 Receiving objects:   8% (22/268)
 Receiving objects:   9% (25/268)
-
-.. parsed-literal::
-
-    Receiving objects:  10% (27/268)
+Receiving objects:  10% (27/268)
 Receiving objects:  11% (30/268)
 Receiving objects:  12% (33/268)
 Receiving objects:  13% (35/268)
@@ -139,120 +136,117 @@ Receiving objects:  22% (59/268)
 .. parsed-literal::
 
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-Receiving objects:  24% (65/268)
 
 .. parsed-literal::
 
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+    Receiving objects:  24% (65/268)
 
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     Resolving deltas:   0% (0/117)
 Resolving deltas:   1% (2/117)
 Resolving deltas:   2% (3/117)
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 Resolving deltas: 100% (117/117)
 Resolving deltas: 100% (117/117), done.
 
@@ -260,10 +254,15 @@ Resolving deltas: 100% (117/117), done.
 .. code:: ipython3
 
     # Installing requirements
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" tqdm
     %pip install -r segmenter/requirements.txt
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -271,28 +270,19 @@ Resolving deltas: 100% (117/117), done.
 
 .. parsed-literal::
 
-    Requirement already satisfied: torch in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 1)) (2.1.0+cpu)
-    Requirement already satisfied: click in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 2)) (8.1.7)
-    Requirement already satisfied: numpy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 3)) (1.23.5)
-
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-.. parsed-literal::
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-    Collecting einops (from -r segmenter/requirements.txt (line 4))
-      Using cached einops-0.7.0-py3-none-any.whl.metadata (13 kB)
+    Requirement already satisfied: torch in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 1)) (2.2.2+cpu)
+    Requirement already satisfied: click in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 2)) (8.1.7)
+    Requirement already satisfied: numpy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 3)) (1.23.5)
+    Requirement already satisfied: einops in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 4)) (0.3.2)
 
 
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     Collecting python-hostlist (from -r segmenter/requirements.txt (line 5))
       Using cached python_hostlist-1.23.0-py3-none-any.whl
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-    Requirement already satisfied: tqdm in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 6)) (4.66.2)
-    Requirement already satisfied: requests in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 7)) (2.31.0)
-    Requirement already satisfied: pyyaml in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 8)) (6.0.1)
+    Requirement already satisfied: tqdm in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 6)) (4.66.2)
+    Requirement already satisfied: requests in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 7)) (2.31.0)
+    Requirement already satisfied: pyyaml in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from -r segmenter/requirements.txt (line 8)) (6.0.1)
 
 
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@@ -311,99 +301,79 @@ Resolving deltas: 100% (117/117), done.
 
     Collecting mmsegmentation==0.14.1 (from -r segmenter/requirements.txt (line 11))
       Using cached mmsegmentation-0.14.1-py3-none-any.whl.metadata (8.3 kB)
-    Requirement already satisfied: torchvision in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from timm==0.4.12->-r segmenter/requirements.txt (line 9)) (0.16.0+cpu)
+    Requirement already satisfied: torchvision in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from timm==0.4.12->-r segmenter/requirements.txt (line 9)) (0.17.2+cpu)
+    Requirement already satisfied: addict in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from mmcv==1.3.8->-r segmenter/requirements.txt (line 10)) (2.4.0)
+    Requirement already satisfied: Pillow in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from mmcv==1.3.8->-r segmenter/requirements.txt (line 10)) (10.3.0)
+    Requirement already satisfied: yapf in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from mmcv==1.3.8->-r segmenter/requirements.txt (line 10)) (0.40.2)
+    Requirement already satisfied: matplotlib in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (3.7.0)
+    Requirement already satisfied: prettytable in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (3.10.0)
+    Requirement already satisfied: filelock in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch->-r segmenter/requirements.txt (line 1)) (3.13.4)
+    Requirement already satisfied: typing-extensions>=4.8.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch->-r segmenter/requirements.txt (line 1)) (4.11.0)
+    Requirement already satisfied: sympy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch->-r segmenter/requirements.txt (line 1)) (1.12)
+    Requirement already satisfied: networkx in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch->-r segmenter/requirements.txt (line 1)) (3.1)
+    Requirement already satisfied: jinja2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch->-r segmenter/requirements.txt (line 1)) (3.1.3)
+    Requirement already satisfied: fsspec in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch->-r segmenter/requirements.txt (line 1)) (2024.2.0)
 
 
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-    Collecting addict (from mmcv==1.3.8->-r segmenter/requirements.txt (line 10))
-      Using cached addict-2.4.0-py3-none-any.whl.metadata (1.0 kB)
-    Requirement already satisfied: Pillow in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from mmcv==1.3.8->-r segmenter/requirements.txt (line 10)) (10.2.0)
+    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->-r segmenter/requirements.txt (line 7)) (3.3.2)
+    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->-r segmenter/requirements.txt (line 7)) (3.7)
+    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->-r segmenter/requirements.txt (line 7)) (2.2.1)
+    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->-r segmenter/requirements.txt (line 7)) (2024.2.2)
 
 
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-    Collecting yapf (from mmcv==1.3.8->-r segmenter/requirements.txt (line 10))
-      Using cached yapf-0.40.2-py3-none-any.whl.metadata (45 kB)
+    Requirement already satisfied: MarkupSafe>=2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jinja2->torch->-r segmenter/requirements.txt (line 1)) (2.1.5)
+    Requirement already satisfied: contourpy>=1.0.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from matplotlib->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (1.1.1)
+    Requirement already satisfied: cycler>=0.10 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from matplotlib->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (0.12.1)
+    Requirement already satisfied: fonttools>=4.22.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from matplotlib->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (4.51.0)
+    Requirement already satisfied: kiwisolver>=1.0.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from matplotlib->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (1.4.5)
+    Requirement already satisfied: packaging>=20.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from matplotlib->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (24.0)
+    Requirement already satisfied: pyparsing>=2.3.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from matplotlib->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (3.1.2)
+    Requirement already satisfied: python-dateutil>=2.7 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from matplotlib->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (2.9.0.post0)
+    Requirement already satisfied: importlib-resources>=3.2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from matplotlib->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (6.4.0)
+    Requirement already satisfied: wcwidth in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from prettytable->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (0.2.13)
+    Requirement already satisfied: mpmath>=0.19 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from sympy->torch->-r segmenter/requirements.txt (line 1)) (1.3.0)
+    Requirement already satisfied: importlib-metadata>=6.6.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from yapf->mmcv==1.3.8->-r segmenter/requirements.txt (line 10)) (7.1.0)
+    Requirement already satisfied: platformdirs>=3.5.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from yapf->mmcv==1.3.8->-r segmenter/requirements.txt (line 10)) (4.2.0)
+    Requirement already satisfied: tomli>=2.0.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from yapf->mmcv==1.3.8->-r segmenter/requirements.txt (line 10)) (2.0.1)
 
 
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-    Requirement already satisfied: matplotlib in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (3.7.5)
-    Requirement already satisfied: prettytable in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (3.10.0)
-    Requirement already satisfied: filelock in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch->-r segmenter/requirements.txt (line 1)) (3.13.1)
-    Requirement already satisfied: typing-extensions in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch->-r segmenter/requirements.txt (line 1)) (4.10.0)
-    Requirement already satisfied: sympy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch->-r segmenter/requirements.txt (line 1)) (1.12)
-    Requirement already satisfied: networkx in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch->-r segmenter/requirements.txt (line 1)) (3.1)
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-    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->-r segmenter/requirements.txt (line 7)) (3.6)
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-    Requirement already satisfied: contourpy>=1.0.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from matplotlib->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (1.1.1)
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-.. parsed-literal::
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-    Collecting tomli>=2.0.1 (from yapf->mmcv==1.3.8->-r segmenter/requirements.txt (line 10))
-      Using cached tomli-2.0.1-py3-none-any.whl.metadata (8.9 kB)
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+    Requirement already satisfied: zipp>=0.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from importlib-metadata>=6.6.0->yapf->mmcv==1.3.8->-r segmenter/requirements.txt (line 10)) (3.18.1)
+    Requirement already satisfied: six>=1.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib->mmsegmentation==0.14.1->-r segmenter/requirements.txt (line 11)) (1.16.0)
 
 
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     Using cached timm-0.4.12-py3-none-any.whl (376 kB)
     Using cached mmsegmentation-0.14.1-py3-none-any.whl (201 kB)
-    Using cached einops-0.7.0-py3-none-any.whl (44 kB)
-    Using cached addict-2.4.0-py3-none-any.whl (3.8 kB)
-    Using cached yapf-0.40.2-py3-none-any.whl (254 kB)
-    Using cached tomli-2.0.1-py3-none-any.whl (12 kB)
 
 
 .. parsed-literal::
 
-    Installing collected packages: python-hostlist, addict, tomli, einops, yapf, mmsegmentation, mmcv, timm
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    Installing collected packages: python-hostlist, mmsegmentation, mmcv, timm
 
 
 .. parsed-literal::
 
-      Attempting uninstall: tomli
-        Found existing installation: tomli 1.2.3
-        Uninstalling tomli-1.2.3:
-          Successfully uninstalled tomli-1.2.3
+      Attempting uninstall: timm
+        Found existing installation: timm 0.9.16
+        Uninstalling timm-0.9.16:
+          Successfully uninstalled timm-0.9.16
 
 
 .. parsed-literal::
 
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    black 21.7b0 requires tomli<2.0.0,>=0.2.6, but you have tomli 2.0.1 which is incompatible.
-    Successfully installed addict-2.4.0 einops-0.7.0 mmcv-1.3.8 mmsegmentation-0.14.1 python-hostlist-1.23.0 timm-0.4.12 tomli-2.0.1 yapf-0.40.2
+    mobileclip 0.1.0 requires timm>=0.9.5, but you have timm 0.4.12 which is incompatible.
+    mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.2.2+cpu which is incompatible.
+    mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.17.2+cpu which is incompatible.
+    Successfully installed mmcv-1.3.8 mmsegmentation-0.14.1 python-hostlist-1.23.0 timm-0.4.12
 
 
 .. parsed-literal::
@@ -417,11 +387,13 @@ Resolving deltas: 100% (117/117), done.
     import yaml
     
     # Fetch the notebook utils script from the openvino_notebooks repo
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file, load_image
 
 We’ll need ``timm``, ``mmsegmentation``, ``einops`` and ``mmcv``, to use
@@ -497,17 +469,12 @@ Load normalization settings from config file.
 .. code:: ipython3
 
     from segmenter.segm.data.utils import STATS
+    
     # load normalization name, in our case "vit" since we are using transformer
     normalization_name = config["dataset_kwargs"]["normalization"]
     # load normalization params, mean and std from STATS
     normalization = STATS[normalization_name]
 
-
-.. parsed-literal::
-
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
-
-
 Preparing preprocessing and visualization functions
 ---------------------------------------------------
 
@@ -624,7 +591,7 @@ example image ``coco_hollywood.jpg``.
     # load image with PIL
     image = load_image("https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_hollywood.jpg")
     # load_image reads the image in BGR format, [:,:,::-1] reshape transfroms it to RGB
-    pil_image = Image.fromarray(image[:,:,::-1])
+    pil_image = Image.fromarray(image[:, :, ::-1])
     
     # preprocess image with normalization params loaded in previous steps
     image = preprocess(pil_image, normalization)
@@ -632,13 +599,15 @@ example image ``coco_hollywood.jpg``.
     # inference function needs some meta parameters, where we specify that we don't flip images in inference mode
     im_meta = dict(flip=False)
     # perform inference with function from repository
-    original_results = inference(model=pytorch_model,
-                                 ims=[image],
-                                 ims_metas=[im_meta],
-                                 ori_shape=image.shape[2:4],
-                                 window_size=config["inference_kwargs"]["window_size"],
-                                 window_stride=config["inference_kwargs"]["window_stride"],
-                                 batch_size=2)
+    original_results = inference(
+        model=pytorch_model,
+        ims=[image],
+        ims_metas=[im_meta],
+        ori_shape=image.shape[2:4],
+        window_size=config["inference_kwargs"]["window_size"],
+        window_stride=config["inference_kwargs"]["window_stride"],
+        batch_size=2,
+    )
 
 After inference is complete, we need to transform output to segmentation
 mask where each class has specified color, using helper functions from
@@ -655,7 +624,7 @@ previous steps.
 
 
 
-.. image:: 204-segmenter-semantic-segmentation-with-output_files/204-segmenter-semantic-segmentation-with-output_21_0.png
+.. image:: segmenter-semantic-segmentation-with-output_files/segmenter-semantic-segmentation-with-output_21_0.png
 
 
 
@@ -704,28 +673,32 @@ they are not a problem.
     # make dummy input with correct shapes obtained from config file
     dummy_input = torch.randn(batch_size, channels, image_size, image_size)
     
-    model = ov.convert_model(pytorch_model, example_input=dummy_input, input=([batch_size, channels, image_size, image_size], ))
+    model = ov.convert_model(
+        pytorch_model,
+        example_input=dummy_input,
+        input=([batch_size, channels, image_size, image_size],),
+    )
     # serialize model for saving IR
     ov.save_model(model, MODEL_DIR / "segmenter.xml")
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/204-segmenter-semantic-segmentation/./segmenter/segm/model/utils.py:69: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/segmenter-semantic-segmentation/./segmenter/segm/model/utils.py:69: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if H % patch_size > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/204-segmenter-semantic-segmentation/./segmenter/segm/model/utils.py:71: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/segmenter-semantic-segmentation/./segmenter/segm/model/utils.py:71: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if W % patch_size > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/204-segmenter-semantic-segmentation/./segmenter/segm/model/vit.py:122: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/segmenter-semantic-segmentation/./segmenter/segm/model/vit.py:122: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if x.shape[1] != pos_embed.shape[1]:
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/204-segmenter-semantic-segmentation/./segmenter/segm/model/decoder.py:100: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/segmenter-semantic-segmentation/./segmenter/segm/model/decoder.py:100: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       masks = rearrange(masks, "b (h w) n -> b n h w", h=int(GS))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/204-segmenter-semantic-segmentation/./segmenter/segm/model/utils.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/segmenter-semantic-segmentation/./segmenter/segm/model/utils.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if extra_h > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/204-segmenter-semantic-segmentation/./segmenter/segm/model/utils.py:87: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/segmenter-semantic-segmentation/./segmenter/segm/model/utils.py:87: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if extra_w > 0:
 
 
@@ -760,7 +733,7 @@ any additional custom code required to process input.
     
         """
     
-        def __init__(self, model_path: Path, device:str = "CPU"):
+        def __init__(self, model_path: Path, device: str = "CPU"):
             """
             Constructor method.
             Initializes OpenVINO model and sets all required attributes
@@ -813,8 +786,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -837,13 +810,15 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     # perform inference with same function as in case of PyTorch model from repository
-    results = inference(model=model,
-                        ims=[image],
-                        ims_metas=[im_meta],
-                        ori_shape=image.shape[2:4],
-                        window_size=model.config["inference_kwargs"]["window_size"],
-                        window_stride=model.config["inference_kwargs"]["window_stride"],
-                        batch_size=2)
+    results = inference(
+        model=model,
+        ims=[image],
+        ims_metas=[im_meta],
+        ori_shape=image.shape[2:4],
+        window_size=model.config["inference_kwargs"]["window_size"],
+        window_stride=model.config["inference_kwargs"]["window_stride"],
+        batch_size=2,
+    )
 
 .. code:: ipython3
 
@@ -856,7 +831,7 @@ select device from dropdown list for running inference using OpenVINO
 
 
 
-.. image:: 204-segmenter-semantic-segmentation-with-output_files/204-segmenter-semantic-segmentation-with-output_32_0.png
+.. image:: segmenter-semantic-segmentation-with-output_files/segmenter-semantic-segmentation-with-output_32_0.png
 
 
 
@@ -925,7 +900,7 @@ to measure the inference performance of the model.
 
 .. parsed-literal::
 
-    [ INFO ] Read model took 24.36 ms
+    [ INFO ] Read model took 23.64 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     im (node: im) : f32 / [...] / [2,3,512,512]
@@ -943,13 +918,9 @@ to measure the inference performance of the model.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 341.36 ms
+    [ INFO ] Compile model took 356.63 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
-
-
-.. parsed-literal::
-
     [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
@@ -986,19 +957,19 @@ to measure the inference performance of the model.
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 203.29 ms
+    [ INFO ] First inference took 202.08 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            1728 iterations
-    [ INFO ] Duration:         120391.49 ms
+    [ INFO ] Count:            1698 iterations
+    [ INFO ] Duration:         120587.19 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        416.77 ms
-    [ INFO ]    Average:       417.75 ms
-    [ INFO ]    Min:           333.14 ms
-    [ INFO ]    Max:           505.65 ms
-    [ INFO ] Throughput:   28.71 FPS
+    [ INFO ]    Median:        425.03 ms
+    [ INFO ]    Average:       425.44 ms
+    [ INFO ]    Min:           356.57 ms
+    [ INFO ]    Max:           515.02 ms
+    [ INFO ] Throughput:   28.16 FPS
 
diff --git a/docs/notebooks/204-segmenter-semantic-segmentation-with-output_files/204-segmenter-semantic-segmentation-with-output_21_0.jpg b/docs/notebooks/segmenter-semantic-segmentation-with-output_files/segmenter-semantic-segmentation-with-output_21_0.jpg
similarity index 100%
rename from docs/notebooks/204-segmenter-semantic-segmentation-with-output_files/204-segmenter-semantic-segmentation-with-output_21_0.jpg
rename to docs/notebooks/segmenter-semantic-segmentation-with-output_files/segmenter-semantic-segmentation-with-output_21_0.jpg
diff --git a/docs/notebooks/204-segmenter-semantic-segmentation-with-output_files/204-segmenter-semantic-segmentation-with-output_21_0.png b/docs/notebooks/segmenter-semantic-segmentation-with-output_files/segmenter-semantic-segmentation-with-output_21_0.png
similarity index 100%
rename from docs/notebooks/204-segmenter-semantic-segmentation-with-output_files/204-segmenter-semantic-segmentation-with-output_21_0.png
rename to docs/notebooks/segmenter-semantic-segmentation-with-output_files/segmenter-semantic-segmentation-with-output_21_0.png
diff --git a/docs/notebooks/204-segmenter-semantic-segmentation-with-output_files/204-segmenter-semantic-segmentation-with-output_32_0.jpg b/docs/notebooks/segmenter-semantic-segmentation-with-output_files/segmenter-semantic-segmentation-with-output_32_0.jpg
similarity index 100%
rename from docs/notebooks/204-segmenter-semantic-segmentation-with-output_files/204-segmenter-semantic-segmentation-with-output_32_0.jpg
rename to docs/notebooks/segmenter-semantic-segmentation-with-output_files/segmenter-semantic-segmentation-with-output_32_0.jpg
diff --git a/docs/notebooks/204-segmenter-semantic-segmentation-with-output_files/204-segmenter-semantic-segmentation-with-output_32_0.png b/docs/notebooks/segmenter-semantic-segmentation-with-output_files/segmenter-semantic-segmentation-with-output_32_0.png
similarity index 100%
rename from docs/notebooks/204-segmenter-semantic-segmentation-with-output_files/204-segmenter-semantic-segmentation-with-output_32_0.png
rename to docs/notebooks/segmenter-semantic-segmentation-with-output_files/segmenter-semantic-segmentation-with-output_32_0.png
diff --git a/docs/notebooks/248-segmind-vegart-with-output.rst b/docs/notebooks/segmind-vegart-with-output.rst
similarity index 88%
rename from docs/notebooks/248-segmind-vegart-with-output.rst
rename to docs/notebooks/segmind-vegart-with-output.rst
index ae6bfb513a0..45266e95e9e 100644
--- a/docs/notebooks/248-segmind-vegart-with-output.rst
+++ b/docs/notebooks/segmind-vegart-with-output.rst
@@ -81,7 +81,7 @@ Prerequisites
 .. code:: ipython3
 
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
-    torch transformers diffusers "git+https://github.com/huggingface/optimum-intel.git" gradio "openvino>=2023.3.0"
+    "torch>=2.1" transformers diffusers "git+https://github.com/huggingface/optimum-intel.git" "gradio>=4.19" "openvino>=2023.3.0" "peft==0.6.2"
 
 
 .. parsed-literal::
@@ -113,7 +113,7 @@ pipeline on disk.
     
     model_id = "segmind/Segmind-Vega"
     adapter_id = "segmind/Segmind-VegaRT"
-    pt_model_dir = Path('segmind-vegart')
+    pt_model_dir = Path("segmind-vegart")
     
     if not pt_model_dir.exists():
         pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
@@ -121,7 +121,7 @@ pipeline on disk.
         pipe.load_lora_weights(adapter_id)
         pipe.fuse_lora()
     
-        pipe.save_pretrained('segmind-vegart')
+        pipe.save_pretrained("segmind-vegart")
         del pipe
         gc.collect()
 
@@ -189,6 +189,7 @@ back to image format.
     from diffusers import AutoencoderTiny
     import gc
     
+    
     class VAEEncoder(torch.nn.Module):
         def __init__(self, vae):
             super().__init__()
@@ -196,7 +197,8 @@ back to image format.
     
         def forward(self, sample):
             return self.vae.encode(sample)
-        
+    
+    
     class VAEDecoder(torch.nn.Module):
         def __init__(self, vae):
             super().__init__()
@@ -205,21 +207,22 @@ back to image format.
         def forward(self, latent_sample):
             return self.vae.decode(latent_sample)
     
+    
     def convert_tiny_vae(model_id, output_path):
         tiny_vae = AutoencoderTiny.from_pretrained(model_id)
         tiny_vae.eval()
         vae_encoder = VAEEncoder(tiny_vae)
-        ov_model = ov.convert_model(vae_encoder, example_input=torch.zeros((1,3,512,512)))
+        ov_model = ov.convert_model(vae_encoder, example_input=torch.zeros((1, 3, 512, 512)))
         ov.save_model(ov_model, output_path / "vae_encoder/openvino_model.xml")
         tiny_vae.save_config(output_path / "vae_encoder")
         vae_decoder = VAEDecoder(tiny_vae)
-        ov_model = ov.convert_model(vae_decoder, example_input=torch.zeros((1,4,64,64)))
+        ov_model = ov.convert_model(vae_decoder, example_input=torch.zeros((1, 4, 64, 64)))
         ov.save_model(ov_model, output_path / "vae_decoder/openvino_model.xml")
         tiny_vae.save_config(output_path / "vae_decoder")
         del tiny_vae
         del ov_model
         gc.collect()
-        
+    
     
     if not skip_convert_model:
         !optimum-cli export openvino --model $sdxl_model_id --task stable-diffusion-xl $model_dir --fp16
@@ -242,7 +245,7 @@ For saving time, we will not cover image-to-image generation in this
 notebook. As we already mentioned, Segmind-Vega is compatible with
 Stable Diffusion XL pipeline, the steps required to run Stable Diffusion
 XL inference for image-to-image task were discussed in this
-`notebook <248-stable-dffision-xl-with-output.html>`__.
+`notebook `__.
 
 Select inference device for text-to-image generation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -257,8 +260,8 @@ Select inference device for text-to-image generation
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -315,7 +318,7 @@ Select inference device for text-to-image generation
 
 
 
-.. image:: 248-segmind-vegart-with-output_files/248-segmind-vegart-with-output_12_1.png
+.. image:: segmind-vegart-with-output_files/segmind-vegart-with-output_12_1.png
 
 
 
@@ -357,7 +360,7 @@ improve model inference speed.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -374,8 +377,13 @@ improve model inference speed.
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     int8_pipe = None
     
@@ -398,11 +406,12 @@ model inputs for calibration we should customize ``CompiledModel``.
 
     UNET_INT8_OV_PATH = model_dir / "optimized_unet" / "openvino_model.xml"
     
+    
     def disable_progress_bar(pipeline, disable=True):
         if not hasattr(pipeline, "_progress_bar_config"):
-            pipeline._progress_bar_config = {'disable': disable}
+            pipeline._progress_bar_config = {"disable": disable}
         else:
-            pipeline._progress_bar_config['disable'] = disable
+            pipeline._progress_bar_config["disable"] = disable
 
 .. code:: ipython3
 
@@ -543,7 +552,7 @@ sensitive ``Convolution`` layers in FP16 precision.
 
 
 
-.. image:: 248-segmind-vegart-with-output_files/248-segmind-vegart-with-output_23_2.png
+.. image:: segmind-vegart-with-output_files/segmind-vegart-with-output_23_2.png
 
 
 Compare UNet file size
@@ -678,7 +687,7 @@ launch the interactive demo.
     
     use_quantized_model = widgets.Checkbox(
         value=quantized_model_present,
-        description='Use quantized model',
+        description="Use quantized model",
         disabled=not quantized_model_present,
     )
     
@@ -708,7 +717,13 @@ launch the interactive demo.
     
     def generate_from_text(text, seed, num_steps, height, width):
         set_seed(seed)
-        result = text2image_pipe(text, num_inference_steps=num_steps, guidance_scale=1.0, height=height, width=width).images[0]
+        result = text2image_pipe(
+            text,
+            num_inference_steps=num_steps,
+            guidance_scale=1.0,
+            height=height,
+            width=width,
+        ).images[0]
         return result
     
     
@@ -721,16 +736,42 @@ launch the interactive demo.
                 height_input = gr.Slider(label="Height", value=512, minimum=256, maximum=1024, step=32)
                 width_input = gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=32)
                 btn = gr.Button()
-            out = gr.Image(label="Result (Quantized)" if use_quantized_model.value else "Result (Original)", type="pil", width=512)
-            btn.click(generate_from_text, [positive_input, seed_input, steps_input, height_input, width_input], out)
-            gr.Examples([
-                ["cute cat", 999], 
-                ["underwater world coral reef, colorful jellyfish, 35mm, cinematic lighting, shallow depth of field,  ultra quality, masterpiece, realistic", 89],
-                ["a photo realistic happy white poodle dog ​​playing in the grass, extremely detailed, high res, 8k, masterpiece, dynamic angle", 1569],
-                ["Astronaut on Mars watching sunset, best quality, cinematic effects,", 65245],
-                ["Black and white street photography of a rainy night in New York, reflections on wet pavement", 48199],
-                ["cinematic photo detailed closeup portraid of a Beautiful cyberpunk woman, robotic parts, cables, lights, text; , high quality photography, 3 point lighting, flash with softbox, 4k, Canon EOS R3, hdr, smooth, sharp focus, high resolution, award winning photo, 80mm, f2.8, bokeh . 35mm photograph, film, bokeh, professional, 4k, highly detailed, high quality photography, 3 point lighting, flash with softbox, 4k, Canon EOS R3, hdr, smooth, sharp focus, high resolution, award winning photo, 80mm, f2.8, bokeh", 48199]
-            ], [positive_input, seed_input])
+            out = gr.Image(
+                label=("Result (Quantized)" if use_quantized_model.value else "Result (Original)"),
+                type="pil",
+                width=512,
+            )
+            btn.click(
+                generate_from_text,
+                [positive_input, seed_input, steps_input, height_input, width_input],
+                out,
+            )
+            gr.Examples(
+                [
+                    ["cute cat", 999],
+                    [
+                        "underwater world coral reef, colorful jellyfish, 35mm, cinematic lighting, shallow depth of field,  ultra quality, masterpiece, realistic",
+                        89,
+                    ],
+                    [
+                        "a photo realistic happy white poodle dog ​​playing in the grass, extremely detailed, high res, 8k, masterpiece, dynamic angle",
+                        1569,
+                    ],
+                    [
+                        "Astronaut on Mars watching sunset, best quality, cinematic effects,",
+                        65245,
+                    ],
+                    [
+                        "Black and white street photography of a rainy night in New York, reflections on wet pavement",
+                        48199,
+                    ],
+                    [
+                        "cinematic photo detailed closeup portraid of a Beautiful cyberpunk woman, robotic parts, cables, lights, text; , high quality photography, 3 point lighting, flash with softbox, 4k, Canon EOS R3, hdr, smooth, sharp focus, high resolution, award winning photo, 80mm, f2.8, bokeh . 35mm photograph, film, bokeh, professional, 4k, highly detailed, high quality photography, 3 point lighting, flash with softbox, 4k, Canon EOS R3, hdr, smooth, sharp focus, high resolution, award winning photo, 80mm, f2.8, bokeh",
+                        48199,
+                    ],
+                ],
+                [positive_input, seed_input],
+            )
     
     # if you are launching remotely, specify server_name and server_port
     # demo.launch(server_name='your server name', server_port='server port in int')
diff --git a/docs/notebooks/248-segmind-vegart-with-output_files/248-segmind-vegart-with-output_12_1.jpg b/docs/notebooks/segmind-vegart-with-output_files/segmind-vegart-with-output_12_1.jpg
similarity index 100%
rename from docs/notebooks/248-segmind-vegart-with-output_files/248-segmind-vegart-with-output_12_1.jpg
rename to docs/notebooks/segmind-vegart-with-output_files/segmind-vegart-with-output_12_1.jpg
diff --git a/docs/notebooks/248-segmind-vegart-with-output_files/248-segmind-vegart-with-output_12_1.png b/docs/notebooks/segmind-vegart-with-output_files/segmind-vegart-with-output_12_1.png
similarity index 100%
rename from docs/notebooks/248-segmind-vegart-with-output_files/248-segmind-vegart-with-output_12_1.png
rename to docs/notebooks/segmind-vegart-with-output_files/segmind-vegart-with-output_12_1.png
diff --git a/docs/notebooks/248-segmind-vegart-with-output_files/248-segmind-vegart-with-output_23_2.jpg b/docs/notebooks/segmind-vegart-with-output_files/segmind-vegart-with-output_23_2.jpg
similarity index 100%
rename from docs/notebooks/248-segmind-vegart-with-output_files/248-segmind-vegart-with-output_23_2.jpg
rename to docs/notebooks/segmind-vegart-with-output_files/segmind-vegart-with-output_23_2.jpg
diff --git a/docs/notebooks/248-segmind-vegart-with-output_files/248-segmind-vegart-with-output_23_2.png b/docs/notebooks/segmind-vegart-with-output_files/segmind-vegart-with-output_23_2.png
similarity index 100%
rename from docs/notebooks/248-segmind-vegart-with-output_files/248-segmind-vegart-with-output_23_2.png
rename to docs/notebooks/segmind-vegart-with-output_files/segmind-vegart-with-output_23_2.png
diff --git a/docs/notebooks/282-siglip-zero-shot-image-classification-with-output.rst b/docs/notebooks/siglip-zero-shot-image-classification-with-output.rst
similarity index 88%
rename from docs/notebooks/282-siglip-zero-shot-image-classification-with-output.rst
rename to docs/notebooks/siglip-zero-shot-image-classification-with-output.rst
index 3f398bd1b6d..8776a4e67a5 100644
--- a/docs/notebooks/282-siglip-zero-shot-image-classification-with-output.rst
+++ b/docs/notebooks/siglip-zero-shot-image-classification-with-output.rst
@@ -45,7 +45,7 @@ Table of contents:
 -  `Interactive inference <#interactive-inference>`__
 
 .. |Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
-   :target: https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/282-siglip-zero-shot-image-classification/282-siglip-zero-shot-image-classification.ipynb
+   :target: https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/siglip-zero-shot-image-classification/siglip-zero-shot-image-classification.ipynb
 
 Instantiate model
 -----------------
@@ -89,7 +89,29 @@ tokenizer and preparing the images.
 
 .. code:: ipython3
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu gradio "openvino>=2023.3.0" "transformers[torch]>=4.37" sentencepiece protobuf scipy datasets nncf
+    import platform
+    
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "gradio>=4.19" "openvino>=2023.3.0" "transformers>=4.37" "torch>=2.1" Pillow sentencepiece protobuf scipy datasets nncf
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -112,14 +134,14 @@ tokenizer and preparing the images.
 
 .. parsed-literal::
 
-    2024-03-13 00:31:36.705887: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:31:36.740422: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 00:44:12.841205: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:44:12.876405: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 00:31:37.337284: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:44:13.472113: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Run PyTorch model inference
@@ -177,19 +199,32 @@ similarity score for the final result.
 
 .. code:: ipython3
 
-    from urllib.request import urlretrieve
+    import requests
     from pathlib import Path
     import torch
     from PIL import Image
     
     image_path = Path("test_image.jpg")
-    urlretrieve(
+    r = requests.get(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
-        image_path,
     )
+    
+    with image_path.open("wb") as f:
+        f.write(r.content)
     image = Image.open(image_path)
     
-    input_labels = ['cat', 'dog', 'wolf', 'tiger', 'man', 'horse', 'frog', 'tree', 'house', 'computer']
+    input_labels = [
+        "cat",
+        "dog",
+        "wolf",
+        "tiger",
+        "man",
+        "horse",
+        "frog",
+        "tree",
+        "house",
+        "computer",
+    ]
     text_descriptions = [f"This is a photo of a {label}" for label in input_labels]
     
     inputs = processor(text=text_descriptions, images=[image], padding="max_length", return_tensors="pt")
@@ -198,7 +233,7 @@ similarity score for the final result.
         model.config.torchscript = False
         results = model(**inputs)
     
-    logits_per_image = results['logits_per_image']  # this is the image-text similarity score
+    logits_per_image = results["logits_per_image"]  # this is the image-text similarity score
     
     probs = logits_per_image.softmax(dim=1).detach().numpy()
     visualize_result(image, input_labels, probs[0])
@@ -210,7 +245,7 @@ similarity score for the final result.
 
 
 
-.. image:: 282-siglip-zero-shot-image-classification-with-output_files/282-siglip-zero-shot-image-classification-with-output_6_1.png
+.. image:: siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_6_1.png
 
 
 Convert model to OpenVINO Intermediate Representation (IR) format
@@ -246,15 +281,15 @@ object ready to load on the device and start making predictions.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/siglip/modeling_siglip.py:362: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/siglip/modeling_siglip.py:362: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/siglip/modeling_siglip.py:380: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/siglip/modeling_siglip.py:380: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim):
 
 
@@ -277,8 +312,8 @@ Select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -317,7 +352,7 @@ Run OpenVINO model
 
 
 
-.. image:: 282-siglip-zero-shot-image-classification-with-output_files/282-siglip-zero-shot-image-classification-with-output_13_1.png
+.. image:: siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_13_1.png
 
 
 Great! Looks like we got the same result.
@@ -356,8 +391,10 @@ model.
     from io import BytesIO
     from PIL import Image
     from requests.packages.urllib3.exceptions import InsecureRequestWarning
+    
     requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
     
+    
     def check_text_data(data):
         """
         Check if the given data is text-based.
@@ -368,6 +405,7 @@ model.
             return all(isinstance(x, str) for x in data)
         return False
     
+    
     def get_pil_from_url(url):
         """
         Downloads and converts an image from a URL to a PIL Image object.
@@ -376,6 +414,7 @@ model.
         image = Image.open(BytesIO(response.content))
         return image.convert("RGB")
     
+    
     def collate_fn(example, image_column="image_url", text_column="caption"):
         """
         Preprocesses an example by loading and transforming image and text data.
@@ -399,8 +438,13 @@ model.
         except Exception:
             return None
     
-        inputs = processor(text=example[text_column], images=[image], return_tensors="pt", padding="max_length")
-        if inputs['input_ids'].shape[1] > model.config.text_config.max_position_embeddings:
+        inputs = processor(
+            text=example[text_column],
+            images=[image],
+            return_tensors="pt",
+            padding="max_length",
+        )
+        if inputs["input_ids"].shape[1] > model.config.text_config.max_position_embeddings:
             return None
         return inputs
 
@@ -410,6 +454,7 @@ model.
     from datasets import load_dataset
     from tqdm.notebook import tqdm
     
+    
     def prepare_calibration_data(dataloader, init_steps):
         """
         This function prepares calibration data from a dataloader for a specified number of initialization steps.
@@ -450,7 +495,7 @@ model.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1461: FutureWarning: The repository for conceptual_captions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conceptual_captions
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1461: FutureWarning: The repository for conceptual_captions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conceptual_captions
     You can avoid this message in future by passing the argument `trust_remote_code=True`.
     Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
       warnings.warn(
@@ -485,9 +530,7 @@ Create a quantized model from the pre-trained ``FP16`` model.
     nncf.set_log_level(logging.ERROR)
     
     if len(calibration_data) == 0:
-        raise RuntimeError(
-            'Calibration dataset is empty. Please check internet connection and try to download images manually.'
-        )
+        raise RuntimeError("Calibration dataset is empty. Please check internet connection and try to download images manually.")
     
     calibration_dataset = nncf.Dataset(calibration_data)
     quantized_ov_model = nncf.quantize(
@@ -604,7 +647,18 @@ model are similar to the PyTorch model.
     from scipy.special import softmax
     
     
-    input_labels = ['cat', 'dog', 'wolf', 'tiger', 'man', 'horse', 'frog', 'tree', 'house', 'computer']
+    input_labels = [
+        "cat",
+        "dog",
+        "wolf",
+        "tiger",
+        "man",
+        "horse",
+        "frog",
+        "tree",
+        "house",
+        "computer",
+    ]
     text_descriptions = [f"This is a photo of a {label}" for label in input_labels]
     
     inputs = processor(text=text_descriptions, images=[image], return_tensors="pt", padding="max_length")
@@ -618,11 +672,11 @@ model are similar to the PyTorch model.
 
 .. parsed-literal::
 
-    [{'dog': 0.99}, {'cat': 0.0}, {'horse': 0.0}, {'wolf': 0.0}, {'tiger': 0.0}]
+    [{'dog': 0.99}, {'horse': 0.0}, {'cat': 0.0}, {'wolf': 0.0}, {'tiger': 0.0}]
 
 
 
-.. image:: 282-siglip-zero-shot-image-classification-with-output_files/282-siglip-zero-shot-image-classification-with-output_24_1.png
+.. image:: siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_24_1.png
 
 
 Compare File Size
@@ -634,10 +688,10 @@ Compare File Size
 
     from pathlib import Path
     
-    fp16_model_path = 'siglip-base-patch16-224.xml'
+    fp16_model_path = "siglip-base-patch16-224.xml"
     ov.save_model(ov_model, fp16_model_path)
     
-    int8_model_path = 'siglip-base-patch16-224_int8.xml'
+    int8_model_path = "siglip-base-patch16-224_int8.xml"
     ov.save_model(quantized_ov_model, int8_model_path)
     
     fp16_ir_model_size = Path(fp16_model_path).with_suffix(".bin").stat().st_size / 1024 / 1024
@@ -671,6 +725,7 @@ approximately estimate the speed up of the dynamic quantized models.
 
     import time
     
+    
     def calculate_inference_time(model_path, calibration_data):
         model = ov.compile_model(model_path, device.value)
         output_layer = model.output(0)
@@ -692,7 +747,7 @@ approximately estimate the speed up of the dynamic quantized models.
 
 .. parsed-literal::
 
-    Performance speed up: 2.103
+    Performance speed up: 2.117
 
 
 Interactive inference
diff --git a/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_13_1.png b/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_13_1.png
new file mode 100644
index 00000000000..4f4106d0098
--- /dev/null
+++ b/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_13_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a171655a1f69c15f58fe8db7c91075695a3e504bca94e0bbd76d610c22a96ad
+size 581043
diff --git a/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_24_1.png b/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_24_1.png
new file mode 100644
index 00000000000..156a28aacb6
--- /dev/null
+++ b/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_24_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:beeadbab0dee7744db3c5541d8002ec04e28e12bd1236179940343143d2420d3
+size 580997
diff --git a/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_6_1.png b/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_6_1.png
new file mode 100644
index 00000000000..4f4106d0098
--- /dev/null
+++ b/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_6_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a171655a1f69c15f58fe8db7c91075695a3e504bca94e0bbd76d610c22a96ad
+size 581043
diff --git a/docs/notebooks/210-slowfast-video-recognition-with-output.rst b/docs/notebooks/slowfast-video-recognition-with-output.rst
similarity index 99%
rename from docs/notebooks/210-slowfast-video-recognition-with-output.rst
rename to docs/notebooks/slowfast-video-recognition-with-output.rst
index 0c9edf604ca..37e78430e33 100644
--- a/docs/notebooks/210-slowfast-video-recognition-with-output.rst
+++ b/docs/notebooks/slowfast-video-recognition-with-output.rst
@@ -62,9 +62,14 @@ Install necessary packages
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.3.0" fvcore --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2023.3.0" torch opencv-python fvcore tqdm --extra-index-url https://download.pytorch.org/whl/cpu
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -79,7 +84,6 @@ Imports and Settings
 
     import json
     import math
-    import sys
     import cv2
     import torch
     import numpy as np
@@ -88,7 +92,14 @@ Imports and Settings
     from IPython.display import Video
     import openvino as ov
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 .. code:: ipython3
@@ -114,7 +125,10 @@ each action. Read more about the dataset and the paper
     
     # load the pretrained model from the repository
     model = torch.hub.load(
-        repo_or_dir=MODEL_REPOSITORY, model=MODEL_NAME, pretrained=True, skip_validation=True
+        repo_or_dir=MODEL_REPOSITORY,
+        model=MODEL_NAME,
+        pretrained=True,
+        skip_validation=True,
     )
     
     # set the device to allocate tensors to. for example, "cpu" or "cuda"
@@ -467,9 +481,7 @@ mapping to a dict for later use.
 
 .. code:: ipython3
 
-    CLASSNAMES_SOURCE = (
-        "https://dl.fbaipublicfiles.com/pyslowfast/dataset/class_names/kinetics_classnames.json"
-    )
+    CLASSNAMES_SOURCE = "https://dl.fbaipublicfiles.com/pyslowfast/dataset/class_names/kinetics_classnames.json"
     CLASSNAMES_FILE = "kinetics_classnames.json"
     
     download_file(url=CLASSNAMES_SOURCE, directory=DATA_DIR, show_progress=True)
@@ -560,7 +572,7 @@ helper functions to implement the preprocessing steps.
         width = frame.shape[1]
         y_offset = int(math.ceil((height - size) / 2))
         x_offset = int(math.ceil((width - size) / 2))
-        cropped = frame[y_offset:y_offset + size, x_offset:x_offset + size, :]
+        cropped = frame[y_offset : y_offset + size, x_offset : x_offset + size, :]
         assert cropped.shape[0] == size, "Image height not cropped properly"
         assert cropped.shape[1] == size, "Image width not cropped properly"
         return cropped
@@ -590,7 +602,7 @@ helper functions to implement the preprocessing steps.
         slow_pathway = np.take(
             frames,
             indices=np.linspace(0, frames.shape[1] - 1, frames.shape[1] // alpha).astype(np.int_),
-            axis=1
+            axis=1,
         )
         frame_list = [slow_pathway, fast_pathway]
         return frame_list
@@ -658,9 +670,7 @@ model.
             ret, frame = video_cap.read()
             frames.append(frame)
         # prepare the inputs
-        inputs = process_inputs(
-            frames=frames, num_frames=num_frames, crop_size=crop_size, mean=mean, std=std
-        )
+        inputs = process_inputs(frames=frames, num_frames=num_frames, crop_size=crop_size, mean=mean, std=std)
     
         if isinstance(model, ov.CompiledModel):
             # openvino compiled model
@@ -672,7 +682,7 @@ model.
             predictions = predictions.detach().cpu().numpy()
     
         def softmax(x):
-            return (np.exp(x) / np.exp(x).sum(axis=None))
+            return np.exp(x) / np.exp(x).sum(axis=None)
     
         # apply activation
         predictions = softmax(predictions)
@@ -748,7 +758,6 @@ either be compiled and inferred or serialized.
     IR_PATH = MODEL_DIR / "slowfast-r50.xml"
     
     
-    
     # serialize model for saving IR
     ov.save_model(model=model, output_model=str(IR_PATH), compress_to_fp16=False)
 
@@ -778,8 +787,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
diff --git a/docs/notebooks/262-softvc-voice-conversion-with-output.rst b/docs/notebooks/softvc-voice-conversion-with-output.rst
similarity index 69%
rename from docs/notebooks/262-softvc-voice-conversion-with-output.rst
rename to docs/notebooks/softvc-voice-conversion-with-output.rst
index d5ed3b862a8..c2785403f9c 100644
--- a/docs/notebooks/262-softvc-voice-conversion-with-output.rst
+++ b/docs/notebooks/softvc-voice-conversion-with-output.rst
@@ -38,7 +38,7 @@ Prerequisites
     %pip install -q --upgrade pip setuptools
     %pip install -q "openvino>=2023.2.0"
     !git clone https://github.com/svc-develop-team/so-vits-svc -b 4.1-Stable
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  tqdm librosa "torch>=2.1.0" "torchaudio>=2.1.0" faiss-cpu gradio "numpy==1.23.5" "fairseq==0.12.2" praat-parselmouth
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  tqdm librosa "torch>=2.1.0" "torchaudio>=2.1.0" faiss-cpu "gradio>=4.19" "numpy>=1.23.5" "fairseq==0.12.2" praat-parselmouth
 
 Download pretrained models and configs. We use a recommended encoder
 `ContentVec `__ and models from `a
@@ -51,24 +51,50 @@ own `__.
 .. code:: ipython3
 
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
     
     # ContentVec
-    download_file("https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt", "checkpoint_best_legacy_500.pt", directory="so-vits-svc/pretrain/")
+    download_file(
+        "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt",
+        "checkpoint_best_legacy_500.pt",
+        directory="so-vits-svc/pretrain/",
+    )
     
-    # pretrained models and configs from a collection of so-vits-svc-4.0 models. You can use other models. 
-    download_file("https://huggingface.co/therealvul/so-vits-svc-4.0/resolve/main/Rainbow%20Dash%20(singing)/kmeans_10000.pt", "kmeans_10000.pt", directory="so-vits-svc/logs/44k/")
-    download_file("https://huggingface.co/therealvul/so-vits-svc-4.0/resolve/main/Rainbow%20Dash%20(singing)/config.json", "config.json", directory="so-vits-svc/configs/")
-    download_file("https://huggingface.co/therealvul/so-vits-svc-4.0/resolve/main/Rainbow%20Dash%20(singing)/G_30400.pth", "G_30400.pth", directory="so-vits-svc/logs/44k/")
-    download_file("https://huggingface.co/therealvul/so-vits-svc-4.0/resolve/main/Rainbow%20Dash%20(singing)/D_30400.pth", "D_30400.pth", directory="so-vits-svc/logs/44k/")
+    # pretrained models and configs from a collection of so-vits-svc-4.0 models. You can use other models.
+    download_file(
+        "https://huggingface.co/therealvul/so-vits-svc-4.0/resolve/main/Rainbow%20Dash%20(singing)/kmeans_10000.pt",
+        "kmeans_10000.pt",
+        directory="so-vits-svc/logs/44k/",
+    )
+    download_file(
+        "https://huggingface.co/therealvul/so-vits-svc-4.0/resolve/main/Rainbow%20Dash%20(singing)/config.json",
+        "config.json",
+        directory="so-vits-svc/configs/",
+    )
+    download_file(
+        "https://huggingface.co/therealvul/so-vits-svc-4.0/resolve/main/Rainbow%20Dash%20(singing)/G_30400.pth",
+        "G_30400.pth",
+        directory="so-vits-svc/logs/44k/",
+    )
+    download_file(
+        "https://huggingface.co/therealvul/so-vits-svc-4.0/resolve/main/Rainbow%20Dash%20(singing)/D_30400.pth",
+        "D_30400.pth",
+        directory="so-vits-svc/logs/44k/",
+    )
     
     # a wav sample
-    download_file("https://huggingface.co/datasets/santifiorino/spinetta/resolve/main/spinetta/000.wav", "000.wav", directory="so-vits-svc/raw/")
+    download_file(
+        "https://huggingface.co/datasets/santifiorino/spinetta/resolve/main/spinetta/000.wav",
+        "000.wav",
+        directory="so-vits-svc/raw/",
+    )
 
 Use the original model to run an inference
 ------------------------------------------
@@ -88,20 +114,20 @@ Define the Sovits Model.
 
     from inference.infer_tool import Svc
     
-    model = Svc("logs/44k/G_30400.pth", "configs/config.json", device='cpu')
+    model = Svc("logs/44k/G_30400.pth", "configs/config.json", device="cpu")
 
 Define ``kwargs`` and make an inference.
 
 .. code:: ipython3
 
     kwargs = {
-        'raw_audio_path': 'raw/000.wav',  # path to a source audio 
-        'spk': 'Rainbow Dash (singing)',  # speaker ID in which the source audio should be converted. 
-        'tran': 0, 
-        'slice_db': -40, 
-        'cluster_infer_ratio': 0, 
-        'auto_predict_f0': False, 
-        'noice_scale': 0.4, 
+        "raw_audio_path": "raw/000.wav",  # path to a source audio
+        "spk": "Rainbow Dash (singing)",  # speaker ID in which the source audio should be converted.
+        "tran": 0,
+        "slice_db": -40,
+        "cluster_infer_ratio": 0,
+        "auto_predict_f0": False,
+        "noice_scale": 0.4,
     }
     
     audio = model.slice_inference(**kwargs)
@@ -152,19 +178,19 @@ without need to look inside.
     model.net_g_ms.forward = model.net_g_ms.infer
     
     net_g_kwargs = {
-        'c': dummy_c,
-        'f0': dummy_f0,
-        'uv': dummy_uv,
-        'g': dummy_g,
-        'noice_scale': torch.tensor(0.35),  # need to wrap numeric and boolean values for conversion
-        'seed': torch.tensor(52468),
-        'predict_f0': torch.tensor(False),
-        'vol': torch.tensor(0)
+        "c": dummy_c,
+        "f0": dummy_f0,
+        "uv": dummy_uv,
+        "g": dummy_g,
+        "noice_scale": torch.tensor(0.35),  # need to wrap numeric and boolean values for conversion
+        "seed": torch.tensor(52468),
+        "predict_f0": torch.tensor(False),
+        "vol": torch.tensor(0),
     }
     core = ov.Core()
     
     
-    net_g_model_xml_path = Path('models/ov_net_g_model.xml')
+    net_g_model_xml_path = Path("models/ov_net_g_model.xml")
     
     if not net_g_model_xml_path.exists():
         converted_model = ov.convert_model(model.net_g_ms, example_input=net_g_kwargs)
@@ -187,8 +213,8 @@ Select a device from dropdown list for running inference using OpenVINO.
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -205,16 +231,16 @@ on a device.
         def __init__(self, net_g_model_xml_path):
             super().__init__()
             self.net_g_model = core.compile_model(net_g_model_xml_path, device.value)
-            
+    
         def infer(self, c, *, f0, uv, g, noice_scale=0.35, seed=52468, predict_f0=False, vol=None):
             if vol is None:  # None is not allowed as an input
                 results = self.net_g_model((c, f0, uv, g, noice_scale, seed, predict_f0))
             else:
                 results = self.net_g_model((c, f0, uv, g, noice_scale, seed, predict_f0, vol))
-            
+    
             return torch.from_numpy(results[0]), torch.from_numpy(results[1])
     
-            
+    
     model.net_g_ms = NetGModelWrapper(net_g_model_xml_path)
     audio = model.slice_inference(**kwargs)
 
@@ -236,10 +262,10 @@ Interactive inference
     import gradio as gr
     
     
-    src_audio = gr.Audio(label="Source Audio", type='filepath')
-    output_audio = gr.Audio(label="Output Audio", type='numpy')
+    src_audio = gr.Audio(label="Source Audio", type="filepath")
+    output_audio = gr.Audio(label="Output Audio", type="numpy")
     
-    title = 'SoftVC VITS Singing Voice Conversion with Gradio'
+    title = "SoftVC VITS Singing Voice Conversion with Gradio"
     description = f'Gradio Demo for SoftVC VITS Singing Voice Conversion and OpenVINO™. Upload a source audio, then click the "Submit" button to inference. Audio sample rate should be {model.target_sample}'
     
     
@@ -248,24 +274,38 @@ Interactive inference
         kwargs["tran"] = tran
         kwargs["slice_db"] = slice_db
         kwargs["noice_scale"] = noice_scale
-     
+    
         audio = model.slice_inference(**kwargs)
     
         return model.target_sample, audio
     
     
     demo = gr.Interface(
-        infer, 
+        infer,
         [
             src_audio,
             gr.Slider(-100, 100, value=0, label="Pitch shift", step=1),
-            gr.Slider(-80, -20, value=-30, label="Slice db", step=10, info="The default is -30, noisy audio can be -30, dry sound can be -50 to preserve breathing."),
-            gr.Slider(0, 1, value=0.4, label="Noise scale", step=0.1, info="Noise level will affect pronunciation and sound quality, which is more metaphysical"),
+            gr.Slider(
+                -80,
+                -20,
+                value=-30,
+                label="Slice db",
+                step=10,
+                info="The default is -30, noisy audio can be -30, dry sound can be -50 to preserve breathing.",
+            ),
+            gr.Slider(
+                0,
+                1,
+                value=0.4,
+                label="Noise scale",
+                step=0.1,
+                info="Noise level will affect pronunciation and sound quality, which is more metaphysical",
+            ),
         ],
-        output_audio, 
+        output_audio,
         title=title,
-        description=description, 
-        examples=[['raw/000.wav', 0, -30, 0.4, False]]
+        description=description,
+        examples=[["raw/000.wav", 0, -30, 0.4, False]],
     )
     
     try:
diff --git a/docs/notebooks/270-sound-generation-audioldm2-with-output.rst b/docs/notebooks/sound-generation-audioldm2-with-output.rst
similarity index 99%
rename from docs/notebooks/270-sound-generation-audioldm2-with-output.rst
rename to docs/notebooks/sound-generation-audioldm2-with-output.rst
index a855fb02da9..86262e6ed65 100644
--- a/docs/notebooks/270-sound-generation-audioldm2-with-output.rst
+++ b/docs/notebooks/sound-generation-audioldm2-with-output.rst
@@ -51,7 +51,7 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q accelerate "diffusers>=0.21.0" transformers torch gradio "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q accelerate "diffusers>=0.21.0" transformers "torch>=2.1" "gradio>=4.19" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "openvino>=2024.0.0"
 
 
@@ -97,7 +97,7 @@ sample.
         prompt,
         negative_prompt=negative_prompt,
         num_inference_steps=150,
-        audio_length_in_s=3.0
+        audio_length_in_s=3.0,
     ).audios[0]
     
     sampling_rate = 16000
@@ -158,6 +158,7 @@ The pipeline consists of seven important parts:
 
     models_base_folder = Path("models")
     
+    
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -191,6 +192,7 @@ against the text prompt by computing similarity scores.
         def forward(self, input_ids, attention_mask):
             return self.encoder.get_text_features(input_ids, attention_mask)
     
+    
     clap_text_encoder_ir_path = models_base_folder / "clap_text_encoder.xml"
     
     if not clap_text_encoder_ir_path.exists():
@@ -329,10 +331,10 @@ projected outputs from the two text encoders.
     if not language_model_ir_path.exists():
         pipe.language_model.config.torchscript = True
         pipe.language_model.eval()
-        pipe.language_model.__call__ = partial(pipe.language_model.__call__, kwargs={
-            "past_key_values": None,
-            "use_cache": False,
-            "return_dict": False})
+        pipe.language_model.__call__ = partial(
+            pipe.language_model.__call__,
+            kwargs={"past_key_values": None, "use_cache": False, "return_dict": False},
+        )
         with torch.no_grad():
             ov_model = ov.convert_model(
                 pipe.language_model,  # model instance
@@ -343,7 +345,7 @@ projected outputs from the two text encoders.
         ov_model.inputs[0].get_node().set_element_type(ov.Type.i64)
         ov_model.inputs[1].get_node().set_partial_shape(ov.PartialShape([1, -1, 768]))
         ov_model.inputs[1].get_node().set_element_type(ov.Type.f32)
-        
+    
         ov_model.validate_nodes_and_infer_types()
     
         ov.save_model(ov_model, language_model_ir_path)
@@ -425,9 +427,9 @@ Diffusion model.
         ov_model.inputs[3].get_node().set_partial_shape(ov.PartialShape((2, -1, 1024)))
         ov_model.inputs[4].get_node().set_partial_shape(ov.PartialShape((2, -1)))
         ov_model.validate_nodes_and_infer_types()
-            
+    
         ov.save_model(ov_model, unet_ir_path)
-        
+    
         del ov_model
         cleanup_torchscript_cache()
         gc.collect()
@@ -459,12 +461,13 @@ process are converted back into images using the VAE decoder. During
 inference, we will see that we **only need the VAE decoder**. You can
 find instructions on how to convert the encoder part in a stable
 diffusion
-`notebook <225-stable-diffusion-text-to-image-with-output.html>`__.
+`notebook `__.
 
 .. code:: ipython3
 
     vae_ir_path = models_base_folder / "vae.xml"
     
+    
     class VAEDecoderWrapper(torch.nn.Module):
         def __init__(self, vae):
             super().__init__()
@@ -474,6 +477,7 @@ diffusion
         def forward(self, latents):
             return self.vae.decode(latents)
     
+    
     if not vae_ir_path.exists():
         vae_decoder = VAEDecoderWrapper(pipe.vae)
         latents = torch.zeros((1, 8, 175, 16))
@@ -557,6 +561,7 @@ model inference into the ``get_text_features`` method.
             last_hidden_state = self.encoder([input_ids, attention_mask])[0]
             return torch.from_numpy(last_hidden_state)
     
+    
     class OVT5EncoderWrapper:
         def __init__(self, encoder_ir, config):
             self.encoder = core.compile_model(encoder_ir, device.value)
@@ -566,7 +571,8 @@ model inference into the ``get_text_features`` method.
         def __call__(self, input_ids, **_):
             last_hidden_state = self.encoder(input_ids)[0]
             return torch.from_numpy(last_hidden_state)[None, ...]
-        
+    
+    
     class OVVocoderWrapper:
         def __init__(self, vocoder_ir, config):
             self.vocoder = core.compile_model(vocoder_ir, device.value)
@@ -575,47 +581,43 @@ model inference into the ``get_text_features`` method.
         def __call__(self, mel_spectrogram, **_):
             waveform = self.vocoder(mel_spectrogram)[0]
             return torch.from_numpy(waveform)
-        
+    
+    
     class OVProjectionModelWrapper:
         def __init__(self, proj_model_ir, config):
             self.proj_model = core.compile_model(proj_model_ir, device.value)
             self.config = config
             self.output_type = namedtuple("ProjectionOutput", ["hidden_states", "attention_mask"])
     
-        def __call__(
-            self, hidden_states,
-            hidden_states_1,
-            attention_mask,
-            attention_mask_1, **_
-        ):
-            output = self.proj_model({
-                "hidden_states": hidden_states,
-                "hidden_states_1": hidden_states_1,
-                "attention_mask": attention_mask,
-                "attention_mask_1": attention_mask_1,
-            })
+        def __call__(self, hidden_states, hidden_states_1, attention_mask, attention_mask_1, **_):
+            output = self.proj_model(
+                {
+                    "hidden_states": hidden_states,
+                    "hidden_states_1": hidden_states_1,
+                    "attention_mask": attention_mask,
+                    "attention_mask_1": attention_mask_1,
+                }
+            )
             return self.output_type(torch.from_numpy(output[0]), torch.from_numpy(output[1]))
-        
+    
+    
     class OVUnetWrapper:
         def __init__(self, unet_ir, config):
             self.unet = core.compile_model(unet_ir, device.value)
             self.config = config
     
-        def __call__(
-            self, sample,
-            timestep,
-            encoder_hidden_states,
-            encoder_hidden_states_1,
-            encoder_attention_mask_1, **_
-        ):
-            output = self.unet({
-                "sample": sample,
-                "timestep": timestep,
-                "encoder_hidden_states": encoder_hidden_states,
-                "encoder_hidden_states_1": encoder_hidden_states_1,
-                "encoder_attention_mask_1": encoder_attention_mask_1,
-            })
-            return (torch.from_numpy(output[0]), )
+        def __call__(self, sample, timestep, encoder_hidden_states, encoder_hidden_states_1, encoder_attention_mask_1, **_):
+            output = self.unet(
+                {
+                    "sample": sample,
+                    "timestep": timestep,
+                    "encoder_hidden_states": encoder_hidden_states,
+                    "encoder_hidden_states_1": encoder_hidden_states_1,
+                    "encoder_attention_mask_1": encoder_attention_mask_1,
+                }
+            )
+            return (torch.from_numpy(output[0]),)
+    
     
     class OVVaeDecoderWrapper:
         def __init__(self, vae_ir, config):
@@ -626,14 +628,9 @@ model inference into the ``get_text_features`` method.
         def decode(self, latents, **_):
             last_hidden_state = self.vae(latents)[0]
             return self.output_type(torch.from_numpy(last_hidden_state))
-        
-    def generate_language_model(
-        gpt_2: ov.CompiledModel,
-        inputs_embeds: torch.Tensor,
-        attention_mask: torch.Tensor,
-        max_new_tokens: int = 8,
-        **_
-    ) -> torch.Tensor:
+    
+    
+    def generate_language_model(gpt_2: ov.CompiledModel, inputs_embeds: torch.Tensor, attention_mask: torch.Tensor, max_new_tokens: int = 8, **_) -> torch.Tensor:
         """
         Generates a sequence of hidden-states from the language model, conditioned on the embedding inputs.
         """
@@ -643,7 +640,7 @@ model inference into the ``get_text_features`` method.
         attention_mask = attention_mask.cpu().numpy()
         for _ in range(max_new_tokens):
             # forward pass to get next hidden states
-            output = gpt_2({"inputs_embeds":inputs_embeds, "attention_mask":attention_mask})
+            output = gpt_2({"inputs_embeds": inputs_embeds, "attention_mask": attention_mask})
     
             next_hidden_states = output[0]
     
@@ -652,7 +649,6 @@ model inference into the ``get_text_features`` method.
             attention_mask = np.concatenate([attention_mask, np.ones((attention_mask.shape[0], 1))], axis=1)
         return torch.from_numpy(inputs_embeds[:, -max_new_tokens:, :])
 
-
 Now we initialize the wrapper objects and load them to the HF pipeline
 
 .. code:: ipython3
@@ -662,7 +658,7 @@ Now we initialize the wrapper objects and load them to the HF pipeline
     pipe.config.return_dict = False
     
     np.random.seed(0)
-    torch.manual_seed(0) 
+    torch.manual_seed(0)
     
     pipe.text_encoder = OVClapEncoderWrapper(clap_text_encoder_ir_path, pipe.text_encoder.config)
     pipe.text_encoder_2 = OVT5EncoderWrapper(t5_text_encoder_ir_path, pipe.text_encoder_2.config)
@@ -681,7 +677,7 @@ Now we initialize the wrapper objects and load them to the HF pipeline
         prompt,
         negative_prompt=negative_prompt,
         num_inference_steps=150,
-        audio_length_in_s=3.0
+        audio_length_in_s=3.0,
     ).audios[0]
     
     sampling_rate = 16000
@@ -729,18 +725,25 @@ package `__
 
     import gradio as gr
     
-    def _generate(prompt, negative_prompt, audio_length_in_s,
-                  num_inference_steps, _=gr.Progress(track_tqdm=True)):
+    
+    def _generate(
+        prompt,
+        negative_prompt,
+        audio_length_in_s,
+        num_inference_steps,
+        _=gr.Progress(track_tqdm=True),
+    ):
         """Gradio backing function."""
         audio_values = pipe(
             prompt,
             negative_prompt=negative_prompt,
             num_inference_steps=num_inference_steps,
-            audio_length_in_s=audio_length_in_s
+            audio_length_in_s=audio_length_in_s,
         )
         waveform = audio_values[0].squeeze() * 2**15
         return (sampling_rate, waveform.astype(np.int16))
     
+    
     demo = gr.Interface(
         _generate,
         inputs=[
@@ -753,11 +756,9 @@ package `__
                 value=7,
                 label="Audio Length (s)",
             ),
-            gr.Slider(label="Inference Steps", step=5, value=150, minimum=50, maximum=250)
-        ],
-        outputs=[
-            "audio"
+            gr.Slider(label="Inference Steps", step=5, value=150, minimum=50, maximum=250),
         ],
+        outputs=["audio"],
         examples=[
             ["birds singing in the forest", "Low quality", 7, 150],
             ["The sound of a hammer hitting a wooden surface", "", 4, 200],
diff --git a/docs/notebooks/116-sparsity-optimization-with-output.rst b/docs/notebooks/sparsity-optimization-with-output.rst
similarity index 86%
rename from docs/notebooks/116-sparsity-optimization-with-output.rst
rename to docs/notebooks/sparsity-optimization-with-output.rst
index c44d34e2461..0702349fa2f 100644
--- a/docs/notebooks/116-sparsity-optimization-with-output.rst
+++ b/docs/notebooks/sparsity-optimization-with-output.rst
@@ -45,14 +45,24 @@ Prerequisites
 .. code:: ipython3
 
     %pip install -q "openvino>=2023.1.0"
-    %pip install -q "git+https://github.com/huggingface/optimum-intel.git" datasets onnx transformers>=4.33.0 --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "git+https://github.com/huggingface/optimum-intel.git" "torch>=2.1" datasets onnx transformers>=4.33.0 --extra-index-url https://download.pytorch.org/whl/cpu
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -73,6 +83,11 @@ Imports
     from huggingface_hub import hf_hub_download
 
 
+.. parsed-literal::
+
+    OpenVINO Tokenizer version is not compatible with OpenVINO version. Installed OpenVINO version: 2024.0.0,OpenVINO Tokenizers requires . OpenVINO Tokenizers models will not be added during export.
+
+
 .. parsed-literal::
 
     INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
@@ -80,19 +95,20 @@ Imports
 
 .. parsed-literal::
 
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
-
-
-.. parsed-literal::
-
-    2024-03-12 22:46:23.659626: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:46:23.694199: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 00:51:56.238501: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:51:56.274301: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 22:46:24.261905: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:51:56.850457: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
 
 
 Download, quantize and sparsify the model, using Hugging Face Optimum API
@@ -131,11 +147,6 @@ model card on Hugging Face.
     Compiling the model to CPU ...
 
 
-.. parsed-literal::
-
-    device must be of type  but got  instead
-
-
 .. parsed-literal::
 
     [{'label': 'negative', 'score': 0.9982142448425293}]
@@ -150,13 +161,13 @@ the IRs into a single folder.
     quantized_sparse_dir = Path("bert_80pc_sparse_quantized_ir")
     quantized_sparse_dir.mkdir(parents=True, exist_ok=True)
     
-    # following return path to specified filename in cache folder (which we've with the 
+    # following return path to specified filename in cache folder (which we've with the
     ov_ir_xml_path = hf_hub_download(repo_id=model_id, filename="openvino_model.xml")
     ov_ir_bin_path = hf_hub_download(repo_id=model_id, filename="openvino_model.bin")
     
     # copy IRs to the folder
     shutil.copy(ov_ir_xml_path, quantized_sparse_dir)
-    shutil.copy(ov_ir_bin_path, quantized_sparse_dir)                                
+    shutil.copy(ov_ir_bin_path, quantized_sparse_dir)
 
 
 
@@ -228,7 +239,7 @@ as an example. It is recommended to tune based on your applications.
 
 .. parsed-literal::
 
-    [ INFO ] Read model took 60.53 ms
+    [ INFO ] Read model took 61.16 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [?,?]
@@ -243,7 +254,7 @@ as an example. It is recommended to tune based on your applications.
 
 .. parsed-literal::
 
-    [ INFO ] Reshape model took 23.14 ms
+    [ INFO ] Reshape model took 22.79 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,64]
@@ -256,7 +267,7 @@ as an example. It is recommended to tune based on your applications.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 1247.60 ms
+    [ INFO ] Compile model took 1249.77 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: torch_jit
@@ -291,21 +302,21 @@ as an example. It is recommended to tune based on your applications.
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 28.98 ms
+    [ INFO ] First inference took 28.22 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            8968 iterations
-    [ INFO ] Duration:         60028.76 ms
+    [ INFO ] Count:            9052 iterations
+    [ INFO ] Duration:         60042.04 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        26.62 ms
-    [ INFO ]    Average:       26.65 ms
-    [ INFO ]    Min:           25.60 ms
-    [ INFO ]    Max:           42.76 ms
-    [ INFO ] Throughput:   149.40 FPS
+    [ INFO ]    Median:        26.38 ms
+    [ INFO ]    Average:       26.41 ms
+    [ INFO ]    Min:           24.29 ms
+    [ INFO ]    Max:           41.86 ms
+    [ INFO ] Throughput:   150.76 FPS
 
 
 Benchmark quantized sparse inference performance
@@ -321,7 +332,7 @@ for which a layer will be enabled.
 .. code:: ipython3
 
     # Dump benchmarking config for dense inference
-    # "CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE" controls minimum sparsity rate for weights to consider 
+    # "CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE" controls minimum sparsity rate for weights to consider
     # for sparse optimization at the runtime.
     with (quantized_sparse_dir / "perf_config_sparse.json").open("w") as outfile:
         outfile.write(
@@ -370,7 +381,7 @@ for which a layer will be enabled.
 
 .. parsed-literal::
 
-    [ INFO ] Read model took 84.42 ms
+    [ INFO ] Read model took 68.00 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [?,?]
@@ -381,7 +392,11 @@ for which a layer will be enabled.
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
     [ INFO ] Reshaping model: 'input_ids': [1,64], 'attention_mask': [1,64], 'token_type_ids': [1,64]
-    [ INFO ] Reshape model took 23.54 ms
+
+
+.. parsed-literal::
+
+    [ INFO ] Reshape model took 23.17 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,64]
@@ -390,10 +405,6 @@ for which a layer will be enabled.
     [ INFO ] Model outputs:
     [ INFO ]     logits (node: logits) : f32 / [...] / [1,2]
     [Step 7/11] Loading the model to the device
-
-
-.. parsed-literal::
-
     [ ERROR ] Exception from src/inference/src/cpp/core.cpp:106:
     Exception from src/inference/src/dev/plugin.cpp:54:
     Exception from src/plugins/intel_cpu/src/config.cpp:208:
@@ -401,9 +412,9 @@ for which a layer will be enabled.
     
     
     Traceback (most recent call last):
-      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 408, in main
+      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 408, in main
         compiled_model = benchmark.core.compile_model(model, benchmark.device, device_config)
-      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/runtime/ie_api.py", line 515, in compile_model
+      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/runtime/ie_api.py", line 515, in compile_model
         super().compile_model(model, device_name, {} if config is None else config),
     RuntimeError: Exception from src/inference/src/cpp/core.cpp:106:
     Exception from src/inference/src/dev/plugin.cpp:54:
diff --git a/docs/notebooks/speculative-sampling-with-output.rst b/docs/notebooks/speculative-sampling-with-output.rst
new file mode 100644
index 00000000000..14fdffc3717
--- /dev/null
+++ b/docs/notebooks/speculative-sampling-with-output.rst
@@ -0,0 +1,424 @@
+Text Generation via Speculative Sampling, KV Caching, and OpenVINO™
+===================================================================
+
+As model sizes grow, Generative AI implementations require significant
+inference resources. This not only increases the cost per generation
+from a prompt, but also increases the power consumption used to serve
+such requests.
+
+Inference optimizations for text generation are essential for reducing
+costs and power consumption. When optimizing the inference process, the
+amount of time and energy required to generate text can be significantly
+reduced. This can lead to cost savings in terms of hardware and
+software, as well as reduced power consumption. Additionally, inference
+optimizations can help improve the accuracy of text generation as well
+as the speed at which it can be generated. This can lead to an improved
+user experience and increased efficiency in text-generation tasks. In
+summary, inference optimizations for text generation are essential to
+reduce costs and power consumption, while also improving the accuracy
+and speed of text generation.
+
+Another necessary condition is that the optimizations are compatible
+with each other. That is, implementing a certain optimization should not
+preclude other optimizations. There are several levels of optimizations
+that can provide significant speedup without “bumping into each other”
+in a way that will compromise overall efficiency.
+
+For details on this method, please refer to the paper by Chen et al,
+http://arxiv.org/abs/2302.01318. Additionally, there’s an interesting
+proof of correctness of speculative sampling (showing that the original
+distribution is preserved) by Leviathan et al,
+http://arxiv.org/abs/2211.17192
+
+Our blog article describing this implementation with OpenVino is
+available at openvino.ai
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+
+   -  `Select inference device <#select-inference-device>`__
+
+-  `Create autoregressive and speculative forms of sampling with KV
+   Cache
+   support <#create-autoregressive-and-speculative-forms-of-sampling-with-kv-cache-support>`__
+
+   -  `Setup imports <#setup-imports>`__
+   -  `Prepare autoregressive
+      sampling <#prepare-autoregressive-sampling>`__
+   -  `Prepare speculative sampling <#prepare-speculative-sampling>`__
+
+-  `Main generation function <#main-generation-function>`__
+
+   -  `Download and Convert Model <#download-and-convert-model>`__
+
+Prerequisites
+-------------
+
+
+
+First, we should install the `Hugging Face
+Optimum `__ library
+accelerated by OpenVINO integration. The Hugging Face Optimum Intel API
+is a high-level API that enables us to convert and quantize models from
+the Hugging Face Transformers library to the OpenVINO™ IR format. For
+more details, refer to the `Hugging Face Optimum Intel
+documentation `__.
+
+We will also need to install transformers (HuggingFace) and some other
+useful modules.
+
+.. code:: ipython3
+
+    %pip install -q --upgrade pip
+    %pip uninstall -q -y openvino-dev openvino openvino-nightly
+    %pip install -q --upgrade transformers "torch>=2.1" "gradio>=4.19" openvino-nightly accelerate onnx ipywidgets "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "git+https://github.com/huggingface/optimum-intel.git"
+
+Select inference device
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Select the device from dropdown list for running inference using
+OpenVINO.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    import openvino as ov
+    
+    core = ov.Core()
+    
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='CPU')
+
+
+
+Create autoregressive and speculative forms of sampling with KV Cache support
+-----------------------------------------------------------------------------
+
+
+
+Text generation is often done in an autoregressive fashion. We will all
+support a KV cache (aka Past Value Cache) in the code. Note that we are
+using greedy sampling. We do not adjust other text generation parameters
+(e.g. temperature) so keep this illustration of speculative sampling as
+simple and understandable as possible.
+
+Setup imports
+~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import time
+    import numpy as np
+    import gradio as gr
+
+Prepare autoregressive sampling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    def autoregressive_sampling_with_pkv(input, model, N=30):
+        input_ids, attention_mask = input.input_ids, input.attention_mask
+        seq_len = input_ids.shape[-1]
+        position_ids = np.arange(0, seq_len, dtype=np.int64).reshape([-1, seq_len])
+    
+        # in all subsequent inferences we feed tokens one by one,
+        # but for the first one we feed the whole encoded prompt
+        request = model.create_infer_request()
+        request.infer((input_ids, attention_mask, position_ids, np.array([0])))
+        next_token = np.argmax(request.results["logits"][:, -1]).reshape([1])
+    
+        all_tokens = []
+        all_tokens.extend(input_ids[0])
+        all_tokens.append(next_token[0])
+    
+        while seq_len < N:
+            input_ids = next_token.reshape([1, 1])
+            attention_mask = np.concatenate((attention_mask, np.array([1]).reshape([1, 1])), axis=1)
+            position_ids = np.array([attention_mask.shape[1]]).reshape([1, 1])
+    
+            request.infer((input_ids, attention_mask, position_ids, np.array([0])))
+            next_token = np.argmax(request.results["logits"][:, -1])
+            all_tokens.append(next_token)
+            seq_len += 1
+    
+        return all_tokens
+
+Prepare speculative sampling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+-  Step 1: With speculative sampling, we first generate K samples from
+   the draft model (in an autoregressive manner).
+-  Step 2: These are now candidates to examine using the main model
+   (step 2) using a batch size of K.
+-  Step 3: We go through each K predicted tokens, and if tokens differ,
+   we stop and keep the last token predicted by the main model.
+-  Step 4: We update KV-cache dropping keys & values for differing
+   tokens and repeat Step 1.
+
+.. code:: ipython3
+
+    def update_state(request, seq_len):
+        for state in request.query_state():
+            old_seq_len = state.state.shape[2]
+            if seq_len >= old_seq_len:
+                continue
+            # After the inference request, key/values have shape [BATCH_SIZE, seq_len + K, vocab_size].
+            # Increment the sequence length by the number of matched tokens, and
+            # trim the KV cache to match the new sequence length.
+            state.state = ov.Tensor(state.state.data[:, :, :seq_len])
+    
+    
+    def speculative_sampling_with_pkv(input, draft_model, main_model, K, N=30, **kwargs):
+        input_ids, attention_mask = input.input_ids, input.attention_mask
+        # seq_len number of key/values or number of already processed input tokens
+        seq_len = input_ids.shape[-1]
+        position_ids = np.arange(0, seq_len, dtype=np.int64).reshape([-1, seq_len])
+    
+        draft_request = draft_model.create_infer_request()
+        draft_request.infer((input_ids, attention_mask, position_ids, np.array([0])))
+    
+        main_request = main_model.create_infer_request()
+        main_request.infer((input_ids, attention_mask, position_ids, np.array([0])))
+        first_token = np.argmax(main_request.results["logits"][:, -1]).reshape([1])
+    
+        all_tokens = []
+        all_tokens.extend(input_ids[0])
+        all_tokens.append(first_token[0])
+    
+        accum_draft_tokens = []
+        while seq_len < N:
+            next_token = first_token
+            for i in range(K):
+                input_ids = next_token.reshape([1, 1])
+                attention_mask = np.concatenate((attention_mask, np.array([1]).reshape([1, 1])), axis=1)
+                position_ids = np.array([attention_mask.shape[1]]).reshape([1, 1])
+    
+                draft_request.infer((input_ids, attention_mask, position_ids, np.array([0])))
+                next_token = np.argmax(draft_request.results["logits"][:, -1])
+                accum_draft_tokens.append(next_token)
+    
+            # main model will give also K out tokens
+            # feed the same first token to the main model and do not give the last token generated by the draft
+            input_ids = np.concatenate((first_token.reshape([1]), accum_draft_tokens[:-1])).reshape([1, -1])
+            attention_mask = np.ones((1, seq_len + K))
+            position_ids = np.arange(seq_len, seq_len + K, dtype=np.int64).reshape([1, -1])
+    
+            main_request.infer((input_ids, attention_mask, position_ids, np.array([0])))
+            next_tokens = np.argmax(main_request.results["logits"], axis=-1)[0]
+    
+            # if disagrees from the very beggining then context will be expanded only for one element
+            # all elements match then context will be expanded to K elements
+            for disagree_idx, (t1, t2) in enumerate(zip(accum_draft_tokens, next_tokens)):
+                if t1 != t2:
+                    break
+    
+            first_token = next_tokens[disagree_idx]
+            all_tokens.extend(next_tokens[: disagree_idx + 1])
+            seq_len += disagree_idx + 1
+    
+            # cut key/values depending on the position where disagreement starts
+            update_state(draft_request, seq_len)
+            update_state(main_request, seq_len)
+    
+            attention_mask = np.ones((1, seq_len))
+            accum_draft_tokens = []
+        all_tokens.extend(accum_draft_tokens)
+        return all_tokens
+
+Main generation function
+------------------------
+
+
+
+Download and Convert Model
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Optimum Intel can be used to load optimized models from the `Hugging
+Face Hub `__ and
+create pipelines to run an inference with OpenVINO Runtime using Hugging
+Face APIs. For speculative decoding we need to manually update states,
+therefore we will use directly openvino inference api, and optimum only
+for model conversion. >To download Llama-2-7b-chat-hf, you will need to
+accept license agreement. You must be a registered user in Hugging
+Face Hub. Please visit HuggingFace model
+`card `__,
+carefully read terms of usage and click accept button. You will need to
+use an access token for the code below to run. For more information on
+access tokens, refer to this section of the documentation.
+
+.. code:: ipython3
+
+    from pathlib import Path
+    
+    main_model_id = "meta-llama/Llama-2-7b-chat-hf"
+    main_model_path = Path("Llama-2-7b-chat-hf")
+    draft_model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+    draft_model_path = Path("TinyLlama-1.1B-Chat-v1.0")
+    
+    from transformers import AutoTokenizer
+    
+    main_tokenizer = AutoTokenizer.from_pretrained(main_model_id)
+    draft_tokenizer = AutoTokenizer.from_pretrained(draft_model_id)
+
+.. code:: ipython3
+
+    # In order for speculative sampling to work, both main and draft tokenizers should be the same.
+    token_test_txt = "text to ensure tokenizers work the same, as of 2024"
+    tokens_1 = draft_tokenizer(token_test_txt, return_tensors="pt").input_ids
+    tokens_2 = main_tokenizer(token_test_txt, return_tensors="pt").input_ids
+    
+    assert all((tokens_1 - tokens_2)[0] == 0)
+
+.. code:: ipython3
+
+    if not main_model_path.exists():
+        !optimum-cli export openvino --model $main_model_id --weight-format fp16 $main_model_path
+    if not draft_model_path.exists():
+        !optimum-cli export openvino --model $draft_model_id --weight-format fp16 $draft_model_path
+
+Infer directly using OpenVINO Inference Pipeline
+
+.. code:: ipython3
+
+    core = ov.Core()
+    draft_ov_model = core.read_model(draft_model_path / "openvino_model.xml")
+    draft_model = core.compile_model(draft_ov_model, device_name="CPU")
+    
+    main_ov_model = core.read_model(main_model_path / "openvino_model.xml")
+    main_model = core.compile_model(main_ov_model, device_name="CPU")
+
+.. code:: ipython3
+
+    def main(
+        prompt: str,
+        n_tokens_to_generate: int = 75,
+        K: int = 5,
+        seed: int = 5555,
+    ):
+        # seed numpy rng
+        np.random.seed(seed)
+        tokenized = main_tokenizer(prompt, return_tensors="pt")
+    
+        def run_autoregressive_sampling_fn(decode_fn, tokenized, **kwargs):
+            start = time.perf_counter()
+            output_ids = decode_fn(tokenized, **kwargs)
+            text = main_tokenizer.decode(output_ids, skip_special_tokens=True)
+            elapsed_time = time.perf_counter() - start
+            return text, elapsed_time
+    
+        def run_speculative_sampling_fn(decode_fn, input_ids, **kwargs):
+            start = time.perf_counter()
+            output_ids = decode_fn(input_ids, **kwargs)
+            text = main_tokenizer.decode(output_ids, skip_special_tokens=True)
+            elapsed_time = time.perf_counter() - start
+            return text, elapsed_time
+    
+        autoregressive_text, autoregressive_time = run_autoregressive_sampling_fn(
+            autoregressive_sampling_with_pkv,
+            tokenized,
+            model=main_model,
+            N=n_tokens_to_generate,
+        )
+    
+        speculative_text, speculative_time = run_speculative_sampling_fn(
+            speculative_sampling_with_pkv,
+            tokenized,
+            main_model=main_model,
+            draft_model=draft_model,
+            N=n_tokens_to_generate,
+            K=K,
+        )
+    
+        # Format results for output in gradio
+        out = "\n" + "Autoregressive Decode" + "\n" + "---------------------" + "\n"
+        out = out + f"Time = {autoregressive_time:.2f}s" + "\n" + f"Text = {autoregressive_text}" + "\n"
+        out = out + "\n" + "Speculative Decode" + "\n" + "------------------" + "\n"
+        out = out + f"Time = {speculative_time:.2f}s" + "\n" + f"Text = {speculative_text}"
+        return out
+
+.. code:: ipython3
+
+    res = main("Alan Turing was a", n_tokens_to_generate=100)
+    print(res)
+
+
+.. parsed-literal::
+
+    2024-04-17 10:21:41.642283: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-17 10:21:41.644834: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
+    2024-04-17 10:21:41.677055: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+    2024-04-17 10:21:41.677093: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
+    2024-04-17 10:21:41.677119: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+    2024-04-17 10:21:41.683198: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
+    2024-04-17 10:21:41.683977: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    2024-04-17 10:21:42.477656: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
+
+.. parsed-literal::
+
+    
+    Autoregressive Decode
+    ---------------------
+    Time = 44.39s
+    Text = Alan Turing was a British mathematician, computer scientist, and codebreaker who played a pivotal role in cracking the German Enigma code during World War II. He was also a pioneer in the field of artificial intelligence and made significant contributions to the development of computer science.
+    
+    Turing was born on June 23, 1912, in London, England. He was educated at Cambridge University, where he earned a degree in mathematics in 
+    
+    Speculative Decode
+    ------------------
+    Time = 22.96s
+    Text = Alan Turing was a British mathematician, computer scientist, and codebreaker who played a pivotal role in cracking the German Enigma code during World War II. He was also a pioneer in the field of artificial intelligence and made significant contributions to the development of computer science.
+    
+    Turing was born on June 23, 1912, in London, England. He was educated at Cambridge University, where he earned a degree in mathematics in 1
+
+
+.. code:: ipython3
+
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            f"""
+            # Speculative Sampling Demo
+            ## The output will show a comparison of Autoregressive Sampling vs Speculative Sampling
+            - Main Model: {main_model_id}
+            - Draft Model: {draft_model_id}
+            - K = 5
+            """
+        )
+        with gr.Row():
+            inp = gr.Textbox(
+                "Alan Turing was a",
+                placeholder="THIS CANNOT BE EMPTY",
+                label="Input Prompt",
+            )
+            out = gr.Textbox(label="Output")
+        btn = gr.Button("Run")
+        btn.click(fn=main, inputs=inp, outputs=out)
+    
+    demo.launch()
diff --git a/docs/notebooks/107-speech-recognition-quantization-data2vec-with-output.rst b/docs/notebooks/speech-recognition-quantization-data2vec-with-output.rst
similarity index 99%
rename from docs/notebooks/107-speech-recognition-quantization-data2vec-with-output.rst
rename to docs/notebooks/speech-recognition-quantization-data2vec-with-output.rst
index 38d945e74af..80358d9f51b 100644
--- a/docs/notebooks/107-speech-recognition-quantization-data2vec-with-output.rst
+++ b/docs/notebooks/speech-recognition-quantization-data2vec-with-output.rst
@@ -98,6 +98,7 @@ Convert model to OpenVINO Intermediate Representation
 .. code:: ipython3
 
     from pathlib import Path
+    
     # Set model directory
     MODEL_DIR = Path("model")
     MODEL_DIR.mkdir(exist_ok=True)
@@ -122,7 +123,6 @@ Convert model to OpenVINO Intermediate Representation
         print("Read IR model from {}".format(ir_model_path))
         ov_model = core.read_model(ir_model_path)
 
-
 Prepare inference data
 ~~~~~~~~~~~~~~~~~~~~~~
 
@@ -143,9 +143,14 @@ dataset.
     
     # define preprocessing function for converting audio to input values for model
     def map_to_input(batch):
-        preprocessed_signal = processor(batch["audio"]["array"], return_tensors="pt", padding="longest", sampling_rate=batch['audio']['sampling_rate'])
+        preprocessed_signal = processor(
+            batch["audio"]["array"],
+            return_tensors="pt",
+            padding="longest",
+            sampling_rate=batch["audio"]["sampling_rate"],
+        )
         input_values = preprocessed_signal.input_values
-        batch['input_values'] = input_values
+        batch["input_values"] = input_values
         return batch
     
     
@@ -176,7 +181,7 @@ For reference, see the same function provided for OpenVINO model.
     
     # inference function for pytorch
     def torch_infer(model, sample):
-        logits = model(torch.Tensor(sample['input_values'])).logits
+        logits = model(torch.Tensor(sample["input_values"])).logits
         # take argmax and decode
         predicted_ids = torch.argmax(logits, dim=-1)
         transcription = processor.batch_decode(predicted_ids)
@@ -186,7 +191,7 @@ For reference, see the same function provided for OpenVINO model.
     # inference function for openvino
     def ov_infer(model, sample):
         output = model.output(0)
-        logits = model(np.array(sample['input_values']))[output]
+        logits = model(np.array(sample["input_values"]))[output]
         predicted_ids = np.argmax(logits, axis=-1)
         transcription = processor.batch_decode(torch.from_numpy(predicted_ids))
         return transcription
@@ -200,8 +205,8 @@ Select inference device for OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -281,7 +286,7 @@ library.
             # run infer function on sample
             transcription = infer_fn(model, sample)
             # update metric on sample result
-            wer.update(transcription, [sample['text']])
+            wer.update(transcription, [sample["text"]])
         # finalize metric calculation
         result = wer.compute()
         return result
@@ -306,8 +311,8 @@ library.
 
 .. code:: ipython3
 
-    print(f'[PyTorch]   Word Error Rate: {pt_result:.4f}')
-    print(f'[OpenVino]  Word Error Rate: {ov_result:.4f}')
+    print(f"[PyTorch]   Word Error Rate: {pt_result:.4f}")
+    print(f"[OpenVino]  Word Error Rate: {ov_result:.4f}")
 
 
 .. parsed-literal::
@@ -477,7 +482,7 @@ saved using ``ov.save_model`` function.
 
 .. code:: ipython3
 
-    MODEL_NAME = 'quantized_data2vec_base'
+    MODEL_NAME = "quantized_data2vec_base"
     quantized_model_path = Path(f"{MODEL_NAME}_openvino_model/{MODEL_NAME}_quantized.xml")
     ov.save_model(quantized_model, quantized_model_path)
 
@@ -529,7 +534,7 @@ using ``core.compile_model``. After that, we can reuse the same
 Compare Performance of the Original and Quantized Models
 --------------------------------------------------------
 
-`Benchmark
+ `Benchmark
 Tool `__
 is used to measure the inference performance of the ``FP16`` and
 ``INT8`` models.
@@ -720,8 +725,8 @@ compare it with the ``FP16`` result.
 .. code:: ipython3
 
     int8_ov_result = compute_wer(dataset, int8_compiled_model, ov_infer)
-    print(f'[OpenVino FP16] Word Error Rate: {ov_result:.4}')
-    print(f'[OpenVino INT8] Word Error Rate: {int8_ov_result:.4f}')
+    print(f"[OpenVino FP16] Word Error Rate: {ov_result:.4}")
+    print(f"[OpenVino INT8] Word Error Rate: {int8_ov_result:.4f}")
 
 
 
diff --git a/docs/notebooks/107-speech-recognition-quantization-wav2vec2-with-output.rst b/docs/notebooks/speech-recognition-quantization-wav2vec2-with-output.rst
similarity index 97%
rename from docs/notebooks/107-speech-recognition-quantization-wav2vec2-with-output.rst
rename to docs/notebooks/speech-recognition-quantization-wav2vec2-with-output.rst
index eff951b8ea5..414f26f6f15 100644
--- a/docs/notebooks/107-speech-recognition-quantization-wav2vec2-with-output.rst
+++ b/docs/notebooks/speech-recognition-quantization-wav2vec2-with-output.rst
@@ -42,6 +42,11 @@ Table of contents:
     %pip install -q soundfile librosa "transformers>=4.36.2" --extra-index-url https://download.pytorch.org/whl/cpu
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -50,95 +55,91 @@ Table of contents:
 .. parsed-literal::
 
     Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cpu
-    Requirement already satisfied: datasets in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2.18.0)
+    Requirement already satisfied: datasets in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2.18.0)
+    Requirement already satisfied: torchmetrics>=0.11.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (1.3.2)
+    Requirement already satisfied: torch>=2.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2.2.2+cpu)
 
 
 .. parsed-literal::
 
-    Collecting torchmetrics>=0.11.0
-      Using cached torchmetrics-1.3.1-py3-none-any.whl.metadata (19 kB)
-    Requirement already satisfied: torch>=2.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2.1.0+cpu)
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+    Requirement already satisfied: numpy>=1.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from datasets) (1.23.5)
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+    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from datasets) (24.0)
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 .. parsed-literal::
 
-    Collecting lightning-utilities>=0.8.0 (from torchmetrics>=0.11.0)
-      Using cached lightning_utilities-0.10.1-py3-none-any.whl.metadata (4.8 kB)
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 .. parsed-literal::
 
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-    Requirement already satisfied: multidict<7.0,>=4.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from aiohttp->datasets) (6.0.5)
-    Requirement already satisfied: yarl<2.0,>=1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from aiohttp->datasets) (1.9.4)
-    Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from aiohttp->datasets) (4.0.3)
+    Requirement already satisfied: setuptools in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from lightning-utilities>=0.8.0->torchmetrics>=0.11.0) (69.5.1)
+    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (3.3.2)
+    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (3.7)
+    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2.2.1)
+    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2024.2.2)
 
 
 .. parsed-literal::
 
-    Requirement already satisfied: setuptools in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from lightning-utilities>=0.8.0->torchmetrics>=0.11.0) (69.1.1)
-    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (3.3.2)
-    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (3.6)
-    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2.2.1)
-    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2024.2.2)
-    Requirement already satisfied: MarkupSafe>=2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jinja2->torch>=2.1.0) (2.1.5)
+    Requirement already satisfied: MarkupSafe>=2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jinja2->torch>=2.1.0) (2.1.5)
 
 
 .. parsed-literal::
 
-    Requirement already satisfied: python-dateutil>=2.8.2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pandas->datasets) (2.9.0.post0)
-    Requirement already satisfied: pytz>=2020.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pandas->datasets) (2024.1)
-    Requirement already satisfied: tzdata>=2022.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pandas->datasets) (2024.1)
-    Requirement already satisfied: mpmath>=0.19 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from sympy->torch>=2.1.0) (1.3.0)
-    Requirement already satisfied: six>=1.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)
+    Requirement already satisfied: python-dateutil>=2.8.2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pandas->datasets) (2.9.0.post0)
+    Requirement already satisfied: pytz>=2020.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pandas->datasets) (2024.1)
+    Requirement already satisfied: tzdata>=2022.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pandas->datasets) (2024.1)
+    Requirement already satisfied: mpmath>=0.19 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from sympy->torch>=2.1.0) (1.3.0)
 
 
 .. parsed-literal::
 
-    Using cached torchmetrics-1.3.1-py3-none-any.whl (840 kB)
-    Using cached lightning_utilities-0.10.1-py3-none-any.whl (24 kB)
+    Requirement already satisfied: six>=1.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)
 
 
 .. parsed-literal::
 
-    Installing collected packages: lightning-utilities, torchmetrics
-
-
-.. parsed-literal::
-
-    Successfully installed lightning-utilities-0.10.1 torchmetrics-1.3.1
-
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
 
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -212,15 +213,15 @@ IR).
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:594: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:594: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:633: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:633: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
 
 
@@ -246,9 +247,14 @@ dataset.
     
     # define preprocessing function for converting audio to input values for model
     def map_to_input(batch):
-        preprocessed_signal = processor(batch["audio"]["array"], return_tensors="pt", padding="longest", sampling_rate=batch['audio']['sampling_rate'])
+        preprocessed_signal = processor(
+            batch["audio"]["array"],
+            return_tensors="pt",
+            padding="longest",
+            sampling_rate=batch["audio"]["sampling_rate"],
+        )
         input_values = preprocessed_signal.input_values
-        batch['input_values'] = input_values
+        batch["input_values"] = input_values
         return batch
     
     
@@ -258,7 +264,7 @@ dataset.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1461: FutureWarning: The repository for patrickvonplaten/librispeech_asr_dummy contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/patrickvonplaten/librispeech_asr_dummy
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1461: FutureWarning: The repository for patrickvonplaten/librispeech_asr_dummy contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/patrickvonplaten/librispeech_asr_dummy
     You can avoid this message in future by passing the argument `trust_remote_code=True`.
     Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
       warnings.warn(
@@ -291,7 +297,7 @@ steps:
    For this model, ignored scope was selected experimentally, based on
    result of quantization with accuracy control. For understanding how
    it works please check following
-   `notebook <122-quantizing-model-with-accuracy-control-with-output.html>`__
+   `notebook `__
 3. Serialize OpenVINO IR model using ``ov.save_model`` function.
 
 .. code:: ipython3
@@ -299,6 +305,7 @@ steps:
     import nncf
     from nncf.parameters import ModelType
     
+    
     def transform_fn(data_item):
         """
         Extract the model's input from the data item.
@@ -332,14 +339,14 @@ steps:
 
 .. parsed-literal::
 
-    2024-03-12 22:26:40.835104: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:26:40.866034: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 00:53:31.426814: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:53:31.462819: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 22:26:41.461559: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:53:32.048658: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -463,7 +470,7 @@ steps:
 
 .. code:: ipython3
 
-    MODEL_NAME = 'quantized_wav2vec2_base'
+    MODEL_NAME = "quantized_wav2vec2_base"
     quantized_model_path = Path(f"{MODEL_NAME}_openvino_model/{MODEL_NAME}_quantized.xml")
     ov.save_model(quantized_model, quantized_model_path)
 
@@ -506,8 +513,8 @@ first. Please select one from available devices from dropdown list:
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -565,9 +572,10 @@ library.
     from torchmetrics import WordErrorRate
     from tqdm.notebook import tqdm
     
+    
     # inference function for pytorch
     def torch_infer(model, sample):
-        logits = model(torch.Tensor(sample['input_values'])).logits
+        logits = model(torch.Tensor(sample["input_values"])).logits
         # take argmax and decode
         predicted_ids = torch.argmax(logits, dim=-1)
         transcription = processor.batch_decode(predicted_ids)
@@ -576,7 +584,7 @@ library.
     
     # inference function for openvino
     def ov_infer(model, sample):
-        logits = model(np.array(sample['input_values']))[0]
+        logits = model(np.array(sample["input_values"]))[0]
         predicted_ids = np.argmax(logits, axis=-1)
         transcription = processor.batch_decode(torch.from_numpy(predicted_ids))
         return transcription
@@ -588,7 +596,7 @@ library.
             # run infer function on sample
             transcription = infer_fn(model, sample)
             # update metric on sample result
-            wer.update(transcription, [sample['text']])
+            wer.update(transcription, [sample["text"]])
         # finalize metric calculation
         result = wer.compute()
         return result
@@ -609,14 +617,14 @@ quantized model.
     pt_result = compute_wer(dataset, torch_model, torch_infer)
     ov_result = compute_wer(dataset, compiled_fp32_ov_model, ov_infer)
     int8_ov_result = compute_wer(dataset, compiled_model, ov_infer)
-    print(f'[PyTorch]       Word Error Rate: {pt_result:.4f}')
-    print(f'[OpenVino FP16] Word Error Rate: {ov_result:.4}')
-    print(f'[OpenVino INT8] Word Error Rate: {int8_ov_result:.4f}')
+    print(f"[PyTorch]       Word Error Rate: {pt_result:.4f}")
+    print(f"[OpenVino FP16] Word Error Rate: {ov_result:.4}")
+    print(f"[OpenVino INT8] Word Error Rate: {int8_ov_result:.4f}")
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:62: FutureWarning: Importing `WordErrorRate` from `torchmetrics` was deprecated and will be removed in 2.0. Import `WordErrorRate` from `torchmetrics.text` instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:62: FutureWarning: Importing `WordErrorRate` from `torchmetrics` was deprecated and will be removed in 2.0. Import `WordErrorRate` from `torchmetrics.text` instead.
       _future_warning(
 
 
@@ -686,11 +694,7 @@ models.
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-
-
-.. parsed-literal::
-
-    [ INFO ] Read model took 27.55 ms
+    [ INFO ] Read model took 27.44 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     input_values (node: input_values) : f32 / [...] / [?,?]
@@ -699,7 +703,11 @@ models.
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
     [ INFO ] Reshaping model: 'input_values': [1,30480]
-    [ INFO ] Reshape model took 29.21 ms
+
+
+.. parsed-literal::
+
+    [ INFO ] Reshape model took 30.09 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     input_values (node: input_values) : f32 / [...] / [1,30480]
@@ -710,7 +718,7 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 531.22 ms
+    [ INFO ] Compile model took 524.97 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -749,21 +757,21 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 61.24 ms
+    [ INFO ] First inference took 57.09 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            5484 iterations
-    [ INFO ] Duration:         120219.99 ms
+    [ INFO ] Count:            5502 iterations
+    [ INFO ] Duration:         120210.49 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        131.07 ms
-    [ INFO ]    Average:       131.36 ms
-    [ INFO ]    Min:           107.53 ms
-    [ INFO ]    Max:           328.44 ms
-    [ INFO ] Throughput:   45.62 FPS
+    [ INFO ]    Median:        130.91 ms
+    [ INFO ]    Average:       130.93 ms
+    [ INFO ]    Min:           69.78 ms
+    [ INFO ]    Max:           154.76 ms
+    [ INFO ] Throughput:   45.77 FPS
 
 
 .. code:: ipython3
@@ -776,10 +784,6 @@ models.
 
     [Step 1/11] Parsing and validating input arguments
     [ INFO ] Parsing input parameters
-
-
-.. parsed-literal::
-
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
@@ -794,7 +798,11 @@ models.
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 40.21 ms
+
+
+.. parsed-literal::
+
+    [ INFO ] Read model took 41.61 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     input_values (node: input_values) : f32 / [...] / [?,?]
@@ -807,7 +815,7 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] Reshape model took 35.76 ms
+    [ INFO ] Reshape model took 35.71 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     input_values (node: input_values) : f32 / [...] / [1,30480]
@@ -818,7 +826,7 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 1294.11 ms
+    [ INFO ] Compile model took 1265.41 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
 
@@ -861,19 +869,19 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 58.50 ms
+    [ INFO ] First inference took 55.32 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            8268 iterations
-    [ INFO ] Duration:         120137.02 ms
+    [ INFO ] Count:            8274 iterations
+    [ INFO ] Duration:         120148.65 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        87.16 ms
-    [ INFO ]    Average:       87.05 ms
-    [ INFO ]    Min:           73.56 ms
-    [ INFO ]    Max:           112.73 ms
-    [ INFO ] Throughput:   68.82 FPS
+    [ INFO ]    Median:        86.99 ms
+    [ INFO ]    Average:       86.98 ms
+    [ INFO ]    Min:           44.88 ms
+    [ INFO ]    Max:           112.37 ms
+    [ INFO ] Throughput:   68.86 FPS
 
diff --git a/docs/notebooks/211-speech-to-text-with-output.rst b/docs/notebooks/speech-to-text-with-output.rst
similarity index 96%
rename from docs/notebooks/211-speech-to-text-with-output.rst
rename to docs/notebooks/speech-to-text-with-output.rst
index 90af565b2b1..ee560da3736 100644
--- a/docs/notebooks/211-speech-to-text-with-output.rst
+++ b/docs/notebooks/speech-to-text-with-output.rst
@@ -48,7 +48,7 @@ Imports
 
 .. code:: ipython3
 
-    %pip install -q "librosa>=0.8.1" "matplotlib<3.8" "openvino-dev>=2024.0.0" "numpy<1.24" "ruamel.yaml"
+    %pip install -q "librosa>=0.8.1" "scipy" "matplotlib<3.8" "openvino-dev>=2024.0.0" "numpy<1.24" "ruamel.yaml" "torch" "tqdm"
 
 .. code:: ipython3
 
@@ -64,12 +64,15 @@ Imports
     import numpy as np
     import scipy
     import openvino as ov
+    
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 Settings
@@ -91,7 +94,7 @@ In this part, all variables used in the notebook are set.
 Download and Convert Public Model
 ---------------------------------
 
-If it is your first run, models
+ If it is your first run, models
 will be downloaded and converted here. It my take a few minutes. Use
 ``omz_downloader`` and ``omz_converter``, which are command-line tools
 from the ``openvino-dev`` package.
@@ -110,8 +113,8 @@ Representation (OpenVINO IR).
 .. code:: ipython3
 
     # Check if a model is already downloaded (to the download directory).
-    path_to_model_weights = Path(f'{download_folder}/public/{model_name}/models')
-    downloaded_model_file = list(path_to_model_weights.glob('*.pth'))
+    path_to_model_weights = Path(f"{download_folder}/public/{model_name}/models")
+    downloaded_model_file = list(path_to_model_weights.glob("*.pth"))
     
     if not path_to_model_weights.is_dir() or len(downloaded_model_file) == 0:
         download_command = f"omz_downloader --name {model_name} --output_dir {download_folder} --precision {precision}"
@@ -121,10 +124,10 @@ Representation (OpenVINO IR).
 
 .. code:: ipython3
 
-    def convert_model(model_path:Path, converted_model_path:Path):
+    def convert_model(model_path: Path, converted_model_path: Path):
         """
         helper function for converting QuartzNet model to IR
-        The function accepts path to directory with dowloaded packages, weights and configs using OMZ downloader, 
+        The function accepts path to directory with dowloaded packages, weights and configs using OMZ downloader,
         initialize model and convert to OpenVINO model and serialize it to IR.
         Params:
           model_path: path to model modules, weights and configs downloaded via omz_downloader
@@ -134,51 +137,58 @@ Representation (OpenVINO IR).
         """
         # add model path to PYTHONPATH for access to downloaded modules
         sys.path.append(str(model_path))
-        
+    
         # import necessary classes
         from ruamel.yaml import YAML
     
         from nemo.collections.asr import JasperEncoder, JasperDecoderForCTC
         from nemo.core import NeuralModuleFactory, DeviceType
     
-        YAML = YAML(typ='safe')
+        YAML = YAML(typ="safe")
     
         # utility fornction fr replacing 1d convolutions to 2d for better efficiency
         def convert_to_2d(model):
             for name, l in model.named_children():
                 layer_type = l.__class__.__name__
-                if layer_type == 'Conv1d':
-                    new_layer = nn.Conv2d(l.in_channels, l.out_channels,
-                                          (1, l.kernel_size[0]), (1, l.stride[0]),
-                                          (0, l.padding[0]), (1, l.dilation[0]),
-                                          l.groups, False if l.bias is None else True, l.padding_mode)
+                if layer_type == "Conv1d":
+                    new_layer = nn.Conv2d(
+                        l.in_channels,
+                        l.out_channels,
+                        (1, l.kernel_size[0]),
+                        (1, l.stride[0]),
+                        (0, l.padding[0]),
+                        (1, l.dilation[0]),
+                        l.groups,
+                        False if l.bias is None else True,
+                        l.padding_mode,
+                    )
                     params = l.state_dict()
-                    params['weight'] = params['weight'].unsqueeze(2)
+                    params["weight"] = params["weight"].unsqueeze(2)
                     new_layer.load_state_dict(params)
                     setattr(model, name, new_layer)
-                elif layer_type == 'BatchNorm1d':
+                elif layer_type == "BatchNorm1d":
                     new_layer = nn.BatchNorm2d(l.num_features, l.eps)
                     new_layer.load_state_dict(l.state_dict())
                     new_layer.eval()
                     setattr(model, name, new_layer)
                 else:
                     convert_to_2d(l)
-        
+    
         # model class
         class QuartzNet(torch.nn.Module):
             def __init__(self, model_config, encoder_weights, decoder_weights):
                 super().__init__()
-                with open(model_config, 'r') as config:
+                with open(model_config, "r") as config:
                     model_args = YAML.load(config)
                 _ = NeuralModuleFactory(placement=DeviceType.CPU)
     
-                encoder_params = model_args['init_params']['encoder_params']['init_params']
+                encoder_params = model_args["init_params"]["encoder_params"]["init_params"]
                 self.encoder = JasperEncoder(**encoder_params)
-                self.encoder.load_state_dict(torch.load(encoder_weights, map_location='cpu'))
+                self.encoder.load_state_dict(torch.load(encoder_weights, map_location="cpu"))
     
-                decoder_params = model_args['init_params']['decoder_params']['init_params']
+                decoder_params = model_args["init_params"]["decoder_params"]["init_params"]
                 self.decoder = JasperDecoderForCTC(**decoder_params)
-                self.decoder.load_state_dict(torch.load(decoder_weights, map_location='cpu'))
+                self.decoder.load_state_dict(torch.load(decoder_weights, map_location="cpu"))
     
                 self.encoder._prepare_for_deployment()
                 self.decoder._prepare_for_deployment()
@@ -192,7 +202,7 @@ Representation (OpenVINO IR).
     
                 shape = i_log_probs.shape
                 return i_log_probs.reshape(shape[0], shape[1], shape[3])
-        
+    
         # path to configs and weights for creating model instane
         model_config = model_path / ".nemo_tmp/module.yaml"
         encoder_weights = model_path / ".nemo_tmp/JasperEncoder.pt"
@@ -209,8 +219,8 @@ Representation (OpenVINO IR).
 .. code:: ipython3
 
     # Check if a model is already converted (in the model directory).
-    path_to_converted_weights = Path(f'{model_folder}/public/{model_name}/{precision}/{model_name}.bin')
-    path_to_converted_model = Path(f'{model_folder}/public/{model_name}/{precision}/{model_name}.xml')
+    path_to_converted_weights = Path(f"{model_folder}/public/{model_name}/{precision}/{model_name}.bin")
+    path_to_converted_model = Path(f"{model_folder}/public/{model_name}/{precision}/{model_name}.xml")
     
     if not path_to_converted_weights.is_file():
         downloaded_model_path = Path("output/public/quartznet-15x5-en/models")
@@ -278,10 +288,10 @@ with a ``sampling rate`` of 16 kHz.
 
 .. code:: ipython3
 
-    # Download the audio from the openvino_notebooks storage 
+    # Download the audio from the openvino_notebooks storage
     file_name = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/audio/" + audio_file_name,
-        directory=data_folder
+        directory=data_folder,
     )
     
     audio, sampling_rate = librosa.load(path=str(file_name), sr=16000)
@@ -317,16 +327,16 @@ spectrogram.
 .. code:: ipython3
 
     plt.figure()
-    librosa.display.waveshow(y=audio, sr=sampling_rate, max_points=50000, x_axis='time', offset=0.0);
+    librosa.display.waveshow(y=audio, sr=sampling_rate, max_points=50000, x_axis="time", offset=0.0)
     plt.show()
     specto_audio = librosa.stft(audio)
     specto_audio = librosa.amplitude_to_db(np.abs(specto_audio), ref=np.max)
     print(specto_audio.shape)
-    librosa.display.specshow(specto_audio, sr=sampling_rate, x_axis='time', y_axis='hz');
+    librosa.display.specshow(specto_audio, sr=sampling_rate, x_axis="time", y_axis="hz");
 
 
 
-.. image:: 211-speech-to-text-with-output_files/211-speech-to-text-with-output_20_0.png
+.. image:: speech-to-text-with-output_files/speech-to-text-with-output_20_0.png
 
 
 .. parsed-literal::
@@ -335,7 +345,7 @@ spectrogram.
 
 
 
-.. image:: 211-speech-to-text-with-output_files/211-speech-to-text-with-output_20_2.png
+.. image:: speech-to-text-with-output_files/speech-to-text-with-output_20_2.png
 
 
 Change Type of Data
@@ -351,7 +361,7 @@ multiply each value by the max value of ``int16`` and convert it to
 .. code:: ipython3
 
     if max(np.abs(audio)) <= 1:
-        audio = (audio * (2**15 - 1))
+        audio = audio * (2**15 - 1)
     audio = audio.astype(np.int16)
 
 Convert Audio to Mel Spectrum
@@ -375,8 +385,17 @@ article =2023.1.0"
-    %pip install -q --upgrade-strategy eager "invisible-watermark>=0.2.0" "transformers>=4.33" "accelerate" "onnx" "onnxruntime" safetensors "diffusers>=0.22.0"
-    %pip install -q gradio
+    %pip install -q --upgrade-strategy eager "invisible-watermark>=0.2.0" "transformers>=4.33" "accelerate" "onnx" "torch>=2.1" safetensors "diffusers>=0.22.0" "peft==0.6.2"
+    %pip install -q "gradio>=4.19"
 
 SSD-1B Base model
 -----------------
@@ -119,8 +119,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -190,7 +190,7 @@ disk and up to 64GB RAM for the SDXL model after exporting.
 
 
 
-.. image:: 248-ssd-b1-with-output_files/248-ssd-b1-with-output_9_1.png
+.. image:: ssd-b1-with-output_files/ssd-b1-with-output_9_1.png
 
 
 
@@ -204,7 +204,13 @@ loaded from disk.
     
     
     prompt = "cute cat 4k, high-res, masterpiece, best quality, soft lighting, dynamic angle"
-    image = text2image_pipe(prompt, num_inference_steps=15, height=512, width=512, generator=np.random.RandomState(314)).images[0]
+    image = text2image_pipe(
+        prompt,
+        num_inference_steps=15,
+        height=512,
+        width=512,
+        generator=np.random.RandomState(314),
+    ).images[0]
     image
 
 
@@ -216,7 +222,7 @@ loaded from disk.
 
 
 
-.. image:: 248-ssd-b1-with-output_files/248-ssd-b1-with-output_11_1.png
+.. image:: ssd-b1-with-output_files/ssd-b1-with-output_11_1.png
 
 
 
@@ -233,8 +239,16 @@ Image2Image Generation Interactive Demo
     prompt = "An astronaut riding a green horse"
     neg_prompt = "ugly, blurry, poor quality"
     
+    
     def generate_from_text(text_promt, neg_prompt, seed, num_steps):
-        result = text2image_pipe(text_promt, negative_prompt=neg_prompt, num_inference_steps=num_steps, generator=np.random.RandomState(seed), height=512, width=512).images[0]
+        result = text2image_pipe(
+            text_promt,
+            negative_prompt=neg_prompt,
+            num_inference_steps=num_steps,
+            generator=np.random.RandomState(seed),
+            height=512,
+            width=512,
+        ).images[0]
         return result
     
     
@@ -247,14 +261,41 @@ Image2Image Generation Interactive Demo
                 steps_input = gr.Slider(label="Steps", value=10, step=1)
                 btn = gr.Button()
             out = gr.Image(label="Result", type="pil", width=512)
-            btn.click(generate_from_text, [positive_input, neg_input, seed_input, steps_input], out)
-            gr.Examples([
-                [prompt, neg_prompt, 999, 20], 
-                ["underwater world coral reef, colorful jellyfish, 35mm, cinematic lighting, shallow depth of field,  ultra quality, masterpiece, realistic", neg_prompt, 89, 20],
-                ["a photo realistic happy white poodle dog ​​playing in the grass, extremely detailed, high res, 8k, masterpiece, dynamic angle", neg_prompt, 1569, 15],
-                ["Astronaut on Mars watching sunset, best quality, cinematic effects,", neg_prompt, 65245, 12],
-                ["Black and white street photography of a rainy night in New York, reflections on wet pavement", neg_prompt, 48199, 10]
-            ], [positive_input, neg_input, seed_input, steps_input])
+            btn.click(
+                generate_from_text,
+                [positive_input, neg_input, seed_input, steps_input],
+                out,
+            )
+            gr.Examples(
+                [
+                    [prompt, neg_prompt, 999, 20],
+                    [
+                        "underwater world coral reef, colorful jellyfish, 35mm, cinematic lighting, shallow depth of field,  ultra quality, masterpiece, realistic",
+                        neg_prompt,
+                        89,
+                        20,
+                    ],
+                    [
+                        "a photo realistic happy white poodle dog ​​playing in the grass, extremely detailed, high res, 8k, masterpiece, dynamic angle",
+                        neg_prompt,
+                        1569,
+                        15,
+                    ],
+                    [
+                        "Astronaut on Mars watching sunset, best quality, cinematic effects,",
+                        neg_prompt,
+                        65245,
+                        12,
+                    ],
+                    [
+                        "Black and white street photography of a rainy night in New York, reflections on wet pavement",
+                        neg_prompt,
+                        48199,
+                        10,
+                    ],
+                ],
+                [positive_input, neg_input, seed_input, steps_input],
+            )
     
     try:
         demo.queue().launch(debug=False)
@@ -323,7 +364,7 @@ Infer the original model
 
 
 
-.. image:: 248-ssd-b1-with-output_files/248-ssd-b1-with-output_16_3.png
+.. image:: ssd-b1-with-output_files/ssd-b1-with-output_16_3.png
 
 
 
@@ -369,7 +410,7 @@ file.
             with torch.no_grad():
                 converted_model = ov.convert_model(model, example_input=example_input)
             ov.save_model(converted_model, xml_path)
-            
+    
             # cleanup memory
             torch._C._jit_clear_class_registry()
             torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
@@ -399,7 +440,7 @@ VAE decoder.
 
 .. code:: ipython3
 
-    VAE_OV_PATH = Path('model/vae_decoder.xml')
+    VAE_OV_PATH = Path("model/vae_decoder.xml")
     
     
     class VAEDecoderWrapper(torch.nn.Module):
@@ -409,7 +450,7 @@ VAE decoder.
     
         def forward(self, latents):
             return self.vae.decode(latents)
-        
+    
     
     pipe.vae.eval()
     vae_decoder = VAEDecoderWrapper(pipe.vae)
@@ -426,7 +467,7 @@ text encoder hidden state.
 
 .. code:: ipython3
 
-    UNET_OV_PATH = Path('model/unet_ir.xml')
+    UNET_OV_PATH = Path("model/unet_ir.xml")
     
     
     class UNETWrapper(torch.nn.Module):
@@ -434,24 +475,31 @@ text encoder hidden state.
             super().__init__()
             self.unet = unet
     
-        def forward(self, sample=None, timestep=None, encoder_hidden_states=None, timestep_cond=None, text_embeds=None, time_ids=None):
-            
+        def forward(
+            self,
+            sample=None,
+            timestep=None,
+            encoder_hidden_states=None,
+            timestep_cond=None,
+            text_embeds=None,
+            time_ids=None,
+        ):
             return self.unet.forward(
                 sample,
-                timestep, 
-                encoder_hidden_states, 
+                timestep,
+                encoder_hidden_states,
                 timestep_cond=timestep_cond,
-                added_cond_kwargs={'text_embeds': text_embeds, 'time_ids': time_ids}
+                added_cond_kwargs={"text_embeds": text_embeds, "time_ids": time_ids},
             )
     
     
     example_input = {
-        'sample': torch.rand([1, 4, 128, 128], dtype=torch.float32),
-        'timestep': torch.from_numpy(np.array(1, dtype=float)),
-        'encoder_hidden_states': torch.rand([1, 77, 2048], dtype=torch.float32),
-        'timestep_cond': torch.rand([1, 256], dtype=torch.float32),
-        'text_embeds': torch.rand([1, 1280], dtype=torch.float32),
-        'time_ids': torch.rand([1, 6], dtype=torch.float32),
+        "sample": torch.rand([1, 4, 128, 128], dtype=torch.float32),
+        "timestep": torch.from_numpy(np.array(1, dtype=float)),
+        "encoder_hidden_states": torch.rand([1, 77, 2048], dtype=torch.float32),
+        "timestep_cond": torch.rand([1, 256], dtype=torch.float32),
+        "text_embeds": torch.rand([1, 1280], dtype=torch.float32),
+        "time_ids": torch.rand([1, 6], dtype=torch.float32),
     }
     
     
@@ -481,18 +529,22 @@ a sequence of latent text embeddings.
             input_ids=None,
             output_hidden_states=None,
         ):
-            encoder_outputs = self.encoder(input_ids, output_hidden_states=output_hidden_states, return_dict=torch.tensor(True))
+            encoder_outputs = self.encoder(
+                input_ids,
+                output_hidden_states=output_hidden_states,
+                return_dict=torch.tensor(True),
+            )
     
             return encoder_outputs[0], list(encoder_outputs.hidden_states)
 
 .. code:: ipython3
 
-    TEXT_ENCODER_1_OV_PATH = Path('model/text_encoder_1.xml')
-    TEXT_ENCODER_2_OV_PATH = Path('model/text_encoder_2.xml')
+    TEXT_ENCODER_1_OV_PATH = Path("model/text_encoder_1.xml")
+    TEXT_ENCODER_2_OV_PATH = Path("model/text_encoder_2.xml")
     
     inputs = {
-        'input_ids': torch.ones((1, 77), dtype=torch.long),
-        'output_hidden_states': torch.tensor(True),
+        "input_ids": torch.ones((1, 77), dtype=torch.long),
+        "output_hidden_states": torch.tensor(True),
     }
 
 .. code:: ipython3
@@ -524,8 +576,8 @@ Select device from dropdown list for running inference using OpenVINO.
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
     
@@ -562,50 +614,44 @@ interaction with original ``DiffusionPipeline`` class.
     
     class EncoderWrapper:
         dtype = torch.float32  # accessed in the original workflow
-        
+    
         def __init__(self, encoder, orig_encoder):
             self.encoder = encoder
-            self.modules = orig_encoder.modules  # accessed in the original workflow 
-            self.config = orig_encoder.config  # accessed in the original workflow 
+            self.modules = orig_encoder.modules  # accessed in the original workflow
+            self.config = orig_encoder.config  # accessed in the original workflow
     
         def __call__(self, input_ids, **kwargs):
-            output_hidden_states = kwargs['output_hidden_states']
-            inputs = {
-                'input_ids': input_ids,
-                'output_hidden_states': output_hidden_states
-            }
+            output_hidden_states = kwargs["output_hidden_states"]
+            inputs = {"input_ids": input_ids, "output_hidden_states": output_hidden_states}
             output = self.encoder(inputs)
     
             hidden_states = []
             hidden_states_len = len(output)
             for i in range(1, hidden_states_len):
                 hidden_states.append(torch.from_numpy(output[i]))
-            
-            BaseModelOutputWithPooling = namedtuple("BaseModelOutputWithPooling", 'last_hidden_state hidden_states')
+    
+            BaseModelOutputWithPooling = namedtuple("BaseModelOutputWithPooling", "last_hidden_state hidden_states")
             output = BaseModelOutputWithPooling(torch.from_numpy(output[0]), hidden_states)
             return output
 
 .. code:: ipython3
 
     class UnetWrapper:
-    
         def __init__(self, unet, unet_orig):
             self.unet = unet
-            self.config = unet_orig.config  # accessed in the original workflow 
-            self.add_embedding = unet_orig.add_embedding  # accessed in the original workflow 
+            self.config = unet_orig.config  # accessed in the original workflow
+            self.add_embedding = unet_orig.add_embedding  # accessed in the original workflow
     
         def __call__(self, *args, **kwargs):
-    
             latent_model_input, t = args
             inputs = {
-                'sample': latent_model_input,
-                'timestep': t,
-                'encoder_hidden_states': kwargs['encoder_hidden_states'],
-                'timestep_cond': kwargs['timestep_cond'],
-                'text_embeds': kwargs['added_cond_kwargs']['text_embeds'],
-                'time_ids': kwargs['added_cond_kwargs']['time_ids']
+                "sample": latent_model_input,
+                "timestep": t,
+                "encoder_hidden_states": kwargs["encoder_hidden_states"],
+                "timestep_cond": kwargs["timestep_cond"],
+                "text_embeds": kwargs["added_cond_kwargs"]["text_embeds"],
+                "time_ids": kwargs["added_cond_kwargs"]["time_ids"],
             }
-            
     
             output = self.unet(inputs)
     
@@ -614,23 +660,23 @@ interaction with original ``DiffusionPipeline`` class.
 .. code:: ipython3
 
     class VAEWrapper:
-        dtype = torch.float32  # accessed in the original workflow 
-        
+        dtype = torch.float32  # accessed in the original workflow
+    
         def __init__(self, vae, vae_orig):
             self.vae = vae
-            self.config = vae_orig.config  # accessed in the original workflow 
+            self.config = vae_orig.config  # accessed in the original workflow
     
         def decode(self, latents, return_dict=False):
             output = self.vae(latents)[0]
             output = torch.from_numpy(output)
-            
+    
             return [output]
 
 And insert wrappers instances in the pipeline:
 
 .. code:: ipython3
 
-    pipe.unet = UnetWrapper(compiled_unet,pipe.unet)
+    pipe.unet = UnetWrapper(compiled_unet, pipe.unet)
     pipe.text_encoder = EncoderWrapper(compiled_text_encoder, pipe.text_encoder)
     pipe.text_encoder_2 = EncoderWrapper(compiled_text_encoder_2, pipe.text_encoder_2)
     pipe.vae = VAEWrapper(compiled_vae, pipe.vae)
@@ -655,7 +701,7 @@ Inference
 
 
 
-.. image:: 248-ssd-b1-with-output_files/248-ssd-b1-with-output_41_1.png
+.. image:: ssd-b1-with-output_files/ssd-b1-with-output_41_1.png
 
 
 
@@ -674,8 +720,16 @@ Image2Image Generation with LCM Interactive Demo
     
     
     def generate_from_text(text_promt, neg_prompt, seed, num_steps):
-        result = pipe(text_promt, negative_prompt=neg_prompt, num_inference_steps=num_steps, guidance_scale=1.0, generator=torch.Generator().manual_seed(seed), height=1024, width=1024).images[0]
-        
+        result = pipe(
+            text_promt,
+            negative_prompt=neg_prompt,
+            num_inference_steps=num_steps,
+            guidance_scale=1.0,
+            generator=torch.Generator().manual_seed(seed),
+            height=1024,
+            width=1024,
+        ).images[0]
+    
         return result
     
     
@@ -688,14 +742,41 @@ Image2Image Generation with LCM Interactive Demo
                 steps_input = gr.Slider(label="Steps", value=4, minimum=2, maximum=8, step=1)
                 btn = gr.Button()
             out = gr.Image(label="Result", type="pil", width=1024)
-            btn.click(generate_from_text, [positive_input, neg_input, seed_input, steps_input], out)
-            gr.Examples([
-                [prompt, neg_prompt, 999, 4], 
-                ["underwater world coral reef, colorful jellyfish, 35mm, cinematic lighting, shallow depth of field,  ultra quality, masterpiece, realistic", neg_prompt, 89, 4],
-                ["a photo realistic happy white poodle dog ​​playing in the grass, extremely detailed, high res, 8k, masterpiece, dynamic angle", neg_prompt, 1569, 4],
-                ["Astronaut on Mars watching sunset, best quality, cinematic effects,", neg_prompt, 65245, 4],
-                ["Black and white street photography of a rainy night in New York, reflections on wet pavement", neg_prompt, 48199, 4]
-            ], [positive_input, neg_input, seed_input, steps_input])
+            btn.click(
+                generate_from_text,
+                [positive_input, neg_input, seed_input, steps_input],
+                out,
+            )
+            gr.Examples(
+                [
+                    [prompt, neg_prompt, 999, 4],
+                    [
+                        "underwater world coral reef, colorful jellyfish, 35mm, cinematic lighting, shallow depth of field,  ultra quality, masterpiece, realistic",
+                        neg_prompt,
+                        89,
+                        4,
+                    ],
+                    [
+                        "a photo realistic happy white poodle dog ​​playing in the grass, extremely detailed, high res, 8k, masterpiece, dynamic angle",
+                        neg_prompt,
+                        1569,
+                        4,
+                    ],
+                    [
+                        "Astronaut on Mars watching sunset, best quality, cinematic effects,",
+                        neg_prompt,
+                        65245,
+                        4,
+                    ],
+                    [
+                        "Black and white street photography of a rainy night in New York, reflections on wet pavement",
+                        neg_prompt,
+                        48199,
+                        4,
+                    ],
+                ],
+                [positive_input, neg_input, seed_input, steps_input],
+            )
     
     try:
         demo.queue().launch(debug=False)
diff --git a/docs/notebooks/248-ssd-b1-with-output_files/248-ssd-b1-with-output_11_1.jpg b/docs/notebooks/ssd-b1-with-output_files/ssd-b1-with-output_11_1.jpg
similarity index 100%
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diff --git a/docs/notebooks/248-ssd-b1-with-output_files/248-ssd-b1-with-output_11_1.png b/docs/notebooks/ssd-b1-with-output_files/ssd-b1-with-output_11_1.png
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diff --git a/docs/notebooks/248-ssd-b1-with-output_files/248-ssd-b1-with-output_16_3.jpg b/docs/notebooks/ssd-b1-with-output_files/ssd-b1-with-output_16_3.jpg
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diff --git a/docs/notebooks/248-ssd-b1-with-output_files/248-ssd-b1-with-output_16_3.png b/docs/notebooks/ssd-b1-with-output_files/ssd-b1-with-output_16_3.png
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diff --git a/docs/notebooks/248-ssd-b1-with-output_files/248-ssd-b1-with-output_41_1.jpg b/docs/notebooks/ssd-b1-with-output_files/ssd-b1-with-output_41_1.jpg
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diff --git a/docs/notebooks/248-ssd-b1-with-output_files/248-ssd-b1-with-output_41_1.png b/docs/notebooks/ssd-b1-with-output_files/ssd-b1-with-output_41_1.png
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diff --git a/docs/notebooks/248-ssd-b1-with-output_files/248-ssd-b1-with-output_9_1.jpg b/docs/notebooks/ssd-b1-with-output_files/ssd-b1-with-output_9_1.jpg
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rename to docs/notebooks/ssd-b1-with-output_files/ssd-b1-with-output_9_1.jpg
diff --git a/docs/notebooks/248-ssd-b1-with-output_files/248-ssd-b1-with-output_9_1.png b/docs/notebooks/ssd-b1-with-output_files/ssd-b1-with-output_9_1.png
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diff --git a/docs/notebooks/278-stable-diffusion-ip-adapter-with-output.rst b/docs/notebooks/stable-diffusion-ip-adapter-with-output.rst
similarity index 79%
rename from docs/notebooks/278-stable-diffusion-ip-adapter-with-output.rst
rename to docs/notebooks/stable-diffusion-ip-adapter-with-output.rst
index cfb914dc61b..fb02e519e31 100644
--- a/docs/notebooks/278-stable-diffusion-ip-adapter-with-output.rst
+++ b/docs/notebooks/stable-diffusion-ip-adapter-with-output.rst
@@ -52,7 +52,7 @@ Prerequisites
 
     import platform
     
-    %pip install -q torch transformers accelerate diffusers "openvino>=2023.3.0" gradio opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "torch>=2.1" transformers accelerate diffusers "openvino>=2023.3.0" "gradio>=4.19" opencv-python "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -60,6 +60,11 @@ Prerequisites
         %pip install -q "matplotlib>=3.4,<3.7"
 
 
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
+
 .. parsed-literal::
 
     DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
@@ -113,7 +118,7 @@ both text and image prompts.
 
 As discussed before, we will also use LCM LoRA for speeding generation
 process. You can find more information about LCM LoRA in this
-`notebook `__.
+`notebook `__.
 For applying LCM LoRA, we should use ``load_lora_weights`` method.
 Additionally, LCM requires using LCMScheduler for efficient generation.
 
@@ -132,9 +137,27 @@ Additionally, LCM requires using LCMScheduler for efficient generation.
     lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
     models_dir = Path("model")
     
-    load_original_pipeline = not all([(models_dir / model_name).exists() for model_name in ["text_encoder.xml", "image_encoder.xml", "unet.xml", "vae_decoder.xml", "vae_encoder.xml"]])
+    load_original_pipeline = not all(
+        [
+            (models_dir / model_name).exists()
+            for model_name in [
+                "text_encoder.xml",
+                "image_encoder.xml",
+                "unet.xml",
+                "vae_decoder.xml",
+                "vae_encoder.xml",
+            ]
+        ]
+    )
     
-    def get_pipeline_components(stable_diffusion_id, ip_adapter_id, ip_adapter_weight_name , lcm_lora_id, ip_adapter_scale=0.6):
+    
+    def get_pipeline_components(
+        stable_diffusion_id,
+        ip_adapter_id,
+        ip_adapter_weight_name,
+        lcm_lora_id,
+        ip_adapter_scale=0.6,
+    ):
         image_encoder = CLIPVisionModelWithProjection.from_pretrained("h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = AutoPipelineForText2Image.from_pretrained(stable_diffusion_id, image_encoder=image_encoder)
         pipeline.load_lora_weights(lcm_lora_id)
@@ -142,30 +165,55 @@ Additionally, LCM requires using LCMScheduler for efficient generation.
         pipeline.load_ip_adapter(ip_adapter_id, subfolder="models", weight_name=ip_adapter_weight_name)
         pipeline.set_ip_adapter_scale(0.6)
         scheduler = LCMScheduler.from_pretrained(stable_diffusion_id, subfolder="scheduler")
-        return pipeline.tokenizer, pipeline.feature_extractor, scheduler, pipeline.text_encoder, pipeline.image_encoder, pipeline.unet, pipeline.vae
+        return (
+            pipeline.tokenizer,
+            pipeline.feature_extractor,
+            scheduler,
+            pipeline.text_encoder,
+            pipeline.image_encoder,
+            pipeline.unet,
+            pipeline.vae,
+        )
+    
     
     if load_original_pipeline:
-        tokenizer, feature_extractor, scheduler, text_encoder, image_encoder, unet, vae = get_pipeline_components(stable_diffusion_id, ip_adapter_id, ip_adapter_weight_name, lcm_lora_id)
+        (
+            tokenizer,
+            feature_extractor,
+            scheduler,
+            text_encoder,
+            image_encoder,
+            unet,
+            vae,
+        ) = get_pipeline_components(stable_diffusion_id, ip_adapter_id, ip_adapter_weight_name, lcm_lora_id)
         scheduler.save_pretrained(models_dir / "scheduler")
     else:
-        tokenizer, feature_extractor, scheduler, text_encoder, image_encoder, unet, vae = None, None, None, None, None, None, None
+        tokenizer, feature_extractor, scheduler, text_encoder, image_encoder, unet, vae = (
+            None,
+            None,
+            None,
+            None,
+            None,
+            None,
+            None,
+        )
 
 
 .. parsed-literal::
 
-    2024-03-13 00:17:00.749663: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:17:00.783556: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 00:59:39.892365: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 00:59:39.926975: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 00:17:01.376270: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 00:59:40.524026: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
 
 
@@ -175,12 +223,6 @@ Additionally, LCM requires using LCMScheduler for efficient generation.
     Loading pipeline components...:   0%|          | 0/7 [00:00= 64:
 
 
@@ -381,23 +427,25 @@ image in pipeline, we can discuss it in inference examples.
     VAE_ENCODER_PATH = models_dir / "vae_encoder.xml"
     
     if not VAE_DECODER_PATH.exists():
+    
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-        
+    
             def forward(self, latents):
                 return self.vae.decode(latents)
     
         vae_decoder = VAEDecoderWrapper(vae)
         with torch.no_grad():
-            ov_model = ov.convert_model(vae_decoder, example_input=torch.ones([1,4,64,64]))
+            ov_model = ov.convert_model(vae_decoder, example_input=torch.ones([1, 4, 64, 64]))
         ov.save_model(ov_model, VAE_DECODER_PATH)
         del ov_model
         cleanup_torchscript_cache()
         del vae_decoder
     
     if not VAE_ENCODER_PATH.exists():
+    
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -405,6 +453,7 @@ image in pipeline, we can discuss it in inference examples.
     
             def forward(self, image):
                 return self.vae.encode(x=image)["latent_dist"].sample()
+    
         vae_encoder = VAEEncoderWrapper(vae)
         vae_encoder.eval()
         image = torch.zeros((1, 3, 512, 512))
@@ -420,16 +469,16 @@ image in pipeline, we can discuss it in inference examples.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
-    	%2494 : Float(1, 4, 64, 64, strides=[16384, 4096, 64, 1], requires_grad=0, device=cpu) = aten::randn(%2488, %2489, %2490, %2491, %2492, %2493) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/torch_utils.py:80:0
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
+    	%2494 : Float(1, 4, 64, 64, strides=[16384, 4096, 64, 1], requires_grad=0, device=cpu) = aten::randn(%2488, %2489, %2490, %2491, %2492, %2493) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/torch_utils.py:80:0
     This may cause errors in trace checking. To disable trace checking, pass check_trace=False to torch.jit.trace()
       _check_trace(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
     Tensor-likes are not close!
     
-    Mismatched elements: 10262 / 16384 (62.6%)
-    Greatest absolute difference: 0.001510024070739746 at index (0, 1, 63, 63) (up to 1e-05 allowed)
-    Greatest relative difference: 0.002872670623917016 at index (0, 3, 63, 59) (up to 1e-05 allowed)
+    Mismatched elements: 10269 / 16384 (62.7%)
+    Greatest absolute difference: 0.0017402172088623047 at index (0, 1, 63, 63) (up to 1e-05 allowed)
+    Greatest relative difference: 0.0036845125929832485 at index (0, 3, 63, 59) (up to 1e-05 allowed)
       _check_trace(
 
 
@@ -457,7 +506,13 @@ hidden states.
     
     if not TEXT_ENCODER_PATH.exists():
         with torch.no_grad():
-            ov_model = ov.convert_model(text_encoder, example_input=torch.ones([1,77], dtype=torch.long), input=[(1,77),])
+            ov_model = ov.convert_model(
+                text_encoder,
+                example_input=torch.ones([1, 77], dtype=torch.long),
+                input=[
+                    (1, 77),
+                ],
+            )
         ov.save_model(ov_model, TEXT_ENCODER_PATH)
         del ov_model
         cleanup_torchscript_cache()
@@ -469,11 +524,11 @@ hidden states.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input_shape[-1] > 1 or self.sliding_window is not None:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if past_key_values_length > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:289: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:289: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
 
 
@@ -516,15 +571,17 @@ encoder (VAE).
     
     from transformers import CLIPTokenizer, CLIPImageProcessor
     from diffusers import DiffusionPipeline
-    from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
+    from diffusers.pipelines.stable_diffusion.pipeline_output import (
+        StableDiffusionPipelineOutput,
+    )
     from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
     
     
-    def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
+    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
-        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio 
+        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-        
+    
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -545,7 +602,7 @@ encoder (VAE).
     ):
         """A helper function to create random tensors on the desired `device` with the desired `dtype`. When
         passing a list of generators, you can seed each batch size individually.
-        
+    
         """
         batch_size = shape[0]
         rand_device = torch.device("cpu")
@@ -556,23 +613,21 @@ encoder (VAE).
     
         if isinstance(generator, list):
             shape = (1,) + shape[1:]
-            latents = [
-                torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
-                for i in range(batch_size)
-            ]
+            latents = [torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) for i in range(batch_size)]
             latents = torch.cat(latents, dim=0)
         else:
             latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype)
     
         return latents
     
+    
     def preprocess(image: PIL.Image.Image, height, width):
         """
         Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-        
+    
         Parameters:
           image (PIL.Image.Image): input image
         Returns:
@@ -580,10 +635,8 @@ encoder (VAE).
            meta (Dict): dictionary with preprocessing metadata info
         """
         src_width, src_height = image.size
-        dst_width, dst_height = scale_fit_to_window(
-            height, width, src_width, src_height)
-        image = np.array(image.resize((dst_width, dst_height),
-                         resample=PIL.Image.Resampling.LANCZOS))[None, :]
+        dst_width, dst_height = scale_fit_to_window(height, width, src_width, src_height)
+        image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
         pad_width = width - dst_width
         pad_height = height - dst_height
         pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
@@ -593,6 +646,7 @@ encoder (VAE).
         image = image.transpose(0, 3, 1, 2)
         return image, {"padding": pad, "src_width": src_width, "src_height": src_height}
     
+    
     class OVStableDiffusionPipeline(DiffusionPipeline):
         def __init__(
             self,
@@ -603,7 +657,7 @@ encoder (VAE).
             scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
             image_encoder: ov.Model,
             feature_extractor: CLIPImageProcessor,
-            vae_encoder: ov.Model
+            vae_encoder: ov.Model,
         ):
             """
             Pipeline for text-to-image generation using Stable Diffusion and IP-Adapter with OpenVINO
@@ -621,7 +675,7 @@ encoder (VAE).
                     A scheduler to be used in combination with unet to denoise the encoded image latents
                 image_encoder (ov.Model):
                     IP-Adapter image encoder for embedding input image as input prompt for generation
-                feature_extractor : 
+                feature_extractor :
             """
             super().__init__()
             self.scheduler = scheduler
@@ -650,7 +704,7 @@ encoder (VAE).
             width: Optional[int] = None,
             generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
             latents: Optional[torch.FloatTensor] = None,
-            strength: float = 1.,
+            strength: float = 1.0,
             **kwargs,
         ):
             """
@@ -676,7 +730,7 @@ encoder (VAE).
                 output_type (`str`, *optional*, defaults to "pil"):
                     The output format of the generate image. Choose between
                     [PIL](https://pillow.readthedocs.io/en/stable/): PIL.Image.Image or np.array.
-                height (int, *optional*, 512): 
+                height (int, *optional*, 512):
                     Generated image height
                 width (int, *optional*, 512):
                     Generated image width
@@ -688,18 +742,22 @@ encoder (VAE).
                     generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                     tensor is generated by sampling using the supplied random `generator`.
             Returns:
-                Dictionary with keys: 
+                Dictionary with keys:
                     sample - the last generated image PIL.Image.Image or np.arrayhttps://huggingface.co/latent-consistency/lcm-lora-sdv1-5
                     iterations - *optional* (if gif=True) images for all diffusion steps, List of PIL.Image.Image or np.array.
             """
             do_classifier_free_guidance = guidance_scale > 1.0
             # get prompt text embeddings
-            text_embeddings = self._encode_prompt(prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt)
+            text_embeddings = self._encode_prompt(
+                prompt,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                negative_prompt=negative_prompt,
+            )
             # get ip-adapter image embeddings
             image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image)
             if do_classifier_free_guidance:
                 image_embeds = np.concatenate([negative_image_embeds, image_embeds])
-            
+    
             # set timesteps
             accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
             extra_set_kwargs = {}
@@ -711,7 +769,16 @@ encoder (VAE).
             latent_timestep = timesteps[:1]
     
             # get the initial random noise unless the user supplied it
-            latents, meta = self.prepare_latents(1, 4, height or self.height, width or self.width, generator=generator, latents=latents, image=image, latent_timestep=latent_timestep)
+            latents, meta = self.prepare_latents(
+                1,
+                4,
+                height or self.height,
+                width or self.width,
+                generator=generator,
+                latents=latents,
+                image=image,
+                latent_timestep=latent_timestep,
+            )
     
             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
@@ -735,15 +802,26 @@ encoder (VAE).
                     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     
                 # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs)["prev_sample"].numpy()
+                latents = self.scheduler.step(
+                    torch.from_numpy(noise_pred),
+                    t,
+                    torch.from_numpy(latents),
+                    **extra_step_kwargs,
+                )["prev_sample"].numpy()
     
             # scale and decode the image latents with vae
             image = self.vae_decoder(latents * (1 / 0.18215))[0]
     
             image = self.postprocess_image(image, meta, output_type)
             return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=False)
-        
-        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True, negative_prompt:Union[str, List[str]] = None):
+    
+        def _encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+            negative_prompt: Union[str, List[str]] = None,
+        ):
             """
             Encodes the prompt into text encoder hidden states.
     
@@ -767,16 +845,13 @@ encoder (VAE).
             )
             text_input_ids = text_inputs.input_ids
     
-            text_embeddings = self.text_encoder(
-                text_input_ids)[0]
+            text_embeddings = self.text_encoder(text_input_ids)[0]
     
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(
-                    text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(
-                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
+                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
+                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
     
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
@@ -810,9 +885,24 @@ encoder (VAE).
     
             return text_embeddings
     
-    
-        def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype=torch.float32, generator=None, latents=None, image=None, latent_timestep=None):
-            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        def prepare_latents(
+            self,
+            batch_size,
+            num_channels_latents,
+            height,
+            width,
+            dtype=torch.float32,
+            generator=None,
+            latents=None,
+            image=None,
+            latent_timestep=None,
+        ):
+            shape = (
+                batch_size,
+                num_channels_latents,
+                height // self.vae_scale_factor,
+                width // self.vae_scale_factor,
+            )
             if isinstance(generator, list) and len(generator) != batch_size:
                 raise ValueError(
                     f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
@@ -831,13 +921,12 @@ encoder (VAE).
             image_latents = image_latents * 0.18215
             latents = self.scheduler.add_noise(torch.from_numpy(image_latents), latents, latent_timestep).numpy()
             return latents, meta
-            
     
-        def postprocess_image(self, image:np.ndarray, meta:Dict, output_type:str = "pil"):
+        def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
             """
-            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initial image size (if required), 
+            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initial image size (if required),
             normalize and convert to [0, 255] pixels range. Optionally, converts it from np.ndarray to PIL.Image format
-            
+    
             Parameters:
                 image (np.ndarray):
                     Generated image
@@ -863,13 +952,11 @@ encoder (VAE).
                 image = self.numpy_to_pil(image)
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [img.resize((orig_width, orig_height),
-                                        PIL.Image.Resampling.LANCZOS) for img in image]
+                    image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
             else:
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [cv2.resize(img, (orig_width, orig_width))
-                             for img in image]
+                    image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
             return image
     
         def encode_image(self, image, num_images_per_prompt=1):
@@ -883,16 +970,16 @@ encoder (VAE).
             uncond_image_embeds = np.zeros(image_embeds.shape)
             return image_embeds, uncond_image_embeds
     
-        def get_timesteps(self, num_inference_steps:int, strength:float):
+        def get_timesteps(self, num_inference_steps: int, strength: float):
             """
             Helper function for getting scheduler timesteps for generation
             In case of image-to-image generation, it updates number of steps according to strength
-            
+    
             Parameters:
                num_inference_steps (int):
                   number of inference steps for generation
                strength (float):
-                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. 
+                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
                    Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.
             """
             # get the original timestep using init_timestep
@@ -956,7 +1043,16 @@ Select inference device from dropdown list.
     tokenizer = AutoTokenizer.from_pretrained(models_dir / "tokenizer")
     feature_extractor = CLIPImageProcessor.from_pretrained(models_dir / "feature_extractor")
     
-    ov_pipe = OVStableDiffusionPipeline(vae_decoder, text_encoder, tokenizer, unet, scheduler, image_encoder, feature_extractor, vae_encoder)
+    ov_pipe = OVStableDiffusionPipeline(
+        vae_decoder,
+        text_encoder,
+        tokenizer,
+        unet,
+        scheduler,
+        image_encoder,
+        feature_extractor,
+        vae_encoder,
+    )
 
 
 .. parsed-literal::
@@ -980,7 +1076,7 @@ can get variation of the same image.
     def visualize_results(images, titles):
         """
         Helper function for results visualization
-        
+    
         Parameters:
            orig_img (PIL.Image.Image): original image
            processed_img (PIL.Image.Image): processed image after editing
@@ -992,8 +1088,14 @@ can get variation of the same image.
         im_w, im_h = images[0].size
         is_horizontal = im_h <= im_w
         figsize = (10, 15 * len(images)) if is_horizontal else (15 * len(images), 10)
-        fig, axs = plt.subplots(1 if is_horizontal else len(images), len(images) if is_horizontal else 1, figsize=figsize, sharex='all', sharey='all')
-        fig.patch.set_facecolor('white')
+        fig, axs = plt.subplots(
+            1 if is_horizontal else len(images),
+            len(images) if is_horizontal else 1,
+            figsize=figsize,
+            sharex="all",
+            sharey="all",
+        )
+        fig.patch.set_facecolor("white")
         list_axes = list(axs.flat)
         for a in list_axes:
             a.set_xticklabels([])
@@ -1003,8 +1105,8 @@ can get variation of the same image.
             a.grid(False)
         for image, title, ax in zip(images, titles, list_axes):
             ax.imshow(np.array(image))
-            ax.set_title(title, fontsize=20) 
-        fig.subplots_adjust(wspace=0.0 if is_horizontal else 0.01 , hspace=0.01 if is_horizontal else 0.0)
+            ax.set_title(title, fontsize=20)
+        fig.subplots_adjust(wspace=0.0 if is_horizontal else 0.01, hspace=0.01 if is_horizontal else 0.0)
         fig.tight_layout()
         return fig
 
@@ -1014,7 +1116,14 @@ can get variation of the same image.
     
     image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png")
     
-    result = ov_pipe(prompt='', ip_adapter_image=image, gaidance_scale=1, negative_prompt="", num_inference_steps=4, generator=generator)
+    result = ov_pipe(
+        prompt="",
+        ip_adapter_image=image,
+        gaidance_scale=1,
+        negative_prompt="",
+        num_inference_steps=4,
+        generator=generator,
+    )
     
     fig = visualize_results([image, result.images[0]], ["input image", "result"])
 
@@ -1026,7 +1135,7 @@ can get variation of the same image.
 
 
 
-.. image:: 278-stable-diffusion-ip-adapter-with-output_files/278-stable-diffusion-ip-adapter-with-output_22_1.png
+.. image:: stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_22_1.png
 
 
 Generation conditioned by image and text
@@ -1043,7 +1152,14 @@ extension for each other, for example we can use a text prompt to add
 
     generator = torch.Generator(device="cpu").manual_seed(576)
     
-    result = ov_pipe(prompt='best quality, high quality, wearing sunglasses', ip_adapter_image=image, gaidance_scale=1, negative_prompt="monochrome, low-res, bad anatomy, worst quality, low quality", num_inference_steps=4, generator=generator)
+    result = ov_pipe(
+        prompt="best quality, high quality, wearing sunglasses",
+        ip_adapter_image=image,
+        gaidance_scale=1,
+        negative_prompt="monochrome, low-res, bad anatomy, worst quality, low quality",
+        num_inference_steps=4,
+        generator=generator,
+    )
 
 
 
@@ -1058,7 +1174,7 @@ extension for each other, for example we can use a text prompt to add
 
 
 
-.. image:: 278-stable-diffusion-ip-adapter-with-output_files/278-stable-diffusion-ip-adapter-with-output_25_0.png
+.. image:: stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_25_0.png
 
 
 Generation image blending
@@ -1074,7 +1190,15 @@ achieve image blending effect.
     image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg")
     ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/river.png")
     
-    result = ov_pipe(prompt='best quality, high quality', image=image, ip_adapter_image=ip_image, gaidance_scale=1, generator=generator, strength=0.7, num_inference_steps=8)
+    result = ov_pipe(
+        prompt="best quality, high quality",
+        image=image,
+        ip_adapter_image=ip_image,
+        gaidance_scale=1,
+        generator=generator,
+        strength=0.7,
+        num_inference_steps=8,
+    )
 
 
 
@@ -1089,7 +1213,7 @@ achieve image blending effect.
 
 
 
-.. image:: 278-stable-diffusion-ip-adapter-with-output_files/278-stable-diffusion-ip-adapter-with-output_28_0.png
+.. image:: stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_28_0.png
 
 
 Interactive demo
@@ -1103,15 +1227,50 @@ Now, you can try model using own images and text prompts.
 
     import gradio as gr
     
-    def generate_from_text(positive_prompt, negative_prompt, ip_adapter_image, seed, num_steps, guidance_scale, _=gr.Progress(track_tqdm=True)):
+    
+    def generate_from_text(
+        positive_prompt,
+        negative_prompt,
+        ip_adapter_image,
+        seed,
+        num_steps,
+        guidance_scale,
+        _=gr.Progress(track_tqdm=True),
+    ):
         generator = torch.Generator(device="cpu").manual_seed(seed)
-        result = ov_pipe(positive_prompt, ip_adapter_image=ip_adapter_image, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_steps, generator=generator)
+        result = ov_pipe(
+            positive_prompt,
+            ip_adapter_image=ip_adapter_image,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_steps,
+            generator=generator,
+        )
         return result.images[0]
     
     
-    def generate_from_image(img, ip_adapter_image, positive_prompt, negative_prompt, seed, num_steps, guidance_scale, strength, _=gr.Progress(track_tqdm=True)):
+    def generate_from_image(
+        img,
+        ip_adapter_image,
+        positive_prompt,
+        negative_prompt,
+        seed,
+        num_steps,
+        guidance_scale,
+        strength,
+        _=gr.Progress(track_tqdm=True),
+    ):
         generator = torch.Generator(device="cpu").manual_seed(seed)
-        result = ov_pipe(positive_prompt, image=img, ip_adapter_image=ip_adapter_image, negative_prompt=negative_prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale, strength=strength, generator=generator)
+        result = ov_pipe(
+            positive_prompt,
+            image=img,
+            ip_adapter_image=ip_adapter_image,
+            negative_prompt=negative_prompt,
+            num_inference_steps=num_steps,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            generator=generator,
+        )
         return result.images[0]
     
     
@@ -1134,11 +1293,33 @@ Now, you can try model using own images and text prompts.
                         )
                 out = gr.Image(label="Result", type="pil")
             btn = gr.Button()
-            btn.click(generate_from_text, [text_input, neg_text_input, ip_adapter_input, seed_input, steps_input, guidance_scale_input], out)
-            gr.Examples([
-                ["https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/woman.png", 'best quality, high quality', "low resolution"],
-                ["https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/statue.png", "wearing a hat", ""],
-            ], [ip_adapter_input, text_input, neg_text_input])
+            btn.click(
+                generate_from_text,
+                [
+                    text_input,
+                    neg_text_input,
+                    ip_adapter_input,
+                    seed_input,
+                    steps_input,
+                    guidance_scale_input,
+                ],
+                out,
+            )
+            gr.Examples(
+                [
+                    [
+                        "https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/woman.png",
+                        "best quality, high quality",
+                        "low resolution",
+                    ],
+                    [
+                        "https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/statue.png",
+                        "wearing a hat",
+                        "",
+                    ],
+                ],
+                [ip_adapter_input, text_input, neg_text_input],
+            )
         with gr.Tab("Image-to-Image generation"):
             with gr.Row():
                 with gr.Column():
@@ -1161,12 +1342,27 @@ Now, you can try model using own images and text prompts.
             i2i_btn = gr.Button()
             i2i_btn.click(
                 generate_from_image,
-                [i2i_input, i2i_ip_adapter_input, i2i_text_input, i2i_neg_text_input, i2i_seed_input, i2i_steps_input, i2i_guidance_scale, strength_input],
+                [
+                    i2i_input,
+                    i2i_ip_adapter_input,
+                    i2i_text_input,
+                    i2i_neg_text_input,
+                    i2i_seed_input,
+                    i2i_steps_input,
+                    i2i_guidance_scale,
+                    strength_input,
+                ],
                 i2i_out,
             )
-            gr.Examples([
-                ["https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/river.png", "https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/statue.png"],
-            ], [i2i_ip_adapter_input, i2i_input])
+            gr.Examples(
+                [
+                    [
+                        "https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/river.png",
+                        "https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/statue.png",
+                    ],
+                ],
+                [i2i_ip_adapter_input, i2i_input],
+            )
     try:
         demo.queue().launch(debug=False)
     except Exception:
diff --git a/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_22_1.png b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_22_1.png
new file mode 100644
index 00000000000..7ee48445585
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_22_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84f09a472548bd938c27d3ba4e3c03b66ed0a64fce319b70937d1d5b61b9de1c
+size 956094
diff --git a/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_25_0.png b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_25_0.png
new file mode 100644
index 00000000000..ca0c8f3e208
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_25_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3eec685c3b7629968c231833d760b6580f3ee73ac11e2d47b460e8517b58b7dd
+size 938259
diff --git a/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_28_0.png b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_28_0.png
new file mode 100644
index 00000000000..246ab19ff17
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_28_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5884d4db0559aa8c6afa898a628e23de55876013ca9c9f485f57479bdaca8aff
+size 600723
diff --git a/docs/notebooks/225-stable-diffusion-text-to-image-with-output.rst b/docs/notebooks/stable-diffusion-text-to-image-with-output.rst
similarity index 86%
rename from docs/notebooks/225-stable-diffusion-text-to-image-with-output.rst
rename to docs/notebooks/stable-diffusion-text-to-image-with-output.rst
index 3764da62687..56372e5967d 100644
--- a/docs/notebooks/225-stable-diffusion-text-to-image-with-output.rst
+++ b/docs/notebooks/stable-diffusion-text-to-image-with-output.rst
@@ -84,7 +84,7 @@ not, you do not have to do anything. Just run the notebook.**
 
 
    ## login to huggingfacehub to get access to pretrained model
-   [back to top ⬆️](#Table-of-contents:)
+
    from huggingface_hub import notebook_login, whoami
 
    try:
@@ -103,10 +103,10 @@ solutions based on Stable Diffusion.
 .. code:: ipython3
 
     %pip install -q "openvino>=2023.1.0"
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "diffusers[torch]>=0.9.0"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "diffusers>=0.9.0" "torch>=2.1"
     %pip install -q "huggingface-hub>=0.9.1"
-    %pip install -q gradio
-    %pip install -q transformers
+    %pip install -q "gradio>=4.19"
+    %pip install -q transformers Pillow opencv-python tqdm
 
 Create PyTorch Models pipeline
 ------------------------------
@@ -122,7 +122,7 @@ First, load the pre-trained weights of all components of the model.
 
     from diffusers import StableDiffusionPipeline
     import gc
-    
+
     pipe = StableDiffusionPipeline.from_pretrained("prompthero/openjourney").to("cpu")
     text_encoder = pipe.text_encoder
     text_encoder.eval()
@@ -130,7 +130,7 @@ First, load the pre-trained weights of all components of the model.
     unet.eval()
     vae = pipe.vae
     vae.eval()
-    
+
     del pipe
     gc.collect()
 
@@ -313,9 +313,10 @@ hidden states.
     from pathlib import Path
     import torch
     import openvino as ov
-    
+
     TEXT_ENCODER_OV_PATH = Path("text_encoder.xml")
-    
+
+
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -323,12 +324,13 @@ hidden states.
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
-    
-    def convert_encoder(text_encoder: torch.nn.Module, ir_path:Path):
+
+
+    def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
         """
-        Convert Text Encoder mode. 
-        Function accepts text encoder model, and prepares example inputs for conversion, 
-        Parameters: 
+        Convert Text Encoder mode.
+        Function accepts text encoder model, and prepares example inputs for conversion,
+        Parameters:
             text_encoder (torch.nn.Module): text_encoder model from Stable Diffusion pipeline
             ir_path (Path): File for storing model
         Returns:
@@ -337,22 +339,28 @@ hidden states.
         input_ids = torch.ones((1, 77), dtype=torch.long)
         # switch model to inference mode
         text_encoder.eval()
-    
+
         # disable gradients calculation for reducing memory consumption
         with torch.no_grad():
             # Export model to IR format
-            ov_model = ov.convert_model(text_encoder, example_input=input_ids, input=[(1,77),])
+            ov_model = ov.convert_model(
+                text_encoder,
+                example_input=input_ids,
+                input=[
+                    (1, 77),
+                ],
+            )
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        print(f'Text Encoder successfully converted to IR and saved to {ir_path}')
-        
-    
+        print(f"Text Encoder successfully converted to IR and saved to {ir_path}")
+
+
     if not TEXT_ENCODER_OV_PATH.exists():
         convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
     else:
         print(f"Text encoder will be loaded from {TEXT_ENCODER_OV_PATH}")
-    
+
     del text_encoder
     gc.collect()
 
@@ -405,20 +413,17 @@ Model predicts the ``sample`` state for the next step.
 .. code:: ipython3
 
     import numpy as np
-    
-    UNET_OV_PATH = Path('unet.xml')
-    
-    dtype_mapping = {
-        torch.float32: ov.Type.f32,
-        torch.float64: ov.Type.f64
-    }
-    
-    
-    def convert_unet(unet:torch.nn.Module, ir_path:Path):
+
+    UNET_OV_PATH = Path("unet.xml")
+
+    dtype_mapping = {torch.float32: ov.Type.f32, torch.float64: ov.Type.f64}
+
+
+    def convert_unet(unet: torch.nn.Module, ir_path: Path):
         """
-        Convert U-net model to IR format. 
-        Function accepts unet model, prepares example inputs for conversion, 
-        Parameters: 
+        Convert U-net model to IR format.
+        Function accepts unet model, prepares example inputs for conversion,
+        Parameters:
             unet (StableDiffusionPipeline): unet from Stable Diffusion pipeline
             ir_path (Path): File for storing model
         Returns:
@@ -435,16 +440,16 @@ Model predicts the ``sample`` state for the next step.
             shape = ov.PartialShape(tuple(input_tensor.shape))
             element_type = dtype_mapping[input_tensor.dtype]
             input_info.append((shape, element_type))
-    
+
         unet.eval()
         with torch.no_grad():
             ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        print(f'Unet successfully converted to IR and saved to {ir_path}')
-    
-    
+        print(f"Unet successfully converted to IR and saved to {ir_path}")
+
+
     if not UNET_OV_PATH.exists():
         convert_unet(unet, UNET_OV_PATH)
         gc.collect()
@@ -506,79 +511,84 @@ of the pipeline, it will be better to convert them to separate models.
 .. code:: ipython3
 
     VAE_ENCODER_OV_PATH = Path("vae_encoder.xml")
-    
+
+
     def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path):
         """
-        Convert VAE model for encoding to IR format. 
-        Function accepts vae model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion, 
-        Parameters: 
-            vae (torch.nn.Module): VAE model from StableDiffusio pipeline 
+        Convert VAE model for encoding to IR format.
+        Function accepts vae model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion,
+        Parameters:
+            vae (torch.nn.Module): VAE model from StableDiffusio pipeline
             ir_path (Path): File for storing model
         Returns:
             None
         """
+
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-    
+
             def forward(self, image):
                 return self.vae.encode(x=image)["latent_dist"].sample()
+
         vae_encoder = VAEEncoderWrapper(vae)
         vae_encoder.eval()
         image = torch.zeros((1, 3, 512, 512))
         with torch.no_grad():
-            ov_model = ov.convert_model(vae_encoder, example_input=image, input=[((1,3,512,512),)])
+            ov_model = ov.convert_model(vae_encoder, example_input=image, input=[((1, 3, 512, 512),)])
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        print(f'VAE encoder successfully converted to IR and saved to {ir_path}')
-    
-    
+        print(f"VAE encoder successfully converted to IR and saved to {ir_path}")
+
+
     if not VAE_ENCODER_OV_PATH.exists():
         convert_vae_encoder(vae, VAE_ENCODER_OV_PATH)
     else:
         print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH}")
-    
-    VAE_DECODER_OV_PATH = Path('vae_decoder.xml')
-    
+
+    VAE_DECODER_OV_PATH = Path("vae_decoder.xml")
+
+
     def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path):
         """
-        Convert VAE model for decoding to IR format. 
-        Function accepts vae model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion, 
-        Parameters: 
+        Convert VAE model for decoding to IR format.
+        Function accepts vae model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion,
+        Parameters:
             vae (torch.nn.Module): VAE model frm StableDiffusion pipeline
             ir_path (Path): File for storing model
         Returns:
             None
         """
+
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-    
+
             def forward(self, latents):
                 return self.vae.decode(latents)
-        
+
         vae_decoder = VAEDecoderWrapper(vae)
         latents = torch.zeros((1, 4, 64, 64))
-    
+
         vae_decoder.eval()
         with torch.no_grad():
-            ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1,4,64,64),)])
+            ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1, 4, 64, 64),)])
         ov.save_model(ov_model, ir_path)
         del ov_model
         cleanup_torchscript_cache()
-        print(f'VAE decoder successfully converted to IR and saved to {ir_path}')
-    
-    
+        print(f"VAE decoder successfully converted to IR and saved to {ir_path}")
+
+
     if not VAE_DECODER_OV_PATH.exists():
         convert_vae_decoder(vae, VAE_DECODER_OV_PATH)
     else:
         print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH}")
-    
+
     del vae
     gc.collect()
 
@@ -591,7 +601,7 @@ of the pipeline, it will be better to convert them to separate models.
       _check_trace(
     /home/ea/work/ov_venv/lib/python3.8/site-packages/torch/jit/_trace.py:1084: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
     Tensor-likes are not close!
-    
+
     Mismatched elements: 10371 / 16384 (63.3%)
     Greatest absolute difference: 0.0014181137084960938 at index (0, 2, 63, 63) (up to 1e-05 allowed)
     Greatest relative difference: 0.006298586412390911 at index (0, 3, 63, 59) (up to 1e-05 allowed)
@@ -667,21 +677,21 @@ of the variational auto encoder.
 
     import inspect
     from typing import List, Optional, Union, Dict
-    
+
     import PIL
     import cv2
-    
+
     from transformers import CLIPTokenizer
     from diffusers.pipelines.pipeline_utils import DiffusionPipeline
     from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
     from openvino.runtime import Model
-    
-    
-    def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
+
+
+    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
-        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio 
+        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-        
+
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -693,15 +703,15 @@ of the variational auto encoder.
         """
         im_scale = min(dst_height / image_height, dst_width / image_width)
         return int(im_scale * image_width), int(im_scale * image_height)
-    
-    
+
+
     def preprocess(image: PIL.Image.Image):
         """
         Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-        
+
         Parameters:
           image (PIL.Image.Image): input image
         Returns:
@@ -709,10 +719,8 @@ of the variational auto encoder.
            meta (Dict): dictionary with preprocessing metadata info
         """
         src_width, src_height = image.size
-        dst_width, dst_height = scale_fit_to_window(
-            512, 512, src_width, src_height)
-        image = np.array(image.resize((dst_width, dst_height),
-                         resample=PIL.Image.Resampling.LANCZOS))[None, :]
+        dst_width, dst_height = scale_fit_to_window(512, 512, src_width, src_height)
+        image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
         pad_width = 512 - dst_width
         pad_height = 512 - dst_height
         pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
@@ -721,8 +729,8 @@ of the variational auto encoder.
         image = 2.0 * image - 1.0
         image = image.transpose(0, 3, 1, 2)
         return image, {"padding": pad, "src_width": src_width, "src_height": src_height}
-    
-    
+
+
     class OVStableDiffusionPipeline(DiffusionPipeline):
         def __init__(
             self,
@@ -762,7 +770,7 @@ of the variational auto encoder.
             self.height = 512
             self.width = 512
             self.tokenizer = tokenizer
-    
+
         def __call__(
             self,
             prompt: Union[str, List[str]],
@@ -805,31 +813,35 @@ of the variational auto encoder.
                 gif (bool, *optional*, False):
                     Flag for storing all steps results or not.
             Returns:
-                Dictionary with keys: 
+                Dictionary with keys:
                     sample - the last generated image PIL.Image.Image or np.array
                     iterations - *optional* (if gif=True) images for all diffusion steps, List of PIL.Image.Image or np.array.
             """
             if seed is not None:
                 np.random.seed(seed)
-    
+
             img_buffer = []
             do_classifier_free_guidance = guidance_scale > 1.0
             # get prompt text embeddings
-            text_embeddings = self._encode_prompt(prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt)
-            
+            text_embeddings = self._encode_prompt(
+                prompt,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                negative_prompt=negative_prompt,
+            )
+
             # set timesteps
             accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
             extra_set_kwargs = {}
             if accepts_offset:
                 extra_set_kwargs["offset"] = 1
-    
+
             self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
             timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
             latent_timestep = timesteps[:1]
-    
+
             # get the initial random noise unless the user supplied it
             latents, meta = self.prepare_latents(image, latent_timestep)
-    
+
             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
             # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
@@ -838,36 +850,47 @@ of the variational auto encoder.
             extra_step_kwargs = {}
             if accepts_eta:
                 extra_step_kwargs["eta"] = eta
-    
+
             for i, t in enumerate(self.progress_bar(timesteps)):
                 # expand the latents if you are doing classifier free guidance
                 latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-    
+
                 # predict the noise residual
                 noise_pred = self.unet([latent_model_input, t, text_embeddings])[self._unet_output]
                 # perform guidance
                 if do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
                     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-    
+
                 # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs)["prev_sample"].numpy()
+                latents = self.scheduler.step(
+                    torch.from_numpy(noise_pred),
+                    t,
+                    torch.from_numpy(latents),
+                    **extra_step_kwargs,
+                )["prev_sample"].numpy()
                 if gif:
                     image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
                     image = self.postprocess_image(image, meta, output_type)
                     img_buffer.extend(image)
-    
+
             # scale and decode the image latents with vae
             image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
-    
+
             image = self.postprocess_image(image, meta, output_type)
-            return {"sample": image, 'iterations': img_buffer}
-        
-        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True, negative_prompt:Union[str, List[str]] = None):
+            return {"sample": image, "iterations": img_buffer}
+
+        def _encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+            negative_prompt: Union[str, List[str]] = None,
+        ):
             """
             Encodes the prompt into text encoder hidden states.
-    
+
             Parameters:
                 prompt (str or list(str)): prompt to be encoded
                 num_images_per_prompt (int): number of images that should be generated per prompt
@@ -877,7 +900,7 @@ of the variational auto encoder.
                 text_embeddings (np.ndarray): text encoder hidden states
             """
             batch_size = len(prompt) if isinstance(prompt, list) else 1
-    
+
             # tokenize input prompts
             text_inputs = self.tokenizer(
                 prompt,
@@ -887,18 +910,15 @@ of the variational auto encoder.
                 return_tensors="np",
             )
             text_input_ids = text_inputs.input_ids
-    
-            text_embeddings = self.text_encoder(
-                text_input_ids)[self._text_encoder_output]
-    
+
+            text_embeddings = self.text_encoder(text_input_ids)[self._text_encoder_output]
+
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(
-                    text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(
-                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
-    
+                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
+                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
+
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
                 uncond_tokens: List[str]
@@ -916,26 +936,25 @@ of the variational auto encoder.
                     truncation=True,
                     return_tensors="np",
                 )
-    
+
                 uncond_embeddings = self.text_encoder(uncond_input.input_ids)[self._text_encoder_output]
-    
+
                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 seq_len = uncond_embeddings.shape[1]
                 uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
                 uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
-    
+
                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
-    
+
             return text_embeddings
-    
-    
-        def prepare_latents(self, image:PIL.Image.Image = None, latent_timestep:torch.Tensor = None):
+
+        def prepare_latents(self, image: PIL.Image.Image = None, latent_timestep: torch.Tensor = None):
             """
             Function for getting initial latents for starting generation
-            
+
             Parameters:
                 image (PIL.Image.Image, *optional*, None):
                     Input image for generation, if not provided randon noise will be used as starting point
@@ -956,12 +975,12 @@ of the variational auto encoder.
             latents = self.vae_encoder(input_image)[self._vae_e_output] * 0.18215
             latents = self.scheduler.add_noise(torch.from_numpy(latents), torch.from_numpy(noise), latent_timestep).numpy()
             return latents, meta
-    
-        def postprocess_image(self, image:np.ndarray, meta:Dict, output_type:str = "pil"):
+
+        def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
             """
-            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required), 
+            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
             normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-            
+
             Parameters:
                 image (np.ndarray):
                     Generated image
@@ -987,34 +1006,32 @@ of the variational auto encoder.
                 image = self.numpy_to_pil(image)
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [img.resize((orig_width, orig_height),
-                                        PIL.Image.Resampling.LANCZOS) for img in image]
+                    image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
             else:
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [cv2.resize(img, (orig_width, orig_width))
-                             for img in image]
+                    image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
             return image
-    
-        def get_timesteps(self, num_inference_steps:int, strength:float):
+
+        def get_timesteps(self, num_inference_steps: int, strength: float):
             """
             Helper function for getting scheduler timesteps for generation
             In case of image-to-image generation, it updates number of steps according to strength
-            
+
             Parameters:
                num_inference_steps (int):
                   number of inference steps for generation
                strength (float):
-                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. 
+                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
                    Values that approach 1.0 enable lots of variations but will also produce images that are not semantically consistent with the input.
             """
             # get the original timestep using init_timestep
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-    
+
             t_start = max(num_inference_steps - init_timestep, 0)
             timesteps = self.scheduler.timesteps[t_start:]
-    
-            return timesteps, num_inference_steps - t_start 
+
+            return timesteps, num_inference_steps - t_start
 
 Configure Inference Pipeline
 ----------------------------
@@ -1032,14 +1049,14 @@ Select device from dropdown list for running inference using OpenVINO.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -1053,7 +1070,6 @@ Select device from dropdown list for running inference using OpenVINO.
 
 .. code:: ipython3
 
-    
     text_enc = core.compile_model(TEXT_ENCODER_OV_PATH, device.value)
 
 .. code:: ipython3
@@ -1063,7 +1079,7 @@ Select device from dropdown list for running inference using OpenVINO.
 .. code:: ipython3
 
     ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device.value != "CPU" else {}
-    
+
     vae_decoder = core.compile_model(VAE_DECODER_OV_PATH, device.value, ov_config)
     vae_encoder = core.compile_model(VAE_ENCODER_OV_PATH, device.value, ov_config)
 
@@ -1074,21 +1090,17 @@ Let us define them and put all components together
 
     from transformers import CLIPTokenizer
     from diffusers.schedulers import LMSDiscreteScheduler
-    
-    lms = LMSDiscreteScheduler(
-        beta_start=0.00085, 
-        beta_end=0.012, 
-        beta_schedule="scaled_linear"
-    )
-    tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
-    
+
+    lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+
     ov_pipe = OVStableDiffusionPipeline(
         tokenizer=tokenizer,
         text_encoder=text_enc,
         unet=unet_model,
         vae_encoder=vae_encoder,
         vae_decoder=vae_decoder,
-        scheduler=lms
+        scheduler=lms,
     )
 
 Text-to-Image generation
@@ -1106,15 +1118,18 @@ latent state initialization and number of steps.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    sample_text = ('cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting, epic composition. '
-                   'A golden daylight, hyper-realistic environment. '
-                   'Hyper and intricate detail, photo-realistic. '
-                   'Cinematic and volumetric light. '
-                   'Epic concept art. '
-                   'Octane render and Unreal Engine, trending on artstation')
-    text_prompt = widgets.Text(value=sample_text, description='your text')
-    num_steps = widgets.IntSlider(min=1, max=50, value=20, description='steps:')
-    seed = widgets.IntSlider(min=0, max=10000000, description='seed: ', value=42)
+
+    sample_text = (
+        "cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting, epic composition. "
+        "A golden daylight, hyper-realistic environment. "
+        "Hyper and intricate detail, photo-realistic. "
+        "Cinematic and volumetric light. "
+        "Epic concept art. "
+        "Octane render and Unreal Engine, trending on artstation"
+    )
+    text_prompt = widgets.Text(value=sample_text, description="your text")
+    num_steps = widgets.IntSlider(min=1, max=50, value=20, description="steps:")
+    seed = widgets.IntSlider(min=0, max=10000000, description="seed: ", value=42)
     widgets.VBox([text_prompt, seed, num_steps])
 
 
@@ -1128,10 +1143,10 @@ latent state initialization and number of steps.
 
 .. code:: ipython3
 
-    print('Pipeline settings')
-    print(f'Input text: {text_prompt.value}')
-    print(f'Seed: {seed.value}')
-    print(f'Number of steps: {num_steps.value}')
+    print("Pipeline settings")
+    print(f"Input text: {text_prompt.value}")
+    print(f"Seed: {seed.value}")
+    print(f"Number of steps: {num_steps.value}")
 
 
 .. parsed-literal::
@@ -1159,20 +1174,27 @@ contains list of intermediate results for each step.
 
 .. code:: ipython3
 
-    final_image = result['sample'][0]
-    if result['iterations']:
-        all_frames = result['iterations']
+    final_image = result["sample"][0]
+    if result["iterations"]:
+        all_frames = result["iterations"]
         img = next(iter(all_frames))
-        img.save(fp='result.gif', format='GIF', append_images=iter(all_frames), save_all=True, duration=len(all_frames) * 5, loop=0)
-    final_image.save('result.png')
+        img.save(
+            fp="result.gif",
+            format="GIF",
+            append_images=iter(all_frames),
+            save_all=True,
+            duration=len(all_frames) * 5,
+            loop=0,
+        )
+    final_image.save("result.png")
 
 Now is show time!
 
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
-    text = '\n\t'.join(text_prompt.value.split('.'))
+
+    text = "\n\t".join(text_prompt.value.split("."))
     print("Input text:")
     print("\t" + text)
     display(final_image)
@@ -1190,7 +1212,7 @@ Now is show time!
 
 
 
-.. image:: 225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_33_1.png
+.. image:: stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_33_1.png
 
 
 Nice. As you can see, the picture has quite a high definition 🔥.
@@ -1209,15 +1231,15 @@ semantically consistent with the input.
 
 .. code:: ipython3
 
-    text_prompt_i2i = widgets.Text(value='amazing watercolor painting', description='your text')
-    num_steps_i2i = widgets.IntSlider(min=1, max=50, value=10, description='steps:')
-    seed_i2i = widgets.IntSlider(min=0, max=1024, description='seed: ', value=42)
+    text_prompt_i2i = widgets.Text(value="amazing watercolor painting", description="your text")
+    num_steps_i2i = widgets.IntSlider(min=1, max=50, value=10, description="steps:")
+    seed_i2i = widgets.IntSlider(min=0, max=1024, description="seed: ", value=42)
     image_widget = widgets.FileUpload(
-        accept='',
+        accept="",
         multiple=False,
-        description='Upload image',
+        description="Upload image",
     )
-    strength = widgets.FloatSlider(min=0, max=1, description='strength: ', value=0.5)
+    strength = widgets.FloatSlider(min=0, max=1, description="strength: ", value=0.5)
     widgets.VBox([text_prompt_i2i, seed_i2i, num_steps_i2i, image_widget, strength])
 
 
@@ -1232,33 +1254,41 @@ semantically consistent with the input.
 .. code:: ipython3
 
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
+    open("notebook_utils.py", "w").write(r.text)
+
     from notebook_utils import download_file
 
 .. code:: ipython3
 
     import io
-    
+
     default_image_path = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
-        filename="coco.jpg"
+        filename="coco.jpg",
     )
-    
+
     # read uploaded image
-    image = PIL.Image.open(io.BytesIO(image_widget.value[-1]['content']) if image_widget.value else str(default_image_path))
-    print('Pipeline settings')
-    print(f'Input text: {text_prompt_i2i.value}')
-    print(f'Seed: {seed_i2i.value}')
-    print(f'Number of steps: {num_steps_i2i.value}')
-    print(f'Strength: {strength.value}')
+    image = PIL.Image.open(io.BytesIO(image_widget.value[-1]["content"]) if image_widget.value else str(default_image_path))
+    print("Pipeline settings")
+    print(f"Input text: {text_prompt_i2i.value}")
+    print(f"Seed: {seed_i2i.value}")
+    print(f"Number of steps: {num_steps_i2i.value}")
+    print(f"Strength: {strength.value}")
     print("Input image:")
     display(image)
-    processed_image = ov_pipe(text_prompt_i2i.value, image, num_inference_steps=num_steps_i2i.value, seed=seed_i2i.value, strength=strength.value)
+    processed_image = ov_pipe(
+        text_prompt_i2i.value,
+        image,
+        num_inference_steps=num_steps_i2i.value,
+        seed=seed_i2i.value,
+        strength=strength.value,
+    )
 
 
 .. parsed-literal::
@@ -1272,7 +1302,7 @@ semantically consistent with the input.
 
 
 
-.. image:: 225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_38_1.png
+.. image:: stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_38_1.png
 
 
 
@@ -1283,16 +1313,23 @@ semantically consistent with the input.
 
 .. code:: ipython3
 
-    final_image_i2i = processed_image['sample'][0]
-    if processed_image['iterations']:
-        all_frames_i2i = processed_image['iterations']
+    final_image_i2i = processed_image["sample"][0]
+    if processed_image["iterations"]:
+        all_frames_i2i = processed_image["iterations"]
         img = next(iter(all_frames_i2i))
-        img.save(fp='result_i2i.gif', format='GIF', append_images=iter(all_frames_i2i), save_all=True, duration=len(all_frames_i2i) * 5, loop=0)
-    final_image_i2i.save('result_i2i.png')
+        img.save(
+            fp="result_i2i.gif",
+            format="GIF",
+            append_images=iter(all_frames_i2i),
+            save_all=True,
+            duration=len(all_frames_i2i) * 5,
+            loop=0,
+        )
+    final_image_i2i.save("result_i2i.png")
 
 .. code:: ipython3
 
-    text_i2i = '\n\t'.join(text_prompt_i2i.value.split('.'))
+    text_i2i = "\n\t".join(text_prompt_i2i.value.split("."))
     print("Input text:")
     print("\t" + text_i2i)
     display(final_image_i2i)
@@ -1305,7 +1342,7 @@ semantically consistent with the input.
 
 
 
-.. image:: 225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_40_1.png
+.. image:: stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_40_1.png
 
 
 Interactive demo
@@ -1316,17 +1353,18 @@ Interactive demo
 .. code:: ipython3
 
     import gradio as gr
-    
+
+
     def generate_from_text(text, seed, num_steps, _=gr.Progress(track_tqdm=True)):
         result = ov_pipe(text, num_inference_steps=num_steps, seed=seed)
         return result["sample"][0]
-    
-    
+
+
     def generate_from_image(img, text, seed, num_steps, strength, _=gr.Progress(track_tqdm=True)):
         result = ov_pipe(text, img, num_inference_steps=num_steps, seed=seed, strength=strength)
         return result["sample"][0]
-    
-    
+
+
     with gr.Blocks() as demo:
         with gr.Tab("Text-to-Image generation"):
             with gr.Row():
@@ -1351,14 +1389,26 @@ Interactive demo
             sample_i2i_text = "amazing watercolor painting"
             i2i_btn.click(
                 generate_from_image,
-                [i2i_input, i2i_text_input, i2i_seed_input, i2i_steps_input, strength_input],
+                [
+                    i2i_input,
+                    i2i_text_input,
+                    i2i_seed_input,
+                    i2i_steps_input,
+                    strength_input,
+                ],
                 i2i_out,
             )
             gr.Examples(
                 [[str(default_image_path), sample_i2i_text, 42, 10, 0.5]],
-                [i2i_input, i2i_text_input, i2i_seed_input, i2i_steps_input, strength_input],
+                [
+                    i2i_input,
+                    i2i_text_input,
+                    i2i_seed_input,
+                    i2i_steps_input,
+                    strength_input,
+                ],
             )
-    
+
     try:
         demo.queue().launch(debug=False)
     except Exception:
@@ -1371,7 +1421,7 @@ Interactive demo
 .. parsed-literal::
 
     Running on local URL:  http://127.0.0.1:7860
-    
+
     To create a public link, set `share=True` in `launch()`.
 
 
diff --git a/docs/notebooks/225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_33_1.png b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_33_1.png
similarity index 100%
rename from docs/notebooks/225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_33_1.png
rename to docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_33_1.png
diff --git a/docs/notebooks/225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_38_1.png b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_38_1.png
similarity index 100%
rename from docs/notebooks/225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_38_1.png
rename to docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_38_1.png
diff --git a/docs/notebooks/225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_40_1.png b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_40_1.png
similarity index 100%
rename from docs/notebooks/225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_40_1.png
rename to docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_40_1.png
diff --git a/docs/notebooks/276-stable-diffusion-torchdynamo-backend-with-output.rst b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output.rst
similarity index 72%
rename from docs/notebooks/276-stable-diffusion-torchdynamo-backend-with-output.rst
rename to docs/notebooks/stable-diffusion-torchdynamo-backend-with-output.rst
index a97fe657f36..c36ba0c3027 100644
--- a/docs/notebooks/276-stable-diffusion-torchdynamo-backend-with-output.rst
+++ b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output.rst
@@ -1,6 +1,12 @@
 Stable Diffusion v2.1 using OpenVINO TorchDynamo backend
 ========================================================
 
+.. container:: alert alert-block alert-danger
+
+   Important note: Currently, PyTorch does not support torch.compile
+   feature on Windows officially. Please follow these instructions if
+   you want to run this tutorial on Windows.
+
 Stable Diffusion v2 is the next generation of Stable Diffusion model a
 Text-to-Image latent diffusion model created by the researchers and
 engineers from `Stability AI `__ and
@@ -49,7 +55,14 @@ Table of contents:
     import sys
     from IPython.display import HTML, display
     
-    sys.path.append("../utils")
+    # Fetch `ipython_exit` module
+    import requests
+    
+    r = requests.get("https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/ipython_exit.py")
+    
+    with open("ipython_exit.py", "w") as f:
+        f.write(r.text)
+    
     from ipython_exit import exit
     
     if sys.platform == "win32":
@@ -63,10 +76,15 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q torch transformers diffusers gradio ipywidgets --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "torch>=2.1" transformers diffusers "gradio>=4.19" ipywidgets --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "openvino>=2023.3.0"
 
 
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
+
 .. parsed-literal::
 
     DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
@@ -82,6 +100,12 @@ Prerequisites
     DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     
 
+.. parsed-literal::
+
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    openvino-tokenizers 2024.2.0.0.dev20240416 requires openvino~=2024.2.0.0.dev, but you have openvino 2024.0.0 which is incompatible.
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -100,19 +124,19 @@ Prerequisites
 
 .. parsed-literal::
 
-    2024-03-13 00:13:56.113209: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:13:56.146913: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 01:02:01.093954: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 01:02:01.129165: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 00:13:56.835981: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 01:02:01.757086: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
 
 
@@ -175,9 +199,9 @@ options `__
 
-..
-
-   **Note**: Currently, PyTorch does not support torch.compile feature
-   on Windows officially. Please follow `these
-   instructions `__
-   if you want to access it on Windows.
-
 Run Image generation
 ~~~~~~~~~~~~~~~~~~~~
 
@@ -258,7 +279,7 @@ Run Image generation
 
 
 
-.. image:: 276-stable-diffusion-torchdynamo-backend-with-output_files/276-stable-diffusion-torchdynamo-backend-with-output_15_1.png
+.. image:: stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_15_1.png
 
 
 
@@ -274,21 +295,37 @@ pipeline. Optionally, you can also change some input parameters.
 .. code:: ipython3
 
     time_stamps = []
+    
+    
     def callback(iter, t, latents):
         time_stamps.append(time.time())
     
     
     def error_str(error, title="Error"):
-        return f"""#### {title}
-                {error}""" if error else ""
+        return (
+            f"""#### {title}
+                {error}"""
+            if error
+            else ""
+        )
     
     
     def on_mode_change(mode):
-        return gr.update(visible=mode == modes['img2img']), \
-            gr.update(visible=mode == modes['txt2img'])
+        return gr.update(visible=mode == modes["img2img"]), gr.update(visible=mode == modes["txt2img"])
     
     
-    def inference(inf_mode, prompt, guidance=7.5, steps=25, width=768, height=768, seed=-1, img=None, strength=0.5, neg_prompt=""):
+    def inference(
+        inf_mode,
+        prompt,
+        guidance=7.5,
+        steps=25,
+        width=768,
+        height=768,
+        seed=-1,
+        img=None,
+        strength=0.5,
+        neg_prompt="",
+    ):
         if seed == -1:
             seed = random.randint(0, 10000000)
         generator = torch.Generator().manual_seed(seed)
@@ -297,51 +334,66 @@ pipeline. Optionally, you can also change some input parameters.
         global time_stamps, pipe
         time_stamps = []
         try:
-            if inf_mode == modes['txt2img']:
+            if inf_mode == modes["txt2img"]:
                 if type(pipe).__name__ != "StableDiffusionPipeline":
                     pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
                     pipe.unet = torch.compile(pipe.unet, backend="openvino")
-                res = pipe(prompt,
-                           negative_prompt=neg_prompt,
-                           num_inference_steps=int(steps),
-                           guidance_scale=guidance,
-                           width=width,
-                           height=height,
-                           generator=generator,
-                           callback=callback,
-                           callback_steps=1).images
-            elif inf_mode == modes['img2img']:
+                res = pipe(
+                    prompt,
+                    negative_prompt=neg_prompt,
+                    num_inference_steps=int(steps),
+                    guidance_scale=guidance,
+                    width=width,
+                    height=height,
+                    generator=generator,
+                    callback=callback,
+                    callback_steps=1,
+                ).images
+            elif inf_mode == modes["img2img"]:
                 if img is None:
-                    return None, None, gr.update(visible=True, value=error_str("Image is required for Image to Image mode"))
+                    return (
+                        None,
+                        None,
+                        gr.update(
+                            visible=True,
+                            value=error_str("Image is required for Image to Image mode"),
+                        ),
+                    )
                 if type(pipe).__name__ != "StableDiffusionImg2ImgPipeline":
                     pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
                     pipe.unet = torch.compile(pipe.unet, backend="openvino")
-                res = pipe(prompt,
-                           negative_prompt=neg_prompt,
-                           image=img,
-                           num_inference_steps=int(steps),
-                           strength=strength,
-                           guidance_scale=guidance,
-                           generator=generator,
-                           callback=callback,
-                           callback_steps=1).images           
+                res = pipe(
+                    prompt,
+                    negative_prompt=neg_prompt,
+                    image=img,
+                    num_inference_steps=int(steps),
+                    strength=strength,
+                    guidance_scale=guidance,
+                    generator=generator,
+                    callback=callback,
+                    callback_steps=1,
+                ).images
         except Exception as e:
             return None, None, gr.update(visible=True, value=error_str(e))
-        
+    
         warmup_duration = time_stamps[1] - time_stamps[0]
         generation_rate = (steps - 1) / (time_stamps[-1] - time_stamps[1])
         res_info = "Warm up time: " + str(round(warmup_duration, 2)) + " secs "
-        if (generation_rate >= 1.0):
+        if generation_rate >= 1.0:
             res_info = res_info + ", Performance: " + str(round(generation_rate, 2)) + " it/s "
         else:
             res_info = res_info + ", Performance: " + str(round(1 / generation_rate, 2)) + " s/it "
     
-        return res, gr.update(visible=True, value=res_info), gr.update(visible=False, value=None)
+        return (
+            res,
+            gr.update(visible=True, value=res_info),
+            gr.update(visible=False, value=None),
+        )
     
     
     modes = {
-        'txt2img': 'Text to Image',
-        'img2img': 'Image to Image',
+        "txt2img": "Text to Image",
+        "img2img": "Image to Image",
     }
     
     with gr.Blocks(css="style.css") as demo:
@@ -351,11 +403,17 @@ pipeline. Optionally, you can also change some input parameters.
             """
         )
         with gr.Row():
-    
             with gr.Column(scale=60):
                 with gr.Group():
-                    prompt = gr.Textbox("a photograph of an astronaut riding a horse", label="Prompt", max_lines=2)
-                    neg_prompt = gr.Textbox("frames, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur", label="Negative prompt")
+                    prompt = gr.Textbox(
+                        "a photograph of an astronaut riding a horse",
+                        label="Prompt",
+                        max_lines=2,
+                    )
+                    neg_prompt = gr.Textbox(
+                        "frames, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur",
+                        label="Negative prompt",
+                    )
                     res_img = gr.Gallery(label="Generated images", show_label=False)
                 error_output = gr.Markdown(visible=False)
     
@@ -363,11 +421,17 @@ pipeline. Optionally, you can also change some input parameters.
                 generate = gr.Button(value="Generate")
     
                 with gr.Group():
-                    inf_mode = gr.Dropdown(list(modes.values()), label="Inference Mode", value=modes['txt2img'])
-                    
+                    inf_mode = gr.Dropdown(list(modes.values()), label="Inference Mode", value=modes["txt2img"])
+    
                     with gr.Column(visible=False) as i2i:
                         image = gr.Image(label="Image", height=128, type="pil")
-                        strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
+                        strength = gr.Slider(
+                            label="Transformation strength",
+                            minimum=0,
+                            maximum=1,
+                            step=0.01,
+                            value=0.5,
+                        )
     
                 with gr.Group():
                     with gr.Row() as txt2i:
@@ -379,16 +443,25 @@ pipeline. Optionally, you can also change some input parameters.
                         steps = gr.Slider(label="Steps", value=20, minimum=1, maximum=50, step=1)
                         guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
     
-                    seed = gr.Slider(-1, 10000000, label='Seed (-1 = random)', value=-1, step=1)
-                
+                    seed = gr.Slider(-1, 10000000, label="Seed (-1 = random)", value=-1, step=1)
+    
                 res_info = gr.Markdown(visible=False)
     
-        inf_mode.change(on_mode_change, inputs=[inf_mode], outputs=[
-                        i2i, txt2i], queue=False)
+        inf_mode.change(on_mode_change, inputs=[inf_mode], outputs=[i2i, txt2i], queue=False)
+    
+        inputs = [
+            inf_mode,
+            prompt,
+            guidance,
+            steps,
+            width,
+            height,
+            seed,
+            image,
+            strength,
+            neg_prompt,
+        ]
     
-        inputs = [inf_mode, prompt, guidance, steps,
-                  width, height, seed, image, strength, neg_prompt]
-        
         outputs = [res_img, res_info, error_output]
         prompt.submit(inference, inputs=inputs, outputs=outputs)
         generate.click(inference, inputs=inputs, outputs=outputs)
diff --git a/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_15_1.jpg b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_15_1.jpg
new file mode 100644
index 00000000000..031b96105ff
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_15_1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df6b732c8923a562843961ded9a24ddc9ead773513df30d6fb8def875c1565bc
+size 29462
diff --git a/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_15_1.png b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_15_1.png
new file mode 100644
index 00000000000..f81fbb33433
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_15_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ae542d1fce059293778d7c921eb3dec3b2cb3538f7c0943742ede02696affb9
+size 385559
diff --git a/docs/notebooks/236-stable-diffusion-v2-infinite-zoom-with-output.rst b/docs/notebooks/stable-diffusion-v2-infinite-zoom-with-output.rst
similarity index 90%
rename from docs/notebooks/236-stable-diffusion-v2-infinite-zoom-with-output.rst
rename to docs/notebooks/stable-diffusion-v2-infinite-zoom-with-output.rst
index f3b70fa431c..427eca0149d 100644
--- a/docs/notebooks/236-stable-diffusion-v2-infinite-zoom-with-output.rst
+++ b/docs/notebooks/stable-diffusion-v2-infinite-zoom-with-output.rst
@@ -20,9 +20,9 @@ fantastic world of diffusion models for everyone!
 
 In previous notebooks, we already discussed how to run `Text-to-Image
 generation and Image-to-Image generation using Stable Diffusion
-v1 <225-stable-diffusion-text-to-image-with-output.html>`__
+v1 `__
 and `controlling its generation process using
-ControlNet <235-controlnet-stable-diffusio235-controlnet-stable-diffusion-with-output.html>`__.
+ControlNet <./controlnet-stable-diffusion/controlnet-stable-diffusion.ipynb>`__.
 Now is turn of Stable Diffusion v2.
 
 Stable Diffusion v2: What’s new?
@@ -149,7 +149,7 @@ install required packages
 
 .. code:: ipython3
 
-    %pip install -q "diffusers>=0.14.0" "transformers>=4.25.1" gradio "openvino>=2023.1.0" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "diffusers>=0.14.0" "transformers>=4.25.1" "gradio>=4.19" "openvino>=2023.1.0" "torch>=2.1" Pillow opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
 
 Stable Diffusion in Diffusers library
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -170,9 +170,9 @@ The code below demonstrates how to create
 .. code:: ipython3
 
     from diffusers import StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler
-
+    
     model_id_inpaint = "stabilityai/stable-diffusion-2-inpainting"
-
+    
     pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained(model_id_inpaint)
     scheduler_inpaint = DPMSolverMultistepScheduler.from_config(pipe_inpaint.scheduler.config)
 
@@ -194,14 +194,14 @@ The code below demonstrates how to create
 .. code:: ipython3
 
     import gc
-
+    
     text_encoder_inpaint = pipe_inpaint.text_encoder
     text_encoder_inpaint.eval()
     unet_inpaint = pipe_inpaint.unet
     unet_inpaint.eval()
     vae_inpaint = pipe_inpaint.vae
     vae_inpaint.eval()
-
+    
     del pipe_inpaint
     gc.collect();
 
@@ -211,10 +211,10 @@ Convert models to OpenVINO Intermediate representation (IR) format
 
 
 Conversion part of model stayed remain as in `Text-to-Image generation
-notebook <236-stable-diffusion-v2-text-to-image-with-output.html>`__. Except
-U-Net now has 9 channels, which now calculated like 4 for U-Net
-generated latents channels + 4 for latent representation of masked image
-+ 1 channel resized mask.
+notebook <./stable-diffusion-v2-text-to-image.ipynb>`__. Except U-Net
+now has 9 channels, which now calculated like 4 for U-Net generated
+latents channels + 4 for latent representation of masked image + 1
+channel resized mask.
 
 .. code:: ipython3
 
@@ -222,7 +222,7 @@ generated latents channels + 4 for latent representation of masked image
     import torch
     import numpy as np
     import openvino as ov
-
+    
     sd2_inpainting_model_dir = Path("sd2_inpainting")
     sd2_inpainting_model_dir.mkdir(exist_ok=True)
 
@@ -235,9 +235,9 @@ generated latents channels + 4 for latent representation of masked image
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
-
-
-    def convert_encoder(text_encoder: torch.nn.Module, ir_path:Path):
+    
+    
+    def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
         """
         Convert Text Encoder model to IR.
         Function accepts pipeline, prepares example inputs for conversion
@@ -251,22 +251,28 @@ generated latents channels + 4 for latent representation of masked image
             input_ids = torch.ones((1, 77), dtype=torch.long)
             # switch model to inference mode
             text_encoder.eval()
-
+    
             # disable gradients calculation for reducing memory consumption
             with torch.no_grad():
                 # export model
                 ov_model = ov.convert_model(
                     text_encoder,  # model instance
                     example_input=input_ids,  # example inputs for model tracing
-                    input=([1,77],)  # input shape for conversion
+                    input=([1, 77],),  # input shape for conversion
                 )
                 ov.save_model(ov_model, ir_path)
                 del ov_model
                 cleanup_torchscript_cache()
-            print('Text Encoder successfully converted to IR')
-
-
-    def convert_unet(unet:torch.nn.Module, ir_path:Path, num_channels:int = 4, width:int = 64, height:int = 64):
+            print("Text Encoder successfully converted to IR")
+    
+    
+    def convert_unet(
+        unet: torch.nn.Module,
+        ir_path: Path,
+        num_channels: int = 4,
+        width: int = 64,
+        height: int = 64,
+    ):
         """
         Convert Unet model to IR format.
         Function accepts pipeline, prepares example inputs for conversion
@@ -279,10 +285,7 @@ generated latents channels + 4 for latent representation of masked image
         Returns:
             None
         """
-        dtype_mapping = {
-            torch.float32: ov.Type.f32,
-            torch.float64: ov.Type.f64
-        }
+        dtype_mapping = {torch.float32: ov.Type.f32, torch.float64: ov.Type.f64}
         if not ir_path.exists():
             # prepare inputs
             encoder_hidden_state = torch.ones((2, 77, 1024))
@@ -296,20 +299,16 @@ generated latents channels + 4 for latent representation of masked image
                 shape = ov.PartialShape(tuple(input_tensor.shape))
                 element_type = dtype_mapping[input_tensor.dtype]
                 input_info.append((shape, element_type))
-
+    
             with torch.no_grad():
-                ov_model = ov.convert_model(
-                    unet,
-                    example_input=dummy_inputs,
-                    input=input_info
-                )
+                ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
             ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
-            print('U-Net successfully converted to IR')
-
-
-    def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path, width:int = 512, height:int = 512):
+            print("U-Net successfully converted to IR")
+    
+    
+    def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path, width: int = 512, height: int = 512):
         """
         Convert VAE model to IR format.
         VAE model, creates wrapper class for export only necessary for inference part,
@@ -322,27 +321,28 @@ generated latents channels + 4 for latent representation of masked image
         Returns:
             None
         """
+    
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-
+    
             def forward(self, image):
                 return self.vae.encode(x=image)["latent_dist"].sample()
-
+    
         if not ir_path.exists():
             vae_encoder = VAEEncoderWrapper(vae)
             vae_encoder.eval()
             image = torch.zeros((1, 3, width, height))
             with torch.no_grad():
-                ov_model = ov.convert_model(vae_encoder, example_input=image, input=([1,3, width, height],))
+                ov_model = ov.convert_model(vae_encoder, example_input=image, input=([1, 3, width, height],))
             ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
-            print('VAE encoder successfully converted to IR')
-
-
-    def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path, width:int = 64, height:int = 64):
+            print("VAE encoder successfully converted to IR")
+    
+    
+    def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path, width: int = 64, height: int = 64):
         """
         Convert VAE decoder model to IR format.
         Function accepts VAE model, creates wrapper class for export only necessary for inference part,
@@ -355,35 +355,36 @@ generated latents channels + 4 for latent representation of masked image
         Returns:
             None
         """
+    
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-
+    
             def forward(self, latents):
                 return self.vae.decode(latents)
-
+    
         if not ir_path.exists():
             vae_decoder = VAEDecoderWrapper(vae)
             latents = torch.zeros((1, 4, width, height))
-
+    
             vae_decoder.eval()
             with torch.no_grad():
-                ov_model = ov.convert_model(vae_decoder, example_input=latents, input=([1,4, width, height],))
+                ov_model = ov.convert_model(vae_decoder, example_input=latents, input=([1, 4, width, height],))
             ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
-            print('VAE decoder successfully converted to IR')
+            print("VAE decoder successfully converted to IR")
 
 .. code:: ipython3
 
     TEXT_ENCODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "text_encoder.xml"
-
+    
     if not TEXT_ENCODER_OV_PATH_INPAINT.exists():
         convert_encoder(text_encoder_inpaint, TEXT_ENCODER_OV_PATH_INPAINT)
     else:
         print(f"Text encoder will be loaded from {TEXT_ENCODER_OV_PATH_INPAINT}")
-
+    
     del text_encoder_inpaint
     gc.collect();
 
@@ -395,7 +396,7 @@ generated latents channels + 4 for latent representation of masked image
 
 .. code:: ipython3
 
-    UNET_OV_PATH_INPAINT = sd2_inpainting_model_dir / 'unet.xml'
+    UNET_OV_PATH_INPAINT = sd2_inpainting_model_dir / "unet.xml"
     if not UNET_OV_PATH_INPAINT.exists():
         convert_unet(unet_inpaint, UNET_OV_PATH_INPAINT, num_channels=9, width=64, height=64)
         del unet_inpaint
@@ -413,19 +414,19 @@ generated latents channels + 4 for latent representation of masked image
 
 .. code:: ipython3
 
-    VAE_ENCODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / 'vae_encoder.xml'
-
+    VAE_ENCODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "vae_encoder.xml"
+    
     if not VAE_ENCODER_OV_PATH_INPAINT.exists():
         convert_vae_encoder(vae_inpaint, VAE_ENCODER_OV_PATH_INPAINT, 512, 512)
     else:
         print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH_INPAINT}")
-
-    VAE_DECODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / 'vae_decoder.xml'
+    
+    VAE_DECODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "vae_decoder.xml"
     if not VAE_DECODER_OV_PATH_INPAINT.exists():
         convert_vae_decoder(vae_inpaint, VAE_DECODER_OV_PATH_INPAINT, 64, 64)
     else:
         print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH_INPAINT}")
-
+    
     del vae_inpaint
     gc.collect();
 
@@ -450,61 +451,61 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
 
     import inspect
     from typing import List, Optional, Union, Dict
-
+    
     import PIL
     import cv2
-
+    
     from transformers import CLIPTokenizer
     from diffusers import DiffusionPipeline
     from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-
-
-    def prepare_mask_and_masked_image(image:PIL.Image.Image, mask:PIL.Image.Image):
+    
+    
+    def prepare_mask_and_masked_image(image: PIL.Image.Image, mask: PIL.Image.Image):
         """
         Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
         converted to ``np.array`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
         ``image`` and ``1`` for the ``mask``.
-
+    
         The ``image`` will be converted to ``np.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
         binarized (``mask > 0.5``) and cast to ``np.float32`` too.
-
+    
         Args:
             image (Union[np.array, PIL.Image]): The image to inpaint.
                 It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array``
             mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
                 It can be a ``PIL.Image``, or a ``height x width`` ``np.array``.
-
+    
         Returns:
             tuple[np.array]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
                 dimensions: ``batch x channels x height x width``.
         """
         if isinstance(image, (PIL.Image.Image, np.ndarray)):
             image = [image]
-
+    
         if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
             image = [np.array(i.convert("RGB"))[None, :] for i in image]
             image = np.concatenate(image, axis=0)
         elif isinstance(image, list) and isinstance(image[0], np.ndarray):
             image = np.concatenate([i[None, :] for i in image], axis=0)
-
+    
         image = image.transpose(0, 3, 1, 2)
         image = image.astype(np.float32) / 127.5 - 1.0
-
+    
         # preprocess mask
         if isinstance(mask, (PIL.Image.Image, np.ndarray)):
             mask = [mask]
-
+    
         if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
             mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
             mask = mask.astype(np.float32) / 255.0
         elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
             mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
+    
         mask[mask < 0.5] = 0
         mask[mask >= 0.5] = 1
-
+    
         masked_image = image * (mask < 0.5)
-
+    
         return mask, masked_image
 
 
@@ -558,7 +559,7 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
             self.width = self.unet.input(0).shape[3] * 8
             self.tokenizer = tokenizer
             self.register_to_config(_progress_bar_config={})
-
+    
         def prepare_mask_latents(
             self,
             mask,
@@ -569,7 +570,7 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
         ):
             """
             Prepare mask as Unet nput and encode input masked image to latent space using vae encoder
-
+    
             Parameters:
               mask (np.array): input mask array
               masked_image (np.array): masked input image tensor
@@ -582,19 +583,15 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
             """
             mask = torch.nn.functional.interpolate(torch.from_numpy(mask), size=(height // 8, width // 8))
             mask = mask.numpy()
-
+    
             # encode the mask image into latents space so we can concatenate it to the latents
             latents = self.vae_encoder(masked_image)[self._vae_e_output]
             masked_image_latents = latents * 0.18215
-
+    
             mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask
-            masked_image_latents = (
-                np.concatenate([masked_image_latents] * 2)
-                if do_classifier_free_guidance
-                else masked_image_latents
-            )
+            masked_image_latents = np.concatenate([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
             return mask, masked_image_latents
-
+    
         def __call__(
             self,
             prompt: Union[str, List[str]],
@@ -653,17 +650,15 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
             # prepare mask
             mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
             # set timesteps
-            accepts_offset = "offset" in set(
-                inspect.signature(self.scheduler.set_timesteps).parameters.keys()
-            )
+            accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
             extra_set_kwargs = {}
             if accepts_offset:
                 extra_set_kwargs["offset"] = 1
-
+    
             self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
             timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, 1)
             latent_timestep = timesteps[:1]
-
+    
             # get the initial random noise unless the user supplied it
             latents, meta = self.prepare_latents(latent_timestep)
             mask, masked_image_latents = self.prepare_mask_latents(
@@ -671,40 +666,28 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
                 masked_image,
                 do_classifier_free_guidance=do_classifier_free_guidance,
             )
-
+    
             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
             # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
             # and should be between [0, 1]
-            accepts_eta = "eta" in set(
-                inspect.signature(self.scheduler.step).parameters.keys()
-            )
+            accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
             extra_step_kwargs = {}
             if accepts_eta:
                 extra_step_kwargs["eta"] = eta
-
+    
             for t in self.progress_bar(timesteps):
                 # expand the latents if we are doing classifier free guidance
-                latent_model_input = (
-                    np.concatenate([latents] * 2)
-                    if do_classifier_free_guidance
-                    else latents
-                )
+                latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                latent_model_input = np.concatenate(
-                    [latent_model_input, mask, masked_image_latents], axis=1
-                )
+                latent_model_input = np.concatenate([latent_model_input, mask, masked_image_latents], axis=1)
                 # predict the noise residual
-                noise_pred = self.unet(
-                    [latent_model_input, np.array(t, dtype=np.float32), text_embeddings]
-                )[self._unet_output]
+                noise_pred = self.unet([latent_model_input, np.array(t, dtype=np.float32), text_embeddings])[self._unet_output]
                 # perform guidance
                 if do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
-                    noise_pred = noise_pred_uncond + guidance_scale * (
-                        noise_pred_text - noise_pred_uncond
-                    )
-
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+    
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(
                     torch.from_numpy(noise_pred),
@@ -714,14 +697,20 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
                 )["prev_sample"].numpy()
             # scale and decode the image latents with vae
             image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
-
+    
             image = self.postprocess_image(image, meta, output_type)
             return {"sample": image}
-
-        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True, negative_prompt:Union[str, List[str]] = None):
+    
+        def _encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+            negative_prompt: Union[str, List[str]] = None,
+        ):
             """
             Encodes the prompt into text encoder hidden states.
-
+    
             Parameters:
                 prompt (str or list(str)): prompt to be encoded
                 num_images_per_prompt (int): number of images that should be generated per prompt
@@ -731,7 +720,7 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
                 text_embeddings (np.ndarray): text encoder hidden states
             """
             batch_size = len(prompt) if isinstance(prompt, list) else 1
-
+    
             # tokenize input prompts
             text_inputs = self.tokenizer(
                 prompt,
@@ -741,18 +730,15 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
                 return_tensors="np",
             )
             text_input_ids = text_inputs.input_ids
-
-            text_embeddings = self.text_encoder(
-                text_input_ids)[self._text_encoder_output]
-
+    
+            text_embeddings = self.text_encoder(text_input_ids)[self._text_encoder_output]
+    
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(
-                    text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(
-                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
-
+                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
+                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
+    
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
                 uncond_tokens: List[str]
@@ -770,25 +756,25 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
                     truncation=True,
                     return_tensors="np",
                 )
-
+    
                 uncond_embeddings = self.text_encoder(uncond_input.input_ids)[self._text_encoder_output]
-
+    
                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 seq_len = uncond_embeddings.shape[1]
                 uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
                 uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
-
+    
                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
-
+    
             return text_embeddings
-
-        def prepare_latents(self, latent_timestep:torch.Tensor = None):
+    
+        def prepare_latents(self, latent_timestep: torch.Tensor = None):
             """
             Function for getting initial latents for starting generation
-
+    
             Parameters:
                 latent_timestep (torch.Tensor, *optional*, None):
                     Predicted by scheduler initial step for image generation, required for latent image mixing with nosie
@@ -802,12 +788,12 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
             if isinstance(self.scheduler, LMSDiscreteScheduler):
                 noise = noise * self.scheduler.sigmas[0].numpy()
             return noise, {}
-
-        def postprocess_image(self, image:np.ndarray, meta:Dict, output_type:str = "pil"):
+    
+        def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
             """
             Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
             normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-
+    
             Parameters:
                 image (np.ndarray):
                     Generated image
@@ -833,20 +819,18 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
                 image = self.numpy_to_pil(image)
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [img.resize((orig_width, orig_height),
-                                        PIL.Image.Resampling.LANCZOS) for img in image]
+                    image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
             else:
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [cv2.resize(img, (orig_width, orig_width))
-                             for img in image]
+                    image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
             return image
-
-        def get_timesteps(self, num_inference_steps:int, strength:float):
+    
+        def get_timesteps(self, num_inference_steps: int, strength: float):
             """
             Helper function for getting scheduler timesteps for generation
             In case of image-to-image generation, it updates number of steps according to strength
-
+    
             Parameters:
                num_inference_steps (int):
                   number of inference steps for generation
@@ -856,10 +840,10 @@ We will reuse ``OVStableDiffusionPipeline`` basic utilities in
             """
             # get the original timestep using init_timestep
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
+    
             t_start = max(num_inference_steps - init_timestep, 0)
             timesteps = self.scheduler.timesteps[t_start:]
-
+    
             return timesteps, num_inference_steps - t_start
 
 Zoom Video Generation
@@ -894,20 +878,22 @@ generation is finished, we record frames in reversed order.
 .. code:: ipython3
 
     from tqdm import trange
+    
+    
     def generate_video(
-        pipe:OVStableDiffusionInpaintingPipeline,
-        prompt:Union[str, List[str]],
-        negative_prompt:Union[str, List[str]],
-        guidance_scale:float = 7.5,
-        num_inference_steps:int = 20,
-        num_frames:int = 20,
-        mask_width:int = 128,
-        seed:int = 9999,
-        zoom_in:bool = False,
+        pipe: OVStableDiffusionInpaintingPipeline,
+        prompt: Union[str, List[str]],
+        negative_prompt: Union[str, List[str]],
+        guidance_scale: float = 7.5,
+        num_inference_steps: int = 20,
+        num_frames: int = 20,
+        mask_width: int = 128,
+        seed: int = 9999,
+        zoom_in: bool = False,
     ):
         """
         Zoom video generation function
-
+    
         Parameters:
           pipe (OVStableDiffusionInpaintingPipeline): inpainting pipeline.
           prompt (str or List[str]): The prompt or prompts to guide the image generation.
@@ -925,15 +911,15 @@ generation is finished, we record frames in reversed order.
         Returns:
           output_path (str): Path where generated video loacated.
         """
-
+    
         height = 512
         width = height
-
+    
         current_image = PIL.Image.new(mode="RGBA", size=(height, width))
         mask_image = np.array(current_image)[:, :, 3]
         mask_image = PIL.Image.fromarray(255 - mask_image).convert("RGB")
         current_image = current_image.convert("RGB")
-        pipe.set_progress_bar_config(desc='Generating initial image...')
+        pipe.set_progress_bar_config(desc="Generating initial image...")
         init_images = pipe(
             prompt=prompt,
             negative_prompt=negative_prompt,
@@ -944,26 +930,29 @@ generation is finished, we record frames in reversed order.
             num_inference_steps=num_inference_steps,
         )["sample"]
         pipe.set_progress_bar_config()
-
+    
         image_grid(init_images, rows=1, cols=1)
-
+    
         num_outpainting_steps = num_frames
         num_interpol_frames = 30
-
+    
         current_image = init_images[0]
         all_frames = []
         all_frames.append(current_image)
-        for i in trange(num_outpainting_steps, desc=f'Generating {num_outpainting_steps} additional images...'):
+        for i in trange(
+            num_outpainting_steps,
+            desc=f"Generating {num_outpainting_steps} additional images...",
+        ):
             prev_image_fix = current_image
-
+    
             prev_image = shrink_and_paste_on_blank(current_image, mask_width)
-
+    
             current_image = prev_image
-
+    
             # create mask (black image with white mask_width width edges)
             mask_image = np.array(current_image)[:, :, 3]
             mask_image = PIL.Image.fromarray(255 - mask_image).convert("RGB")
-
+    
             # inpainting step
             current_image = current_image.convert("RGB")
             images = pipe(
@@ -977,7 +966,7 @@ generation is finished, we record frames in reversed order.
             )["sample"]
             current_image = images[0]
             current_image.paste(prev_image, mask=prev_image)
-
+    
             # interpolation steps bewteen 2 inpainted images (=sequential zoom and crop)
             for j in range(num_interpol_frames - 1):
                 interpol_image = current_image
@@ -990,16 +979,16 @@ generation is finished, we record frames in reversed order.
                         height - interpol_width,
                     )
                 )
-
+    
                 interpol_image = interpol_image.resize((height, width))
-
+    
                 # paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
                 interpol_width2 = round((1 - (height - 2 * mask_width) / (height - 2 * interpol_width)) / 2 * height)
                 prev_image_fix_crop = shrink_and_paste_on_blank(prev_image_fix, interpol_width2)
                 interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
                 all_frames.append(interpol_image)
             all_frames.append(current_image)
-
+    
         video_file_name = f"infinite_zoom_{'in' if zoom_in else 'out'}"
         fps = 30
         save_path = video_file_name + ".mp4"
@@ -1008,44 +997,42 @@ generation is finished, we record frames in reversed order.
 
 .. code:: ipython3
 
-    def shrink_and_paste_on_blank(current_image:PIL.Image.Image, mask_width:int):
+    def shrink_and_paste_on_blank(current_image: PIL.Image.Image, mask_width: int):
         """
         Decreases size of current_image by mask_width pixels from each side,
         then adds a mask_width width transparent frame,
         so that the image the function returns is the same size as the input.
-
+    
         Parameters:
             current_image (PIL.Image): input image to transform
             mask_width (int): width in pixels to shrink from each side
         Returns:
            prev_image (PIL.Image): resized image with extended borders
         """
-
+    
         height = current_image.height
         width = current_image.width
-
+    
         # shrink down by mask_width
         prev_image = current_image.resize((height - 2 * mask_width, width - 2 * mask_width))
         prev_image = prev_image.convert("RGBA")
         prev_image = np.array(prev_image)
-
+    
         # create blank non-transparent image
         blank_image = np.array(current_image.convert("RGBA")) * 0
         blank_image[:, :, 3] = 1
-
+    
         # paste shrinked onto blank
-        blank_image[
-            mask_width : height - mask_width, mask_width : width - mask_width, :
-        ] = prev_image
+        blank_image[mask_width : height - mask_width, mask_width : width - mask_width, :] = prev_image
         prev_image = PIL.Image.fromarray(blank_image)
-
+    
         return prev_image
-
-
-    def image_grid(imgs:List[PIL.Image.Image], rows:int, cols:int):
+    
+    
+    def image_grid(imgs: List[PIL.Image.Image], rows: int, cols: int):
         """
         Insert images to grid
-
+    
         Parameters:
             imgs (List[PIL.Image.Image]): list of images for making grid
             rows (int): number of rows in grid
@@ -1054,19 +1041,25 @@ generation is finished, we record frames in reversed order.
             grid (PIL.Image): image with input images collage
         """
         assert len(imgs) == rows * cols
-
+    
         w, h = imgs[0].size
         grid = PIL.Image.new("RGB", size=(cols * w, rows * h))
-
+    
         for i, img in enumerate(imgs):
             grid.paste(img, box=(i % cols * w, i // cols * h))
         return grid
-
-
-    def write_video(file_path:str, frames:List[PIL.Image.Image], fps:float, reversed_order:bool = True, gif:bool = True):
+    
+    
+    def write_video(
+        file_path: str,
+        frames: List[PIL.Image.Image],
+        fps: float,
+        reversed_order: bool = True,
+        gif: bool = True,
+    ):
         """
         Writes frames to an mp4 video file and optionaly to gif
-
+    
         Parameters:
             file_path (str): Path to output video, must end with .mp4
             frames (List of PIL.Image): list of frames
@@ -1078,17 +1071,17 @@ generation is finished, we record frames in reversed order.
         """
         if reversed_order:
             frames.reverse()
-
+    
         w, h = frames[0].size
         fourcc = cv2.VideoWriter_fourcc("m", "p", "4", "v")
         # fourcc = cv2.VideoWriter_fourcc(*'avc1')
         writer = cv2.VideoWriter(file_path, fourcc, fps, (w, h))
-
+    
         for frame in frames:
             np_frame = np.array(frame.convert("RGB"))
             cv_frame = cv2.cvtColor(np_frame, cv2.COLOR_RGB2BGR)
             writer.write(cv_frame)
-
+    
         writer.release()
         if gif:
             frames[0].save(
@@ -1111,8 +1104,8 @@ class
 .. code:: ipython3
 
     core = ov.Core()
-
-    tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
+    
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
 
 Select inference device
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -1124,14 +1117,14 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-
+    
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
-
+    
     device
 
 
@@ -1146,13 +1139,13 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device.value != "CPU" else {}
-
-
+    
+    
     text_enc_inpaint = core.compile_model(TEXT_ENCODER_OV_PATH_INPAINT, device.value)
     unet_model_inpaint = core.compile_model(UNET_OV_PATH_INPAINT, device.value)
     vae_decoder_inpaint = core.compile_model(VAE_DECODER_OV_PATH_INPAINT, device.value, ov_config)
     vae_encoder_inpaint = core.compile_model(VAE_ENCODER_OV_PATH_INPAINT, device.value, ov_config)
-
+    
     ov_pipe_inpaint = OVStableDiffusionInpaintingPipeline(
         tokenizer=tokenizer,
         text_encoder=text_enc_inpaint,
@@ -1171,7 +1164,8 @@ Run Infinite Zoom video generation
 
     import gradio as gr
     from socket import gethostbyname, gethostname
-
+    
+    
     def generate(
         prompt,
         negative_prompt,
@@ -1193,8 +1187,8 @@ Run Infinite Zoom video generation
             zoom_in=zoom_in,
         )
         return video_path.replace(".mp4", ".gif")
-
-
+    
+    
     gr.close_all()
     demo = gr.Interface(
         generate,
@@ -1220,6 +1214,20 @@ Run Infinite Zoom video generation
 
     Running on local URL:  http://127.0.0.1:7860
     Running on public URL: https://372deef95f8b1d0168.gradio.live
-
+    
     This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)
 
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/notebooks/236-stable-diffusion-v2-optimum-demo-with-output.rst b/docs/notebooks/stable-diffusion-v2-optimum-demo-with-output.rst
similarity index 89%
rename from docs/notebooks/236-stable-diffusion-v2-optimum-demo-with-output.rst
rename to docs/notebooks/stable-diffusion-v2-optimum-demo-with-output.rst
index 7fc85733aae..1a113eeee37 100644
--- a/docs/notebooks/236-stable-diffusion-v2-optimum-demo-with-output.rst
+++ b/docs/notebooks/stable-diffusion-v2-optimum-demo-with-output.rst
@@ -28,7 +28,7 @@ this
 
 .. code:: ipython3
 
-    %pip install -q "optimum-intel[openvino,diffusers]@git+https://github.com/huggingface/optimum-intel.git" "ipywidgets" "transformers>=4.33.0" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "optimum-intel[openvino,diffusers]@git+https://github.com/huggingface/optimum-intel.git" "ipywidgets" "transformers>=4.33.0" "torch>=2.1" --extra-index-url https://download.pytorch.org/whl/cpu
 
 Stable Diffusion pipeline should brings 6 elements together, a text
 encoder model with a tokenizer, a UNet model with and scheduler, and an
@@ -47,7 +47,8 @@ OpenVINO’s integration into Optimum.
 .. code:: ipython3
 
     import warnings
-    warnings.filterwarnings('ignore')
+    
+    warnings.filterwarnings("ignore")
 
 Showing Info Available Devices
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -64,13 +65,13 @@ If you just have either an iGPU or dGPU that will be assigned to
 
 .. code:: ipython3
 
-    from openvino.runtime import Core
+    import openvino as ov
     
-    ie = Core()
-    devices = ie.available_devices
+    core = ov.Core()
+    devices = core.available_devices
     
     for device in devices:
-        device_name = ie.get_property(device, "FULL_DEVICE_NAME")
+        device_name = core.get_property(device, "FULL_DEVICE_NAME")
         print(f"{device}: {device_name}")
 
 
@@ -93,7 +94,7 @@ Select device from dropdown list for running inference using OpenVINO.
     import ipywidgets as widgets
     
     device = widgets.Dropdown(
-        options=ie.available_devices + ["AUTO"],
+        options=core.available_devices + ["AUTO"],
         value="CPU",
         description="Device:",
         disabled=False,
@@ -130,7 +131,7 @@ Using full precision model in choice device with ``OVStableDiffusionPipeline``
 
     import gc
     
-    # Generate an image. 
+    # Generate an image.
     prompt = "red car in snowy forest, epic vista, beautiful landscape, 4k, 8k"
     output_ov = ov_pipe(prompt, num_inference_steps=17, output_type="pil").images[0]
     output_ov.save("image.png")
@@ -145,7 +146,7 @@ Using full precision model in choice device with ``OVStableDiffusionPipeline``
 
 
 
-.. image:: 236-stable-diffusion-v2-optimum-demo-with-output_files/236-stable-diffusion-v2-optimum-demo-with-output_11_1.png
+.. image:: stable-diffusion-v2-optimum-demo-with-output_files/stable-diffusion-v2-optimum-demo-with-output_11_1.png
 
 
 
diff --git a/docs/notebooks/236-stable-diffusion-v2-optimum-demo-with-output_files/236-stable-diffusion-v2-optimum-demo-with-output_11_1.jpg b/docs/notebooks/stable-diffusion-v2-optimum-demo-with-output_files/stable-diffusion-v2-optimum-demo-with-output_11_1.jpg
similarity index 100%
rename from docs/notebooks/236-stable-diffusion-v2-optimum-demo-with-output_files/236-stable-diffusion-v2-optimum-demo-with-output_11_1.jpg
rename to docs/notebooks/stable-diffusion-v2-optimum-demo-with-output_files/stable-diffusion-v2-optimum-demo-with-output_11_1.jpg
diff --git a/docs/notebooks/236-stable-diffusion-v2-optimum-demo-with-output_files/236-stable-diffusion-v2-optimum-demo-with-output_11_1.png b/docs/notebooks/stable-diffusion-v2-optimum-demo-with-output_files/stable-diffusion-v2-optimum-demo-with-output_11_1.png
similarity index 100%
rename from docs/notebooks/236-stable-diffusion-v2-optimum-demo-with-output_files/236-stable-diffusion-v2-optimum-demo-with-output_11_1.png
rename to docs/notebooks/stable-diffusion-v2-optimum-demo-with-output_files/stable-diffusion-v2-optimum-demo-with-output_11_1.png
diff --git a/docs/notebooks/236-stable-diffusion-v2-text-to-image-demo-with-output.rst b/docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output.rst
similarity index 84%
rename from docs/notebooks/236-stable-diffusion-v2-text-to-image-demo-with-output.rst
rename to docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output.rst
index 9b862a14f97..c5b9893462e 100644
--- a/docs/notebooks/236-stable-diffusion-v2-text-to-image-demo-with-output.rst
+++ b/docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output.rst
@@ -12,14 +12,14 @@ less restrictive filtering of the dataset. All of these features give us
 promising results for selecting a wide range of input text prompts!
 
 **Note:** This is a shorter version of the
-`236-stable-diffusion-v2-text-to-image `__
+`stable-diffusion-v2-text-to-image `__
 notebook for demo purposes and to get started quickly. This version does
 not have the full implementation of the helper utilities needed to
 convert the models from PyTorch to ONNX to OpenVINO, and the OpenVINO
 ``OVStableDiffusionPipeline`` within the notebook directly. If you would
 like to see the full implementation of stable diffusion for text to
 image, please visit
-`236-stable-diffusion-v2-text-to-image `__.
+`stable-diffusion-v2-text-to-image `__.
 
 Table of contents:
 ^^^^^^^^^^^^^^^^^^
@@ -59,7 +59,7 @@ pipelines `__.
 
 .. code:: ipython3
 
-    %pip install -q "diffusers>=0.14.0" "openvino>=2023.1.0" "transformers>=4.31" accelerate "urllib3==1.26.15" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "diffusers>=0.14.0" "openvino>=2023.1.0" "transformers>=4.31" accelerate "torch>=2.1" Pillow opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
 
 
 .. parsed-literal::
@@ -171,7 +171,12 @@ pipelines in OpenVINO on our own data!
 
 .. code:: ipython3
 
-    from implementation.conversion_helper_utils import convert_encoder, convert_unet, convert_vae_decoder, convert_vae_encoder 
+    from implementation.conversion_helper_utils import (
+        convert_encoder,
+        convert_unet,
+        convert_vae_decoder,
+        convert_vae_encoder,
+    )
     
     # Convert the Text-to-Image models from PyTorch -> Onnx -> OpenVINO
     # 1. Convert the Text Encoder
@@ -207,14 +212,14 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    from openvino.runtime import Core
+    import openvino as ov
     
-    core = Core()
+    core = ov.Core()
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -272,7 +277,7 @@ As part of the ``OVStableDiffusionPipeline()`` class:
     from implementation.ov_stable_diffusion_pipeline import OVStableDiffusionPipeline
     
     scheduler = LMSDiscreteScheduler.from_config(conf)
-    tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
     
     ov_pipe = OVStableDiffusionPipeline(
         tokenizer=tokenizer,
@@ -280,13 +285,13 @@ As part of the ``OVStableDiffusionPipeline()`` class:
         unet=unet_model,
         vae_encoder=vae_encoder,
         vae_decoder=vae_decoder,
-        scheduler=scheduler
+        scheduler=scheduler,
     )
 
 
 .. parsed-literal::
 
-    /home/ea/work/openvino_notebooks/notebooks/236-stable-diffusion-v2/implementation/ov_stable_diffusion_pipeline.py:10: FutureWarning: Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.
+    /home/ea/work/openvino_notebooks/notebooks/stable-diffusion-v2/implementation/ov_stable_diffusion_pipeline.py:10: FutureWarning: Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.
       from diffusers.pipeline_utils import DiffusionPipeline
 
 
@@ -322,10 +327,18 @@ explanation of how it works can be found in this
 
     import ipywidgets as widgets
     
-    text_prompt = widgets.Textarea(value="valley in the Alps at sunset, epic vista, beautiful landscape, 4k, 8k", description='positive prompt', layout=widgets.Layout(width="auto"))
-    negative_prompt = widgets.Textarea(value="frames, borderline, text, charachter, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur", description='negative prompt', layout=widgets.Layout(width="auto"))
-    num_steps = widgets.IntSlider(min=1, max=50, value=25, description='steps:')
-    seed = widgets.IntSlider(min=0, max=10000000, description='seed: ', value=42)
+    text_prompt = widgets.Textarea(
+        value="valley in the Alps at sunset, epic vista, beautiful landscape, 4k, 8k",
+        description="positive prompt",
+        layout=widgets.Layout(width="auto"),
+    )
+    negative_prompt = widgets.Textarea(
+        value="frames, borderline, text, charachter, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur",
+        description="negative prompt",
+        layout=widgets.Layout(width="auto"),
+    )
+    num_steps = widgets.IntSlider(min=1, max=50, value=25, description="steps:")
+    seed = widgets.IntSlider(min=0, max=10000000, description="seed: ", value=42)
     widgets.VBox([text_prompt, negative_prompt, seed, num_steps])
 
 
@@ -340,8 +353,12 @@ explanation of how it works can be found in this
 .. code:: ipython3
 
     # Run inference pipeline
-    result = ov_pipe(text_prompt.value, negative_prompt=negative_prompt.value, num_inference_steps=num_steps.value, 
-                     seed=seed.value)
+    result = ov_pipe(
+        text_prompt.value,
+        negative_prompt=negative_prompt.value,
+        num_inference_steps=num_steps.value,
+        seed=seed.value,
+    )
 
 
 
@@ -352,13 +369,13 @@ explanation of how it works can be found in this
 
 .. code:: ipython3
 
-    final_image = result['sample'][0]
-    final_image.save('result.png')
+    final_image = result["sample"][0]
+    final_image.save("result.png")
     final_image
 
 
 
 
-.. image:: 236-stable-diffusion-v2-text-to-image-demo-with-output_files/236-stable-diffusion-v2-text-to-image-demo-with-output_24_0.png
+.. image:: stable-diffusion-v2-text-to-image-demo-with-output_files/stable-diffusion-v2-text-to-image-demo-with-output_24_0.png
 
 
diff --git a/docs/notebooks/236-stable-diffusion-v2-text-to-image-demo-with-output_files/236-stable-diffusion-v2-text-to-image-demo-with-output_24_0.jpg b/docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output_files/stable-diffusion-v2-text-to-image-demo-with-output_24_0.jpg
similarity index 100%
rename from docs/notebooks/236-stable-diffusion-v2-text-to-image-demo-with-output_files/236-stable-diffusion-v2-text-to-image-demo-with-output_24_0.jpg
rename to docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output_files/stable-diffusion-v2-text-to-image-demo-with-output_24_0.jpg
diff --git a/docs/notebooks/236-stable-diffusion-v2-text-to-image-demo-with-output_files/236-stable-diffusion-v2-text-to-image-demo-with-output_24_0.png b/docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output_files/stable-diffusion-v2-text-to-image-demo-with-output_24_0.png
similarity index 100%
rename from docs/notebooks/236-stable-diffusion-v2-text-to-image-demo-with-output_files/236-stable-diffusion-v2-text-to-image-demo-with-output_24_0.png
rename to docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output_files/stable-diffusion-v2-text-to-image-demo-with-output_24_0.png
diff --git a/docs/notebooks/236-stable-diffusion-v2-text-to-image-with-output.rst b/docs/notebooks/stable-diffusion-v2-text-to-image-with-output.rst
similarity index 91%
rename from docs/notebooks/236-stable-diffusion-v2-text-to-image-with-output.rst
rename to docs/notebooks/stable-diffusion-v2-text-to-image-with-output.rst
index 1a7e17cef2a..dbece89e181 100644
--- a/docs/notebooks/236-stable-diffusion-v2-text-to-image-with-output.rst
+++ b/docs/notebooks/stable-diffusion-v2-text-to-image-with-output.rst
@@ -20,9 +20,9 @@ fantastic world of diffusion models for everyone!
 
 In previous notebooks, we already discussed how to run `Text-to-Image
 generation and Image-to-Image generation using Stable Diffusion
-v1 <225-stable-diffusion-text-to-image-with-output.html>`__
+v1 `__
 and `controlling its generation process using
-ControlNet <235-controlnet-stable-diffusio235-controlnet-stable-diffusion-with-output.html>`__.
+ControlNet <./controlnet-stable-diffusion/controlnet-stable-diffusion.ipynb>`__.
 Now is turn of Stable Diffusion v2.
 
 Stable Diffusion v2: What’s new?
@@ -75,8 +75,8 @@ Notebook contains the following steps:
 
 **Note:** This is the full version of the Stable Diffusion text-to-image
 implementation. If you would like to get started and run the notebook
-quickly, check out `236-stable-diffusion-v2-text-to-image-demo
-notebook `__.
+quickly, check out `stable-diffusion-v2-text-to-image-demo
+notebook `__.
 
 Table of contents:
 ^^^^^^^^^^^^^^^^^^
@@ -113,7 +113,7 @@ install required packages
 
 .. code:: ipython3
 
-    %pip install -q "diffusers>=0.14.0" "openvino>=2023.1.0" "datasets>=2.14.6" "transformers>=4.25.1" gradio --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "diffusers>=0.14.0" "openvino>=2023.1.0" "datasets>=2.14.6" "transformers>=4.25.1" "gradio>=4.19" "torch>=2.1" Pillow opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "nncf>=2.9.0"
 
 
@@ -142,7 +142,7 @@ and original model
 Stable Diffusion in Diffusers library
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-To work with Stable Diffusion
+ To work with Stable Diffusion
 v2, we will use Hugging Face
 `Diffusers `__ library. To
 experiment with Stable Diffusion models, Diffusers exposes the
@@ -228,7 +228,7 @@ hidden states.
     import torch
     import openvino as ov
     
-    TEXT_ENCODER_OV_PATH = sd2_1_model_dir / 'text_encoder.xml'
+    TEXT_ENCODER_OV_PATH = sd2_1_model_dir / "text_encoder.xml"
     
     
     def cleanup_torchscript_cache():
@@ -240,11 +240,11 @@ hidden states.
         torch.jit._state._clear_class_state()
     
     
-    def convert_encoder(text_encoder: torch.nn.Module, ir_path:Path):
+    def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
         """
-        Convert Text Encoder model to IR. 
-        Function accepts pipeline, prepares example inputs for conversion 
-        Parameters: 
+        Convert Text Encoder model to IR.
+        Function accepts pipeline, prepares example inputs for conversion
+        Parameters:
             text_encoder (torch.nn.Module): text encoder PyTorch model
             ir_path (Path): File for storing model
         Returns:
@@ -261,12 +261,13 @@ hidden states.
                 ov_model = ov.convert_model(
                     text_encoder,  # model instance
                     example_input=input_ids,  # example inputs for model tracing
-                    input=([1,77],)  # input shape for conversion
+                    input=([1, 77],),  # input shape for conversion
                 )
                 ov.save_model(ov_model, ir_path)
                 del ov_model
                 cleanup_torchscript_cache()
-            print('Text Encoder successfully converted to IR')
+            print("Text Encoder successfully converted to IR")
+    
     
     if not TEXT_ENCODER_OV_PATH.exists():
         convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
@@ -312,14 +313,20 @@ such use cases required to modify number of input channels.
 
     import numpy as np
     
-    UNET_OV_PATH = sd2_1_model_dir / 'unet.xml'
+    UNET_OV_PATH = sd2_1_model_dir / "unet.xml"
     
     
-    def convert_unet(unet:torch.nn.Module, ir_path:Path, num_channels:int = 4, width:int = 64, height:int = 64):
+    def convert_unet(
+        unet: torch.nn.Module,
+        ir_path: Path,
+        num_channels: int = 4,
+        width: int = 64,
+        height: int = 64,
+    ):
         """
-        Convert Unet model to IR format. 
-        Function accepts pipeline, prepares example inputs for conversion 
-        Parameters: 
+        Convert Unet model to IR format.
+        Function accepts pipeline, prepares example inputs for conversion
+        Parameters:
             unet (torch.nn.Module): UNet PyTorch model
             ir_path (Path): File for storing model
             num_channels (int, optional, 4): number of input channels
@@ -328,10 +335,7 @@ such use cases required to modify number of input channels.
         Returns:
             None
         """
-        dtype_mapping = {
-            torch.float32: ov.Type.f32,
-            torch.float64: ov.Type.f64
-        }
+        dtype_mapping = {torch.float32: ov.Type.f32, torch.float64: ov.Type.f64}
         if not ir_path.exists():
             # prepare inputs
             encoder_hidden_state = torch.ones((2, 77, 1024))
@@ -347,15 +351,11 @@ such use cases required to modify number of input channels.
                 input_info.append((shape, element_type))
     
             with torch.no_grad():
-                ov_model = ov.convert_model(
-                    unet, 
-                    example_input=dummy_inputs,
-                    input=input_info
-                )
+                ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
             ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
-            print('U-Net successfully converted to IR')
+            print("U-Net successfully converted to IR")
     
     
     if not UNET_OV_PATH.exists():
@@ -394,15 +394,15 @@ RAM (recommended at least 32GB).
 
 .. code:: ipython3
 
-    VAE_ENCODER_OV_PATH = sd2_1_model_dir / 'vae_encoder.xml'
+    VAE_ENCODER_OV_PATH = sd2_1_model_dir / "vae_encoder.xml"
     
     
-    def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path, width:int = 512, height:int = 512):
+    def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path, width: int = 512, height: int = 512):
         """
-        Convert VAE model to IR format. 
-        VAE model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for onversion 
-        Parameters: 
+        Convert VAE model to IR format.
+        VAE model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for onversion
+        Parameters:
             vae (torch.nn.Module): VAE PyTorch model
             ir_path (Path): File for storing model
             width (int, optional, 512): input width
@@ -410,6 +410,7 @@ RAM (recommended at least 32GB).
         Returns:
             None
         """
+    
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -423,26 +424,27 @@ RAM (recommended at least 32GB).
             vae_encoder.eval()
             image = torch.zeros((1, 3, width, height))
             with torch.no_grad():
-                ov_model = ov.convert_model(vae_encoder, example_input=image, input=([1,3, width, height],))
+                ov_model = ov.convert_model(vae_encoder, example_input=image, input=([1, 3, width, height],))
             ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
-            print('VAE encoder successfully converted to IR')
+            print("VAE encoder successfully converted to IR")
     
     
-    def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path, width:int = 64, height:int = 64):
+    def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path, width: int = 64, height: int = 64):
         """
-        Convert VAE decoder model to IR format. 
-        Function accepts VAE model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion 
-        Parameters: 
-            vae (torch.nn.Module): VAE model 
+        Convert VAE decoder model to IR format.
+        Function accepts VAE model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion
+        Parameters:
+            vae (torch.nn.Module): VAE model
             ir_path (Path): File for storing model
             width (int, optional, 64): input width
             height (int, optional, 64): input height
         Returns:
             None
         """
+    
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -457,18 +459,19 @@ RAM (recommended at least 32GB).
     
             vae_decoder.eval()
             with torch.no_grad():
-                ov_model = ov.convert_model(vae_decoder, example_input=latents, input=([1,4, width, height],))
+                ov_model = ov.convert_model(vae_decoder, example_input=latents, input=([1, 4, width, height],))
             ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
-            print('VAE decoder successfully converted to IR')
+            print("VAE decoder successfully converted to IR")
+    
     
     if not VAE_ENCODER_OV_PATH.exists():
         convert_vae_encoder(vae, VAE_ENCODER_OV_PATH, 768, 768)
     else:
         print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH}")
     
-    VAE_DECODER_OV_PATH = sd2_1_model_dir / 'vae_decoder.xml'
+    VAE_DECODER_OV_PATH = sd2_1_model_dir / "vae_decoder.xml"
     
     if not VAE_DECODER_OV_PATH.exists():
         convert_vae_decoder(vae, VAE_DECODER_OV_PATH, 96, 96)
@@ -527,7 +530,7 @@ it is recommended to look into `Elucidating the Design Space of
 Diffusion-Based Generative Models `__.
 
 The chart above looks very similar to Stable Diffusion V1 from
-`notebook <225-stable-diffusion-text-to-image-with-output.html>`__,
+`notebook `__,
 but there is some small difference in details:
 
 -  Changed input resolution for U-Net model.
@@ -559,11 +562,11 @@ but there is some small difference in details:
     from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
     
     
-    def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
+    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
-        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio 
+        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-        
+    
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -583,7 +586,7 @@ but there is some small difference in details:
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-        
+    
         Parameters:
           image (PIL.Image.Image): input image
         Returns:
@@ -591,10 +594,8 @@ but there is some small difference in details:
            meta (Dict): dictionary with preprocessing metadata info
         """
         src_width, src_height = image.size
-        dst_width, dst_height = scale_fit_to_window(
-            512, 512, src_width, src_height)
-        image = np.array(image.resize((dst_width, dst_height),
-                         resample=PIL.Image.Resampling.LANCZOS))[None, :]
+        dst_width, dst_height = scale_fit_to_window(512, 512, src_width, src_height)
+        image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
         pad_width = 512 - dst_width
         pad_height = 512 - dst_height
         pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
@@ -688,7 +689,7 @@ but there is some small difference in details:
                 strength (int, *optional*, 1.0):
                     strength between initial image and generated in Image-to-Image pipeline, do not used in Text-to-Image
             Returns:
-                Dictionary with keys: 
+                Dictionary with keys:
                     sample - the last generated image PIL.Image.Image or np.array
             """
             if seed is not None:
@@ -698,7 +699,11 @@ but there is some small difference in details:
             # corresponds to doing no classifier free guidance.
             do_classifier_free_guidance = guidance_scale > 1.0
             # get prompt text embeddings
-            text_embeddings = self._encode_prompt(prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt)
+            text_embeddings = self._encode_prompt(
+                prompt,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                negative_prompt=negative_prompt,
+            )
             # set timesteps
             accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
             extra_set_kwargs = {}
@@ -741,7 +746,13 @@ but there is some small difference in details:
             image = self.postprocess_image(image, meta, output_type)
             return {"sample": image}
     
-        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True, negative_prompt:Union[str, List[str]] = None):
+        def _encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+            negative_prompt: Union[str, List[str]] = None,
+        ):
             """
             Encodes the prompt into text encoder hidden states.
     
@@ -765,16 +776,13 @@ but there is some small difference in details:
             )
             text_input_ids = text_inputs.input_ids
     
-            text_embeddings = self.text_encoder(
-                text_input_ids)[self._text_encoder_output]
+            text_embeddings = self.text_encoder(text_input_ids)[self._text_encoder_output]
     
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(
-                    text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(
-                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
+                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
+                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
     
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
@@ -808,10 +816,10 @@ but there is some small difference in details:
     
             return text_embeddings
     
-        def prepare_latents(self, image:PIL.Image.Image = None, latent_timestep:torch.Tensor = None):
+        def prepare_latents(self, image: PIL.Image.Image = None, latent_timestep: torch.Tensor = None):
             """
             Function for getting initial latents for starting generation
-            
+    
             Parameters:
                 image (PIL.Image.Image, *optional*, None):
                     Input image for generation, if not provided randon noise will be used as starting point
@@ -834,11 +842,11 @@ but there is some small difference in details:
             latents = self.scheduler.add_noise(torch.from_numpy(latents), torch.from_numpy(noise), latent_timestep).numpy()
             return latents, meta
     
-        def postprocess_image(self, image:np.ndarray, meta:Dict, output_type:str = "pil"):
+        def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
             """
-            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required), 
+            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
             normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-            
+    
             Parameters:
                 image (np.ndarray):
                     Generated image
@@ -864,25 +872,23 @@ but there is some small difference in details:
                 image = self.numpy_to_pil(image)
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [img.resize((orig_width, orig_height),
-                                        PIL.Image.Resampling.LANCZOS) for img in image]
+                    image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
             else:
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [cv2.resize(img, (orig_width, orig_width))
-                             for img in image]
+                    image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
             return image
     
-        def get_timesteps(self, num_inference_steps:int, strength:float):
+        def get_timesteps(self, num_inference_steps: int, strength: float):
             """
             Helper function for getting scheduler timesteps for generation
             In case of image-to-image generation, it updates number of steps according to strength
-            
+    
             Parameters:
                num_inference_steps (int):
                   number of inference steps for generation
                strength (float):
-                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. 
+                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
                    Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.
             """
             # get the original timestep using init_timestep
@@ -891,7 +897,7 @@ but there is some small difference in details:
             t_start = max(num_inference_steps - init_timestep, 0)
             timesteps = self.scheduler.timesteps[t_start:]
     
-            return timesteps, num_inference_steps - t_start 
+            return timesteps, num_inference_steps - t_start
 
 Configure Inference Pipeline
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -907,8 +913,8 @@ First, you should create instances of OpenVINO Model.
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -939,8 +945,8 @@ Let us define them and put all components together.
 
     from transformers import CLIPTokenizer
     
-    scheduler = DDIMScheduler.from_config(conf)     # DDIMScheduler is used because UNet quantization produces better results with it
-    tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
+    scheduler = DDIMScheduler.from_config(conf)  # DDIMScheduler is used because UNet quantization produces better results with it
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
     
     ov_pipe = OVStableDiffusionPipeline(
         tokenizer=tokenizer,
@@ -948,7 +954,7 @@ Let us define them and put all components together.
         unet=unet_model,
         vae_encoder=vae_encoder,
         vae_decoder=vae_decoder,
-        scheduler=scheduler
+        scheduler=scheduler,
     )
 
 Quantization
@@ -992,7 +998,7 @@ improve model inference speed.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
     
@@ -1009,8 +1015,13 @@ improve model inference speed.
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
     
     int8_ov_pipe = None
     
@@ -1281,7 +1292,7 @@ launch the interactive demo.
     
     use_quantized_model = widgets.Checkbox(
         value=True if quantized_model_present else False,
-        description='Use quantized model',
+        description="Use quantized model",
         disabled=not quantized_model_present,
     )
     
diff --git a/docs/notebooks/248-stable-diffusion-xl-with-output.rst b/docs/notebooks/stable-diffusion-xl-with-output.rst
similarity index 54%
rename from docs/notebooks/248-stable-diffusion-xl-with-output.rst
rename to docs/notebooks/stable-diffusion-xl-with-output.rst
index b335da34e6f..e0a6a6fa333 100644
--- a/docs/notebooks/248-stable-diffusion-xl-with-output.rst
+++ b/docs/notebooks/stable-diffusion-xl-with-output.rst
@@ -99,9 +99,9 @@ Install prerequisites
 
 .. code:: ipython3
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "diffusers>=0.18.0" "invisible-watermark>=0.2.0" "transformers>=4.33.0" "accelerate" "onnx" 
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "torch>=2.1" "diffusers>=0.18.0" "invisible-watermark>=0.2.0" "transformers>=4.33.0" "accelerate" "onnx" "peft==0.6.2"
     %pip install -q "git+https://github.com/huggingface/optimum-intel.git"
-    %pip install -q "openvino>=2023.1.0" gradio
+    %pip install -q "openvino>=2023.1.0" "gradio>=4.19" "nncf>=2.9.0"
 
 SDXL Base model
 ---------------
@@ -133,23 +133,6 @@ You can save the model on disk using the ``save_pretrained`` method.
     model_id = "stabilityai/stable-diffusion-xl-base-1.0"
     model_dir = Path("openvino-sd-xl-base-1.0")
 
-
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
-    2023-09-19 18:52:15.570335: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2023-09-19 18:52:15.609718: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2023-09-19 18:52:16.242994: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
-      warnings.warn(
-
-
 Select inference device SDXL Base model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -166,8 +149,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -178,14 +161,54 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
+    Dropdown(description='Device:', index=4, options=('CPU', 'GPU.0', 'GPU.1', 'GPU.2', 'AUTO'), value='AUTO')
+
+
+
+Please select below whether you would like to use weight compression to
+reduce memory footprint. `Optimum
+Intel `__
+supports weight compression via NNCF out of the box. For 8-bit
+compression we provide
+``quantization_config=OVWeightQuantizationConfig(bits=8, ...)`` argument
+to ``from_pretrained()`` method containing number of bits and other
+compression parameters.
+
+.. code:: ipython3
+
+    compress_weights = widgets.Checkbox(
+        description="Apply weight compression",
+        value=True,
+    )
+    
+    compress_weights
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Apply weight compression')
 
 
 
+.. code:: ipython3
+
+    def get_quantization_config(compress_weights):
+        quantization_config = None
+        if compress_weights.value:
+            from optimum.intel import OVWeightQuantizationConfig
+    
+            quantization_config = OVWeightQuantizationConfig(bits=8)
+        return quantization_config
+    
+    
+    quantization_config = get_quantization_config(compress_weights)
+
 .. code:: ipython3
 
     if not model_dir.exists():
-        text2image_pipe = OVStableDiffusionXLPipeline.from_pretrained(model_id, compile=False, device=device.value)
+        text2image_pipe = OVStableDiffusionXLPipeline.from_pretrained(model_id, compile=False, device=device.value, quantization_config=quantization_config)
         text2image_pipe.half()
         text2image_pipe.save_pretrained(model_dir)
         text2image_pipe.compile()
@@ -195,11 +218,171 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    Compiling the vae_decoder...
-    Compiling the unet...
-    Compiling the text_encoder...
-    Compiling the vae_encoder...
-    Compiling the text_encoder_2...
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (794 / 794)          | 100% (794 / 794)                  |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. parsed-literal::
+
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (32 / 32)            | 100% (32 / 32)                    |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. parsed-literal::
+
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (40 / 40)            | 100% (40 / 40)                    |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. parsed-literal::
+
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (74 / 74)            | 100% (74 / 74)                    |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. parsed-literal::
+
+    INFO:nncf:Statistics of the bitwidth distribution:
+    +--------------+---------------------------+-----------------------------------+
+    | Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
+    |              |                           |             (layers)              |
+    +==============+===========================+===================================+
+    | 8            | 100% (195 / 195)          | 100% (195 / 195)                  |
+    +--------------+---------------------------+-----------------------------------+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. parsed-literal::
+
+    Compiling the vae_decoder to AUTO ...
+    Compiling the unet to AUTO ...
+    Compiling the vae_encoder to AUTO ...
+    Compiling the text_encoder to AUTO ...
+    Compiling the text_encoder_2 to AUTO ...
 
 
 Run Text2Image generation pipeline
@@ -219,34 +402,26 @@ numpy random state with a specific seed for results reproducibility.
     import numpy as np
     
     prompt = "cute cat 4k, high-res, masterpiece, best quality, soft lighting, dynamic angle"
-    image = text2image_pipe(prompt, num_inference_steps=15, height=512, width=512, generator=np.random.RandomState(314)).images[0]
+    image = text2image_pipe(
+        prompt,
+        num_inference_steps=15,
+        height=512,
+        width=512,
+        generator=np.random.RandomState(314),
+    ).images[0]
     image.save("cat.png")
     image
 
 
-.. parsed-literal::
-
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/optimum/intel/openvino/modeling_diffusion.py:559: FutureWarning: `shared_memory` is deprecated and will be removed in 2024.0. Value of `shared_memory` is going to override `share_inputs` value. Please use only `share_inputs` explicitly.
-      outputs = self.request(inputs, shared_memory=True)
-
-
 
 .. parsed-literal::
 
       0%|          | 0/15 [00:00=4.19"
 
 Convert model to OpenVINO Intermediate Representation (IR) and compress model weights to INT4 using NNCF
 --------------------------------------------------------------------------------------------------------
@@ -139,11 +139,11 @@ that
 
 .. code:: ipython3
 
-    stateful_model_path = Path("stable-zephyr-3b-stateful/pytorch/dldt/compressed_weights/OV_FP16-4BIT_DEFAULT") 
+    stateful_model_path = Path("stable-zephyr-3b-stateful/pytorch/dldt/compressed_weights/OV_FP16-4BIT_DEFAULT")
     
     convert_script = genai_llm_bench / "convert.py"
     
-    if not (stateful_model_path / "openvino_model.xml").exists()
+    if not (stateful_model_path / "openvino_model.xml").exists():
         !python $convert_script --model_id stabilityai/stable-zephyr-3b --precision FP16 --compress_weights 4BIT_DEFAULT --output stable-zephyr-3b-stateful --force_convert
 
 
@@ -288,7 +288,7 @@ Compare with model without state
 
 .. code:: ipython3
 
-    stateless_model_path = Path("stable-zephyr-3b-stateless/pytorch/dldt/compressed_weights/OV_FP16-4BIT_DEFAULT") 
+    stateless_model_path = Path("stable-zephyr-3b-stateless/pytorch/dldt/compressed_weights/OV_FP16-4BIT_DEFAULT")
     
     if not (stateless_model_path / "openvino_model.xml").exists():
         !python $convert_script --model_id stabilityai/stable-zephyr-3b --precision FP16 --compress_weights 4BIT_DEFAULT --output stable-zephyr-3b-stateless --force_convert --disable-stateful
@@ -425,7 +425,11 @@ sequence of generated token ids that should be decoded using a tokenizer
     from optimum.intel.openvino import OVModelForCausalLM
     from transformers import AutoConfig
     
-    ov_model = OVModelForCausalLM.from_pretrained(stateful_model_path, config=AutoConfig.from_pretrained(stateful_model_path, trust_remote_code=True), device=device.value)
+    ov_model = OVModelForCausalLM.from_pretrained(
+        stateful_model_path,
+        config=AutoConfig.from_pretrained(stateful_model_path, trust_remote_code=True),
+        device=device.value,
+    )
 
 Interactive chatbot demo
 ------------------------
@@ -513,7 +517,7 @@ You can modify them in ``Advanced generation options`` section.
     model_configuration = {
         "start_message": f"<|system|>\n {DEFAULT_SYSTEM_PROMPT }<|endoftext|>",
         "history_template": "<|user|>\n{user}<|endoftext|><|assistant|>\n{assistant}<|endoftext|>",
-        "current_message_template": '<|user|>\n{user}<|endoftext|><|assistant|>\n{assistant}',
+        "current_message_template": "<|user|>\n{user}<|endoftext|><|assistant|>\n{assistant}",
     }
     history_template = model_configuration["history_template"]
     current_message_template = model_configuration["current_message_template"]
@@ -528,9 +532,7 @@ You can modify them in ``Advanced generation options`` section.
         ["Can you explain to me briefly what is Python programming language?"],
         ["Explain the plot of Cinderella in a sentence."],
         ["What are some common mistakes to avoid when writing code?"],
-        [
-            "Write a 100-word blog post on “Benefits of Artificial Intelligence and OpenVINO“"
-        ],
+        ["Write a 100-word blog post on “Benefits of Artificial Intelligence and OpenVINO“"],
     ]
     
     max_new_tokens = 256
@@ -540,9 +542,7 @@ You can modify them in ``Advanced generation options`` section.
         def __init__(self, token_ids):
             self.token_ids = token_ids
     
-        def __call__(
-            self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
-        ) -> bool:
+        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
             for stop_id in self.token_ids:
                 if input_ids[0][-1] == stop_id:
                     return True
@@ -571,9 +571,8 @@ You can modify them in ``Advanced generation options`` section.
         return partial_text
     
     
-    text_processor = model_configuration.get(
-        "partial_text_processor", default_partial_text_processor
-    )
+    text_processor = model_configuration.get("partial_text_processor", default_partial_text_processor)
+    
     
     def convert_history_to_text(history: List[Tuple[str, str]]):
         """
@@ -583,14 +582,7 @@ You can modify them in ``Advanced generation options`` section.
         Returns:
           history in text format
         """
-        text = start_message + "".join(
-            [
-                "".join(
-                    [history_template.format(num=round, user=item[0], assistant=item[1])]
-                )
-                for round, item in enumerate(history[:-1])
-            ]
-        )
+        text = start_message + "".join(["".join([history_template.format(num=round, user=item[0], assistant=item[1])]) for round, item in enumerate(history[:-1])])
         text += "".join(
             [
                 "".join(
@@ -645,9 +637,7 @@ You can modify them in ``Advanced generation options`` section.
             history = [history[-1]]
             messages = convert_history_to_text(history)
             input_ids = tok(messages, return_tensors="pt", **tokenizer_kwargs).input_ids
-        streamer = TextIteratorStreamer(
-            tok, timeout=30.0, skip_prompt=True, skip_special_tokens=True
-        )
+        streamer = TextIteratorStreamer(tok, timeout=30.0, skip_prompt=True, skip_special_tokens=True)
         generate_kwargs = dict(
             input_ids=input_ids,
             max_new_tokens=max_new_tokens,
@@ -759,9 +749,7 @@ You can modify them in ``Advanced generation options`` section.
                                 interactive=True,
                                 info="Penalize repetition — 1.0 to disable.",
                             )
-        gr.Examples(
-            examples, inputs=msg, label="Click on any example and press the 'Submit' button"
-        )
+        gr.Examples(examples, inputs=msg, label="Click on any example and press the 'Submit' button")
     
         submit_event = msg.submit(
             fn=user,
@@ -815,73 +803,3 @@ You can modify them in ``Advanced generation options`` section.
     # demo.launch(share=True)
     # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
     demo.launch(share=True)
-
-
-.. parsed-literal::
-
-    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/gradio/deprecation.py:40: UserWarning: `height` is deprecated in `Interface()`, please use it within `launch()` instead.
-      warnings.warn(value)
-    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/gradio/deprecation.py:43: UserWarning: You have unused kwarg parameters in Textbox, please remove them: {'container': False}
-      warnings.warn(
-
-
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7860
-
-
-::
-
-
-    ---------------------------------------------------------------------------
-
-    KeyboardInterrupt                         Traceback (most recent call last)
-
-    Cell In[9], line 326
-        320 demo.queue(max_size=2)
-        321 # if you are launching remotely, specify server_name and server_port
-        322 #  demo.launch(server_name='your server name', server_port='server port in int')
-        323 # if you have any issue to launch on your platform, you can pass share=True to launch method:
-        324 # demo.launch(share=True)
-        325 # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
-    --> 326 demo.launch(share=True)
-
-
-    File ~/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/gradio/blocks.py:1542, in Blocks.launch(self, inline, inbrowser, share, debug, enable_queue, max_threads, auth, auth_message, prevent_thread_lock, show_error, server_name, server_port, show_tips, height, width, encrypt, favicon_path, ssl_keyfile, ssl_certfile, ssl_keyfile_password, quiet, show_api, file_directories, _frontend)
-       1540 try:
-       1541     if self.share_url is None:
-    -> 1542         self.share_url = networking.setup_tunnel(
-       1543             self.server_name, self.server_port, self.share_token
-       1544         )
-       1545     print(strings.en["SHARE_LINK_DISPLAY"].format(self.share_url))
-       1546     if not (quiet):
-
-
-    File ~/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/gradio/networking.py:168, in setup_tunnel(local_host, local_port, share_token)
-        164     remote_host, remote_port = payload["host"], int(payload["port"])
-        165     tunnel = Tunnel(
-        166         remote_host, remote_port, local_host, local_port, share_token
-        167     )
-    --> 168     address = tunnel.start_tunnel()
-        169     return address
-        170 except Exception as e:
-
-
-    File ~/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/gradio/tunneling.py:61, in Tunnel.start_tunnel(self)
-         59 def start_tunnel(self) -> str:
-         60     binary_path = self.download_binary()
-    ---> 61     self.url = self._start_tunnel(binary_path)
-         62     return self.url
-
-
-    File ~/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/gradio/tunneling.py:97, in Tunnel._start_tunnel(self, binary)
-         95 if self.proc.stdout is None:
-         96     continue
-    ---> 97 line = self.proc.stdout.readline()
-         98 line = line.decode("utf-8")
-         99 if "start proxy success" in line:
-
-
-    KeyboardInterrupt: 
-
diff --git a/docs/notebooks/404-style-transfer-with-output.rst b/docs/notebooks/style-transfer-with-output.rst
similarity index 91%
rename from docs/notebooks/404-style-transfer-with-output.rst
rename to docs/notebooks/style-transfer-with-output.rst
index abfc32d5514..20dd2469101 100644
--- a/docs/notebooks/404-style-transfer-with-output.rst
+++ b/docs/notebooks/style-transfer-with-output.rst
@@ -66,11 +66,13 @@ Install requirements
     %pip install -q opencv-python requests tqdm
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
 
 Imports
 ~~~~~~~
@@ -96,13 +98,14 @@ Pointilism to do the style transfer.
 
 .. code:: ipython3
 
-    # Option to select different styles    
+    # Option to select different styles
     styleButtons = ToggleButtons(
-        options=['MOSAIC', 'RAIN-PRINCESS', 'CANDY', 'UDNIE', 'POINTILISM'],
+        options=["MOSAIC", "RAIN-PRINCESS", "CANDY", "UDNIE", "POINTILISM"],
         description="Click one of the styles you want to use for the style transfer",
         disabled=False,
-        style={'description_width': '300px'})
-       
+        style={"description_width": "300px"},
+    )
+    
     interactive(lambda option: print(option), option=styleButtons)
 
 The Model
@@ -205,8 +208,8 @@ results.
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -239,7 +242,7 @@ respectively. For *fast-neural-style-mosaic-onnx*, there is 1 input and
 Preprocess the image
 ~~~~~~~~~~~~~~~~~~~~
 
-Preprocess the input image
+ Preprocess the input image
 before running the model. Prepare the dimensions and channel order for
 the image to match the original image with the input tensor
 
@@ -254,12 +257,12 @@ the image to match the original image with the input tensor
         Preprocess input image to align with network size
     
         Parameters:
-            :param frame:  input frame 
+            :param frame:  input frame
             :param H:  height of the frame to style transfer model
             :param W:  width of the frame to style transfer model
             :returns: resized and transposed frame
         """
-        image = np.array(frame).astype('float32')
+        image = np.array(frame).astype("float32")
         image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
         image = cv2.resize(src=image, dsize=(H, W), interpolation=cv2.INTER_AREA)
         image = np.transpose(image, [2, 0, 1])
@@ -278,13 +281,13 @@ shape .
 
 .. code:: ipython3
 
-    # Postprocess the result        
+    # Postprocess the result
     def convert_result_to_image(frame, stylized_image) -> np.ndarray:
         """
         Postprocess stylized image for visualization
     
         Parameters:
-            :param frame:  input frame 
+            :param frame:  input frame
             :param stylized_image:  stylized image with specific style applied
             :returns: resized stylized image for visualization
         """
@@ -313,10 +316,10 @@ either using a webcam or a video file.
         3. Run AI inference for style transfer.
         4. Visualize the results.
         Parameters:
-            source: The webcam number to feed the video stream with primary webcam set to "0", or the video path.  
+            source: The webcam number to feed the video stream with primary webcam set to "0", or the video path.
             flip: To be used by VideoPlayer function for flipping capture image.
             use_popup: False for showing encoded frames over this notebook, True for creating a popup window.
-            skip_first_frames: Number of frames to skip at the beginning of the video. 
+            skip_first_frames: Number of frames to skip at the beginning of the video.
         """
         # Create a video player to play with target fps.
         player = None
@@ -338,12 +341,17 @@ either using a webcam or a video file.
                 # If the frame is larger than full HD, reduce size to improve the performance.
                 scale = 720 / max(frame.shape)
                 if scale < 1:
-                    frame = cv2.resize(src=frame, dsize=None, fx=scale, fy=scale,
-                                       interpolation=cv2.INTER_AREA)
+                    frame = cv2.resize(
+                        src=frame,
+                        dsize=None,
+                        fx=scale,
+                        fy=scale,
+                        interpolation=cv2.INTER_AREA,
+                    )
                 # Preprocess the input image.
     
                 image = preprocess_images(frame, H, W)
-               
+    
                 # Measure processing time for the input image.
                 start_time = time.time()
                 # Perform the inference step.
@@ -362,10 +370,17 @@ either using a webcam or a video file.
                 # Visualize the results.
                 f_height, f_width = frame.shape[:2]
                 fps = 1000 / processing_time_det
-                cv2.putText(result_image, text=f"Inference time: {processing_time_det:.1f}ms ({fps:.1f} FPS)", 
-                            org=(20, 40),fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=f_width / 1000,
-                            color=(0, 0, 255), thickness=1, lineType=cv2.LINE_AA)
-                
+                cv2.putText(
+                    result_image,
+                    text=f"Inference time: {processing_time_det:.1f}ms ({fps:.1f} FPS)",
+                    org=(20, 40),
+                    fontFace=cv2.FONT_HERSHEY_COMPLEX,
+                    fontScale=f_width / 1000,
+                    color=(0, 0, 255),
+                    thickness=1,
+                    lineType=cv2.LINE_AA,
+                )
+    
                 # Use this workaround if there is flickering.
                 if use_popup:
                     cv2.imshow(title, result_image)
diff --git a/docs/notebooks/table-question-answering-with-output.rst b/docs/notebooks/table-question-answering-with-output.rst
new file mode 100644
index 00000000000..76dfbea00cf
--- /dev/null
+++ b/docs/notebooks/table-question-answering-with-output.rst
@@ -0,0 +1,503 @@
+Table Question Answering using TAPAS and OpenVINO™
+==================================================
+
+Table Question Answering (Table QA) is the answering a question about an
+information on a given table. You can use the Table Question Answering
+models to simulate SQL execution by inputting a table.
+
+In this tutorial we demonstrate how to perform table question answering
+using OpenVINO. This example based on `TAPAS base model fine-tuned on
+WikiTable Questions
+(WTQ) `__ that
+is based on the paper `TAPAS: Weakly Supervised Table Parsing via
+Pre-training `__.
+
+Answering natural language questions over tables is usually seen as a
+semantic parsing task. To alleviate the collection cost of full logical
+forms, one popular approach focuses on weak supervision consisting of
+denotations instead of logical forms. However, training semantic parsers
+from weak supervision poses difficulties, and in addition, the generated
+logical forms are only used as an intermediate step prior to retrieving
+the denotation. In `this
+paper `__, it is presented TAPAS,
+an approach to question answering over tables without generating logical
+forms. TAPAS trains from weak supervision, and predicts the denotation
+by selecting table cells and optionally applying a corresponding
+aggregation operator to such selection. TAPAS extends BERT’s
+architecture to encode tables as input, initializes from an effective
+joint pre-training of text segments and tables crawled from Wikipedia,
+and is trained end-to-end.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+-  `Use the original model to run an
+   inference <#use-the-original-model-to-run-an-inference>`__
+-  `Convert the original model to OpenVINO Intermediate Representation
+   (IR)
+   format <#convert-the-original-model-to-openvino-intermediate-representation-ir-format>`__
+-  `Run the OpenVINO model <#run-the-openvino-model>`__
+-  `Interactive inference <#interactive-inference>`__
+
+Prerequisites
+~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    
+    %pip install -q torch "transformers>=4.31.0" "torch>=2.1" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2023.2.0" "gradio>=4.0.2"
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. code:: ipython3
+
+    import torch
+    from transformers import TapasForQuestionAnswering
+    from transformers import TapasTokenizer
+    from transformers import pipeline
+    import pandas as pd
+
+
+.. parsed-literal::
+
+    2024-04-18 01:04:30.594476: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 01:04:30.630628: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+
+
+.. parsed-literal::
+
+    2024-04-18 01:04:31.285021: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
+
+Use ``TapasForQuestionAnswering.from_pretrained`` to download a
+pretrained model and ``TapasTokenizer.from_pretrained`` to get a
+tokenizer.
+
+.. code:: ipython3
+
+    model = TapasForQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq")
+    tokenizer = TapasTokenizer.from_pretrained("google/tapas-large-finetuned-wtq")
+    
+    data = {
+        "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
+        "Number of movies": ["87", "53", "69"],
+    }
+    table = pd.DataFrame.from_dict(data)
+    question = "how many movies does Leonardo Di Caprio have?"
+    table
+
+
+
+
+.. raw:: html
+
+    

+    
+    
Sentences ENSentences DE

+      
+        
+          
+          
+          
+        
+      
+      
+        
+          
+          
+          
+        
+        
+          
+          
+          
+        
+        
+          
+          
+          
+        
+      
+    
ActorsNumber of movies
0Brad Pitt87
1Leonardo Di Caprio53
2George Clooney69

+    
+
+
+
+Use the original model to run an inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+We use `this
+example `__ to
+demonstrate how to make an inference. You can use ``pipeline`` from
+``transformer`` library for this purpose.
+
+.. code:: ipython3
+
+    tqa = pipeline(task="table-question-answering", model=model, tokenizer=tokenizer)
+    result = tqa(table=table, query=question)
+    print(f"The answer is {result['cells'][0]}")
+
+
+.. parsed-literal::
+
+    The answer is 53
+
+
+You can read more about the inference output structure in `this
+documentation `__.
+
+Convert the original model to OpenVINO Intermediate Representation (IR) format
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+The original model is a PyTorch module, that can be converted with
+``ov.convert_model`` function directly. We also use ``ov.save_model``
+function to serialize the result of conversion.
+
+.. code:: ipython3
+
+    import openvino as ov
+    from pathlib import Path
+    
+    
+    # Define the input shape
+    batch_size = 1
+    sequence_length = 29
+    
+    # Modify the input shape of the dummy_input dictionary
+    dummy_input = {
+        "input_ids": torch.zeros((batch_size, sequence_length), dtype=torch.long),
+        "attention_mask": torch.zeros((batch_size, sequence_length), dtype=torch.long),
+        "token_type_ids": torch.zeros((batch_size, sequence_length, 7), dtype=torch.long),
+    }
+    
+    
+    ov_model_xml_path = Path("models/ov_model.xml")
+    
+    if not ov_model_xml_path.exists():
+        ov_model = ov.convert_model(model, example_input=dummy_input)
+        ov.save_model(ov_model, ov_model_xml_path)
+
+
+.. parsed-literal::
+
+    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
+
+
+.. parsed-literal::
+
+    [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+      warnings.warn(
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1600: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      self.indices = torch.as_tensor(indices)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1601: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      self.num_segments = torch.as_tensor(num_segments, device=indices.device)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1703: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      batch_size = torch.prod(torch.tensor(list(index.batch_shape())))
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1779: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      [torch.as_tensor([-1], dtype=torch.long), torch.as_tensor(vector_shape, dtype=torch.long)], dim=0
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1782: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      flat_values = values.reshape(flattened_shape.tolist())
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1784: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      out = torch.zeros(int(flat_index.num_segments), dtype=torch.float, device=flat_values.device)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1792: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      torch.as_tensor(index.batch_shape(), dtype=torch.long),
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1793: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      torch.as_tensor([index.num_segments], dtype=torch.long),
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1794: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      torch.as_tensor(vector_shape, dtype=torch.long),
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1799: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      output_values = segment_means.clone().view(new_shape.tolist()).to(values.dtype)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1730: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      batch_shape = torch.as_tensor(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1734: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      num_segments = torch.as_tensor(num_segments)  # create a rank 0 tensor (scalar) containing num_segments (e.g. 64)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1745: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      new_shape = [int(x) for x in new_tensor.tolist()]
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1748: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      multiples = torch.cat([batch_shape, torch.as_tensor([1])], dim=0)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1749: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      indices = indices.repeat(multiples.tolist())
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:316: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      torch.as_tensor(self.config.max_position_embeddings - 1, device=device), position - first_position
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1260: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      indices=torch.min(row_ids, torch.as_tensor(self.config.max_num_rows - 1, device=row_ids.device)),
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1265: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      indices=torch.min(column_ids, torch.as_tensor(self.config.max_num_columns - 1, device=column_ids.device)),
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1957: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * torch.as_tensor(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1962: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * torch.as_tensor(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1998: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      labels_per_column, _ = reduce_sum(torch.as_tensor(labels, dtype=torch.float32, device=labels.device), col_index)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:2021: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      torch.as_tensor(labels, dtype=torch.long, device=labels.device), cell_index
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:2028: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      column_mask = torch.as_tensor(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:2053: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      selected_column_id = torch.as_tensor(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:2058: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+      selected_column_mask = torch.as_tensor(
+
+
+Run the OpenVINO model
+~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Select a device from dropdown list for running inference using OpenVINO.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    core = ov.Core()
+    
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+We use ``ov.compile_model`` to make it ready to use for loading on a
+device. To prepare inputs use the original ``tokenizer``.
+
+.. code:: ipython3
+
+    inputs = tokenizer(table=table, queries=question, padding="max_length", return_tensors="pt")
+    
+    compiled_model = core.compile_model(ov_model_xml_path, device.value)
+    result = compiled_model((inputs["input_ids"], inputs["attention_mask"], inputs["token_type_ids"]))
+
+Now we should postprocess results. For this, we can use the appropriate
+part of the code from
+`postprocess `__
+method of ``TableQuestionAnsweringPipeline``.
+
+.. code:: ipython3
+
+    logits = result[0]
+    logits_aggregation = result[1]
+    
+    
+    predictions = tokenizer.convert_logits_to_predictions(inputs, torch.from_numpy(result[0]))
+    answer_coordinates_batch = predictions[0]
+    aggregators = {}
+    aggregators_prefix = {}
+    answers = []
+    for index, coordinates in enumerate(answer_coordinates_batch):
+        cells = [table.iat[coordinate] for coordinate in coordinates]
+        aggregator = aggregators.get(index, "")
+        aggregator_prefix = aggregators_prefix.get(index, "")
+        answer = {
+            "answer": aggregator_prefix + ", ".join(cells),
+            "coordinates": coordinates,
+            "cells": [table.iat[coordinate] for coordinate in coordinates],
+        }
+        if aggregator:
+            answer["aggregator"] = aggregator
+    
+        answers.append(answer)
+    
+    print(answers[0]["cells"][0])
+
+
+.. parsed-literal::
+
+    53
+
+
+Also, we can use the original pipeline. For this, we should create a
+wrapper for ``TapasForQuestionAnswering`` class replacing ``forward``
+method to use the OpenVINO model for inference and methods and
+attributes of original model class to be integrated into the pipeline.
+
+.. code:: ipython3
+
+    from transformers import TapasConfig
+    
+    
+    # get config for pretrained model
+    config = TapasConfig.from_pretrained("google/tapas-large-finetuned-wtq")
+    
+    
+    class TapasForQuestionAnswering(TapasForQuestionAnswering):  # it is better to keep the class name to avoid warnings
+        def __init__(self, ov_model_path):
+            super().__init__(config)  # pass config from the pretrained model
+            self.tqa_model = core.compile_model(ov_model_path, device.value)
+    
+        def forward(self, input_ids, *, attention_mask, token_type_ids):
+            results = self.tqa_model((input_ids, attention_mask, token_type_ids))
+    
+            return torch.from_numpy(results[0]), torch.from_numpy(results[1])
+    
+    
+    compiled_model = TapasForQuestionAnswering(ov_model_xml_path)
+    tqa = pipeline(task="table-question-answering", model=compiled_model, tokenizer=tokenizer)
+    print(tqa(table=table, query=question)["cells"][0])
+
+
+.. parsed-literal::
+
+    53
+
+
+Interactive inference
+~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import requests
+    
+    import gradio as gr
+    import pandas as pd
+    
+    r = requests.get("https://github.com/openvinotoolkit/openvino_notebooks/files/13215688/eu_city_population_top10.csv")
+    
+    with open("eu_city_population_top10.csv", "w") as f:
+        f.write(r.text)
+    
+    
+    def display_table(csv_file_name):
+        table = pd.read_csv(csv_file_name.name, delimiter=",")
+        table = table.astype(str)
+    
+        return table
+    
+    
+    def highlight_answers(x, coordinates):
+        highlighted_table = pd.DataFrame("", index=x.index, columns=x.columns)
+        for coordinates_i in coordinates:
+            highlighted_table.iloc[coordinates_i[0], coordinates_i[1]] = "background-color: lightgreen"
+    
+        return highlighted_table
+    
+    
+    def infer(query, csv_file_name):
+        table = pd.read_csv(csv_file_name.name, delimiter=",")
+        table = table.astype(str)
+    
+        result = tqa(table=table, query=query)
+        table = table.style.apply(highlight_answers, axis=None, coordinates=result["coordinates"])
+    
+        return result["answer"], table
+    
+    
+    with gr.Blocks(title="TAPAS Table Question Answering") as demo:
+        with gr.Row():
+            with gr.Column():
+                search_query = gr.Textbox(label="Search query")
+                csv_file = gr.File(label="CSV file")
+                infer_button = gr.Button("Submit", variant="primary")
+            with gr.Column():
+                answer = gr.Textbox(label="Result")
+                result_csv_file = gr.Dataframe(label="All data")
+    
+        examples = [
+            [
+                "What is the city with the highest population that is not a capital?",
+                "eu_city_population_top10.csv",
+            ],
+            ["In which country is Madrid?", "eu_city_population_top10.csv"],
+            [
+                "In which cities is the population greater than 2,000,000?",
+                "eu_city_population_top10.csv",
+            ],
+        ]
+        gr.Examples(examples, inputs=[search_query, csv_file])
+    
+        # Callbacks
+        csv_file.upload(display_table, inputs=csv_file, outputs=result_csv_file)
+        csv_file.select(display_table, inputs=csv_file, outputs=result_csv_file)
+        csv_file.change(display_table, inputs=csv_file, outputs=result_csv_file)
+        infer_button.click(infer, inputs=[search_query, csv_file], outputs=[answer, result_csv_file])
+    
+    try:
+        demo.queue().launch(debug=False)
+    except Exception:
+        demo.queue().launch(share=True, debug=False)
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+    
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
diff --git a/docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output.rst b/docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output.rst
new file mode 100644
index 00000000000..0304f8cbd8c
--- /dev/null
+++ b/docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output.rst
@@ -0,0 +1,1126 @@
+Big Transfer Image Classification Model Quantization pipeline with NNCF
+=======================================================================
+
+This tutorial demonstrates the Quantization of the Big Transfer Image
+Classification model, which is fine-tuned on the sub-set of ImageNet
+dataset with 10 class labels with
+`NNCF `__. It uses
+`BiT-M-R50x1/1 `__
+model, which is trained on ImageNet-21k. Big Transfer is a recipe for
+pre-training image classification models on large supervised datasets
+and efficiently fine-tuning them on any given target task. The recipe
+achieves excellent performance on a wide variety of tasks, even when
+using very few labeled examples from the target dataset. This tutorial
+uses OpenVINO backend for performing model quantization in NNCF.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prepare Dataset <#prepare-dataset>`__
+-  `Plotting data samples <#plotting-data-samples>`__
+-  `Model Fine-tuning <#model-fine-tuning>`__
+-  `Perform model optimization (IR)
+   step <#perform-model-optimization-ir-step>`__
+-  `Compute accuracy of the TF
+   model <#compute-accuracy-of-the-tf-model>`__
+-  `Compute accuracy of the OpenVINO
+   model <#compute-accuracy-of-the-openvino-model>`__
+-  `Quantize OpenVINO model using
+   NNCF <#quantize-openvino-model-using-nncf>`__
+-  `Compute accuracy of the quantized
+   model <#compute-accuracy-of-the-quantized-model>`__
+-  `Compare FP32 and INT8 accuracy <#compare-fp32-and-int8-accuracy>`__
+-  `Compare inference results on one
+   picture <#compare-inference-results-on-one-picture>`__
+
+.. code:: ipython3
+
+    import platform
+    
+    %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin' and python_version <= '3.8'"
+    
+    %pip install -q "openvino>=2024.0.0" "nncf>=2.7.0" "tensorflow-hub>=0.15.0" "tensorflow_datasets" tf_keras
+    %pip install -q "scikit-learn>=1.3.2"
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. code:: ipython3
+
+    import os
+    import numpy as np
+    from pathlib import Path
+    
+    from openvino.runtime import Core
+    import openvino as ov
+    import nncf
+    import logging
+    
+    from nncf.common.logging.logger import set_log_level
+    
+    set_log_level(logging.ERROR)
+    
+    from sklearn.metrics import accuracy_score
+    
+    os.environ["TF_USE_LEGACY_KERAS"] = "1"
+    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+    os.environ["TFHUB_CACHE_DIR"] = str(Path("./tfhub_modules").resolve())
+    
+    import tensorflow as tf
+    import tensorflow_datasets as tfds
+    import tensorflow_hub as hub
+    
+    tfds.core.utils.gcs_utils._is_gcs_disabled = True
+    os.environ["NO_GCE_CHECK"] = "true"
+
+
+.. parsed-literal::
+
+    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
+
+
+.. code:: ipython3
+
+    core = Core()
+    tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+    
+    
+    # For top 5 labels.
+    MAX_PREDS = 1
+    TRAINING_BATCH_SIZE = 128
+    BATCH_SIZE = 1
+    IMG_SIZE = (256, 256)  # Default Imagenet image size
+    NUM_CLASSES = 10  # For Imagenette dataset
+    FINE_TUNING_STEPS = 1
+    LR = 1e-5
+    
+    MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)  # From Imagenet dataset
+    STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)  # From Imagenet dataset
+
+Prepare Dataset
+~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    datasets, datasets_info = tfds.load(
+        "imagenette/160px",
+        shuffle_files=True,
+        as_supervised=True,
+        with_info=True,
+        read_config=tfds.ReadConfig(shuffle_seed=0),
+    )
+    train_ds, validation_ds = datasets["train"], datasets["validation"]
+
+
+.. parsed-literal::
+
+    2024-04-17 23:07:11.869026: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-04-17 23:07:11.869251: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+
+
+.. code:: ipython3
+
+    def preprocessing(image, label):
+        image = tf.image.resize(image, IMG_SIZE)
+        image = tf.cast(image, tf.float32) / 255.0
+        label = tf.one_hot(label, NUM_CLASSES)
+        return image, label
+    
+    
+    train_dataset = train_ds.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(TRAINING_BATCH_SIZE).prefetch(tf.data.experimental.AUTOTUNE)
+    validation_dataset = (
+        validation_ds.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(TRAINING_BATCH_SIZE).prefetch(tf.data.experimental.AUTOTUNE)
+    )
+
+.. code:: ipython3
+
+    # Class labels dictionary with imagenette sample names and classes
+    lbl_dict = dict(
+        n01440764="tench",
+        n02102040="English springer",
+        n02979186="cassette player",
+        n03000684="chain saw",
+        n03028079="church",
+        n03394916="French horn",
+        n03417042="garbage truck",
+        n03425413="gas pump",
+        n03445777="golf ball",
+        n03888257="parachute",
+    )
+    
+    # Imagenette samples name index
+    class_idx_dict = [
+        "n01440764",
+        "n02102040",
+        "n02979186",
+        "n03000684",
+        "n03028079",
+        "n03394916",
+        "n03417042",
+        "n03425413",
+        "n03445777",
+        "n03888257",
+    ]
+    
+    
+    def label_func(key):
+        return lbl_dict[key]
+
+Plotting data samples
+~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import matplotlib.pyplot as plt
+    
+    # Get the class labels from the dataset info
+    class_labels = datasets_info.features["label"].names
+    
+    # Display labels along with the examples
+    num_examples_to_display = 4
+    fig, axes = plt.subplots(nrows=1, ncols=num_examples_to_display, figsize=(10, 5))
+    
+    for i, (image, label_index) in enumerate(train_ds.take(num_examples_to_display)):
+        label_name = class_labels[label_index.numpy()]
+    
+        axes[i].imshow(image.numpy())
+        axes[i].set_title(f"{label_func(label_name)}")
+        axes[i].axis("off")
+        plt.tight_layout()
+    plt.show()
+
+
+
+.. image:: tensorflow-bit-image-classification-nncf-quantization-with-output_files/tensorflow-bit-image-classification-nncf-quantization-with-output_9_0.png
+
+
+.. code:: ipython3
+
+    # Get the class labels from the dataset info
+    class_labels = datasets_info.features["label"].names
+    
+    # Display labels along with the examples
+    num_examples_to_display = 4
+    fig, axes = plt.subplots(nrows=1, ncols=num_examples_to_display, figsize=(10, 5))
+    
+    for i, (image, label_index) in enumerate(validation_ds.take(num_examples_to_display)):
+        label_name = class_labels[label_index.numpy()]
+    
+        axes[i].imshow(image.numpy())
+        axes[i].set_title(f"{label_func(label_name)}")
+        axes[i].axis("off")
+        plt.tight_layout()
+    plt.show()
+
+
+
+.. image:: tensorflow-bit-image-classification-nncf-quantization-with-output_files/tensorflow-bit-image-classification-nncf-quantization-with-output_10_0.png
+
+
+Model Fine-tuning
+~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    # Load the Big Transfer model
+    bit_model_url = "https://www.kaggle.com/models/google/bit/frameworks/TensorFlow2/variations/m-r50x1/versions/1"
+    bit_m = hub.KerasLayer(bit_model_url, trainable=True)
+    
+    # Customize the model for the new task
+    model = tf.keras.Sequential([bit_m, tf.keras.layers.Dense(NUM_CLASSES, activation="softmax")])
+    
+    # Compile the model
+    model.compile(
+        optimizer=tf.keras.optimizers.Adam(learning_rate=LR),
+        loss="categorical_crossentropy",
+        metrics=["accuracy"],
+    )
+    
+    # Fine-tune the model
+    model.fit(
+        train_dataset.take(3000),
+        epochs=FINE_TUNING_STEPS,
+        validation_data=validation_dataset.take(1000),
+    )
+    model.save("./bit_tf_model/", save_format="tf")
+
+
+.. parsed-literal::
+
+    
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+.. parsed-literal::
+
+    
+ 89/101 [=========================>....] - ETA: 1:50 - loss: 0.5494 - accuracy: 0.8649
+
+.. parsed-literal::
+
+    
+ 90/101 [=========================>....] - ETA: 1:41 - loss: 0.5439 - accuracy: 0.8661
+
+.. parsed-literal::
+
+    
+ 91/101 [==========================>...] - ETA: 1:32 - loss: 0.5382 - accuracy: 0.8674
+
+.. parsed-literal::
+
+    
+ 92/101 [==========================>...] - ETA: 1:22 - loss: 0.5326 - accuracy: 0.8688
+
+.. parsed-literal::
+
+    
+ 93/101 [==========================>...] - ETA: 1:13 - loss: 0.5273 - accuracy: 0.8700
+
+.. parsed-literal::
+
+    
+ 94/101 [==========================>...] - ETA: 1:04 - loss: 0.5231 - accuracy: 0.8711
+
+.. parsed-literal::
+
+    
+ 95/101 [===========================>..] - ETA: 55s - loss: 0.5177 - accuracy: 0.8725 
+
+.. parsed-literal::
+
+    
+ 96/101 [===========================>..] - ETA: 46s - loss: 0.5131 - accuracy: 0.8736
+
+.. parsed-literal::
+
+    
+ 97/101 [===========================>..] - ETA: 36s - loss: 0.5086 - accuracy: 0.8748
+
+.. parsed-literal::
+
+    
+ 98/101 [============================>.] - ETA: 27s - loss: 0.5044 - accuracy: 0.8758
+
+.. parsed-literal::
+
+    
+ 99/101 [============================>.] - ETA: 18s - loss: 0.5013 - accuracy: 0.8767
+
+.. parsed-literal::
+
+    
+100/101 [============================>.] - ETA: 9s - loss: 0.4974 - accuracy: 0.8775 
+
+.. parsed-literal::
+
+    
+101/101 [==============================] - ETA: 0s - loss: 0.4945 - accuracy: 0.8782
+
+.. parsed-literal::
+
+    
+101/101 [==============================] - 962s 9s/step - loss: 0.4945 - accuracy: 0.8782 - val_loss: 0.0819 - val_accuracy: 0.9800
+
+
+.. parsed-literal::
+
+    WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.
+
+
+Perform model optimization (IR) step
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    ir_path = Path("./bit_ov_model/bit_m_r50x1_1.xml")
+    if not ir_path.exists():
+        print("Initiating model optimization..!!!")
+        ov_model = ov.convert_model("./bit_tf_model")
+        ov.save_model(ov_model, ir_path)
+    else:
+        print(f"IR model {ir_path} already exists.")
+
+
+.. parsed-literal::
+
+    Initiating model optimization..!!!
+
+
+Compute accuracy of the TF model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    tf_model = tf.keras.models.load_model("./bit_tf_model/")
+    
+    tf_predictions = []
+    gt_label = []
+    
+    for _, label in validation_dataset:
+        for cls_label in label:
+            l_list = cls_label.numpy().tolist()
+            gt_label.append(l_list.index(1))
+    
+    for img_batch, label_batch in validation_dataset:
+        tf_result_batch = tf_model.predict(img_batch, verbose=0)
+        for i in range(len(img_batch)):
+            tf_result = tf_result_batch[i]
+            tf_result = tf.reshape(tf_result, [-1])
+            top5_label_idx = np.argsort(tf_result)[-MAX_PREDS::][::-1]
+            tf_predictions.append(top5_label_idx)
+    
+    # Convert the lists to NumPy arrays for accuracy calculation
+    tf_predictions = np.array(tf_predictions)
+    gt_label = np.array(gt_label)
+    
+    tf_acc_score = accuracy_score(tf_predictions, gt_label)
+
+Compute accuracy of the OpenVINO model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Select device for inference:
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    core = ov.Core()
+    
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+.. code:: ipython3
+
+    ov_fp32_model = core.read_model("./bit_ov_model/bit_m_r50x1_1.xml")
+    ov_fp32_model.reshape([1, IMG_SIZE[0], IMG_SIZE[1], 3])
+    
+    # Target device set to CPU (Other options Ex: AUTO/GPU/dGPU/)
+    compiled_model = ov.compile_model(ov_fp32_model, device.value)
+    output = compiled_model.outputs[0]
+    
+    ov_predictions = []
+    for img_batch, _ in validation_dataset:
+        for image in img_batch:
+            image = tf.expand_dims(image, axis=0)
+            pred = compiled_model(image)[output]
+            ov_result = tf.reshape(pred, [-1])
+            top_label_idx = np.argsort(ov_result)[-MAX_PREDS::][::-1]
+            ov_predictions.append(top_label_idx)
+    
+    fp32_acc_score = accuracy_score(ov_predictions, gt_label)
+
+Quantize OpenVINO model using NNCF
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Model Quantization using NNCF
+
+1. Preprocessing and preparing validation samples for NNCF calibration
+2. Perform NNCF Quantization on OpenVINO FP32 model
+3. Serialize Quantized OpenVINO INT8 model
+
+.. code:: ipython3
+
+    def nncf_preprocessing(image, label):
+        image = tf.image.resize(image, IMG_SIZE)
+        image = image - MEAN_RGB
+        image = image / STDDEV_RGB
+        return image
+    
+    
+    val_ds = validation_ds.map(nncf_preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(1).prefetch(tf.data.experimental.AUTOTUNE)
+    
+    calibration_dataset = nncf.Dataset(val_ds)
+    
+    ov_fp32_model = core.read_model("./bit_ov_model/bit_m_r50x1_1.xml")
+    
+    ov_int8_model = nncf.quantize(ov_fp32_model, calibration_dataset, fast_bias_correction=False)
+    
+    ov.save_model(ov_int8_model, "./bit_ov_int8_model/bit_m_r50x1_1_ov_int8.xml")
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+Compute accuracy of the quantized model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    nncf_quantized_model = core.read_model("./bit_ov_int8_model/bit_m_r50x1_1_ov_int8.xml")
+    nncf_quantized_model.reshape([1, IMG_SIZE[0], IMG_SIZE[1], 3])
+    
+    # Target device set to CPU by default
+    compiled_model = ov.compile_model(nncf_quantized_model, device.value)
+    output = compiled_model.outputs[0]
+    
+    ov_predictions = []
+    inp_tensor = nncf_quantized_model.inputs[0]
+    out_tensor = nncf_quantized_model.outputs[0]
+    
+    for img_batch, _ in validation_dataset:
+        for image in img_batch:
+            image = tf.expand_dims(image, axis=0)
+            pred = compiled_model(image)[output]
+            ov_result = tf.reshape(pred, [-1])
+            top_label_idx = np.argsort(ov_result)[-MAX_PREDS::][::-1]
+            ov_predictions.append(top_label_idx)
+    
+    int8_acc_score = accuracy_score(ov_predictions, gt_label)
+
+Compare FP32 and INT8 accuracy
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    print(f"Accuracy of the tensorflow model (fp32): {tf_acc_score * 100: .2f}%")
+    print(f"Accuracy of the OpenVINO optimized model (fp32): {fp32_acc_score * 100: .2f}%")
+    print(f"Accuracy of the OpenVINO quantized model (int8): {int8_acc_score * 100: .2f}%")
+    accuracy_drop = fp32_acc_score - int8_acc_score
+    print(f"Accuracy drop between OV FP32 and INT8 model: {accuracy_drop * 100:.1f}% ")
+
+
+.. parsed-literal::
+
+    Accuracy of the tensorflow model (fp32):  98.00%
+    Accuracy of the OpenVINO optimized model (fp32):  98.20%
+    Accuracy of the OpenVINO quantized model (int8):  97.00%
+    Accuracy drop between OV FP32 and INT8 model: 1.2% 
+
+
+Compare inference results on one picture
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    # Accessing validation sample
+    sample_idx = 50
+    vds = datasets["validation"]
+    
+    if len(vds) > sample_idx:
+        sample = vds.take(sample_idx + 1).skip(sample_idx).as_numpy_iterator().next()
+    else:
+        print("Dataset does not have enough samples...!!!")
+    
+    # Image data
+    sample_data = sample[0]
+    
+    # Label info
+    sample_label = sample[1]
+    
+    # Image data pre-processing
+    image = tf.image.resize(sample_data, IMG_SIZE)
+    image = tf.expand_dims(image, axis=0)
+    image = tf.cast(image, tf.float32) / 255.0
+    
+    
+    # OpenVINO inference
+    def ov_inference(model: ov.Model, image) -> str:
+        compiled_model = ov.compile_model(model, device.value)
+        output = compiled_model.outputs[0]
+        pred = compiled_model(image)[output]
+        ov_result = tf.reshape(pred, [-1])
+        pred_label = np.argsort(ov_result)[-MAX_PREDS::][::-1]
+        return pred_label
+    
+    
+    # OpenVINO FP32 model
+    ov_fp32_model = core.read_model("./bit_ov_model/bit_m_r50x1_1.xml")
+    ov_fp32_model.reshape([1, IMG_SIZE[0], IMG_SIZE[1], 3])
+    
+    # OpenVINO INT8 model
+    ov_int8_model = core.read_model("./bit_ov_int8_model/bit_m_r50x1_1_ov_int8.xml")
+    ov_int8_model.reshape([1, IMG_SIZE[0], IMG_SIZE[1], 3])
+    
+    # OpenVINO FP32 model inference
+    ov_fp32_pred_label = ov_inference(ov_fp32_model, image)
+    
+    print(f"Predicted label for the sample picture by float (fp32) model: {label_func(class_idx_dict[int(ov_fp32_pred_label)])}\n")
+    
+    # OpenVINO FP32 model inference
+    ov_int8_pred_label = ov_inference(ov_int8_model, image)
+    print(f"Predicted label for the sample picture by qunatized (int8) model: {label_func(class_idx_dict[int(ov_int8_pred_label)])}\n")
+    
+    # Plotting the image sample with ground truth
+    plt.figure()
+    plt.imshow(sample_data)
+    plt.title(f"Ground truth: {label_func(class_idx_dict[sample_label])}")
+    plt.axis("off")
+    plt.show()
+
+
+.. parsed-literal::
+
+    Predicted label for the sample picture by float (fp32) model: gas pump
+    
+
+
+.. parsed-literal::
+
+    Predicted label for the sample picture by qunatized (int8) model: gas pump
+    
+
+
+
+.. image:: tensorflow-bit-image-classification-nncf-quantization-with-output_files/tensorflow-bit-image-classification-nncf-quantization-with-output_27_2.png
+
diff --git a/docs/notebooks/127-tensorflow-bit-image-classification-nncf-quantization-with-output_files/127-tensorflow-bit-image-classification-nncf-quantization-with-output_10_2.png b/docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output_files/tensorflow-bit-image-classification-nncf-quantization-with-output_10_0.png
similarity index 100%
rename from docs/notebooks/127-tensorflow-bit-image-classification-nncf-quantization-with-output_files/127-tensorflow-bit-image-classification-nncf-quantization-with-output_10_2.png
rename to docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output_files/tensorflow-bit-image-classification-nncf-quantization-with-output_10_0.png
diff --git a/docs/notebooks/127-tensorflow-bit-image-classification-nncf-quantization-with-output_files/127-tensorflow-bit-image-classification-nncf-quantization-with-output_27_3.png b/docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output_files/tensorflow-bit-image-classification-nncf-quantization-with-output_27_2.png
similarity index 100%
rename from docs/notebooks/127-tensorflow-bit-image-classification-nncf-quantization-with-output_files/127-tensorflow-bit-image-classification-nncf-quantization-with-output_27_3.png
rename to docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output_files/tensorflow-bit-image-classification-nncf-quantization-with-output_27_2.png
diff --git a/docs/notebooks/127-tensorflow-bit-image-classification-nncf-quantization-with-output_files/127-tensorflow-bit-image-classification-nncf-quantization-with-output_9_2.png b/docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output_files/tensorflow-bit-image-classification-nncf-quantization-with-output_9_0.png
similarity index 100%
rename from docs/notebooks/127-tensorflow-bit-image-classification-nncf-quantization-with-output_files/127-tensorflow-bit-image-classification-nncf-quantization-with-output_9_2.png
rename to docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output_files/tensorflow-bit-image-classification-nncf-quantization-with-output_9_0.png
diff --git a/docs/notebooks/101-tensorflow-classification-to-openvino-with-output.rst b/docs/notebooks/tensorflow-classification-to-openvino-with-output.rst
similarity index 55%
rename from docs/notebooks/101-tensorflow-classification-to-openvino-with-output.rst
rename to docs/notebooks/tensorflow-classification-to-openvino-with-output.rst
index f7ec33011b6..76842dab865 100644
--- a/docs/notebooks/101-tensorflow-classification-to-openvino-with-output.rst
+++ b/docs/notebooks/tensorflow-classification-to-openvino-with-output.rst
@@ -38,10 +38,108 @@ Table of contents:
 
 .. code:: ipython3
 
+    import platform
+    
     # Install openvino package
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" "opencv-python"
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+    %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin' and python_version <= '3.8'"
+    %pip install -q tf_keras tensorflow_hub tqdm
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -54,9 +152,13 @@ Imports
 
 .. code:: ipython3
 
+    import os
     import time
     from pathlib import Path
     
+    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+    os.environ["TF_USE_LEGACY_KERAS"] = "1"
+    
     import cv2
     import matplotlib.pyplot as plt
     import numpy as np
@@ -64,27 +166,16 @@ Imports
     import tensorflow as tf
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file
 
-
-.. parsed-literal::
-
-    2024-03-12 22:18:47.211125: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 22:18:47.245088: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-
-
-.. parsed-literal::
-
-    2024-03-12 22:18:47.761261: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
 Settings
 --------
 
@@ -122,12 +213,8 @@ and save it to the disk.
 
 .. parsed-literal::
 
-    2024-03-12 22:18:50.501463: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-03-12 22:18:50.501497: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
-    2024-03-12 22:18:50.501501: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
-    2024-03-12 22:18:50.501645: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
-    2024-03-12 22:18:50.501661: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
-    2024-03-12 22:18:50.501665: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-04-18 01:05:25.005331: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-04-18 01:05:25.005523: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 .. parsed-literal::
@@ -137,14 +224,6 @@ and save it to the disk.
 
 .. parsed-literal::
 
-    2024-03-12 22:18:54.762256: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,1,1,1024]
-    	 [[{{node inputs}}]]
-
-
-.. parsed-literal::
-
-    2024-03-12 22:18:57.938498: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,1,1,1024]
-    	 [[{{node inputs}}]]
     WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 54). These functions will not be directly callable after loading.
 
 
@@ -222,8 +301,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -251,7 +330,7 @@ Get Model Information
 
     input_key = compiled_model.input(0)
     output_key = compiled_model.output(0)
-    network_input_shape = input_key.shape 
+    network_input_shape = input_key.shape
 
 Load an Image
 ~~~~~~~~~~~~~
@@ -266,7 +345,7 @@ network.
     # Download the image from the openvino_notebooks storage
     image_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
-        directory="data"
+        directory="data",
     )
     
     # The MobileNet network expects images in RGB format.
@@ -288,7 +367,7 @@ network.
 
 
 
-.. image:: 101-tensorflow-classification-to-openvino-with-output_files/101-tensorflow-classification-to-openvino-with-output_19_1.png
+.. image:: tensorflow-classification-to-openvino-with-output_files/tensorflow-classification-to-openvino-with-output_19_1.png
 
 
 Do Inference
@@ -307,7 +386,7 @@ Do Inference
     # Download the datasets from the openvino_notebooks storage
     image_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/datasets/imagenet/imagenet_2012.txt",
-        directory="data"
+        directory="data",
     )
     
     # Convert the inference result to a class name.
@@ -354,13 +433,10 @@ performance.
     end = time.perf_counter()
     time_ir = end - start
     
-    print(
-        f"IR model in OpenVINO Runtime/CPU: {time_ir/num_images:.4f} "
-        f"seconds per image, FPS: {num_images/time_ir:.2f}"
-    )
+    print(f"IR model in OpenVINO Runtime/CPU: {time_ir/num_images:.4f} " f"seconds per image, FPS: {num_images/time_ir:.2f}")
 
 
 .. parsed-literal::
 
-    IR model in OpenVINO Runtime/CPU: 0.0011 seconds per image, FPS: 946.40
+    IR model in OpenVINO Runtime/CPU: 0.0011 seconds per image, FPS: 917.77
 
diff --git a/docs/notebooks/tensorflow-classification-to-openvino-with-output_files/tensorflow-classification-to-openvino-with-output_19_1.png b/docs/notebooks/tensorflow-classification-to-openvino-with-output_files/tensorflow-classification-to-openvino-with-output_19_1.png
new file mode 100644
index 00000000000..cca7858e3bc
--- /dev/null
+++ b/docs/notebooks/tensorflow-classification-to-openvino-with-output_files/tensorflow-classification-to-openvino-with-output_19_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbd7b81cc8e7a73ea9bcb8be0c0575134f50b6af8f7de23ee9feed645a4cf66c
+size 387941
diff --git a/docs/notebooks/126-tensorflow-hub-with-output.rst b/docs/notebooks/tensorflow-hub-with-output.rst
similarity index 61%
rename from docs/notebooks/126-tensorflow-hub-with-output.rst
rename to docs/notebooks/tensorflow-hub-with-output.rst
index cdce57355bd..4485966d043 100644
--- a/docs/notebooks/126-tensorflow-hub-with-output.rst
+++ b/docs/notebooks/tensorflow-hub-with-output.rst
@@ -47,9 +47,9 @@ Table of contents:
    -  `Inference <#inference>`__
 
 .. |Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
-   :target: https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/126-tensorflow-hub/126-tensorflow-hub.ipynb
+   :target: https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/tensorflow-hub/tensorflow-hub.ipynb
 .. |Binder| image:: https://mybinder.org/badge_logo.svg
-   :target: https://mybinder.org/v2/gh/eaidova/openvino_notebooks_binder.git/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Fopenvinotoolkit%252Fopenvino_notebooks%26urlpath%3Dtree%252Fopenvino_notebooks%252Fnotebooks%2F126-tensorflow-hub%2F126-tensorflow-hub.ipynb
+   :target: https://mybinder.org/v2/gh/eaidova/openvino_notebooks_binder.git/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Fopenvinotoolkit%252Fopenvino_notebooks%26urlpath%3Dtree%252Fopenvino_notebooks%252Fnotebooks%2Ftensorflow-hub%2Ftensorflow-hub.ipynb
 
 Image classification
 --------------------
@@ -83,25 +83,118 @@ Install required packages
 
     import platform
     
-    %pip install -q tensorflow_hub tensorflow pillow numpy
+    %pip install -q pillow numpy
     %pip install -q "openvino>=2023.2.0"
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
+    
+    %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin' and python_version <= '3.8'"
+    %pip install -q tf_keras tensorflow_hub
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -116,8 +209,11 @@ Import libraries
 
     from pathlib import Path
     import os
-    from urllib.request import urlretrieve
+    import requests
+    
     os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+    os.environ["TF_USE_LEGACY_KERAS"] = "1"
+    os.environ["TFHUB_CACHE_DIR"] = str(Path("./tfhub_modules").resolve())
     
     import tensorflow_hub as hub
     import tensorflow as tf
@@ -132,13 +228,19 @@ Import libraries
 .. code:: ipython3
 
     IMAGE_SHAPE = (224, 224)
-    IMAGE_URL, IMAGE_PATH = "https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg", "data/grace_hopper.jpg"
-    MODEL_URL, MODEL_PATH = "https://www.kaggle.com/models/google/mobilenet-v1/frameworks/tensorFlow2/variations/100-224-classification/versions/2", "models/mobilenet_v2_100_224.xml"
+    IMAGE_URL, IMAGE_PATH = (
+        "https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg",
+        "data/grace_hopper.jpg",
+    )
+    MODEL_URL, MODEL_PATH = (
+        "https://www.kaggle.com/models/google/mobilenet-v1/frameworks/tensorFlow2/variations/100-224-classification/versions/2",
+        "models/mobilenet_v2_100_224.xml",
+    )
 
 Download the classifier
 ~~~~~~~~~~~~~~~~~~~~~~~
 
-Select a MobileNetV2
+ Select a MobileNetV2
 pre-trained model `from TensorFlow
 Hub `__
 and wrap it as a Keras layer with ``hub.KerasLayer``.
@@ -150,14 +252,14 @@ and wrap it as a Keras layer with ``hub.KerasLayer``.
 
 .. parsed-literal::
 
-    2024-03-12 22:55:04.217869: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-03-12 22:55:04.218046: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-04-18 01:06:17.784754: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-04-18 01:06:17.784935: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 Download a single image to try the model on
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The input ``images`` are
+ The input ``images`` are
 expected to have color values in the range [0,1], following the `common
 image input
 conventions `__.
@@ -167,14 +269,17 @@ For this model, the size of the input images is fixed to ``height`` x
 .. code:: ipython3
 
     Path(IMAGE_PATH).parent.mkdir(parents=True, exist_ok=True)
-    grace_hopper, _ = urlretrieve(IMAGE_URL, IMAGE_PATH)
-    grace_hopper = PIL.Image.open(grace_hopper).resize(IMAGE_SHAPE)
+    
+    r = requests.get(IMAGE_URL)
+    with Path(IMAGE_PATH).open("wb") as f:
+        f.write(r.content)
+    grace_hopper = PIL.Image.open(IMAGE_PATH).resize(IMAGE_SHAPE)
     grace_hopper
 
 
 
 
-.. image:: 126-tensorflow-hub-with-output_files/126-tensorflow-hub-with-output_11_0.png
+.. image:: tensorflow-hub-with-output_files/tensorflow-hub-with-output_11_0.png
 
 
 
@@ -225,8 +330,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -291,16 +396,19 @@ dataset labels to decode the predictions:
 
 .. code:: ipython3
 
-    labels_path = tf.keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')
+    labels_path = tf.keras.utils.get_file(
+        "ImageNetLabels.txt",
+        "https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt",
+    )
     imagenet_labels = np.array(open(labels_path).read().splitlines())
     plt.imshow(grace_hopper)
-    plt.axis('off')
+    plt.axis("off")
     predicted_class_name = imagenet_labels[predicted_class]
     _ = plt.title("Prediction: " + predicted_class_name.title())
 
 
 
-.. image:: 126-tensorflow-hub-with-output_files/126-tensorflow-hub-with-output_26_0.png
+.. image:: tensorflow-hub-with-output_files/tensorflow-hub-with-output_26_0.png
 
 
 Image style transfer
@@ -345,11 +453,21 @@ Install required packages
     %pip install -q "openvino>=2023.2.0"
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -358,8 +476,8 @@ Install required packages
 .. code:: ipython3
 
     import os
+    
     os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
-    from urllib.request import urlretrieve
     from pathlib import Path
     
     import openvino as ov
@@ -433,8 +551,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -463,7 +581,9 @@ Inference
     def download_image(src, dst):
         if not Path(dst).exists():
             Path(dst).parent.mkdir(parents=True, exist_ok=True)
-            urlretrieve(src, dst)
+            r = requests.get(src)
+            with open(dst, "wb") as f:
+                f.write(r.content)
         image = cv2.imread(dst)
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert image color to RGB space
         image = image / 255  # Normalize to [0, 1] interval
@@ -474,7 +594,7 @@ Inference
 
     content_image = download_image(CONTENT_IMAGE_URL, CONTENT_IMAGE_PATH)
     style_image = download_image(STYLE_IMAGE_URL, STYLE_IMAGE_PATH)
-    style_image = cv2.resize(style_image, (256,256))  # model was trained on 256x256 images
+    style_image = cv2.resize(style_image, (256, 256))  # model was trained on 256x256 images
 
 .. code:: ipython3
 
@@ -496,5 +616,5 @@ Inference
 
 
 
-.. image:: 126-tensorflow-hub-with-output_files/126-tensorflow-hub-with-output_45_0.png
+.. image:: tensorflow-hub-with-output_files/tensorflow-hub-with-output_45_0.png
 
diff --git a/docs/notebooks/126-tensorflow-hub-with-output_files/126-tensorflow-hub-with-output_11_0.jpg b/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_11_0.jpg
similarity index 100%
rename from docs/notebooks/126-tensorflow-hub-with-output_files/126-tensorflow-hub-with-output_11_0.jpg
rename to docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_11_0.jpg
diff --git a/docs/notebooks/126-tensorflow-hub-with-output_files/126-tensorflow-hub-with-output_11_0.png b/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_11_0.png
similarity index 100%
rename from docs/notebooks/126-tensorflow-hub-with-output_files/126-tensorflow-hub-with-output_11_0.png
rename to docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_11_0.png
diff --git a/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_26_0.png b/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_26_0.png
new file mode 100644
index 00000000000..abeb0cad037
--- /dev/null
+++ b/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_26_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2183d9cfbc5865851a25d7890cafabbb84a4264ba8a792f5550cbe4c2ba901e8
+size 203738
diff --git a/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_45_0.png b/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_45_0.png
new file mode 100644
index 00000000000..77b2f70419d
--- /dev/null
+++ b/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_45_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f4b0fb5d082511d93c8d2d5247da30c6c756c4222a6ac501083058857d5d519
+size 538743
diff --git a/docs/notebooks/120-tensorflow-instance-segmentation-to-openvino-with-output.rst b/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output.rst
similarity index 77%
rename from docs/notebooks/120-tensorflow-instance-segmentation-to-openvino-with-output.rst
rename to docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output.rst
index 4d09c541219..2ec0626ab43 100644
--- a/docs/notebooks/120-tensorflow-instance-segmentation-to-openvino-with-output.rst
+++ b/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output.rst
@@ -62,19 +62,96 @@ Install required packages:
 
     import platform
     
-    %pip install -q "openvino>=2023.1.0" "numpy>=1.21.0" "opencv-python"
+    %pip install -q "openvino>=2023.1.0" "numpy>=1.21.0" "opencv-python" "tqdm"
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
+    
+    %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin' and python_version <= '3.8'"
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -86,12 +163,22 @@ The notebook uses utility functions. The cell below will download the
 .. code:: ipython3
 
     # Fetch the notebook utils script from the openvino_notebooks repo
-    import urllib.request
+    import requests
     
-    urllib.request.urlretrieve(
-        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py",
-        filename="notebook_utils.py",
-    );
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
+
+
+
+
+.. parsed-literal::
+
+    21503
+
+
 
 Imports
 -------
@@ -139,7 +226,9 @@ Define model related variables and create corresponding directories:
     
     openvino_ir_path = ir_model_dir / f"{model_name}.xml"
     
-    tf_model_url = "https://www.kaggle.com/models/tensorflow/mask-rcnn-inception-resnet-v2/frameworks/tensorFlow2/variations/1024x1024/versions/1?tf-hub-format=compressed"
+    tf_model_url = (
+        "https://www.kaggle.com/models/tensorflow/mask-rcnn-inception-resnet-v2/frameworks/tensorFlow2/variations/1024x1024/versions/1?tf-hub-format=compressed"
+    )
     
     tf_model_archive_filename = f"{model_name}.tar.gz"
 
@@ -154,11 +243,7 @@ from TensorFlow Hub:
 
 .. code:: ipython3
 
-    download_file(
-        url=tf_model_url,
-        filename=tf_model_archive_filename,
-        directory=tf_model_dir
-    );
+    download_file(url=tf_model_url, filename=tf_model_archive_filename, directory=tf_model_dir);
 
 
 
@@ -224,8 +309,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -376,12 +461,12 @@ Read the image, resize and convert it to the input shape of the network:
 
 .. parsed-literal::
 
-    
+    
 
 
 
 
-.. image:: 120-tensorflow-instance-segmentation-to-openvino-with-output_files/120-tensorflow-instance-segmentation-to-openvino-with-output_25_1.png
+.. image:: tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_25_1.png
 
 
 Perform Inference
@@ -446,9 +531,7 @@ Define utility functions to visualize the inference results
     from typing import Optional
     
     
-    def add_detection_box(
-        box: np.ndarray, image: np.ndarray, mask: np.ndarray, label: Optional[str] = None
-    ) -> np.ndarray:
+    def add_detection_box(box: np.ndarray, image: np.ndarray, mask: np.ndarray, label: Optional[str] = None) -> np.ndarray:
         """
         Helper function for adding single bounding box to the image
     
@@ -489,7 +572,10 @@ Define utility functions to visualize the inference results
             font_scale = line_thickness / 3
             font_color = (255, 255, 255)
             text_size = cv2.getTextSize(
-                text=label, fontFace=font_face, fontScale=font_scale, thickness=font_thickness
+                text=label,
+                fontFace=font_face,
+                fontScale=font_scale,
+                thickness=font_thickness,
             )[0]
             # Calculate rectangle coordinates
             rectangle_point1 = point1
@@ -539,19 +625,22 @@ Define utility functions to visualize the inference results
         x_max = frame.shape[1] * box[3]
         y_max = frame.shape[0] * box[2]
         rect_src = np.array(
-            [[0, 0], [mask.shape[1], 0], [mask.shape[1], mask.shape[0]], [0, mask.shape[0]]],
+            [
+                [0, 0],
+                [mask.shape[1], 0],
+                [mask.shape[1], mask.shape[0]],
+                [0, mask.shape[0]],
+            ],
             dtype=np.float32,
         )
         rect_dst = np.array(
-            [[x_min, y_min], [x_max, y_min], [x_max, y_max], [x_min, y_max]], dtype=np.float32
+            [[x_min, y_min], [x_max, y_min], [x_max, y_max], [x_min, y_max]],
+            dtype=np.float32,
         )
         M = cv2.getPerspectiveTransform(rect_src[:, :], rect_dst[:, :])
-        mask_frame = cv2.warpPerspective(
-            mask, M, (frame.shape[1], frame.shape[0]), flags=cv2.INTER_CUBIC
-        )
+        mask_frame = cv2.warpPerspective(mask, M, (frame.shape[1], frame.shape[0]), flags=cv2.INTER_CUBIC)
         return mask_frame
 
-
 .. code:: ipython3
 
     from typing import Dict
@@ -585,11 +674,7 @@ Define utility functions to visualize the inference results
         num_detections = inference_result.get("num_detections")
         detection_masks = inference_result.get("detection_masks")
     
-        detections_limit = int(
-            min(detections_limit, num_detections[0])
-            if detections_limit is not None
-            else num_detections[0]
-        )
+        detections_limit = int(min(detections_limit, num_detections[0]) if detections_limit is not None else num_detections[0])
     
         # Normalize detection boxes coordinates to original image size
         original_image_height, original_image_width, _ = image.shape
@@ -608,7 +693,10 @@ Define utility functions to visualize the inference results
             mask_reframed = (mask_reframed > 0.5).astype(np.uint8)
             label = f"{detected_class_name} {score:.2f}"
             result = add_detection_box(
-                box=normalized_detection_boxes[i], image=result, mask=mask_reframed, label=label
+                box=normalized_detection_boxes[i],
+                image=result,
+                mask=mask_reframed,
+                label=label,
             )
     
         plt.imshow(result)
@@ -672,7 +760,7 @@ original test image:
 
 
 
-.. image:: 120-tensorflow-instance-segmentation-to-openvino-with-output_files/120-tensorflow-instance-segmentation-to-openvino-with-output_39_0.png
+.. image:: tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_39_0.png
 
 
 Next Steps
@@ -686,13 +774,13 @@ performance of your application using OpenVINO.
 Async inference pipeline
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-The key advantage of the Async
+ The key advantage of the Async
 API is that when a device is busy with inference, the application can
 perform other tasks in parallel (for example, populating inputs or
 scheduling other requests) rather than wait for the current inference to
 complete first. To understand how to perform async inference using
 openvino, refer to the `Async API
-tutorial <115-async-api-with-output.html>`__.
+tutorial `__.
 
 Integration preprocessing to model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -708,6 +796,6 @@ CPU as part of an application. This will improve selected device
 utilization.
 
 For more information, refer to the `Optimize Preprocessing
-tutorial <118-optimize-preprocessing-with-output.html>`__
-and to the overview of `Preprocessing
+tutorial `__ and
+to the overview of `Preprocessing
 API `__.
diff --git a/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_25_1.png b/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_25_1.png
new file mode 100644
index 00000000000..5479dc43b2c
--- /dev/null
+++ b/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_25_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6bd39d6eb3cbfd9cea347a2187dce7bee617c145fccc54de05dc5e35597da770
+size 395346
diff --git a/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_39_0.png b/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_39_0.png
new file mode 100644
index 00000000000..3e3934b35fe
--- /dev/null
+++ b/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_39_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7490928303dad0c415e87d17a7e92fa1ae6b8c76473ded8d0c7d9cbb98f9066a
+size 392067
diff --git a/docs/notebooks/120-tensorflow-object-detection-to-openvino-with-output.rst b/docs/notebooks/tensorflow-object-detection-to-openvino-with-output.rst
similarity index 77%
rename from docs/notebooks/120-tensorflow-object-detection-to-openvino-with-output.rst
rename to docs/notebooks/tensorflow-object-detection-to-openvino-with-output.rst
index 2902f183f9d..305fac6f8c2 100644
--- a/docs/notebooks/120-tensorflow-object-detection-to-openvino-with-output.rst
+++ b/docs/notebooks/tensorflow-object-detection-to-openvino-with-output.rst
@@ -61,19 +61,95 @@ Install required packages:
 
     import platform
     
-    %pip install -q "openvino>=2023.1.0" "numpy>=1.21.0" "opencv-python"
+    %pip install -q "openvino>=2023.1.0" "numpy>=1.21.0" "opencv-python" "tqdm"
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
+    %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin' and python_version <= '3.8'"
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -85,12 +161,22 @@ The notebook uses utility functions. The cell below will download the
 .. code:: ipython3
 
     # Fetch the notebook utils script from the openvino_notebooks repo
-    import urllib.request
+    import requests
     
-    urllib.request.urlretrieve(
-        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py",
-        filename="notebook_utils.py",
-    );
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
+
+
+
+
+.. parsed-literal::
+
+    21503
+
+
 
 Imports
 -------
@@ -106,6 +192,7 @@ Imports
     import cv2
     import matplotlib.pyplot as plt
     import numpy as np
+    
     # OpenVINO import
     import openvino as ov
     
@@ -152,11 +239,7 @@ from TensorFlow Hub:
 
 .. code:: ipython3
 
-    download_file(
-        url=tf_model_url,
-        filename=tf_model_archive_filename,
-        directory=tf_model_dir
-    )
+    download_file(url=tf_model_url, filename=tf_model_archive_filename, directory=tf_model_dir)
 
 
 
@@ -169,7 +252,7 @@ from TensorFlow Hub:
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/120-tensorflow-object-detection-to-openvino/model/tf/faster_rcnn_resnet50_v1_640x640.tar.gz')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/tensorflow-object-detection-to-openvino/model/tf/faster_rcnn_resnet50_v1_640x640.tar.gz')
 
 
 
@@ -231,8 +314,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -355,7 +438,7 @@ Load and save an image:
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/120-tensorflow-object-detection-to-openvino/data/coco_bike.jpg')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/tensorflow-object-detection-to-openvino/data/coco_bike.jpg')
 
 
 
@@ -383,12 +466,12 @@ Read the image, resize and convert it to the input shape of the network:
 
 .. parsed-literal::
 
-    
+    
 
 
 
 
-.. image:: 120-tensorflow-object-detection-to-openvino-with-output_files/120-tensorflow-object-detection-to-openvino-with-output_25_1.png
+.. image:: tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_25_1.png
 
 
 Perform Inference
@@ -407,7 +490,16 @@ outputs will be used.
 
 .. code:: ipython3
 
-    _, detection_boxes, detection_classes, _, detection_scores, num_detections, _, _ = model_outputs
+    (
+        _,
+        detection_boxes,
+        detection_classes,
+        _,
+        detection_scores,
+        num_detections,
+        _,
+        _,
+    ) = model_outputs
     
     image_detection_boxes = inference_result[detection_boxes]
     print("image_detection_boxes:", image_detection_boxes)
@@ -541,23 +633,51 @@ Define utility functions to visualize the inference results
         box_color = [random.randint(0, 255) for _ in range(3)]
         line_thickness = round(0.002 * (image.shape[0] + image.shape[1]) / 2) + 1
     
-        cv2.rectangle(img=image, pt1=point1, pt2=point2, color=box_color, thickness=line_thickness, lineType=cv2.LINE_AA)
+        cv2.rectangle(
+            img=image,
+            pt1=point1,
+            pt2=point2,
+            color=box_color,
+            thickness=line_thickness,
+            lineType=cv2.LINE_AA,
+        )
     
         if label:
             font_thickness = max(line_thickness - 1, 1)
             font_face = 0
             font_scale = line_thickness / 3
             font_color = (255, 255, 255)
-            text_size = cv2.getTextSize(text=label, fontFace=font_face, fontScale=font_scale, thickness=font_thickness)[0]
+            text_size = cv2.getTextSize(
+                text=label,
+                fontFace=font_face,
+                fontScale=font_scale,
+                thickness=font_thickness,
+            )[0]
             # Calculate rectangle coordinates
             rectangle_point1 = point1
             rectangle_point2 = (point1[0] + text_size[0], point1[1] - text_size[1] - 3)
             # Add filled rectangle
-            cv2.rectangle(img=image, pt1=rectangle_point1, pt2=rectangle_point2, color=box_color, thickness=-1, lineType=cv2.LINE_AA)
+            cv2.rectangle(
+                img=image,
+                pt1=rectangle_point1,
+                pt2=rectangle_point2,
+                color=box_color,
+                thickness=-1,
+                lineType=cv2.LINE_AA,
+            )
             # Calculate text position
             text_position = point1[0], point1[1] - 3
             # Add text with label to filled rectangle
-            cv2.putText(img=image, text=label, org=text_position, fontFace=font_face, fontScale=font_scale, color=font_color, thickness=font_thickness, lineType=cv2.LINE_AA)
+            cv2.putText(
+                img=image,
+                text=label,
+                org=text_position,
+                fontFace=font_face,
+                fontScale=font_scale,
+                color=font_color,
+                thickness=font_thickness,
+                lineType=cv2.LINE_AA,
+            )
         return image
 
 .. code:: ipython3
@@ -567,7 +687,12 @@ Define utility functions to visualize the inference results
     from openvino.runtime.utils.data_helpers import OVDict
     
     
-    def visualize_inference_result(inference_result: OVDict, image: np.ndarray, labels_map: Dict, detections_limit: Optional[int] = None):
+    def visualize_inference_result(
+        inference_result: OVDict,
+        image: np.ndarray,
+        labels_map: Dict,
+        detections_limit: Optional[int] = None,
+    ):
         """
         Helper function for visualizing inference result on the image
     
@@ -587,11 +712,7 @@ Define utility functions to visualize the inference results
         detection_scores: np.ndarray = inference_result.get("detection_scores")
         num_detections: np.ndarray = inference_result.get("num_detections")
     
-        detections_limit = int(
-            min(detections_limit, num_detections[0])
-            if detections_limit is not None
-            else num_detections[0]
-        )
+        detections_limit = int(min(detections_limit, num_detections[0]) if detections_limit is not None else num_detections[0])
     
         # Normalize detection boxes coordinates to original image size
         original_image_height, original_image_width, _ = image.shape
@@ -647,7 +768,7 @@ Zoo `__:
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/120-tensorflow-object-detection-to-openvino/data/coco_91cl.txt')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/tensorflow-object-detection-to-openvino/data/coco_91cl.txt')
 
 
 
@@ -683,7 +804,7 @@ original test image:
 
 
 
-.. image:: 120-tensorflow-object-detection-to-openvino-with-output_files/120-tensorflow-object-detection-to-openvino-with-output_38_0.png
+.. image:: tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_38_0.png
 
 
 Next Steps
@@ -697,13 +818,13 @@ performance of your application using OpenVINO.
 Async inference pipeline
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-The key advantage of the Async
+ The key advantage of the Async
 API is that when a device is busy with inference, the application can
 perform other tasks in parallel (for example, populating inputs or
 scheduling other requests) rather than wait for the current inference to
 complete first. To understand how to perform async inference using
 openvino, refer to the `Async API
-tutorial <115-async-api-with-output.html>`__.
+tutorial `__.
 
 Integration preprocessing to model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -719,6 +840,6 @@ CPU as part of an application. This will improve selected device
 utilization.
 
 For more information, refer to the `Optimize Preprocessing
-tutorial <118-optimize-preprocessing-with-output.html>`__
-and to the overview of `Preprocessing
+tutorial `__ and
+to the overview of `Preprocessing
 API `__.
diff --git a/docs/notebooks/tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_25_1.png b/docs/notebooks/tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_25_1.png
new file mode 100644
index 00000000000..5479dc43b2c
--- /dev/null
+++ b/docs/notebooks/tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_25_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6bd39d6eb3cbfd9cea347a2187dce7bee617c145fccc54de05dc5e35597da770
+size 395346
diff --git a/docs/notebooks/tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_38_0.png b/docs/notebooks/tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_38_0.png
new file mode 100644
index 00000000000..90ea52895da
--- /dev/null
+++ b/docs/notebooks/tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_38_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7512ac3ef1f68e67ecbae9e3cd6e739c0c027367d579e0a700d85d694da9a3b9
+size 391982
diff --git a/docs/notebooks/305-tensorflow-quantization-aware-training-with-output.rst b/docs/notebooks/tensorflow-quantization-aware-training-with-output.rst
similarity index 83%
rename from docs/notebooks/305-tensorflow-quantization-aware-training-with-output.rst
rename to docs/notebooks/tensorflow-quantization-aware-training-with-output.rst
index 39d7d0ab540..19e8a986e5d 100644
--- a/docs/notebooks/305-tensorflow-quantization-aware-training-with-output.rst
+++ b/docs/notebooks/tensorflow-quantization-aware-training-with-output.rst
@@ -54,16 +54,11 @@ models will be stored.
 
 .. code:: ipython3
 
-    import sys
-    import importlib.util
-    
-    %pip install -q "openvino>=2023.1.0" "nncf>=2.5.0"
-    if sys.platform == "win32":
-        if importlib.util.find_spec("tensorflow_datasets"):
-            %pip uninstall -q tensorflow-datasets
-        %pip install -q --upgrade "tfds-nightly"
-    else:
-        %pip install -q "tensorflow-datasets>=4.8.0"
+    %pip install -q "openvino>=2024.0.0" "nncf>=2.9.0"
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64'"
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin'"
+    %pip install -q "tensorflow-datasets>=4.9.0"
 
 
 .. parsed-literal::
@@ -83,8 +78,32 @@ models will be stored.
 
 .. parsed-literal::
 
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 3.20.3 which is incompatible.
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     
 
 .. parsed-literal::
@@ -99,8 +118,6 @@ models will be stored.
     
     import tensorflow as tf
     import tensorflow_datasets as tfds
-    from tensorflow.keras import layers
-    from tensorflow.keras import models
     
     from nncf import NNCFConfig
     from nncf.tensorflow.helpers.model_creation import create_compressed_model
@@ -133,19 +150,19 @@ models will be stored.
     
     fp32_pth_url = "https://storage.openvinotoolkit.org/repositories/nncf/openvino_notebook_ckpts/305_resnet18_imagenette_fp32_v1.h5"
     _ = tf.keras.utils.get_file(fp32_h5_path.resolve(), fp32_pth_url)
-    print(f'Absolute path where the model weights are saved:\n {fp32_h5_path.resolve()}')
+    print(f"Absolute path where the model weights are saved:\n {fp32_h5_path.resolve()}")
 
 
 .. parsed-literal::
 
-    2024-03-13 01:11:54.839379: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 01:11:54.874069: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 01:08:40.624224: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 01:08:40.660171: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 01:11:55.482764: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 01:08:41.258674: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -166,97 +183,87 @@ models will be stored.
 .. parsed-literal::
 
     
-   147456/134604992 [..............................] - ETA: 1:02
+    57344/134604992 [..............................] - ETA: 2:36
 
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-   655360/134604992 [..............................] - ETA: 27s 
+   319488/134604992 [..............................] - ETA: 54s 
 
 .. parsed-literal::
 
     
-  2719744/134604992 [..............................] - ETA: 9s 
+  1343488/134604992 [..............................] - ETA: 18s
+
+.. parsed-literal::
+
+    
+  2850816/134604992 [..............................] - ETA: 10s
+
+.. parsed-literal::
+
+    
+  8183808/134604992 [>.............................] - ETA: 4s 
 
 .. parsed-literal::
 
     
-  6914048/134604992 [>.............................] - ETA: 5s
+ 13860864/134604992 [==>...........................] - ETA: 2s
 
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- 12705792/134604992 [=>............................] - ETA: 3s
+ 17104896/134604992 [==>...........................] - ETA: 2s
 
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- 17588224/134604992 [==>...........................] - ETA: 2s
+ 20733952/134604992 [===>..........................] - ETA: 2s
 
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- 20963328/134604992 [===>..........................] - ETA: 2s
+ 25583616/134604992 [====>.........................] - ETA: 2s
 
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- 23764992/134604992 [====>.........................] - ETA: 2s
+ 28033024/134604992 [=====>........................] - ETA: 2s
 
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+ 31973376/134604992 [======>.......................] - ETA: 1s
 
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+ 36118528/134604992 [=======>......................] - ETA: 1s
 
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- 31449088/134604992 [======>.......................] - ETA: 2s
+ 38920192/134604992 [=======>......................] - ETA: 1s
 
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- 33857536/134604992 [======>.......................] - ETA: 2s
+ 41934848/134604992 [========>.....................] - ETA: 1s
 
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- 36683776/134604992 [=======>......................] - ETA: 2s
+ 45613056/134604992 [=========>....................] - ETA: 1s
 
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- 40517632/134604992 [========>.....................] - ETA: 2s
+ 49029120/134604992 [=========>....................] - ETA: 1s
 
 .. parsed-literal::
 
     
- 41484288/134604992 [========>.....................] - ETA: 2s
-
-.. parsed-literal::
-
-    
- 41934848/134604992 [========>.....................] - ETA: 2s
-
-.. parsed-literal::
-
-    
- 43761664/134604992 [========>.....................] - ETA: 2s
-
-.. parsed-literal::
-
-    
- 47177728/134604992 [=========>....................] - ETA: 2s
-
-.. parsed-literal::
-
-    
- 52133888/134604992 [==========>...................] - ETA: 1s
+ 52027392/134604992 [==========>...................] - ETA: 1s
 
 .. parsed-literal::
 
@@ -266,7 +273,12 @@ models will be stored.
 .. parsed-literal::
 
     
- 52551680/134604992 [==========>...................] - ETA: 2s
+ 54910976/134604992 [===========>..................] - ETA: 1s
+
+.. parsed-literal::
+
+    
+ 56811520/134604992 [===========>..................] - ETA: 1s
 
 .. parsed-literal::
 
@@ -276,12 +288,17 @@ models will be stored.
 .. parsed-literal::
 
     
- 61620224/134604992 [============>.................] - ETA: 1s
+ 60514304/134604992 [============>.................] - ETA: 1s
 
 .. parsed-literal::
 
     
- 64462848/134604992 [=============>................] - ETA: 1s
+ 62939136/134604992 [=============>................] - ETA: 1s
+
+.. parsed-literal::
+
+    
+ 67624960/134604992 [==============>...............] - ETA: 1s
 
 .. parsed-literal::
 
@@ -291,7 +308,7 @@ models will be stored.
 .. parsed-literal::
 
     
- 72540160/134604992 [===============>..............] - ETA: 1s
+ 70328320/134604992 [==============>...............] - ETA: 1s
 
 .. parsed-literal::
 
@@ -301,17 +318,17 @@ models will be stored.
 .. parsed-literal::
 
     
- 77783040/134604992 [================>.............] - ETA: 1s
+ 75784192/134604992 [===============>..............] - ETA: 1s
 
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- 78635008/134604992 [================>.............] - ETA: 1s
+ 79994880/134604992 [================>.............] - ETA: 1s
 
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- 83025920/134604992 [=================>............] - ETA: 1s
+ 81969152/134604992 [=================>............] - ETA: 1s
 
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@@ -321,37 +338,37 @@ models will be stored.
 .. parsed-literal::
 
     
- 88268800/134604992 [==================>...........] - ETA: 1s
+ 86663168/134604992 [==================>...........] - ETA: 1s
 
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- 89120768/134604992 [==================>...........] - ETA: 1s
+ 89120768/134604992 [==================>...........] - ETA: 0s
 
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- 90234880/134604992 [===================>..........] - ETA: 1s
+ 92356608/134604992 [===================>..........] - ETA: 0s
 
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- 92659712/134604992 [===================>..........] - ETA: 1s
+ 94355456/134604992 [====================>.........] - ETA: 0s
 
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+ 96370688/134604992 [====================>.........] - ETA: 0s
 
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- 99606528/134604992 [=====================>........] - ETA: 0s
+ 99729408/134604992 [=====================>........] - ETA: 0s
 
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-104275968/134604992 [======================>.......] - ETA: 0s
+ 99803136/134604992 [=====================>........] - ETA: 0s
 
 .. parsed-literal::
 
@@ -361,17 +378,12 @@ models will be stored.
 .. parsed-literal::
 
     
-109240320/134604992 [=======================>......] - ETA: 0s
+107438080/134604992 [======================>.......] - ETA: 0s
 
 .. parsed-literal::
 
     
-110092288/134604992 [=======================>......] - ETA: 0s
-
-.. parsed-literal::
-
-    
-112508928/134604992 [========================>.....] - ETA: 0s
+110436352/134604992 [=======================>......] - ETA: 0s
 
 .. parsed-literal::
 
@@ -381,37 +393,27 @@ models will be stored.
 .. parsed-literal::
 
     
-120201216/134604992 [=========================>....] - ETA: 0s
+119242752/134604992 [=========================>....] - ETA: 0s
 
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-120578048/134604992 [=========================>....] - ETA: 0s
+122077184/134604992 [==========================>...] - ETA: 0s
 
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-125231104/134604992 [==========================>...] - ETA: 0s
+125616128/134604992 [==========================>...] - ETA: 0s
 
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-125820928/134604992 [===========================>..] - ETA: 0s
+129187840/134604992 [===========================>..] - ETA: 0s
 
 .. parsed-literal::
 
     
-130924544/134604992 [============================>.] - ETA: 0s
-
-.. parsed-literal::
-
-    
-131538944/134604992 [============================>.] - ETA: 0s
-
-.. parsed-literal::
-
-    
-134447104/134604992 [============================>.] - ETA: 0s
+131284992/134604992 [============================>.] - ETA: 0s
 
 .. parsed-literal::
 
@@ -422,7 +424,7 @@ models will be stored.
 .. parsed-literal::
 
     Absolute path where the model weights are saved:
-     /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/305-tensorflow-quantization-aware-training/model/ResNet-18_fp32.h5
+     /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/tensorflow-quantization-aware-training/model/ResNet-18_fp32.h5
 
 
 Dataset Preprocessing
@@ -442,29 +444,34 @@ Download size: 94.18 MiB
 
 .. code:: ipython3
 
-    datasets, datasets_info = tfds.load('imagenette/160px', shuffle_files=True, as_supervised=True, with_info=True,
-                                        read_config=tfds.ReadConfig(shuffle_seed=0))
-    train_dataset, validation_dataset = datasets['train'], datasets['validation']
+    datasets, datasets_info = tfds.load(
+        "imagenette/160px",
+        shuffle_files=True,
+        as_supervised=True,
+        with_info=True,
+        read_config=tfds.ReadConfig(shuffle_seed=0),
+    )
+    train_dataset, validation_dataset = datasets["train"], datasets["validation"]
     fig = tfds.show_examples(train_dataset, datasets_info)
 
 
 .. parsed-literal::
 
-    2024-03-13 01:12:03.781864: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-03-13 01:12:03.781896: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
-    2024-03-13 01:12:03.781901: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
-    2024-03-13 01:12:03.782051: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
-    2024-03-13 01:12:03.782066: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
-    2024-03-13 01:12:03.782070: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
-    2024-03-13 01:12:03.899468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int64 and shape [1]
+    2024-04-18 01:08:48.987051: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-04-18 01:08:48.987083: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
+    2024-04-18 01:08:48.987087: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
+    2024-04-18 01:08:48.987237: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
+    2024-04-18 01:08:48.987252: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
+    2024-04-18 01:08:48.987255: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-04-18 01:08:49.095070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int64 and shape [1]
     	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:12:03.899790: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1]
-    	 [[{{node Placeholder/_1}}]]
-    2024-03-13 01:12:03.971431: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
+    2024-04-18 01:08:49.095393: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int64 and shape [1]
+    	 [[{{node Placeholder/_4}}]]
+    2024-04-18 01:08:49.187279: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
 
 
 
-.. image:: 305-tensorflow-quantization-aware-training-with-output_files/305-tensorflow-quantization-aware-training-with-output_6_1.png
+.. image:: tensorflow-quantization-aware-training-with-output_files/tensorflow-quantization-aware-training-with-output_6_1.png
 
 
 .. code:: ipython3
@@ -477,13 +484,11 @@ Download size: 94.18 MiB
         return image, label
     
     
-    train_dataset = (train_dataset.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE)
-                                  .batch(BATCH_SIZE)
-                                  .prefetch(tf.data.experimental.AUTOTUNE))
+    train_dataset = train_dataset.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(BATCH_SIZE).prefetch(tf.data.experimental.AUTOTUNE)
     
-    validation_dataset = (validation_dataset.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE)
-                                            .batch(BATCH_SIZE)
-                                            .prefetch(tf.data.experimental.AUTOTUNE))
+    validation_dataset = (
+        validation_dataset.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(BATCH_SIZE).prefetch(tf.data.experimental.AUTOTUNE)
+    )
 
 Define a Floating-Point Model
 -----------------------------
@@ -492,29 +497,40 @@ Define a Floating-Point Model
 
 .. code:: ipython3
 
-    def residual_conv_block(filters, stage, block, strides=(1, 1), cut='pre'):
+    def residual_conv_block(filters, stage, block, strides=(1, 1), cut="pre"):
         def layer(input_tensor):
-            x = layers.BatchNormalization(epsilon=2e-5)(input_tensor)
-            x = layers.Activation('relu')(x)
+            x = tf.keras.layers.BatchNormalization(epsilon=2e-5)(input_tensor)
+            x = tf.keras.layers.Activation("relu")(x)
     
             # Defining shortcut connection.
-            if cut == 'pre':
+            if cut == "pre":
                 shortcut = input_tensor
-            elif cut == 'post':
-                shortcut = layers.Conv2D(filters, (1, 1), strides=strides, kernel_initializer='he_uniform', 
-                                         use_bias=False)(x)
+            elif cut == "post":
+                shortcut = tf.keras.layers.Conv2D(
+                    filters,
+                    (1, 1),
+                    strides=strides,
+                    kernel_initializer="he_uniform",
+                    use_bias=False,
+                )(x)
     
             # Continue with convolution layers.
-            x = layers.ZeroPadding2D(padding=(1, 1))(x)
-            x = layers.Conv2D(filters, (3, 3), strides=strides, kernel_initializer='he_uniform', use_bias=False)(x)
+            x = tf.keras.layers.ZeroPadding2D(padding=(1, 1))(x)
+            x = tf.keras.layers.Conv2D(
+                filters,
+                (3, 3),
+                strides=strides,
+                kernel_initializer="he_uniform",
+                use_bias=False,
+            )(x)
     
-            x = layers.BatchNormalization(epsilon=2e-5)(x)
-            x = layers.Activation('relu')(x)
-            x = layers.ZeroPadding2D(padding=(1, 1))(x)
-            x = layers.Conv2D(filters, (3, 3), kernel_initializer='he_uniform', use_bias=False)(x)
+            x = tf.keras.layers.BatchNormalization(epsilon=2e-5)(x)
+            x = tf.keras.layers.Activation("relu")(x)
+            x = tf.keras.layers.ZeroPadding2D(padding=(1, 1))(x)
+            x = tf.keras.layers.Conv2D(filters, (3, 3), kernel_initializer="he_uniform", use_bias=False)(x)
     
             # Add residual connection.
-            x = layers.Add()([x, shortcut])
+            x = tf.keras.layers.Add()([x, shortcut])
             return x
     
         return layer
@@ -522,38 +538,38 @@ Define a Floating-Point Model
     
     def ResNet18(input_shape=None):
         """Instantiates the ResNet18 architecture."""
-        img_input = layers.Input(shape=input_shape, name='data')
+        img_input = tf.keras.layers.Input(shape=input_shape, name="data")
     
         # ResNet18 bottom
-        x = layers.BatchNormalization(epsilon=2e-5, scale=False)(img_input)
-        x = layers.ZeroPadding2D(padding=(3, 3))(x)
-        x = layers.Conv2D(64, (7, 7), strides=(2, 2), kernel_initializer='he_uniform', use_bias=False)(x)
-        x = layers.BatchNormalization(epsilon=2e-5)(x)
-        x = layers.Activation('relu')(x)
-        x = layers.ZeroPadding2D(padding=(1, 1))(x)
-        x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(x)
+        x = tf.keras.layers.BatchNormalization(epsilon=2e-5, scale=False)(img_input)
+        x = tf.keras.layers.ZeroPadding2D(padding=(3, 3))(x)
+        x = tf.keras.layers.Conv2D(64, (7, 7), strides=(2, 2), kernel_initializer="he_uniform", use_bias=False)(x)
+        x = tf.keras.layers.BatchNormalization(epsilon=2e-5)(x)
+        x = tf.keras.layers.Activation("relu")(x)
+        x = tf.keras.layers.ZeroPadding2D(padding=(1, 1))(x)
+        x = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding="valid")(x)
     
         # ResNet18 body
         repetitions = (2, 2, 2, 2)
         for stage, rep in enumerate(repetitions):
             for block in range(rep):
-                filters = 64 * (2 ** stage)
+                filters = 64 * (2**stage)
                 if block == 0 and stage == 0:
-                    x = residual_conv_block(filters, stage, block, strides=(1, 1), cut='post')(x)
+                    x = residual_conv_block(filters, stage, block, strides=(1, 1), cut="post")(x)
                 elif block == 0:
-                    x = residual_conv_block(filters, stage, block, strides=(2, 2), cut='post')(x)
+                    x = residual_conv_block(filters, stage, block, strides=(2, 2), cut="post")(x)
                 else:
-                    x = residual_conv_block(filters, stage, block, strides=(1, 1), cut='pre')(x)
-        x = layers.BatchNormalization(epsilon=2e-5)(x)
-        x = layers.Activation('relu')(x)
+                    x = residual_conv_block(filters, stage, block, strides=(1, 1), cut="pre")(x)
+        x = tf.keras.layers.BatchNormalization(epsilon=2e-5)(x)
+        x = tf.keras.layers.Activation("relu")(x)
     
         # ResNet18 top
-        x = layers.GlobalAveragePooling2D()(x)
-        x = layers.Dense(NUM_CLASSES)(x)
-        x = layers.Activation('softmax')(x)
+        x = tf.keras.layers.GlobalAveragePooling2D()(x)
+        x = tf.keras.layers.Dense(NUM_CLASSES)(x)
+        x = tf.keras.layers.Activation("softmax")(x)
     
         # Create the model.
-        model = models.Model(img_input, x)
+        model = tf.keras.models.Model(img_input, x)
     
         return model
 
@@ -582,23 +598,23 @@ model and a training pipeline.
     # Compile the floating-point model.
     fp32_model.compile(
         loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
-        metrics=[tf.keras.metrics.CategoricalAccuracy(name='acc@1')]
+        metrics=[tf.keras.metrics.CategoricalAccuracy(name="acc@1")],
     )
     
     # Validate the floating-point model.
     test_loss, acc_fp32 = fp32_model.evaluate(
         validation_dataset,
-        callbacks=tf.keras.callbacks.ProgbarLogger(stateful_metrics=['acc@1'])
+        callbacks=tf.keras.callbacks.ProgbarLogger(stateful_metrics=["acc@1"]),
     )
     print(f"\nAccuracy of FP32 model: {acc_fp32:.3f}")
 
 
 .. parsed-literal::
 
-    2024-03-13 01:12:04.910372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int64 and shape [1]
-    	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:12:04.910741: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int64 and shape [1]
-    	 [[{{node Placeholder/_4}}]]
+    2024-04-18 01:08:50.112136: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1]
+    	 [[{{node Placeholder/_1}}]]
+    2024-04-18 01:08:50.112884: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1]
+    	 [[{{node Placeholder/_1}}]]
 
 
 .. parsed-literal::
@@ -624,7 +640,7 @@ model and a training pipeline.
 .. parsed-literal::
 
     
-4/4 [==============================] - 1s 254ms/sample - loss: 0.9807 - acc@1: 0.8220
+4/4 [==============================] - 1s 302ms/sample - loss: 0.9807 - acc@1: 0.8220
 
 
 .. parsed-literal::
@@ -662,9 +678,7 @@ scenario and requires only 3 modifications.
 
 .. code:: ipython3
 
-    nncf_config = register_default_init_args(nncf_config=nncf_config,
-                                             data_loader=train_dataset,
-                                             batch_size=BATCH_SIZE)
+    nncf_config = register_default_init_args(nncf_config=nncf_config, data_loader=train_dataset, batch_size=BATCH_SIZE)
 
 3. Create a wrapped model ready for compression fine-tuning from a
    pre-trained ``FP32`` model and a configuration object.
@@ -676,25 +690,25 @@ scenario and requires only 3 modifications.
 
 .. parsed-literal::
 
-    2024-03-13 01:12:07.816084: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int64 and shape [1]
-    	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:12:07.816469: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [1]
-    	 [[{{node Placeholder/_0}}]]
+    2024-04-18 01:08:52.920250: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_2' with dtype string and shape [1]
+    	 [[{{node Placeholder/_2}}]]
+    2024-04-18 01:08:52.920633: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1]
+    	 [[{{node Placeholder/_1}}]]
 
 
 .. parsed-literal::
 
-    2024-03-13 01:12:08.749313: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
+    2024-04-18 01:08:53.808337: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
 
 
 .. parsed-literal::
 
-    2024-03-13 01:12:09.358441: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
+    2024-04-18 01:08:54.455894: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
 
 
 .. parsed-literal::
 
-    2024-03-13 01:12:17.326475: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
+    2024-04-18 01:09:02.361497: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
 
 
 Evaluate the new model on the validation set after initialization of
@@ -708,13 +722,13 @@ demonstrated here.
     int8_model.compile(
         optimizer=tf.keras.optimizers.Adam(learning_rate=LR),
         loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
-        metrics=[tf.keras.metrics.CategoricalAccuracy(name='acc@1')]
+        metrics=[tf.keras.metrics.CategoricalAccuracy(name="acc@1")],
     )
     
     # Validate the INT8 model.
     test_loss, test_acc = int8_model.evaluate(
         validation_dataset,
-        callbacks=tf.keras.callbacks.ProgbarLogger(stateful_metrics=['acc@1'])
+        callbacks=tf.keras.callbacks.ProgbarLogger(stateful_metrics=["acc@1"]),
     )
 
 
@@ -741,7 +755,7 @@ demonstrated here.
 .. parsed-literal::
 
     
-4/4 [==============================] - 1s 301ms/sample - loss: 0.9766 - acc@1: 0.8120
+4/4 [==============================] - 1s 302ms/sample - loss: 0.9766 - acc@1: 0.8120
 
 
 Fine-tune the Compressed Model
@@ -764,10 +778,11 @@ training pipeline are required. Here is a simple example.
     
     # Validate the INT8 model.
     test_loss, acc_int8 = int8_model.evaluate(
-        validation_dataset, callbacks=tf.keras.callbacks.ProgbarLogger(stateful_metrics=['acc@1']))
+        validation_dataset,
+        callbacks=tf.keras.callbacks.ProgbarLogger(stateful_metrics=["acc@1"]),
+    )
     print(f"\nAccuracy of INT8 model after fine-tuning: {acc_int8:.3f}")
-    print(
-        f"\nAccuracy drop of tuned INT8 model over pre-trained FP32 model: {acc_fp32 - acc_int8:.3f}")
+    print(f"\nAccuracy drop of tuned INT8 model over pre-trained FP32 model: {acc_fp32 - acc_int8:.3f}")
 
 
 .. parsed-literal::
@@ -784,17 +799,17 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
-  1/101 [..............................] - ETA: 11:57 - loss: 0.6168 - acc@1: 0.9844
+  1/101 [..............................] - ETA: 11:52 - loss: 0.6168 - acc@1: 0.9844
 
 .. parsed-literal::
 
     
-  2/101 [..............................] - ETA: 43s - loss: 0.6303 - acc@1: 0.9766  
+  2/101 [..............................] - ETA: 41s - loss: 0.6303 - acc@1: 0.9766  
 
 .. parsed-literal::
 
     
-  3/101 [..............................] - ETA: 42s - loss: 0.6613 - acc@1: 0.9609
+  3/101 [..............................] - ETA: 41s - loss: 0.6613 - acc@1: 0.9609
 
 .. parsed-literal::
 
@@ -809,7 +824,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
-  6/101 [>.............................] - ETA: 40s - loss: 0.6805 - acc@1: 0.9466
+  6/101 [>.............................] - ETA: 39s - loss: 0.6805 - acc@1: 0.9466
 
 .. parsed-literal::
 
@@ -879,7 +894,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 20/101 [====>.........................] - ETA: 33s - loss: 0.6931 - acc@1: 0.9402
+ 20/101 [====>.........................] - ETA: 34s - loss: 0.6931 - acc@1: 0.9402
 
 .. parsed-literal::
 
@@ -914,7 +929,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 27/101 [=======>......................] - ETA: 31s - loss: 0.6963 - acc@1: 0.9372
+ 27/101 [=======>......................] - ETA: 30s - loss: 0.6963 - acc@1: 0.9372
 
 .. parsed-literal::
 
@@ -949,7 +964,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 34/101 [=========>....................] - ETA: 28s - loss: 0.6978 - acc@1: 0.9370
+ 34/101 [=========>....................] - ETA: 27s - loss: 0.6978 - acc@1: 0.9370
 
 .. parsed-literal::
 
@@ -984,7 +999,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 41/101 [===========>..................] - ETA: 25s - loss: 0.7021 - acc@1: 0.9371
+ 41/101 [===========>..................] - ETA: 24s - loss: 0.7021 - acc@1: 0.9371
 
 .. parsed-literal::
 
@@ -1164,12 +1179,12 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 77/101 [=====================>........] - ETA: 9s - loss: 0.7078 - acc@1: 0.9333 
+ 77/101 [=====================>........] - ETA: 10s - loss: 0.7078 - acc@1: 0.9333
 
 .. parsed-literal::
 
-    
- 78/101 [======================>.......] - ETA: 9s - loss: 0.7080 - acc@1: 0.9334
+    
+ 78/101 [======================>.......] - ETA: 9s - loss: 0.7080 - acc@1: 0.9334 
 
 .. parsed-literal::
 
@@ -1224,7 +1239,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 89/101 [=========================>....] - ETA: 4s - loss: 0.7087 - acc@1: 0.9331
+ 89/101 [=========================>....] - ETA: 5s - loss: 0.7087 - acc@1: 0.9331
 
 .. parsed-literal::
 
@@ -1289,7 +1304,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
-101/101 [==============================] - 49s 415ms/step - loss: 0.7134 - acc@1: 0.9299
+101/101 [==============================] - 49s 417ms/step - loss: 0.7134 - acc@1: 0.9299
 
 
 .. parsed-literal::
@@ -1300,7 +1315,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
-  1/101 [..............................] - ETA: 43s - loss: 0.5798 - acc@1: 1.0000
+  1/101 [..............................] - ETA: 41s - loss: 0.5798 - acc@1: 1.0000
 
 .. parsed-literal::
 
@@ -1315,7 +1330,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
-  4/101 [>.............................] - ETA: 39s - loss: 0.6225 - acc@1: 0.9844
+  4/101 [>.............................] - ETA: 40s - loss: 0.6225 - acc@1: 0.9844
 
 .. parsed-literal::
 
@@ -1340,12 +1355,12 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
-  9/101 [=>............................] - ETA: 37s - loss: 0.6435 - acc@1: 0.9740
+  9/101 [=>............................] - ETA: 38s - loss: 0.6435 - acc@1: 0.9740
 
 .. parsed-literal::
 
     
- 10/101 [=>............................] - ETA: 37s - loss: 0.6508 - acc@1: 0.9688
+ 10/101 [=>............................] - ETA: 38s - loss: 0.6508 - acc@1: 0.9688
 
 .. parsed-literal::
 
@@ -1355,7 +1370,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 12/101 [==>...........................] - ETA: 36s - loss: 0.6548 - acc@1: 0.9681
+ 12/101 [==>...........................] - ETA: 37s - loss: 0.6548 - acc@1: 0.9681
 
 .. parsed-literal::
 
@@ -1370,7 +1385,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 15/101 [===>..........................] - ETA: 35s - loss: 0.6590 - acc@1: 0.9656
+ 15/101 [===>..........................] - ETA: 36s - loss: 0.6590 - acc@1: 0.9656
 
 .. parsed-literal::
 
@@ -1380,22 +1395,22 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 17/101 [====>.........................] - ETA: 34s - loss: 0.6583 - acc@1: 0.9665
+ 17/101 [====>.........................] - ETA: 35s - loss: 0.6583 - acc@1: 0.9665
 
 .. parsed-literal::
 
     
- 18/101 [====>.........................] - ETA: 34s - loss: 0.6584 - acc@1: 0.9666
+ 18/101 [====>.........................] - ETA: 35s - loss: 0.6584 - acc@1: 0.9666
 
 .. parsed-literal::
 
     
- 19/101 [====>.........................] - ETA: 33s - loss: 0.6601 - acc@1: 0.9659
+ 19/101 [====>.........................] - ETA: 34s - loss: 0.6601 - acc@1: 0.9659
 
 .. parsed-literal::
 
     
- 20/101 [====>.........................] - ETA: 33s - loss: 0.6586 - acc@1: 0.9656
+ 20/101 [====>.........................] - ETA: 34s - loss: 0.6586 - acc@1: 0.9656
 
 .. parsed-literal::
 
@@ -1405,7 +1420,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 22/101 [=====>........................] - ETA: 32s - loss: 0.6610 - acc@1: 0.9634
+ 22/101 [=====>........................] - ETA: 33s - loss: 0.6610 - acc@1: 0.9634
 
 .. parsed-literal::
 
@@ -1415,7 +1430,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 24/101 [======>.......................] - ETA: 31s - loss: 0.6630 - acc@1: 0.9609
+ 24/101 [======>.......................] - ETA: 32s - loss: 0.6630 - acc@1: 0.9609
 
 .. parsed-literal::
 
@@ -1430,7 +1445,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 27/101 [=======>......................] - ETA: 30s - loss: 0.6631 - acc@1: 0.9604
+ 27/101 [=======>......................] - ETA: 31s - loss: 0.6631 - acc@1: 0.9604
 
 .. parsed-literal::
 
@@ -1440,7 +1455,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 29/101 [=======>......................] - ETA: 29s - loss: 0.6636 - acc@1: 0.9604
+ 29/101 [=======>......................] - ETA: 30s - loss: 0.6636 - acc@1: 0.9604
 
 .. parsed-literal::
 
@@ -1465,7 +1480,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 34/101 [=========>....................] - ETA: 27s - loss: 0.6655 - acc@1: 0.9586
+ 34/101 [=========>....................] - ETA: 28s - loss: 0.6655 - acc@1: 0.9586
 
 .. parsed-literal::
 
@@ -1490,7 +1505,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 39/101 [==========>...................] - ETA: 25s - loss: 0.6692 - acc@1: 0.9569
+ 39/101 [==========>...................] - ETA: 26s - loss: 0.6692 - acc@1: 0.9569
 
 .. parsed-literal::
 
@@ -1500,7 +1515,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 41/101 [===========>..................] - ETA: 24s - loss: 0.6692 - acc@1: 0.9571
+ 41/101 [===========>..................] - ETA: 25s - loss: 0.6692 - acc@1: 0.9571
 
 .. parsed-literal::
 
@@ -1525,7 +1540,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 46/101 [============>.................] - ETA: 22s - loss: 0.6702 - acc@1: 0.9562
+ 46/101 [============>.................] - ETA: 23s - loss: 0.6702 - acc@1: 0.9562
 
 .. parsed-literal::
 
@@ -1620,7 +1635,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
- 65/101 [==================>...........] - ETA: 14s - loss: 0.6750 - acc@1: 0.9523
+ 65/101 [==================>...........] - ETA: 15s - loss: 0.6750 - acc@1: 0.9523
 
 .. parsed-literal::
 
@@ -1805,7 +1820,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
-101/101 [==============================] - 42s 417ms/step - loss: 0.6807 - acc@1: 0.9489
+101/101 [==============================] - 42s 419ms/step - loss: 0.6807 - acc@1: 0.9489
 
 
 .. parsed-literal::
@@ -1831,7 +1846,7 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     
-4/4 [==============================] - 1s 146ms/sample - loss: 0.9760 - acc@1: 0.8160
+4/4 [==============================] - 1s 141ms/sample - loss: 0.9760 - acc@1: 0.8160
 
 
 .. parsed-literal::
@@ -1878,7 +1893,6 @@ Executing this command may take a while.
     ov.save_model(model_ir_fp32, fp32_ir_path, compress_to_fp16=False)
     ov.save_model(model_ir_int8, int8_ir_path, compress_to_fp16=False)
 
-
 Benchmark Model Performance by Computing Inference Time
 -------------------------------------------------------
 
@@ -1911,8 +1925,8 @@ Please select a benchmarking device using the dropdown list:
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices,
-        value='CPU',
-        description='Device:',
+        value="CPU",
+        description="Device:",
         disabled=False,
     )
     
@@ -1930,15 +1944,15 @@ Please select a benchmarking device using the dropdown list:
 .. code:: ipython3
 
     def parse_benchmark_output(benchmark_output):
-        parsed_output = [line for line in benchmark_output if 'FPS' in line]
-        print(*parsed_output, sep='\n')
+        parsed_output = [line for line in benchmark_output if "FPS" in line]
+        print(*parsed_output, sep="\n")
     
     
-    print('Benchmark FP32 model (IR)')
+    print("Benchmark FP32 model (IR)")
     benchmark_output = ! benchmark_app -m $fp32_ir_path -d $device.value -api async -t 15 -shape [1,64,64,3]
     parse_benchmark_output(benchmark_output)
     
-    print('\nBenchmark INT8 model (IR)')
+    print("\nBenchmark INT8 model (IR)")
     benchmark_output = ! benchmark_app -m $int8_ir_path -d $device.value -api async -t 15 -shape [1,64,64,3]
     parse_benchmark_output(benchmark_output)
 
@@ -1950,14 +1964,14 @@ Please select a benchmarking device using the dropdown list:
 
 .. parsed-literal::
 
-    [ INFO ] Throughput:   2840.25 FPS
+    [ INFO ] Throughput:   2821.81 FPS
     
     Benchmark INT8 model (IR)
 
 
 .. parsed-literal::
 
-    [ INFO ] Throughput:   11202.29 FPS
+    [ INFO ] Throughput:   11151.37 FPS
 
 
 Show Device Information for reference.
diff --git a/docs/notebooks/tensorflow-quantization-aware-training-with-output_files/tensorflow-quantization-aware-training-with-output_6_1.png b/docs/notebooks/tensorflow-quantization-aware-training-with-output_files/tensorflow-quantization-aware-training-with-output_6_1.png
new file mode 100644
index 00000000000..60be8223ea8
--- /dev/null
+++ b/docs/notebooks/tensorflow-quantization-aware-training-with-output_files/tensorflow-quantization-aware-training-with-output_6_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6eadcb9ce8d255dab09875466b29e4ba7774cbf18191c857309621898a002891
+size 518753
diff --git a/docs/notebooks/301-tensorflow-training-openvino-nncf-with-output.rst b/docs/notebooks/tensorflow-training-openvino-nncf-with-output.rst
similarity index 62%
rename from docs/notebooks/301-tensorflow-training-openvino-nncf-with-output.rst
rename to docs/notebooks/tensorflow-training-openvino-nncf-with-output.rst
index 466c18b85b9..f4647fb47c6 100644
--- a/docs/notebooks/301-tensorflow-training-openvino-nncf-with-output.rst
+++ b/docs/notebooks/tensorflow-training-openvino-nncf-with-output.rst
@@ -2,10 +2,9 @@ Post-Training Quantization with TensorFlow Classification Model
 ===============================================================
 
 This example demonstrates how to quantize the OpenVINO model that was
-created in `301-tensorflow-training-openvino
-notebook <301-tensorflow-training-openvino-with-output.html>`__, to improve
-inference speed. Quantization is performed with `Post-training
-Quantization with
+created in `tensorflow-training-openvino
+notebook `__, to improve inference
+speed. Quantization is performed with `Post-training Quantization with
 NNCF `__.
 A custom dataloader and metric will be defined, and accuracy and
 performance will be computed for the original IR model and the quantized
@@ -42,13 +41,91 @@ notebook. This will take a while.
 
     import platform
 
-    %pip install -q tensorflow Pillow numpy tqdm nncf
+    %pip install -q Pillow numpy tqdm nncf "openvino>=2023.1"
 
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
 
+    %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version <= '3.8'"
+    %pip install -q tf_keras tqdm
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
 
 .. parsed-literal::
 
@@ -73,30 +150,32 @@ notebook. This will take a while.
 .. code:: ipython3
 
     from pathlib import Path
+    import os
+
+    os.environ["TF_USE_LEGACY_KERAS"] = "1"
+
 
     import tensorflow as tf
 
     model_xml = Path("model/flower/flower_ir.xml")
-    dataset_url = (
-        "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
-    )
+    dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
     data_dir = Path(tf.keras.utils.get_file("flower_photos", origin=dataset_url, untar=True))
 
     if not model_xml.exists():
         print("Executing training notebook. This will take a while...")
-        %run 301-tensorflow-training-openvino.ipynb
+        %run tensorflow-training-openvino.ipynb
 
 
 .. parsed-literal::
 
-    2024-03-13 00:59:54.212886: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:59:54.247629: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 01:11:39.893197: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 01:11:39.928932: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 00:59:54.839388: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 01:11:40.523686: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -114,6 +193,86 @@ notebook. This will take a while.
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
 .. parsed-literal::
 
     3670
@@ -131,12 +290,12 @@ notebook. This will take a while.
 
 .. parsed-literal::
 
-    2024-03-13 01:00:00.957194: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-03-13 01:00:00.957232: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
-    2024-03-13 01:00:00.957237: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
-    2024-03-13 01:00:00.957362: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
-    2024-03-13 01:00:00.957378: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
-    2024-03-13 01:00:00.957382: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-04-18 01:12:09.395264: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-04-18 01:12:09.395300: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
+    2024-04-18 01:12:09.395304: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
+    2024-04-18 01:12:09.395435: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
+    2024-04-18 01:12:09.395449: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
+    2024-04-18 01:12:09.395453: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 .. parsed-literal::
@@ -152,25 +311,25 @@ notebook. This will take a while.
 
 .. parsed-literal::
 
-    2024-03-13 01:00:01.273972: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
-    	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:00:01.274232: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
+    2024-04-18 01:12:09.711710: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
     	 [[{{node Placeholder/_0}}]]
+    2024-04-18 01:12:09.711989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    	 [[{{node Placeholder/_4}}]]
 
 
 
-.. image:: 301-tensorflow-training-openvino-nncf-with-output_files/301-tensorflow-training-openvino-nncf-with-output_3_12.png
+.. image:: tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_28.png
 
 
 .. parsed-literal::
 
-    2024-03-13 01:00:02.318594: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    2024-04-18 01:12:10.676936: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:00:02.319077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    2024-04-18 01:12:10.677180: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:00:02.512508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
-    	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:00:02.512891: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    2024-04-18 01:12:10.818263: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
+    	 [[{{node Placeholder/_0}}]]
+    2024-04-18 01:12:10.818550: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     	 [[{{node Placeholder/_4}}]]
 
 
@@ -182,19 +341,19 @@ notebook. This will take a while.
 
 .. parsed-literal::
 
-    0.008872573 0.7322078
+    0.0 1.0
 
 
 .. parsed-literal::
 
-    2024-03-13 01:00:03.177759: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
+    2024-04-18 01:12:11.693375: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
     	 [[{{node Placeholder/_0}}]]
-    2024-03-13 01:00:03.178061: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
+    2024-04-18 01:12:11.693703: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
     	 [[{{node Placeholder/_0}}]]
 
 
 
-.. image:: 301-tensorflow-training-openvino-nncf-with-output_files/301-tensorflow-training-openvino-nncf-with-output_3_17.png
+.. image:: tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_33.png
 
 
 .. parsed-literal::
@@ -384,484 +543,479 @@ notebook. This will take a while.
 
 .. parsed-literal::
 
-    2024-03-13 01:00:04.215942: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
-    	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:00:04.216563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    2024-04-18 01:12:12.768860: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     	 [[{{node Placeholder/_4}}]]
+    2024-04-18 01:12:12.769608: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
+    	 [[{{node Placeholder/_0}}]]
 
 
 .. parsed-literal::
 
 
- 1/92 [..............................] - ETA: 1:27 - loss: 1.6034 - accuracy: 0.2812
+ 1/92 [..............................] - ETA: 1:32 - loss: 1.6453 - accuracy: 0.1250
 
 .. parsed-literal::
 
     
- 2/92 [..............................] - ETA: 6s - loss: 1.8268 - accuracy: 0.2812
+ 2/92 [..............................] - ETA: 6s - loss: 3.1595 - accuracy: 0.1875
 
 .. parsed-literal::
 
     
- 3/92 [..............................] - ETA: 6s - loss: 1.9325 - accuracy: 0.2500
+ 3/92 [..............................] - ETA: 5s - loss: 3.0478 - accuracy: 0.1771
 
 .. parsed-literal::
 
     
- 4/92 [>.............................] - ETA: 5s - loss: 1.9389 - accuracy: 0.2422
+ 4/92 [>.............................] - ETA: 5s - loss: 3.0508 - accuracy: 0.1875
 
 .. parsed-literal::
 
     
- 5/92 [>.............................] - ETA: 5s - loss: 1.8737 - accuracy: 0.2375
+ 5/92 [>.............................] - ETA: 5s - loss: 2.8188 - accuracy: 0.2000
 
 .. parsed-literal::
 
     
- 6/92 [>.............................] - ETA: 5s - loss: 1.8344 - accuracy: 0.2188
+ 6/92 [>.............................] - ETA: 5s - loss: 2.6227 - accuracy: 0.2135
 
 .. parsed-literal::
 
     
- 7/92 [=>............................] - ETA: 5s - loss: 1.7918 - accuracy: 0.2321
+ 7/92 [=>............................] - ETA: 5s - loss: 2.4793 - accuracy: 0.2277
 
 .. parsed-literal::
 
     
- 8/92 [=>............................] - ETA: 5s - loss: 1.7671 - accuracy: 0.2383
+ 8/92 [=>............................] - ETA: 5s - loss: 2.3750 - accuracy: 0.2070
 
 .. parsed-literal::
 
     
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-    2024-03-13 01:00:10.495383: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [734]
+    2024-04-18 01:12:19.128053: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [734]
+    	 [[{{node Placeholder/_0}}]]
+    2024-04-18 01:12:19.128326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [734]
     	 [[{{node Placeholder/_0}}]]
-    2024-03-13 01:00:10.495657: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [734]
-    	 [[{{node Placeholder/_4}}]]
 
 
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 .. parsed-literal::
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 .. parsed-literal::
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+91/92 [============================>.] - ETA: 0s - loss: 0.5965 - accuracy: 0.7713
 
 .. parsed-literal::
 
     
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+92/92 [==============================] - ETA: 0s - loss: 0.5970 - accuracy: 0.7718
 
 .. parsed-literal::
 
     
-92/92 [==============================] - 6s 64ms/step - loss: 0.5075 - accuracy: 0.8185 - val_loss: 0.6563 - val_accuracy: 0.7425
+92/92 [==============================] - 6s 64ms/step - loss: 0.5970 - accuracy: 0.7718 - val_loss: 0.7530 - val_accuracy: 0.7112
 
 
 
-.. image:: 301-tensorflow-training-openvino-nncf-with-output_files/301-tensorflow-training-openvino-nncf-with-output_3_1452.png
+.. image:: tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_1467.png
 
 
 .. parsed-literal::
 
 
-   1/1 [==============================] - ETA: 0s
+    1/1 [==============================] - ETA: 0s
 
 .. parsed-literal::
 
     
-    1/1 [==============================] - 0s 78ms/step
+1/1 [==============================] - 0s 75ms/step
 
 
 .. parsed-literal::
 
-    This image most likely belongs to sunflowers with a 99.74 percent confidence.
+    This image most likely belongs to sunflowers with a 86.22 percent confidence.
 
 
 .. parsed-literal::
 
-    2024-03-13 01:01:33.965755: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:42.664989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
     	 [[{{node random_flip_input}}]]
-    2024-03-13 01:01:34.051093: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:42.761394: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.061726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:42.772169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
     	 [[{{node random_flip_input}}]]
-    2024-03-13 01:01:34.073459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:42.784031: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.080567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:42.791410: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.087476: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:42.798592: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.098574: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:42.810122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.137415: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:42.851227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
     	 [[{{node sequential_1_input}}]]
 
 
 .. parsed-literal::
 
-    2024-03-13 01:01:34.205352: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:42.923722: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.225505: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:42.945679: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
     	 [[{{node sequential_1_input}}]]
-    2024-03-13 01:01:34.263910: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
+    2024-04-18 01:13:42.987699: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
     	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.288270: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:43.012752: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    	 [[{{node inputs}}]]
+    2024-04-18 01:13:43.090809: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
 
 
 .. parsed-literal::
 
-    2024-03-13 01:01:34.526623: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
-    	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.665874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:43.248083: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
 
 
 .. parsed-literal::
 
-    2024-03-13 01:01:34.801730: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
+    2024-04-18 01:13:43.562029: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
     	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.835860: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:43.598798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.863528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:43.628830: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:01:34.909392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:13:43.679003: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
     WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.
 
@@ -7481,11 +7635,11 @@ notebook. This will take a while.
 
     (1, 180, 180, 3)
     [1,180,180,3]
-    This image most likely belongs to dandelion with a 99.50 percent confidence.
+    This image most likely belongs to dandelion with a 97.36 percent confidence.
 
 
 
-.. image:: 301-tensorflow-training-openvino-nncf-with-output_files/301-tensorflow-training-openvino-nncf-with-output_3_1464.png
+.. image:: tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_1479.png
 
 
 Imports
@@ -7498,8 +7652,6 @@ library.
 
 .. code:: ipython3
 
-    import sys
-
     import matplotlib.pyplot as plt
     import numpy as np
     import nncf
@@ -7508,7 +7660,14 @@ library.
     from PIL import Image
     from sklearn.metrics import accuracy_score
 
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 
@@ -7540,12 +7699,12 @@ The validation dataset already defined in the training notebook.
     img_height = 180
     img_width = 180
     val_dataset = tf.keras.preprocessing.image_dataset_from_directory(
-      data_dir,
-      validation_split=0.2,
-      subset="validation",
-      seed=123,
-      image_size=(img_height, img_width),
-      batch_size=1
+        data_dir,
+        validation_split=0.2,
+        subset="validation",
+        seed=123,
+        image_size=(img_height, img_width),
+        batch_size=1,
     )
 
     for a, b in val_dataset:
@@ -7566,10 +7725,10 @@ The validation dataset already defined in the training notebook.
 
 .. parsed-literal::
 
-    2024-03-13 01:01:37.480859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [734]
-    	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:01:37.481108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [734]
+    2024-04-18 01:13:46.645327: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [734]
     	 [[{{node Placeholder/_4}}]]
+    2024-04-18 01:13:46.645607: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [734]
+    	 [[{{node Placeholder/_0}}]]
 
 
 The validation dataset can be reused in quantization process. But it
@@ -7606,11 +7765,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/live.py", line 32, in run
     
 
@@ -7673,7 +7828,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/live.py", line 223, in refresh
     
 
@@ -7691,7 +7846,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/live.py", line 203, in renderable
     
 
@@ -7709,7 +7864,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/live.py", line 98, in get_renderable
     
 
@@ -7727,7 +7882,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/progress.py", line 1537, in get_renderable
     
 
@@ -7745,7 +7900,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/progress.py", line 1542, in get_renderables
     
 
@@ -7763,7 +7918,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/progress.py", line 1566, in make_tasks_table
     
 
@@ -7781,7 +7936,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/progress.py", line 1571, in <genexpr>
     
 
@@ -7799,7 +7954,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/progress.py", line 528, in __call__
     
 
@@ -7817,7 +7972,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/nncf/common/logging/track_progress.py", line 58, in render
     
 
@@ -7835,7 +7990,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/progress.py", line 787, in render
     
 
@@ -7853,7 +8008,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/rich/progress.py", line 1039, in time_remaining
     
 
@@ -7871,7 +8026,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
     
 
@@ -7889,7 +8044,7 @@ control   File
-    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
+    "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
     te-packages/tensorflow/python/ops/math_ops.py", line 1569, in _truediv_python3
     
 
@@ -7968,9 +8123,9 @@ select device from dropdown list for running inference using OpenVINO
     import ipywidgets as widgets
 
     device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"] if not "GPU" in core.available_devices else ["AUTO", "MULTY:CPU,GPU"],
-        value='AUTO',
-        description='Device:',
+        options=(core.available_devices + ["AUTO"] if not "GPU" in core.available_devices else ["AUTO", "MULTY:CPU,GPU"]),
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
 
@@ -8038,8 +8193,8 @@ Calculate accuracy for the original model and the quantized model.
 
 .. parsed-literal::
 
-    Accuracy of the original model: 0.743
-    Accuracy of the quantized model: 0.749
+    Accuracy of the original model: 0.711
+    Accuracy of the quantized model: 0.707
 
 
 Compare file size of the models.
@@ -8069,9 +8224,8 @@ Run Inference on Quantized Model
 
 Copy the preprocess function from the training notebook and run
 inference on the quantized model with Inference Engine. See the
-`OpenVINO API tutorial <002-openvino-api-with-output.html>`__
-for more information about running inference with Inference Engine
-Python API.
+`OpenVINO API tutorial `__ for more
+information about running inference with Inference Engine Python API.
 
 .. code:: ipython3
 
@@ -8098,20 +8252,18 @@ Python API.
     class_names = sorted([item.name for item in Path(data_dir).iterdir() if item.is_dir()])
 
     # Run inference on an input image...
-    inp_img_url = (
-        "https://upload.wikimedia.org/wikipedia/commons/4/48/A_Close_Up_Photo_of_a_Dandelion.jpg"
-    )
+    inp_img_url = "https://upload.wikimedia.org/wikipedia/commons/4/48/A_Close_Up_Photo_of_a_Dandelion.jpg"
     directory = "output"
     inp_file_name = "A_Close_Up_Photo_of_a_Dandelion.jpg"
-    file_path = Path(directory)/Path(inp_file_name)
+    file_path = Path(directory) / Path(inp_file_name)
     # Download the image if it does not exist yet
     if not Path(inp_file_name).exists():
         download_file(inp_img_url, inp_file_name, directory=directory)
 
     # Pre-process the image and get it ready for inference.
     input_image = pre_process_image(imagePath=file_path)
-    print(f'input image shape: {input_image.shape}')
-    print(f'input layer shape: {input_layer.shape}')
+    print(f"input image shape: {input_image.shape}")
+    print(f"input layer shape: {input_layer.shape}")
 
     res = quantized_compiled_model([input_image])[output_layer]
 
@@ -8120,11 +8272,7 @@ Python API.
     # Show the results
     image = Image.open(file_path)
     plt.imshow(image)
-    print(
-        "This image most likely belongs to {} with a {:.2f} percent confidence.".format(
-            class_names[np.argmax(score)], 100 * np.max(score)
-        )
-    )
+    print("This image most likely belongs to {} with a {:.2f} percent confidence.".format(class_names[np.argmax(score)], 100 * np.max(score)))
 
 
 .. parsed-literal::
@@ -8132,15 +8280,11 @@ Python API.
     'output/A_Close_Up_Photo_of_a_Dandelion.jpg' already exists.
     input image shape: (1, 180, 180, 3)
     input layer shape: [1,180,180,3]
-
-
-.. parsed-literal::
-
-    This image most likely belongs to dandelion with a 99.53 percent confidence.
+    This image most likely belongs to dandelion with a 97.54 percent confidence.
 
 
 
-.. image:: 301-tensorflow-training-openvino-nncf-with-output_files/301-tensorflow-training-openvino-nncf-with-output_27_2.png
+.. image:: tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_27_1.png
 
 
 Compare Inference Speed
@@ -8163,7 +8307,7 @@ number of seconds to run inference. See the
 for more information.
 
 This tutorial uses a wrapper function from `Notebook
-Utils `__.
+Utils `__.
 It prints the ``benchmark_app`` command with the chosen parameters.
 
 In the next cells, inference speed will be measured for the original and
@@ -8205,6 +8349,10 @@ measured for CPU+GPU as well. The number of seconds is set to 15.
     [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
+
+
+.. parsed-literal::
+
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ]
     [ INFO ]
@@ -8212,7 +8360,7 @@ measured for CPU+GPU as well. The number of seconds is set to 15.
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 4.25 ms
+    [ INFO ] Read model took 4.36 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     sequential_1_input (node: sequential_1_input) : f32 / [...] / [1,180,180,3]
@@ -8230,7 +8378,7 @@ measured for CPU+GPU as well. The number of seconds is set to 15.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 105.22 ms
+    [ INFO ] Compile model took 119.88 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: TensorFlow_Frontend_IR
@@ -8245,10 +8393,6 @@ measured for CPU+GPU as well. The number of seconds is set to 15.
     [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
-
-
-.. parsed-literal::
-
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
     [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
     [ INFO ]     INFERENCE_NUM_THREADS: 24
@@ -8269,21 +8413,21 @@ measured for CPU+GPU as well. The number of seconds is set to 15.
     [ INFO ] Fill input 'sequential_1_input' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 3.84 ms
+    [ INFO ] First inference took 4.01 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            55932 iterations
-    [ INFO ] Duration:         15004.24 ms
+    [ INFO ] Count:            55860 iterations
+    [ INFO ] Duration:         15002.77 ms
     [ INFO ] Latency:
     [ INFO ]    Median:        3.03 ms
-    [ INFO ]    Average:       3.03 ms
-    [ INFO ]    Min:           1.72 ms
-    [ INFO ]    Max:           11.92 ms
-    [ INFO ] Throughput:   3727.75 FPS
+    [ INFO ]    Average:       3.04 ms
+    [ INFO ]    Min:           1.44 ms
+    [ INFO ]    Max:           13.17 ms
+    [ INFO ] Throughput:   3723.31 FPS
 
 
 .. code:: ipython3
@@ -8309,7 +8453,7 @@ measured for CPU+GPU as well. The number of seconds is set to 15.
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 4.62 ms
+    [ INFO ] Read model took 4.80 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     sequential_1_input (node: sequential_1_input) : f32 / [...] / [1,180,180,3]
@@ -8327,7 +8471,7 @@ measured for CPU+GPU as well. The number of seconds is set to 15.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 113.92 ms
+    [ INFO ] Compile model took 105.05 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: TensorFlow_Frontend_IR
@@ -8362,19 +8506,23 @@ measured for CPU+GPU as well. The number of seconds is set to 15.
     [ INFO ] Fill input 'sequential_1_input' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 1.72 ms
+
+
+.. parsed-literal::
+
+    [ INFO ] First inference took 2.15 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            178524 iterations
-    [ INFO ] Duration:         15001.12 ms
+    [ INFO ] Count:            178632 iterations
+    [ INFO ] Duration:         15001.70 ms
     [ INFO ] Latency:
     [ INFO ]    Median:        0.94 ms
     [ INFO ]    Average:       0.97 ms
-    [ INFO ]    Min:           0.61 ms
-    [ INFO ]    Max:           13.42 ms
-    [ INFO ] Throughput:   11900.71 FPS
+    [ INFO ]    Min:           0.58 ms
+    [ INFO ]    Max:           7.07 ms
+    [ INFO ] Throughput:   11907.45 FPS
 
diff --git a/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_27_1.png b/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_27_1.png
new file mode 100644
index 00000000000..e4613cff71b
--- /dev/null
+++ b/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_27_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9fb281263ba8f686cb056799d79d05b223669f5fdb4d06edabcfadab0fa7a98
+size 143412
diff --git a/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_1467.png b/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_1467.png
new file mode 100644
index 00000000000..4a0d0fcf7a7
--- /dev/null
+++ b/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_1467.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a1d3b7f61be4ede7104467e41c90a5f12dbf4d50c47588e44ae33d3b62b254a
+size 53913
diff --git a/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_1479.png b/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_1479.png
new file mode 100644
index 00000000000..e4613cff71b
--- /dev/null
+++ b/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_1479.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9fb281263ba8f686cb056799d79d05b223669f5fdb4d06edabcfadab0fa7a98
+size 143412
diff --git a/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_28.png b/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_28.png
new file mode 100644
index 00000000000..89b3ca92feb
--- /dev/null
+++ b/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_28.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f7304b1c669a8fcc26df474661dc8ef0f1e04c15de8aca62a2307fe94ea1567
+size 941151
diff --git a/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_33.png b/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_33.png
new file mode 100644
index 00000000000..0ca5392acb2
--- /dev/null
+++ b/docs/notebooks/tensorflow-training-openvino-nncf-with-output_files/tensorflow-training-openvino-nncf-with-output_3_33.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa7ee379feccd2e22bf9adf46c32ca7d41f49516eb7d99d2e775d133041e5dcd
+size 839192
diff --git a/docs/notebooks/301-tensorflow-training-openvino-with-output.rst b/docs/notebooks/tensorflow-training-openvino-with-output.rst
similarity index 62%
rename from docs/notebooks/301-tensorflow-training-openvino-with-output.rst
rename to docs/notebooks/tensorflow-training-openvino-with-output.rst
index 0719e2fb906..7c8889fdeba 100644
--- a/docs/notebooks/301-tensorflow-training-openvino-with-output.rst
+++ b/docs/notebooks/tensorflow-training-openvino-with-output.rst
@@ -62,7 +62,7 @@ notebook shows the process where we perform the inference step on the
 freshly trained model that is converted to OpenVINO IR with model
 conversion API. For faster inference speed on the model created in this
 notebook, check out the `Post-Training Quantization with TensorFlow
-Classification Model <301-tensorflow-training-openvino-nncf-with-output.html>`__
+Classification Model <./tensorflow-training-openvino-nncf.ipynb>`__
 notebook.
 
 This training code comprises the official `TensorFlow Image
@@ -76,7 +76,100 @@ obtained by executing the code with ‘Runtime->Run All’ or the
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0"
+    import platform
+
+    %pip install -q "openvino>=2023.1.0" "pillow" "tqdm"
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+    %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
+    %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
+    %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
+    %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin' and python_version <= '3.8'"
+    %pip install -q tf_keras
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -120,33 +213,38 @@ Import TensorFlow and Other Libraries
 .. code:: ipython3
 
     import os
-    import sys
     from pathlib import Path
 
+    os.environ["TF_USE_LEGACY_KERAS"] = "1"
+
     import PIL
     import matplotlib.pyplot as plt
     import numpy as np
     import tensorflow as tf
     from PIL import Image
     import openvino as ov
-    from tensorflow import keras
-    from tensorflow.keras import layers
-    from tensorflow.keras.models import Sequential
 
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 
 .. parsed-literal::
 
-    2024-03-13 01:02:24.497427: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 01:02:24.532546: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 01:14:56.038506: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 01:14:56.073274: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 01:02:25.044342: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 01:14:56.589731: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Download and Explore the Dataset
@@ -169,8 +267,9 @@ dataset contains 5 sub-directories, one per class:
 .. code:: ipython3
 
     import pathlib
+
     dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
-    data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
+    data_dir = tf.keras.utils.get_file("flower_photos", origin=dataset_url, untar=True)
     data_dir = pathlib.Path(data_dir)
 
 After downloading, you should now have a copy of the dataset available.
@@ -178,7 +277,7 @@ There are 3,670 total images:
 
 .. code:: ipython3
 
-    image_count = len(list(data_dir.glob('*/*.jpg')))
+    image_count = len(list(data_dir.glob("*/*.jpg")))
     print(image_count)
 
 
@@ -191,13 +290,13 @@ Here are some roses:
 
 .. code:: ipython3
 
-    roses = list(data_dir.glob('roses/*'))
+    roses = list(data_dir.glob("roses/*"))
     PIL.Image.open(str(roses[0]))
 
 
 
 
-.. image:: 301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_14_0.png
+.. image:: tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_14_0.png
 
 
 
@@ -208,7 +307,7 @@ Here are some roses:
 
 
 
-.. image:: 301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_15_0.png
+.. image:: tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_15_0.png
 
 
 
@@ -216,13 +315,13 @@ And some tulips:
 
 .. code:: ipython3
 
-    tulips = list(data_dir.glob('tulips/*'))
+    tulips = list(data_dir.glob("tulips/*"))
     PIL.Image.open(str(tulips[0]))
 
 
 
 
-.. image:: 301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_17_0.png
+.. image:: tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_17_0.png
 
 
 
@@ -233,7 +332,7 @@ And some tulips:
 
 
 
-.. image:: 301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_18_0.png
+.. image:: tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_18_0.png
 
 
 
@@ -269,12 +368,13 @@ Let’s use 80% of the images for training, and 20% for validation.
 .. code:: ipython3
 
     train_ds = tf.keras.preprocessing.image_dataset_from_directory(
-      data_dir,
-      validation_split=0.2,
-      subset="training",
-      seed=123,
-      image_size=(img_height, img_width),
-      batch_size=batch_size)
+        data_dir,
+        validation_split=0.2,
+        subset="training",
+        seed=123,
+        image_size=(img_height, img_width),
+        batch_size=batch_size,
+    )
 
 
 .. parsed-literal::
@@ -289,23 +389,24 @@ Let’s use 80% of the images for training, and 20% for validation.
 
 .. parsed-literal::
 
-    2024-03-13 01:02:28.106945: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-03-13 01:02:28.106977: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
-    2024-03-13 01:02:28.106982: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
-    2024-03-13 01:02:28.107105: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
-    2024-03-13 01:02:28.107122: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
-    2024-03-13 01:02:28.107125: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-04-18 01:14:59.925007: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-04-18 01:14:59.925039: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
+    2024-04-18 01:14:59.925044: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
+    2024-04-18 01:14:59.925173: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
+    2024-04-18 01:14:59.925190: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
+    2024-04-18 01:14:59.925193: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 .. code:: ipython3
 
     val_ds = tf.keras.preprocessing.image_dataset_from_directory(
-      data_dir,
-      validation_split=0.2,
-      subset="validation",
-      seed=123,
-      image_size=(img_height, img_width),
-      batch_size=batch_size)
+        data_dir,
+        validation_split=0.2,
+        subset="validation",
+        seed=123,
+        image_size=(img_height, img_width),
+        batch_size=batch_size,
+    )
 
 
 .. parsed-literal::
@@ -352,14 +453,14 @@ Here are the first 9 images from the training dataset.
 
 .. parsed-literal::
 
-    2024-03-13 01:02:28.449873: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    2024-04-18 01:15:00.258455: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:02:28.450244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
+    2024-04-18 01:15:00.259027: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
     	 [[{{node Placeholder/_0}}]]
 
 
 
-.. image:: 301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_29_1.png
+.. image:: tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_29_1.png
 
 
 You will train a model using these datasets by passing them to
@@ -382,9 +483,9 @@ over the dataset and retrieve batches of images:
 
 .. parsed-literal::
 
-    2024-03-13 01:02:29.296083: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    2024-04-18 01:15:01.067592: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:02:29.296450: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    2024-04-18 01:15:01.067963: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     	 [[{{node Placeholder/_4}}]]
 
 
@@ -436,7 +537,7 @@ range by using a Rescaling layer.
 
 .. code:: ipython3
 
-    normalization_layer = layers.Rescaling(1./255)
+    normalization_layer = tf.keras.layers.Rescaling(1.0 / 255)
 
 Note: The Keras Preprocessing utilities and layers introduced in this
 section are currently experimental and may change.
@@ -455,9 +556,9 @@ calling map:
 
 .. parsed-literal::
 
-    2024-03-13 01:02:29.513870: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
-    	 [[{{node Placeholder/_0}}]]
-    2024-03-13 01:02:29.514493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    2024-04-18 01:15:01.259365: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    	 [[{{node Placeholder/_4}}]]
+    2024-04-18 01:15:01.259741: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     	 [[{{node Placeholder/_4}}]]
 
 
@@ -490,18 +591,20 @@ standard approach.
 
     num_classes = 5
 
-    model = Sequential([
-      layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
-      layers.Conv2D(16, 3, padding='same', activation='relu'),
-      layers.MaxPooling2D(),
-      layers.Conv2D(32, 3, padding='same', activation='relu'),
-      layers.MaxPooling2D(),
-      layers.Conv2D(64, 3, padding='same', activation='relu'),
-      layers.MaxPooling2D(),
-      layers.Flatten(),
-      layers.Dense(128, activation='relu'),
-      layers.Dense(num_classes)
-    ])
+    model = tf.keras.Sequential(
+        [
+            tf.keras.layers.Rescaling(1.0 / 255, input_shape=(img_height, img_width, 3)),
+            tf.keras.layers.Conv2D(16, 3, padding="same", activation="relu"),
+            tf.keras.layers.MaxPooling2D(),
+            tf.keras.layers.Conv2D(32, 3, padding="same", activation="relu"),
+            tf.keras.layers.MaxPooling2D(),
+            tf.keras.layers.Conv2D(64, 3, padding="same", activation="relu"),
+            tf.keras.layers.MaxPooling2D(),
+            tf.keras.layers.Flatten(),
+            tf.keras.layers.Dense(128, activation="relu"),
+            tf.keras.layers.Dense(num_classes),
+        ]
+    )
 
 Compile the Model
 -----------------
@@ -515,9 +618,11 @@ argument.
 
 .. code:: ipython3
 
-    model.compile(optimizer='adam',
-                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-                  metrics=['accuracy'])
+    model.compile(
+        optimizer="adam",
+        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        metrics=["accuracy"],
+    )
 
 Model Summary
 -------------
@@ -625,15 +730,12 @@ inside your model like other layers, and run on the GPU.
 
 .. code:: ipython3
 
-    data_augmentation = keras.Sequential(
-      [
-        layers.RandomFlip("horizontal",
-                          input_shape=(img_height,
-                                       img_width,
-                                       3)),
-        layers.RandomRotation(0.1),
-        layers.RandomZoom(0.1),
-      ]
+    data_augmentation = tf.keras.Sequential(
+        [
+            tf.keras.layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
+            tf.keras.layers.RandomRotation(0.1),
+            tf.keras.layers.RandomZoom(0.1),
+        ]
     )
 
 Let’s visualize what a few augmented examples look like by applying data
@@ -652,14 +754,14 @@ augmentation to the same image several times:
 
 .. parsed-literal::
 
-    2024-03-13 01:02:30.494557: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
-    	 [[{{node Placeholder/_0}}]]
-    2024-03-13 01:02:30.495526: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    2024-04-18 01:15:02.167841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
     	 [[{{node Placeholder/_4}}]]
+    2024-04-18 01:15:02.168220: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
+    	 [[{{node Placeholder/_0}}]]
 
 
 
-.. image:: 301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_57_1.png
+.. image:: tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_57_1.png
 
 
 You will use data augmentation to train a model in a moment.
@@ -684,20 +786,22 @@ it using augmented images.
 
 .. code:: ipython3
 
-    model = Sequential([
-        data_augmentation,
-        layers.Rescaling(1./255),
-        layers.Conv2D(16, 3, padding='same', activation='relu'),
-        layers.MaxPooling2D(),
-        layers.Conv2D(32, 3, padding='same', activation='relu'),
-        layers.MaxPooling2D(),
-        layers.Conv2D(64, 3, padding='same', activation='relu'),
-        layers.MaxPooling2D(),
-        layers.Dropout(0.2),
-        layers.Flatten(),
-        layers.Dense(128, activation='relu'),
-        layers.Dense(num_classes, name="outputs")
-    ])
+    model = tf.keras.Sequential(
+        [
+            data_augmentation,
+            tf.keras.layers.Rescaling(1.0 / 255),
+            tf.keras.layers.Conv2D(16, 3, padding="same", activation="relu"),
+            tf.keras.layers.MaxPooling2D(),
+            tf.keras.layers.Conv2D(32, 3, padding="same", activation="relu"),
+            tf.keras.layers.MaxPooling2D(),
+            tf.keras.layers.Conv2D(64, 3, padding="same", activation="relu"),
+            tf.keras.layers.MaxPooling2D(),
+            tf.keras.layers.Dropout(0.2),
+            tf.keras.layers.Flatten(),
+            tf.keras.layers.Dense(128, activation="relu"),
+            tf.keras.layers.Dense(num_classes, name="outputs"),
+        ]
+    )
 
 Compile and Train the Model
 ---------------------------
@@ -706,9 +810,11 @@ Compile and Train the Model
 
 .. code:: ipython3
 
-    model.compile(optimizer='adam',
-                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-                  metrics=['accuracy'])
+    model.compile(
+        optimizer="adam",
+        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        metrics=["accuracy"],
+    )
 
 .. code:: ipython3
 
@@ -898,11 +1004,7 @@ Compile and Train the Model
 .. code:: ipython3
 
     epochs = 15
-    history = model.fit(
-        train_ds,
-        validation_data=val_ds,
-        epochs=epochs
-    )
+    history = model.fit(train_ds, validation_data=val_ds, epochs=epochs)
 
 
 .. parsed-literal::
@@ -912,484 +1014,479 @@ Compile and Train the Model
 
 .. parsed-literal::
 
-    2024-03-13 01:02:31.608332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
-    	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:02:31.608737: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
+    2024-04-18 01:15:03.232166: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
     	 [[{{node Placeholder/_0}}]]
+    2024-04-18 01:15:03.232504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
+    	 [[{{node Placeholder/_4}}]]
 
 
 .. parsed-literal::
 
 
- 1/92 [..............................] - ETA: 1:24 - loss: 1.6184 - accuracy: 0.1875
+ 1/92 [..............................] - ETA: 1:36 - loss: 1.6139 - accuracy: 0.1562
 
 .. parsed-literal::
 
     
- 2/92 [..............................] - ETA: 6s - loss: 2.2743 - accuracy: 0.2344
+ 2/92 [..............................] - ETA: 6s - loss: 2.6072 - accuracy: 0.2031
 
 .. parsed-literal::
 
     
- 3/92 [..............................] - ETA: 5s - loss: 2.2543 - accuracy: 0.2708
+ 3/92 [..............................] - ETA: 5s - loss: 2.2647 - accuracy: 0.2188
 
 .. parsed-literal::
 
     
- 4/92 [>.............................] - ETA: 5s - loss: 2.1636 - accuracy: 0.2344
+ 4/92 [>.............................] - ETA: 5s - loss: 2.1299 - accuracy: 0.2266
 
 .. parsed-literal::
 
     
- 5/92 [>.............................] - ETA: 5s - loss: 2.0592 - accuracy: 0.2313
+ 5/92 [>.............................] - ETA: 5s - loss: 2.0328 - accuracy: 0.2375
 
 .. parsed-literal::
 
     
- 6/92 [>.............................] - ETA: 5s - loss: 1.9847 - accuracy: 0.2188
+ 6/92 [>.............................] - ETA: 5s - loss: 1.9690 - accuracy: 0.2448
 
 .. parsed-literal::
 
     
- 7/92 [=>............................] - ETA: 5s - loss: 1.9289 - accuracy: 0.2232
+ 7/92 [=>............................] - ETA: 5s - loss: 1.9062 - accuracy: 0.2545
 
 .. parsed-literal::
 
     
- 8/92 [=>............................] - ETA: 5s - loss: 1.8900 - accuracy: 0.2109
+ 8/92 [=>............................] - ETA: 5s - loss: 1.8712 - accuracy: 0.2461
 
 .. parsed-literal::
 
     
- 9/92 [=>............................] - ETA: 5s - loss: 1.8577 - accuracy: 0.2118
+ 9/92 [=>............................] - ETA: 5s - loss: 1.8292 - accuracy: 0.2569
 
 .. parsed-literal::
 
     
-10/92 [==>...........................] - ETA: 5s - loss: 1.8316 - accuracy: 0.2219
+10/92 [==>...........................] - ETA: 4s - loss: 1.8010 - accuracy: 0.2594
 
 .. parsed-literal::
 
     
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-
-    2024-03-13 01:02:37.888562: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [734]
-    	 [[{{node Placeholder/_4}}]]
-    2024-03-13 01:02:37.888844: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [734]
+    2024-04-18 01:15:09.636279: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [734]
+    	 [[{{node Placeholder/_0}}]]
+    2024-04-18 01:15:09.636558: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [734]
     	 [[{{node Placeholder/_4}}]]
 
 
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 .. parsed-literal::
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
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 .. parsed-literal::
 
 
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
@@ -6526,462 +6623,462 @@ Compile and Train the Model
 .. parsed-literal::
 
 
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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 .. parsed-literal::
 
     
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+92/92 [==============================] - ETA: 0s - loss: 0.5243 - accuracy: 0.8028
 
 .. parsed-literal::
 
     
-92/92 [==============================] - 6s 64ms/step - loss: 0.5179 - accuracy: 0.7994 - val_loss: 0.7059 - val_accuracy: 0.7357
+92/92 [==============================] - 6s 63ms/step - loss: 0.5243 - accuracy: 0.8028 - val_loss: 0.6708 - val_accuracy: 0.7520
 
 
 Visualize Training Results
@@ -7926,31 +8023,31 @@ than before, and training and validation accuracy are closer aligned.
 
 .. code:: ipython3
 
-    acc = history.history['accuracy']
-    val_acc = history.history['val_accuracy']
+    acc = history.history["accuracy"]
+    val_acc = history.history["val_accuracy"]
 
-    loss = history.history['loss']
-    val_loss = history.history['val_loss']
+    loss = history.history["loss"]
+    val_loss = history.history["val_loss"]
 
     epochs_range = range(epochs)
 
     plt.figure(figsize=(8, 8))
     plt.subplot(1, 2, 1)
-    plt.plot(epochs_range, acc, label='Training Accuracy')
-    plt.plot(epochs_range, val_acc, label='Validation Accuracy')
-    plt.legend(loc='lower right')
-    plt.title('Training and Validation Accuracy')
+    plt.plot(epochs_range, acc, label="Training Accuracy")
+    plt.plot(epochs_range, val_acc, label="Validation Accuracy")
+    plt.legend(loc="lower right")
+    plt.title("Training and Validation Accuracy")
 
     plt.subplot(1, 2, 2)
-    plt.plot(epochs_range, loss, label='Training Loss')
-    plt.plot(epochs_range, val_loss, label='Validation Loss')
-    plt.legend(loc='upper right')
-    plt.title('Training and Validation Loss')
+    plt.plot(epochs_range, loss, label="Training Loss")
+    plt.plot(epochs_range, val_loss, label="Validation Loss")
+    plt.legend(loc="upper right")
+    plt.title("Training and Validation Loss")
     plt.show()
 
 
 
-.. image:: 301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_66_0.png
+.. image:: tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_66_0.png
 
 
 Predict on New Data
@@ -7967,37 +8064,32 @@ in the training or validation sets.
 .. code:: ipython3
 
     sunflower_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg"
-    sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)
+    sunflower_path = tf.keras.utils.get_file("Red_sunflower", origin=sunflower_url)
 
-    img = keras.preprocessing.image.load_img(
-        sunflower_path, target_size=(img_height, img_width)
-    )
-    img_array = keras.preprocessing.image.img_to_array(img)
+    img = tf.keras.preprocessing.image.load_img(sunflower_path, target_size=(img_height, img_width))
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
     img_array = tf.expand_dims(img_array, 0)  # Create a batch
 
     predictions = model.predict(img_array)
     score = tf.nn.softmax(predictions[0])
 
-    print(
-        "This image most likely belongs to {} with a {:.2f} percent confidence."
-        .format(class_names[np.argmax(score)], 100 * np.max(score))
-    )
+    print("This image most likely belongs to {} with a {:.2f} percent confidence.".format(class_names[np.argmax(score)], 100 * np.max(score)))
 
 
 .. parsed-literal::
 
 
-   1/1 [==============================] - ETA: 0s
+    1/1 [==============================] - ETA: 0s
 
 .. parsed-literal::
 
     
-1/1 [==============================] - 0s 99ms/step
+1/1 [==============================] - 0s 76ms/step
 
 
 .. parsed-literal::
 
-    This image most likely belongs to sunflowers with a 96.93 percent confidence.
+    This image most likely belongs to sunflowers with a 95.17 percent confidence.
 
 
 Save the TensorFlow Model
@@ -8007,62 +8099,62 @@ Save the TensorFlow Model
 
 .. code:: ipython3
 
-    #save the trained model - a new folder flower will be created
-    #and the file "saved_model.pb" is the pre-trained model
+    # save the trained model - a new folder flower will be created
+    # and the file "saved_model.h5" is the pre-trained model
     model_dir = "model"
-    saved_model_dir = f"{model_dir}/flower/saved_model"
-    model.save(saved_model_dir)
+    saved_model_path = f"{model_dir}/flower/saved_model"
+    model.save(saved_model_path)
 
 
 .. parsed-literal::
 
-    2024-03-13 01:04:01.596956: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.043830: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
     	 [[{{node random_flip_input}}]]
-    2024-03-13 01:04:01.681918: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.140167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:04:01.691876: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.150389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
     	 [[{{node random_flip_input}}]]
-    2024-03-13 01:04:01.702720: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.162042: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:04:01.709631: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.169331: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:04:01.716729: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.176583: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:04:01.728191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.188085: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:04:01.769744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.229110: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
     	 [[{{node sequential_1_input}}]]
 
 
 .. parsed-literal::
 
-    2024-03-13 01:04:01.859999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.301829: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:04:01.880443: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.323839: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
     	 [[{{node sequential_1_input}}]]
-    2024-03-13 01:04:01.919461: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
+    2024-04-18 01:16:33.365804: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
     	 [[{{node inputs}}]]
-    2024-03-13 01:04:01.944149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.390823: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:04:02.016302: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.480089: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
 
 
 .. parsed-literal::
 
-    2024-03-13 01:04:02.156624: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.638699: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
-    2024-03-13 01:04:02.294259: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
+    2024-04-18 01:16:33.790660: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
     	 [[{{node inputs}}]]
-    2024-03-13 01:04:02.328020: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
-    	 [[{{node inputs}}]]
-    2024-03-13 01:04:02.355628: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.827134: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
 
 
 .. parsed-literal::
 
-    2024-03-13 01:04:02.402015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    2024-04-18 01:16:33.858808: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
+    	 [[{{node inputs}}]]
+    2024-04-18 01:16:33.909722: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
     	 [[{{node inputs}}]]
     WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.
 
@@ -8080,7 +8172,7 @@ Save the TensorFlow Model
 Convert the TensorFlow model with OpenVINO Model Conversion API
 ---------------------------------------------------------------
 
-To convert the model to
+ To convert the model to
 OpenVINO IR with ``FP16`` precision, use model conversion Python API.
 
 .. code:: ipython3
@@ -8088,7 +8180,7 @@ OpenVINO IR with ``FP16`` precision, use model conversion Python API.
     # Convert the model to ir model format and save it.
     ir_model_path = Path("model/flower")
     ir_model_path.mkdir(parents=True, exist_ok=True)
-    ir_model = ov.convert_model(saved_model_dir, input=[1,180,180,3])
+    ir_model = ov.convert_model(saved_model_path, input=[1, 180, 180, 3])
     ov.save_model(ir_model, ir_model_path / "flower_ir.xml")
 
 Preprocessing Image Function
@@ -8130,8 +8222,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
 
@@ -8148,7 +8240,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. code:: ipython3
 
-    class_names=["daisy", "dandelion", "roses", "sunflowers", "tulips"]
+    class_names = ["daisy", "dandelion", "roses", "sunflowers", "tulips"]
 
     compiled_model = core.compile_model(model=ir_model, device_name=device.value)
 
@@ -8168,7 +8260,7 @@ Run the Inference Step
     inp_img_url = "https://upload.wikimedia.org/wikipedia/commons/4/48/A_Close_Up_Photo_of_a_Dandelion.jpg"
     OUTPUT_DIR = "output"
     inp_file_name = f"A_Close_Up_Photo_of_a_Dandelion.jpg"
-    file_path = Path(OUTPUT_DIR)/Path(inp_file_name)
+    file_path = Path(OUTPUT_DIR) / Path(inp_file_name)
 
     os.makedirs(OUTPUT_DIR, exist_ok=True)
 
@@ -8187,10 +8279,7 @@ Run the Inference Step
     # Show the results
     image = Image.open(file_path)
     plt.imshow(image)
-    print(
-        "This image most likely belongs to {} with a {:.2f} percent confidence."
-        .format(class_names[np.argmax(score)], 100 * np.max(score))
-    )
+    print("This image most likely belongs to {} with a {:.2f} percent confidence.".format(class_names[np.argmax(score)], 100 * np.max(score)))
 
 
 .. parsed-literal::
@@ -8198,11 +8287,11 @@ Run the Inference Step
     'output/A_Close_Up_Photo_of_a_Dandelion.jpg' already exists.
     (1, 180, 180, 3)
     [1,180,180,3]
-    This image most likely belongs to dandelion with a 95.08 percent confidence.
+    This image most likely belongs to dandelion with a 99.97 percent confidence.
 
 
 
-.. image:: 301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_79_1.png
+.. image:: tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_79_1.png
 
 
 The Next Steps
@@ -8217,4 +8306,4 @@ model. To see how to quantize this model with OpenVINO’s `Post-training
 Quantization with NNCF
 Tool `__,
 check out the `Post-Training Quantization with TensorFlow Classification
-Model <301-tensorflow-training-openvino-nncf-with-output.html>`__ notebook.
+Model <./tensorflow-training-openvino-nncf.ipynb>`__ notebook.
diff --git a/docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_14_0.jpg b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_14_0.jpg
similarity index 100%
rename from docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_14_0.jpg
rename to docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_14_0.jpg
diff --git a/docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_14_0.png b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_14_0.png
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rename from docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_14_0.png
rename to docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_14_0.png
diff --git a/docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_15_0.jpg b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_15_0.jpg
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rename to docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_15_0.jpg
diff --git a/docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_15_0.png b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_15_0.png
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diff --git a/docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_17_0.jpg b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_17_0.jpg
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rename from docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_17_0.jpg
rename to docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_17_0.jpg
diff --git a/docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_17_0.png b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_17_0.png
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rename from docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_17_0.png
rename to docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_17_0.png
diff --git a/docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_18_0.jpg b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_18_0.jpg
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rename from docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_18_0.jpg
rename to docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_18_0.jpg
diff --git a/docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_18_0.png b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_18_0.png
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rename from docs/notebooks/301-tensorflow-training-openvino-with-output_files/301-tensorflow-training-openvino-with-output_18_0.png
rename to docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_18_0.png
diff --git a/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_29_1.png b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_29_1.png
new file mode 100644
index 00000000000..89b3ca92feb
--- /dev/null
+++ b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_29_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f7304b1c669a8fcc26df474661dc8ef0f1e04c15de8aca62a2307fe94ea1567
+size 941151
diff --git a/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_57_1.png b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_57_1.png
new file mode 100644
index 00000000000..0d58971d58d
--- /dev/null
+++ b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_57_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8bbfd03d9034dec16effda955b58d0fc87d7d6d04fef53967a1485f2fd92581
+size 649820
diff --git a/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_66_0.png b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_66_0.png
new file mode 100644
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7743c81d25d663d5ef68d607d35a31aa1361108b2507c95461d32d78e6c50e12
+size 55266
diff --git a/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_79_1.png b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_79_1.png
new file mode 100644
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--- /dev/null
+++ b/docs/notebooks/tensorflow-training-openvino-with-output_files/tensorflow-training-openvino-with-output_79_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9fb281263ba8f686cb056799d79d05b223669f5fdb4d06edabcfadab0fa7a98
+size 143412
diff --git a/docs/notebooks/223-text-prediction-with-output.rst b/docs/notebooks/text-prediction-with-output.rst
similarity index 83%
rename from docs/notebooks/223-text-prediction-with-output.rst
rename to docs/notebooks/text-prediction-with-output.rst
index 6bb672ee964..d617f7a4884 100644
--- a/docs/notebooks/223-text-prediction-with-output.rst
+++ b/docs/notebooks/text-prediction-with-output.rst
@@ -109,8 +109,8 @@ used for text generation whereas PersonaGPT is used for Conversation.
 .. code:: ipython3
 
     %pip install -q "openvino>=2023.1.0"
-    %pip install -q gradio
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu transformers[torch]
+    %pip install -q "gradio>=4.19"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu transformers "torch>=2.1"
 
 
 .. parsed-literal::
@@ -123,6 +123,11 @@ used for text generation whereas PersonaGPT is used for Conversation.
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
+
 .. parsed-literal::
 
     DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
@@ -147,12 +152,12 @@ used for text generation whereas PersonaGPT is used for Conversation.
 
     import ipywidgets as widgets
     
-    style = {'description_width': 'initial'}
+    style = {"description_width": "initial"}
     model_name = widgets.Select(
-        options=['PersonaGPT (Converastional)', 'GPT-2', 'GPT-Neo'],
-        value='PersonaGPT (Converastional)',
-        description='Select Model:',
-        disabled=False
+        options=["PersonaGPT (Converastional)", "GPT-2", "GPT-Neo"],
+        value="PersonaGPT (Converastional)",
+        description="Select Model:",
+        disabled=False,
     )
     
     widgets.VBox([model_name])
@@ -175,17 +180,22 @@ Download the Selected Model and Tokenizer from HuggingFace
 
 .. code:: ipython3
 
-    from transformers import GPTNeoForCausalLM, GPT2TokenizerFast, GPT2Tokenizer, GPT2LMHeadModel
+    from transformers import (
+        GPTNeoForCausalLM,
+        GPT2TokenizerFast,
+        GPT2Tokenizer,
+        GPT2LMHeadModel,
+    )
     
     if model_name.value == "PersonaGPT (Converastional)":
-        pt_model = GPT2LMHeadModel.from_pretrained('af1tang/personaGPT')
-        tokenizer = GPT2Tokenizer.from_pretrained('af1tang/personaGPT')
-    elif model_name.value == 'GPT-2':
-        pt_model = GPT2LMHeadModel.from_pretrained('gpt2')
-        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-    elif model_name.value == 'GPT-Neo':
-        pt_model = GPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M')
-        tokenizer = GPT2TokenizerFast.from_pretrained('EleutherAI/gpt-neo-125M')
+        pt_model = GPT2LMHeadModel.from_pretrained("af1tang/personaGPT")
+        tokenizer = GPT2Tokenizer.from_pretrained("af1tang/personaGPT")
+    elif model_name.value == "GPT-2":
+        pt_model = GPT2LMHeadModel.from_pretrained("gpt2")
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+    elif model_name.value == "GPT-Neo":
+        pt_model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")
+        tokenizer = GPT2TokenizerFast.from_pretrained("EleutherAI/gpt-neo-125M")
 
 Convert Pytorch Model to OpenVINO IR
 ------------------------------------
@@ -216,14 +226,31 @@ consumption.
     # define path for saving openvino model
     model_path = Path("model/text_generator.xml")
     
-    example_input = {"input_ids": torch.ones((1, 10), dtype=torch.long), "attention_mask": torch.ones((1, 10), dtype=torch.long)}
+    example_input = {
+        "input_ids": torch.ones((1, 10), dtype=torch.long),
+        "attention_mask": torch.ones((1, 10), dtype=torch.long),
+    }
     pt_model.config.torchscript = True
     
     # convert model to openvino
     if model_name.value == "PersonaGPT (Converastional)":
-        ov_model = ov.convert_model(pt_model, example_input=example_input, input=[('input_ids', [1, -1], ov.Type.i64), ('attention_mask', [1,-1], ov.Type.i64)])
+        ov_model = ov.convert_model(
+            pt_model,
+            example_input=example_input,
+            input=[
+                ("input_ids", [1, -1], ov.Type.i64),
+                ("attention_mask", [1, -1], ov.Type.i64),
+            ],
+        )
     else:
-        ov_model = ov.convert_model(pt_model, example_input=example_input, input=[('input_ids', [1, ov.Dimension(1,128)], ov.Type.i64), ('attention_mask', [1, ov.Dimension(1,128)], ov.Type.i64)])
+        ov_model = ov.convert_model(
+            pt_model,
+            example_input=example_input,
+            input=[
+                ("input_ids", [1, ov.Dimension(1, 128)], ov.Type.i64),
+                ("attention_mask", [1, ov.Dimension(1, 128)], ov.Type.i64),
+            ],
+        )
     
     # serialize openvino model
     ov.save_model(ov_model, str(model_path))
@@ -231,16 +258,27 @@ consumption.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/gpt2/modeling_gpt2.py:801: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/gpt2/modeling_gpt2.py:801: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if batch_size <= 0:
 
 
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1102: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    Tensor-likes are not close!
+    
+    Mismatched elements: 22 / 502630 (0.0%)
+    Greatest absolute difference: 1.3828277587890625e-05 at index (0, 1, 1287) (up to 1e-05 allowed)
+    Greatest relative difference: 0.006159087930808544 at index (0, 5, 8109) (up to 1e-05 allowed)
+      _check_trace(
+
+
 Load the model
 ~~~~~~~~~~~~~~
 
@@ -266,8 +304,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -347,20 +385,20 @@ at later stage.
 
 .. parsed-literal::
 
-    2024-03-12 23:30:05.170186: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 23:30:05.202013: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 01:17:07.121619: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 01:17:07.154872: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 23:30:05.770717: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 01:17:07.673304: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Define Softmax layer
 ~~~~~~~~~~~~~~~~~~~~
 
-A softmax function is used to
+ A softmax function is used to
 convert top-k logits into a probability distribution.
 
 .. code:: ipython3
@@ -368,7 +406,7 @@ convert top-k logits into a probability distribution.
     import numpy as np
     
     
-    def softmax(x : np.array) -> np.array:
+    def softmax(x: np.array) -> np.array:
         e_x = np.exp(x - np.max(x, axis=-1, keepdims=True))
         summation = e_x.sum(axis=-1, keepdims=True)
         return e_x / summation
@@ -384,7 +422,7 @@ the process of generating the next words.
 
 .. code:: ipython3
 
-    def process_logits(cur_length: int, scores: np.array, eos_token_id : int, min_length : int = 0) -> np.array:
+    def process_logits(cur_length: int, scores: np.array, eos_token_id: int, min_length: int = 0) -> np.array:
         """
         Reduce probability for padded indices.
     
@@ -411,7 +449,7 @@ redistribute the probability mass among only those K next words.
 
 .. code:: ipython3
 
-    def get_top_k_logits(scores : np.array, top_k : int) -> np.array:
+    def get_top_k_logits(scores: np.array, top_k: int) -> np.array:
         """
         Perform top-k sampling on the logits scores.
     
@@ -428,8 +466,7 @@ redistribute the probability mass among only those K next words.
         top_k = min(max(top_k, 1), scores.shape[-1])
         top_k_scores = -np.sort(-scores)[:, :top_k]
         indices_to_remove = scores < np.min(top_k_scores)
-        filtred_scores = np.ma.array(scores, mask=indices_to_remove,
-                                     fill_value=filter_value).filled()
+        filtred_scores = np.ma.array(scores, mask=indices_to_remove, fill_value=filter_value).filled()
         return filtred_scores
 
 Main Processing Function
@@ -441,8 +478,13 @@ Generating the predicted sequence.
 
 .. code:: ipython3
 
-    def generate_sequence(input_ids : List[int], attention_mask : List[int], max_sequence_length : int = 128,
-                          eos_token_id : int = eos_token_id, dynamic_shapes : bool = True) -> List[int]:
+    def generate_sequence(
+        input_ids: List[int],
+        attention_mask: List[int],
+        max_sequence_length: int = 128,
+        eos_token_id: int = eos_token_id,
+        dynamic_shapes: bool = True,
+    ) -> List[int]:
         """
         Generates a sequence of tokens using a pre-trained language model.
     
@@ -468,14 +510,12 @@ Generating the predicted sequence.
             outputs = compiled_model({"input_ids": model_input_ids, "attention_mask": model_input_attention_mask})[output_key]
             next_token_logits = outputs[:, cur_input_len - 1, :]
             # pre-process distribution
-            next_token_scores = process_logits(cur_input_len,
-                                               next_token_logits, eos_token_id)
+            next_token_scores = process_logits(cur_input_len, next_token_logits, eos_token_id)
             top_k = 20
             next_token_scores = get_top_k_logits(next_token_scores, top_k)
             # get next token id
             probs = softmax(next_token_scores)
-            next_tokens = np.random.choice(probs.shape[-1], 1,
-                                           p=probs[0], replace=True)
+            next_tokens = np.random.choice(probs.shape[-1], 1, p=probs[0], replace=True)
             # break the loop if max length or end of text token is reached
             if cur_input_len == max_sequence_length or next_tokens[0] == eos_token_id:
                 break
@@ -547,8 +587,12 @@ Wrapper on generate sequence function to support conversation
 
 .. code:: ipython3
 
-    def converse(input: str, history: List[int], eos_token: str = eos_token,
-                 eos_token_id: int = eos_token_id) -> Tuple[str, List[int]]:
+    def converse(
+        input: str,
+        history: List[int],
+        eos_token: str = eos_token,
+        eos_token_id: int = eos_token_id,
+    ) -> Tuple[str, List[int]]:
         """
         Converse with the Model.
     
@@ -582,7 +626,7 @@ Wrapper on generate sequence function to support conversation
         history = np.append(history[0], eos_token_id)
     
         # convert the tokens to text, and then split the responses into lines and retrieve the response from the Model
-        response = ''.join(tokenizer.batch_decode(history)).split(eos_token)[-2]
+        response = "".join(tokenizer.batch_decode(history)).split(eos_token)[-2]
         return response, history
 
 Conversation Class
@@ -621,12 +665,12 @@ The style of inference can be selected in the next cell.
 
 .. code:: ipython3
 
-    style = {'description_width': 'initial'}
+    style = {"description_width": "initial"}
     interactive_mode = widgets.Select(
-        options=['Plain', 'Interactive'],
-        value='Plain',
-        description='Inference Style:',
-        disabled=False
+        options=["Plain", "Interactive"],
+        value="Plain",
+        description="Inference Style:",
+        disabled=False,
     )
     
     widgets.VBox([interactive_mode])
@@ -645,10 +689,15 @@ The style of inference can be selected in the next cell.
     import gradio as gr
     
     if model_name.value == "PersonaGPT (Converastional)":
-        if interactive_mode.value == 'Plain':
+        if interactive_mode.value == "Plain":
             conversation = Conversation()
             user_prompt = None
-            pre_written_prompts = ["Hi,How are you?", "What are you doing?", "I like to dance,do you?", "Can you recommend me some books?"]
+            pre_written_prompts = [
+                "Hi,How are you?",
+                "What are you doing?",
+                "I like to dance,do you?",
+                "Can you recommend me some books?",
+            ]
             # Number of responses generated by model
             n_prompts = 10
             for i in range(n_prompts):
@@ -661,6 +710,7 @@ The style of inference can be selected in the next cell.
                 print(conversation.messages[-1])
                 user_prompt = None
         else:
+    
             def add_text(history, text):
                 history = history + [(text, None)]
                 return history, ""
@@ -681,12 +731,10 @@ The style of inference can be selected in the next cell.
                         txt = gr.Textbox(
                             show_label=False,
                             placeholder="Enter text and press enter, or upload an image",
-                            container=False
+                            container=False,
                         )
     
-                txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(
-                    bot, chatbot, chatbot
-                )
+                txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(bot, chatbot, chatbot)
             try:
                 demo.launch(debug=False)
             except Exception:
@@ -701,59 +749,59 @@ The style of inference can be selected in the next cell.
 .. parsed-literal::
 
     Person: Hi,How are you?
-    PersonaGPT: good how are you?
+    PersonaGPT: i'm good thanks for asking
 
 
 .. parsed-literal::
 
     Person: What are you doing?
-    PersonaGPT: good just hanging out with my friends
+    PersonaGPT: just relaxing before bed
 
 
 .. parsed-literal::
 
     Person: I like to dance,do you?
-    PersonaGPT: not really but i like to play
+    PersonaGPT: no. what do you dance to?
 
 
 .. parsed-literal::
 
     Person: Can you recommend me some books?
-    PersonaGPT: any good suggestions i like romance movies
+    PersonaGPT: what do you do for fun or for a hobby?
 
 
 .. parsed-literal::
 
     Person: Hi,How are you?
-    PersonaGPT: good thanks for asking. what do you do for fun?
+    PersonaGPT: i like to read and play games.
 
 
 .. parsed-literal::
 
     Person: What are you doing?
-    PersonaGPT: i like to read. what about you?
+    PersonaGPT: i'm great thanks for asking.
 
 
 .. parsed-literal::
 
     Person: I like to dance,do you?
-    PersonaGPT: not really but i do play games.
+    PersonaGPT: no. what do you like to do for fun?
 
 
 .. parsed-literal::
 
     Person: Can you recommend me some books?
-    PersonaGPT: sure what kind of games do you play
+    PersonaGPT: what do you like to do for fun?
 
 
 .. parsed-literal::
 
     Person: Hi,How are you?
-    PersonaGPT: i am good do you like movies?
+    PersonaGPT: i am good thanks for asking
 
 
 .. parsed-literal::
 
     Person: What are you doing?
-    PersonaGPT: reading a romance movie. you seen any good ones lately?
+    PersonaGPT: i'm relaxing before bed.
 
diff --git a/docs/notebooks/243-tflite-selfie-segmentation-with-output.rst b/docs/notebooks/tflite-selfie-segmentation-with-output.rst
similarity index 88%
rename from docs/notebooks/243-tflite-selfie-segmentation-with-output.rst
rename to docs/notebooks/tflite-selfie-segmentation-with-output.rst
index 21763f712f5..892281dc639 100644
--- a/docs/notebooks/243-tflite-selfie-segmentation-with-output.rst
+++ b/docs/notebooks/tflite-selfie-segmentation-with-output.rst
@@ -73,7 +73,7 @@ Install required dependencies
 
     import platform
     
-    %pip install -q "openvino>=2023.1.0" "opencv-python"
+    %pip install -q "openvino>=2023.1.0" "opencv-python" "tqdm"
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -103,11 +103,22 @@ Install required dependencies
 
 .. code:: ipython3
 
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
-    );
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
+
+
+
+
+.. parsed-literal::
+
+    21503
+
+
 
 Download pretrained model and test image
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -135,7 +146,7 @@ Download pretrained model and test image
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/243-tflite-selfie-segmentation/selfie_multiclass_256x256.tflite')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/tflite-selfie-segmentation/selfie_multiclass_256x256.tflite')
 
 
 
@@ -148,18 +159,18 @@ Starting from the 2023.0.0 release, OpenVINO supports TFLite model
 conversion. However TFLite model format can be directly passed in
 ``read_model`` (you can find examples of this API usage for TFLite in
 `TFLite to OpenVINO conversion
-tutorial <119-tflite-to-openvino-with-output.html>`__ and
+tutorial `__ and
 tutorial with `basic OpenVINO API
-capabilities <002-openvino-api-with-output.html>`__), it is
-recommended to convert model to OpenVINO Intermediate Representation
-format to apply additional optimizations (e.g. weights compression to
-FP16 format). To convert the TFLite model to OpenVINO IR, model
-conversion Python API can be used. The ``ov.convert_model`` function
-accepts a path to the TFLite model and returns the OpenVINO Model class
-instance which represents this model. The obtained model is ready to use
-and to be loaded on the device using ``compile_model`` or can be saved
-on a disk using the ``ov.save_model`` function reducing loading time for
-the next running. For more information about model conversion, see this
+capabilities `__), it is recommended
+to convert model to OpenVINO Intermediate Representation format to apply
+additional optimizations (e.g. weights compression to FP16 format). To
+convert the TFLite model to OpenVINO IR, model conversion Python API can
+be used. The ``ov.convert_model`` function accepts a path to the TFLite
+model and returns the OpenVINO Model class instance which represents
+this model. The obtained model is ready to use and to be loaded on the
+device using ``compile_model`` or can be saved on a disk using the
+``ov.save_model`` function reducing loading time for the next running.
+For more information about model conversion, see this
 `page `__.
 For TensorFlow Lite, refer to the `models
 support `__.
@@ -236,8 +247,8 @@ Load model
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -280,13 +291,14 @@ Additionally, the input image is represented as an RGB image in UINT8
     img = load_image(test_image_url)
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     
+    
     # Preprocessing helper function
-    def resize_and_pad(image:np.ndarray, height:int = 256, width:int = 256):
+    def resize_and_pad(image: np.ndarray, height: int = 256, width: int = 256):
         """
-        Input preprocessing function, takes input image in np.ndarray format, 
-        resizes it to fit specified height and width with preserving aspect ratio 
+        Input preprocessing function, takes input image in np.ndarray format,
+        resizes it to fit specified height and width with preserving aspect ratio
         and adds padding on bottom or right side to complete target height x width rectangle.
-        
+    
         Parameters:
           image (np.ndarray): input image in np.ndarray format
           height (int, *optional*, 256): target height
@@ -300,13 +312,14 @@ Additionally, the input image is represented as an RGB image in UINT8
             img = cv2.resize(image, (width, np.floor(h / (w / width)).astype(int)))
         else:
             img = cv2.resize(image, (np.floor(w / (h / height)).astype(int), height))
-        
+    
         r_h, r_w = img.shape[:2]
         right_padding = width - r_w
         bottom_padding = height - r_h
         padded_img = cv2.copyMakeBorder(img, 0, bottom_padding, 0, right_padding, cv2.BORDER_CONSTANT)
         return padded_img, (bottom_padding, right_padding)
     
+    
     # Apply preprocessig step - resize and pad input image
     padded_img, pad_info = resize_and_pad(np.array(img))
     
@@ -352,13 +365,14 @@ makeup).
     ]
     SegmentationLabels = SegmentationMap(labels)
     
+    
     # helper for postprocessing output mask
-    def postprocess_mask(out:np.ndarray, pad_info:Tuple[int, int], orig_img_size:Tuple[int, int]):
+    def postprocess_mask(out: np.ndarray, pad_info: Tuple[int, int], orig_img_size: Tuple[int, int]):
         """
-        Posptprocessing function for segmentation mask, accepts model output tensor, 
+        Posptprocessing function for segmentation mask, accepts model output tensor,
         gets labels for each pixel using argmax,
         unpads segmentation mask and resizes it to original image size.
-        
+    
         Parameters:
           out (np.ndarray): model output tensor
           pad_info (Tuple[int, int]): information about padding size from preprocessing step
@@ -375,6 +389,7 @@ makeup).
         label_mask_resized = cv2.resize(label_mask_unpadded, (orig_w, orig_h), interpolation=cv2.INTER_NEAREST)
         return label_mask_resized
     
+    
     # Get info about original image
     image_data = np.array(img)
     orig_img_shape = image_data.shape
@@ -411,8 +426,8 @@ Visualize obtained result
     titles = ["Original image", "Portrait mask", "Removed background", "Blurred background"]
     images = [image_data, output_mask, output_image, output_blurred_image]
     figsize = (16, 16)
-    fig, axs = plt.subplots(2, 2, figsize=figsize, sharex='all', sharey='all')
-    fig.patch.set_facecolor('white')
+    fig, axs = plt.subplots(2, 2, figsize=figsize, sharex="all", sharey="all")
+    fig.patch.set_facecolor("white")
     list_axes = list(axs.flat)
     for i, a in enumerate(list_axes):
         a.set_xticklabels([])
@@ -427,7 +442,7 @@ Visualize obtained result
 
 
 
-.. image:: 243-tflite-selfie-segmentation-with-output_files/243-tflite-selfie-segmentation-with-output_25_0.png
+.. image:: tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_25_0.png
 
 
 Interactive background blurring demo on video
@@ -448,7 +463,14 @@ The following code runs model inference on a video:
     
     
     # Main processing function to run background blurring
-    def run_background_blurring(source:Union[str, int] = 0, flip:bool = False, use_popup:bool = False, skip_first_frames:int = 0, model:ov.Model = ov_model, device:str = "CPU"):
+    def run_background_blurring(
+        source: Union[str, int] = 0,
+        flip: bool = False,
+        use_popup: bool = False,
+        skip_first_frames: int = 0,
+        model: ov.Model = ov_model,
+        device: str = "CPU",
+    ):
         """
         Function for running background blurring inference on video
         Parameters:
@@ -465,16 +487,12 @@ The following code runs model inference on a video:
         compiled_model = core.compile_model(model, device)
         try:
             # Create a video player to play with target fps.
-            player = VideoPlayer(
-                source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames
-            )
+            player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
             # Start capturing.
             player.start()
             if use_popup:
                 title = "Press ESC to Exit"
-                cv2.namedWindow(
-                    winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE
-                )
+                cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
     
             processing_times = collections.deque()
             while True:
@@ -496,7 +514,7 @@ The following code runs model inference on a video:
                 # Get the results.
                 input_image, pad_info = resize_and_pad(frame, 256, 256)
                 normalized_img = np.expand_dims(input_image.astype(np.float32) / 255, 0)
-               
+    
                 start_time = time.time()
                 # model expects RGB image, while video capturing in BGR
                 segmentation_mask = compiled_model(normalized_img[:, :, :, ::-1])[0]
@@ -522,7 +540,7 @@ The following code runs model inference on a video:
                     fontScale=f_width / 1000,
                     color=(255, 0, 0),
                     thickness=1,
-                    lineType=cv2.LINE_AA
+                    lineType=cv2.LINE_AA,
                 )
                 # Use this workaround if there is flickering.
                 if use_popup:
@@ -533,9 +551,7 @@ The following code runs model inference on a video:
                         break
                 else:
                     # Encode numpy array to jpg.
-                    _, encoded_img = cv2.imencode(
-                        ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100]
-                    )
+                    _, encoded_img = cv2.imencode(ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                     # Create an IPython image.
                     i = display.Image(data=encoded_img)
                     # Display the image in this notebook.
@@ -605,7 +621,7 @@ Run:
 
 
 
-.. image:: 243-tflite-selfie-segmentation-with-output_files/243-tflite-selfie-segmentation-with-output_33_0.png
+.. image:: tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_33_0.png
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_25_0.png b/docs/notebooks/tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_25_0.png
new file mode 100644
index 00000000000..67deaabec88
--- /dev/null
+++ b/docs/notebooks/tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_25_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f35e940c90fcad2c7ac07fb1e3ead6ff7973b2bb57c038deab32701f0a3d451
+size 516065
diff --git a/docs/notebooks/tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_33_0.png b/docs/notebooks/tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_33_0.png
new file mode 100644
index 00000000000..c4d06ca79cc
--- /dev/null
+++ b/docs/notebooks/tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_33_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:811a5af0eb02a09a3feba19702e87b6d66547efdc1a028850f7e82a08b4916df
+size 14382
diff --git a/docs/notebooks/119-tflite-to-openvino-with-output.rst b/docs/notebooks/tflite-to-openvino-with-output.rst
similarity index 85%
rename from docs/notebooks/119-tflite-to-openvino-with-output.rst
rename to docs/notebooks/tflite-to-openvino-with-output.rst
index 66ed09c9dc9..71302314417 100644
--- a/docs/notebooks/119-tflite-to-openvino-with-output.rst
+++ b/docs/notebooks/tflite-to-openvino-with-output.rst
@@ -49,23 +49,43 @@ Install requirements
     %pip install -q opencv-python requests tqdm
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
-    );
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+
+
+.. parsed-literal::
+
+    21503
+
+
+
 Imports
 ~~~~~~~
 
@@ -106,7 +126,7 @@ Download TFLite model
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/119-tflite-to-openvino/model/efficientnet_lite0_fp32_2.tflite')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/tflite-to-openvino/model/efficientnet_lite0_fp32_2.tflite')
 
 
 
@@ -148,7 +168,7 @@ Load model using OpenVINO TensorFlow Lite Frontend
 TensorFlow Lite models are supported via ``FrontEnd`` API. You may skip
 conversion to IR and read models directly by OpenVINO runtime API. For
 more examples supported formats reading via Frontend API, please look
-this `tutorial <002-openvino-api-with-output.html>`__.
+this `tutorial <../openvino-api>`__.
 
 .. code:: ipython3
 
@@ -169,7 +189,7 @@ on `TensorFlow Hub `__.
 
     image = load_image("https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bricks.png")
     # load_image reads the image in BGR format, [:,:,::-1] reshape transfroms it to RGB
-    image = Image.fromarray(image[:,:,::-1])
+    image = Image.fromarray(image[:, :, ::-1])
     resized_image = image.resize((224, 224))
     input_tensor = np.expand_dims((np.array(resized_image).astype(np.float32) - 127) / 128, 0)
 
@@ -186,8 +206,8 @@ select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -227,7 +247,7 @@ select device from dropdown list for running inference using OpenVINO
 
 
 
-.. image:: 119-tflite-to-openvino-with-output_files/119-tflite-to-openvino-with-output_16_1.png
+.. image:: tflite-to-openvino-with-output_files/tflite-to-openvino-with-output_16_1.png
 
 
 .. parsed-literal::
@@ -238,7 +258,7 @@ select device from dropdown list for running inference using OpenVINO
 Estimate Model Performance
 --------------------------
 
-`Benchmark
+ `Benchmark
 Tool `__
 is used to measure the inference performance of the model on CPU and
 GPU.
@@ -282,7 +302,7 @@ GPU.
 
 .. parsed-literal::
 
-    [ INFO ] Read model took 9.52 ms
+    [ INFO ] Read model took 9.44 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     images (node: images) : f32 / [...] / [1,224,224,3]
@@ -300,7 +320,7 @@ GPU.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 181.00 ms
+    [ INFO ] Compile model took 190.96 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: TensorFlow_Lite_Frontend_IR
@@ -308,6 +328,10 @@ GPU.
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
     [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
     [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
+
+
+.. parsed-literal::
+
     [ INFO ]   CPU:
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
@@ -335,23 +359,19 @@ GPU.
     [ INFO ] Fill input 'images' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-
-
-.. parsed-literal::
-
-    [ INFO ] First inference took 7.56 ms
+    [ INFO ] First inference took 7.52 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            17412 iterations
-    [ INFO ] Duration:         15009.54 ms
+    [ INFO ] Count:            17502 iterations
+    [ INFO ] Duration:         15008.50 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        5.04 ms
-    [ INFO ]    Average:       5.04 ms
-    [ INFO ]    Min:           3.66 ms
-    [ INFO ]    Max:           13.39 ms
-    [ INFO ] Throughput:   1160.06 FPS
+    [ INFO ]    Median:        5.01 ms
+    [ INFO ]    Average:       5.01 ms
+    [ INFO ]    Min:           3.20 ms
+    [ INFO ]    Max:           15.09 ms
+    [ INFO ] Throughput:   1166.14 FPS
 
diff --git a/docs/notebooks/119-tflite-to-openvino-with-output_files/119-tflite-to-openvino-with-output_16_1.jpg b/docs/notebooks/tflite-to-openvino-with-output_files/tflite-to-openvino-with-output_16_1.jpg
similarity index 100%
rename from docs/notebooks/119-tflite-to-openvino-with-output_files/119-tflite-to-openvino-with-output_16_1.jpg
rename to docs/notebooks/tflite-to-openvino-with-output_files/tflite-to-openvino-with-output_16_1.jpg
diff --git a/docs/notebooks/119-tflite-to-openvino-with-output_files/119-tflite-to-openvino-with-output_16_1.png b/docs/notebooks/tflite-to-openvino-with-output_files/tflite-to-openvino-with-output_16_1.png
similarity index 100%
rename from docs/notebooks/119-tflite-to-openvino-with-output_files/119-tflite-to-openvino-with-output_16_1.png
rename to docs/notebooks/tflite-to-openvino-with-output_files/tflite-to-openvino-with-output_16_1.png
diff --git a/docs/notebooks/109-throughput-tricks-with-output.rst b/docs/notebooks/throughput-tricks-with-output.rst
similarity index 79%
rename from docs/notebooks/109-throughput-tricks-with-output.rst
rename to docs/notebooks/throughput-tricks-with-output.rst
index ff7fded1fd8..9ab92ebecb7 100644
--- a/docs/notebooks/109-throughput-tricks-with-output.rst
+++ b/docs/notebooks/throughput-tricks-with-output.rst
@@ -24,8 +24,7 @@ The quantization and pre-post-processing API are not included here as
 they change the precision (quantization) or processing graph
 (prepostprocessor). You can find examples of how to apply them to
 optimize performance on OpenVINO IR files in
-`111-detection-quantization <111-detection-quantization-with-output.html>`__ and
-`118-optimize-preprocessing <118-optimize-preprocessing-with-output.html>`__.
+`optimize-preprocessing <../optimize-preprocessing>`__.
 
 |image0|
 
@@ -41,7 +40,7 @@ optimize performance on OpenVINO IR files in
    result in different performance.
 
 A similar notebook focused on the latency mode is available
-`here <109-latency-tricks-with-output.html>`__.
+`here `__.
 
 Table of contents:
 ^^^^^^^^^^^^^^^^^^
@@ -80,7 +79,19 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0" "ultralytics<=8.0.178" seaborn onnx --extra-index-url https://download.pytorch.org/whl/cpu
+    import platform
+    
+    %pip install -q "openvino>=2023.1.0" "ultralytics<=8.0.178" seaborn onnx opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
+    
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
 
 
 .. parsed-literal::
@@ -95,11 +106,13 @@ Prerequisites
     from typing import Any, List, Tuple
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     import notebook_utils as utils
 
 Data
@@ -139,14 +152,14 @@ object detection model.
 
 
 
-.. image:: 109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_5_0.jpg
+.. image:: throughput-tricks-with-output_files/throughput-tricks-with-output_5_0.jpg
 
 
 
 
 .. parsed-literal::
 
-    
+    
 
 
 
@@ -185,7 +198,7 @@ PyTorch Hub and small enough to see the difference in performance.
 
 .. parsed-literal::
 
-    YOLOv5 🚀 2023-4-21 Python-3.8.10 torch-2.1.0+cpu CPU
+    YOLOv5 🚀 2023-4-21 Python-3.8.10 torch-2.2.2+cpu CPU
     
 
 
@@ -196,7 +209,7 @@ PyTorch Hub and small enough to see the difference in performance.
 
 .. parsed-literal::
 
-    YOLOv5n summary: 213 layers, 1867405 parameters, 0 gradients
+    YOLOv5n summary: 213 layers, 1867405 parameters, 0 gradients, 4.5 GFLOPs
 
 
 .. parsed-literal::
@@ -249,10 +262,16 @@ second (FPS).
 
 .. code:: ipython3
 
-    from openvino.runtime import AsyncInferQueue
+    from openvino import AsyncInferQueue
     
     
-    def benchmark_model(model: Any, frames: np.ndarray, async_queue: AsyncInferQueue = None, benchmark_name: str = "OpenVINO model", device_name: str = "CPU") -> float:
+    def benchmark_model(
+        model: Any,
+        frames: np.ndarray,
+        async_queue: AsyncInferQueue = None,
+        benchmark_name: str = "OpenVINO model",
+        device_name: str = "CPU",
+    ) -> float:
         """
         Helper function for benchmarking the model. It measures the time and prints results.
         """
@@ -291,14 +310,86 @@ the image.
 
     # https://gist.github.com/AruniRC/7b3dadd004da04c80198557db5da4bda
     classes = [
-        "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
-        "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra",
-        "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
-        "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork",
-        "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut",
-        "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
-        "cell phone", "microwave", "oven", "oaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
-        "hair drier", "toothbrush"
+        "person",
+        "bicycle",
+        "car",
+        "motorcycle",
+        "airplane",
+        "bus",
+        "train",
+        "truck",
+        "boat",
+        "traffic light",
+        "fire hydrant",
+        "stop sign",
+        "parking meter",
+        "bench",
+        "bird",
+        "cat",
+        "dog",
+        "horse",
+        "sheep",
+        "cow",
+        "elephant",
+        "bear",
+        "zebra",
+        "giraffe",
+        "backpack",
+        "umbrella",
+        "handbag",
+        "tie",
+        "suitcase",
+        "frisbee",
+        "skis",
+        "snowboard",
+        "sports ball",
+        "kite",
+        "baseball bat",
+        "baseball glove",
+        "skateboard",
+        "surfboard",
+        "tennis racket",
+        "bottle",
+        "wine glass",
+        "cup",
+        "fork",
+        "knife",
+        "spoon",
+        "bowl",
+        "banana",
+        "apple",
+        "sandwich",
+        "orange",
+        "broccoli",
+        "carrot",
+        "hot dog",
+        "pizza",
+        "donut",
+        "cake",
+        "chair",
+        "couch",
+        "potted plant",
+        "bed",
+        "dining table",
+        "toilet",
+        "tv",
+        "laptop",
+        "mouse",
+        "remote",
+        "keyboard",
+        "cell phone",
+        "microwave",
+        "oven",
+        "oaster",
+        "sink",
+        "refrigerator",
+        "book",
+        "clock",
+        "vase",
+        "scissors",
+        "teddy bear",
+        "hair drier",
+        "toothbrush",
     ]
     
     # Colors for the classes above (Rainbow Color Map).
@@ -323,18 +414,14 @@ the image.
             score = obj[4]
             label = np.argmax(obj[5:])
             # Create a box with pixels coordinates from the box with normalized coordinates [0,1].
-            boxes.append(
-                tuple(map(int, (xmin - ww // 2, ymin - hh // 2, ww, hh)))
-            )
+            boxes.append(tuple(map(int, (xmin - ww // 2, ymin - hh // 2, ww, hh))))
             labels.append(int(label))
             scores.append(float(score))
     
         # Apply non-maximum suppression to get rid of many overlapping entities.
         # See https://paperswithcode.com/method/non-maximum-suppression
         # This algorithm returns indices of objects to keep.
-        indices = cv2.dnn.NMSBoxes(
-            bboxes=boxes, scores=scores, score_threshold=0.25, nms_threshold=0.5
-        )
+        indices = cv2.dnn.NMSBoxes(bboxes=boxes, scores=scores, score_threshold=0.25, nms_threshold=0.5)
     
         # If there are no boxes.
         if len(indices) == 0:
@@ -404,21 +491,25 @@ optimizations applied. We will treat it as our baseline.
     with torch.no_grad():
         result = pytorch_model(torch.as_tensor(video_frames[0])).detach().numpy()[0]
         show_result(result)
-        pytorch_fps = benchmark_model(pytorch_model, frames=torch.as_tensor(video_frames).float(), benchmark_name="PyTorch model")
+        pytorch_fps = benchmark_model(
+            pytorch_model,
+            frames=torch.as_tensor(video_frames).float(),
+            benchmark_name="PyTorch model",
+        )
 
 
 
-.. image:: 109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_15_0.jpg
+.. image:: throughput-tricks-with-output_files/throughput-tricks-with-output_15_0.jpg
 
 
 .. parsed-literal::
 
-    PyTorch model on CPU. First inference time: 0.0262 seconds
+    PyTorch model on CPU. First inference time: 0.0293 seconds
 
 
 .. parsed-literal::
 
-    PyTorch model on CPU: 0.0205 seconds per image (48.72 FPS)
+    PyTorch model on CPU: 0.0190 seconds per image (52.54 FPS)
 
 
 OpenVINO IR model
@@ -459,17 +550,17 @@ step in this notebook.
 
 
 
-.. image:: 109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_18_0.jpg
+.. image:: throughput-tricks-with-output_files/throughput-tricks-with-output_18_0.jpg
 
 
 .. parsed-literal::
 
-    OpenVINO model on CPU. First inference time: 0.0147 seconds
+    OpenVINO model on CPU. First inference time: 0.0114 seconds
 
 
 .. parsed-literal::
 
-    OpenVINO model on CPU: 0.0070 seconds per image (142.58 FPS)
+    OpenVINO model on CPU: 0.0071 seconds per image (141.35 FPS)
 
 
 OpenVINO IR model + bigger batch
@@ -515,23 +606,27 @@ hardware and model.
     
     result = ov_cpu_batch_model(batched_video_frames[0])[ov_cpu_batch_model.output(0)][0]
     show_result(result)
-    ov_cpu_batch_fps = benchmark_model(model=ov_cpu_batch_model, frames=batched_video_frames, benchmark_name="OpenVINO model + bigger batch")
+    ov_cpu_batch_fps = benchmark_model(
+        model=ov_cpu_batch_model,
+        frames=batched_video_frames,
+        benchmark_name="OpenVINO model + bigger batch",
+    )
     
     del ov_cpu_batch_model  # release resources
 
 
 
-.. image:: 109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_21_0.jpg
+.. image:: throughput-tricks-with-output_files/throughput-tricks-with-output_21_0.jpg
 
 
 .. parsed-literal::
 
-    OpenVINO model + bigger batch on CPU. First inference time: 0.0514 seconds
+    OpenVINO model + bigger batch on CPU. First inference time: 0.0369 seconds
 
 
 .. parsed-literal::
 
-    OpenVINO model + bigger batch on CPU: 0.0068 seconds per image (146.52 FPS)
+    OpenVINO model + bigger batch on CPU: 0.0069 seconds per image (144.00 FPS)
 
 
 Asynchronous processing
@@ -570,7 +665,13 @@ the pipeline.
     
         # don't show output for the remaining frames
         infer_queue.set_callback(lambda x, y: {})
-        fps = benchmark_model(model=infer_queue.start_async, frames=video_frames, async_queue=infer_queue, benchmark_name=benchmark_name, device_name=device_name)
+        fps = benchmark_model(
+            model=infer_queue.start_async,
+            frames=video_frames,
+            async_queue=infer_queue,
+            benchmark_name=benchmark_name,
+            device_name=device_name,
+        )
     
         del infer_queue  # release resources
         return fps
@@ -593,23 +694,27 @@ feature, which sets the batch size to the optimal level.
 
     ov_cpu_through_model = core.compile_model(ov_model, device_name="CPU", config={"PERFORMANCE_HINT": "THROUGHPUT"})
     
-    ov_cpu_through_fps = benchmark_async_mode(ov_cpu_through_model, benchmark_name="OpenVINO model", device_name="CPU (THROUGHPUT)")
+    ov_cpu_through_fps = benchmark_async_mode(
+        ov_cpu_through_model,
+        benchmark_name="OpenVINO model",
+        device_name="CPU (THROUGHPUT)",
+    )
     
     del ov_cpu_through_model  # release resources
 
 
 
-.. image:: 109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_25_0.jpg
+.. image:: throughput-tricks-with-output_files/throughput-tricks-with-output_25_0.jpg
 
 
 .. parsed-literal::
 
-    OpenVINO model on CPU (THROUGHPUT). First inference time: 0.0244 seconds
+    OpenVINO model on CPU (THROUGHPUT). First inference time: 0.0259 seconds
 
 
 .. parsed-literal::
 
-    OpenVINO model on CPU (THROUGHPUT): 0.0040 seconds per image (249.04 FPS)
+    OpenVINO model on CPU (THROUGHPUT): 0.0040 seconds per image (249.64 FPS)
 
 
 OpenVINO IR model in throughput mode on GPU
@@ -658,17 +763,17 @@ performance hint.
 
 
 
-.. image:: 109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_29_0.jpg
+.. image:: throughput-tricks-with-output_files/throughput-tricks-with-output_29_0.jpg
 
 
 .. parsed-literal::
 
-    OpenVINO model on AUTO (THROUGHPUT). First inference time: 0.0233 seconds
+    OpenVINO model on AUTO (THROUGHPUT). First inference time: 0.0254 seconds
 
 
 .. parsed-literal::
 
-    OpenVINO model on AUTO (THROUGHPUT): 0.0040 seconds per image (250.58 FPS)
+    OpenVINO model on AUTO (THROUGHPUT): 0.0040 seconds per image (249.49 FPS)
 
 
 OpenVINO IR model in cumulative throughput mode on AUTO
@@ -686,21 +791,25 @@ activate all devices.
 
     ov_auto_cumulative_model = core.compile_model(ov_model, device_name="AUTO", config={"PERFORMANCE_HINT": "CUMULATIVE_THROUGHPUT"})
     
-    ov_auto_cumulative_fps = benchmark_async_mode(ov_auto_cumulative_model, benchmark_name="OpenVINO model", device_name="AUTO (CUMULATIVE THROUGHPUT)")
+    ov_auto_cumulative_fps = benchmark_async_mode(
+        ov_auto_cumulative_model,
+        benchmark_name="OpenVINO model",
+        device_name="AUTO (CUMULATIVE THROUGHPUT)",
+    )
 
 
 
-.. image:: 109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_31_0.jpg
+.. image:: throughput-tricks-with-output_files/throughput-tricks-with-output_31_0.jpg
 
 
 .. parsed-literal::
 
-    OpenVINO model on AUTO (CUMULATIVE THROUGHPUT). First inference time: 0.0204 seconds
+    OpenVINO model on AUTO (CUMULATIVE THROUGHPUT). First inference time: 0.0224 seconds
 
 
 .. parsed-literal::
 
-    OpenVINO model on AUTO (CUMULATIVE THROUGHPUT): 0.0040 seconds per image (251.22 FPS)
+    OpenVINO model on AUTO (CUMULATIVE THROUGHPUT): 0.0040 seconds per image (250.82 FPS)
 
 
 Other tricks
@@ -713,9 +822,8 @@ options, quantization and pre-post-processing or dedicated to latency
 mode. To get even more from your model, please visit `advanced
 throughput
 options `__,
-`109-latency-tricks <109-latency-tricks-with-output.html-with-output.html>`__,
-`111-detection-quantization <111-detection-quantization-with-output.html>`__, and
-`118-optimize-preprocessing <118-optimize-preprocessing-with-output.html>`__.
+`latency-tricks `__, and
+`optimize-preprocessing <../optimize-preprocessing>`__.
 
 Performance comparison
 ----------------------
@@ -734,10 +842,25 @@ steps, just skip them.
 
     from matplotlib import pyplot as plt
     
-    labels = ["PyTorch model", "OpenVINO IR model", "OpenVINO IR model + bigger batch", "OpenVINO IR model in throughput mode", "OpenVINO IR model in throughput mode on GPU",
-              "OpenVINO IR model in throughput mode on AUTO", "OpenVINO IR model in cumulative throughput mode on AUTO"]
+    labels = [
+        "PyTorch model",
+        "OpenVINO IR model",
+        "OpenVINO IR model + bigger batch",
+        "OpenVINO IR model in throughput mode",
+        "OpenVINO IR model in throughput mode on GPU",
+        "OpenVINO IR model in throughput mode on AUTO",
+        "OpenVINO IR model in cumulative throughput mode on AUTO",
+    ]
     
-    fps = [pytorch_fps, ov_cpu_fps, ov_cpu_batch_fps, ov_cpu_through_fps, ov_gpu_fps, ov_auto_fps, ov_auto_cumulative_fps]
+    fps = [
+        pytorch_fps,
+        ov_cpu_fps,
+        ov_cpu_batch_fps,
+        ov_cpu_through_fps,
+        ov_gpu_fps,
+        ov_auto_fps,
+        ov_auto_cumulative_fps,
+    ]
     
     bar_colors = colors[::10] / 255.0
     
@@ -747,12 +870,12 @@ steps, just skip them.
     ax.set_ylabel("Throughput [FPS]")
     ax.set_title("Performance difference")
     
-    plt.xticks(rotation='vertical')
+    plt.xticks(rotation="vertical")
     plt.show()
 
 
 
-.. image:: 109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_34_0.png
+.. image:: throughput-tricks-with-output_files/throughput-tricks-with-output_34_0.png
 
 
 Conclusions
diff --git a/docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_15_0.jpg b/docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_15_0.jpg
similarity index 100%
rename from docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_15_0.jpg
rename to docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_15_0.jpg
diff --git a/docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_18_0.jpg b/docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_18_0.jpg
similarity index 100%
rename from docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_18_0.jpg
rename to docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_18_0.jpg
diff --git a/docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_21_0.jpg b/docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_21_0.jpg
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rename from docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_21_0.jpg
rename to docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_21_0.jpg
diff --git a/docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_25_0.jpg b/docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_25_0.jpg
similarity index 100%
rename from docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_25_0.jpg
rename to docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_25_0.jpg
diff --git a/docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_29_0.jpg b/docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_29_0.jpg
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rename from docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_29_0.jpg
rename to docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_29_0.jpg
diff --git a/docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_31_0.jpg b/docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_31_0.jpg
similarity index 100%
rename from docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_31_0.jpg
rename to docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_31_0.jpg
diff --git a/docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_34_0.png b/docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_34_0.png
new file mode 100644
index 00000000000..aa018f1d9c6
--- /dev/null
+++ b/docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_34_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1fff4df7ad0fd1d1c35bb0f769f45e3bdeb62bd09ebc948a7d0e2890a61b69d5
+size 62466
diff --git a/docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_5_0.jpg b/docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_5_0.jpg
similarity index 100%
rename from docs/notebooks/109-throughput-tricks-with-output_files/109-throughput-tricks-with-output_5_0.jpg
rename to docs/notebooks/throughput-tricks-with-output_files/throughput-tricks-with-output_5_0.jpg
diff --git a/docs/notebooks/251-tiny-sd-image-generation-with-output.rst b/docs/notebooks/tiny-sd-image-generation-with-output.rst
similarity index 84%
rename from docs/notebooks/251-tiny-sd-image-generation-with-output.rst
rename to docs/notebooks/tiny-sd-image-generation-with-output.rst
index 601e918c67c..74dc89af7a3 100644
--- a/docs/notebooks/251-tiny-sd-image-generation-with-output.rst
+++ b/docs/notebooks/tiny-sd-image-generation-with-output.rst
@@ -69,7 +69,7 @@ Install required dependencies
 
 .. code:: ipython3
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu torch torchvision "openvino>=2023.3.0" "diffusers>=0.18.0" "transformers>=4.30.2" "gradio"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "torch>=2.1" torchvision "openvino>=2023.3.0" "diffusers>=0.18.0" "transformers>=4.30.2" "gradio>=4.19"
 
 Create PyTorch Models pipeline
 ------------------------------
@@ -181,11 +181,11 @@ hidden states.
     TEXT_ENCODER_OV_PATH = Path("text_encoder.xml")
     
     
-    def convert_encoder(text_encoder: torch.nn.Module, ir_path:Path):
+    def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
         """
-        Convert Text Encoder mode. 
-        Function accepts text encoder model, and prepares example inputs for conversion, 
-        Parameters: 
+        Convert Text Encoder mode.
+        Function accepts text encoder model, and prepares example inputs for conversion,
+        Parameters:
             text_encoder (torch.nn.Module): text_encoder model from Stable Diffusion pipeline
             ir_path (Path): File for storing model
         Returns:
@@ -198,11 +198,17 @@ hidden states.
         # disable gradients calculation for reducing memory consumption
         with torch.no_grad():
             # Export model to IR format
-            ov_model = ov.convert_model(text_encoder, example_input=input_ids, input=[(1,77),])
+            ov_model = ov.convert_model(
+                text_encoder,
+                example_input=input_ids,
+                input=[
+                    (1, 77),
+                ],
+            )
         ov.save_model(ov_model, ir_path)
         del ov_model
-        print(f'Text Encoder successfully converted to IR and saved to {ir_path}')
-        
+        print(f"Text Encoder successfully converted to IR and saved to {ir_path}")
+    
     
     if not TEXT_ENCODER_OV_PATH.exists():
         convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
@@ -245,19 +251,16 @@ Model predicts the ``sample`` state for the next step.
     import numpy as np
     from openvino import PartialShape, Type
     
-    UNET_OV_PATH = Path('unet.xml')
+    UNET_OV_PATH = Path("unet.xml")
     
-    dtype_mapping = {
-        torch.float32: Type.f32,
-        torch.float64: Type.f64
-    }
+    dtype_mapping = {torch.float32: Type.f32, torch.float64: Type.f64}
     
     
-    def convert_unet(unet:torch.nn.Module, ir_path:Path):
+    def convert_unet(unet: torch.nn.Module, ir_path: Path):
         """
-        Convert U-net model to IR format. 
-        Function accepts unet model, prepares example inputs for conversion, 
-        Parameters: 
+        Convert U-net model to IR format.
+        Function accepts unet model, prepares example inputs for conversion,
+        Parameters:
             unet (StableDiffusionPipeline): unet from Stable Diffusion pipeline
             ir_path (Path): File for storing model
         Returns:
@@ -280,7 +283,7 @@ Model predicts the ``sample`` state for the next step.
             ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
         ov.save_model(ov_model, ir_path)
         del ov_model
-        print(f'Unet successfully converted to IR and saved to {ir_path}')
+        print(f"Unet successfully converted to IR and saved to {ir_path}")
     
     
     if not UNET_OV_PATH.exists():
@@ -331,17 +334,19 @@ of the pipeline, it will be better to convert them to separate models.
 
     VAE_ENCODER_OV_PATH = Path("vae_encodr.xml")
     
+    
     def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path):
         """
-        Convert VAE model for encoding to IR format. 
-        Function accepts vae model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion, 
-        Parameters: 
-            vae (torch.nn.Module): VAE model from StableDiffusio pipeline 
+        Convert VAE model for encoding to IR format.
+        Function accepts vae model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion,
+        Parameters:
+            vae (torch.nn.Module): VAE model from StableDiffusio pipeline
             ir_path (Path): File for storing model
         Returns:
             None
         """
+    
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -349,14 +354,15 @@ of the pipeline, it will be better to convert them to separate models.
     
             def forward(self, image):
                 return self.vae.encode(x=image)["latent_dist"].sample()
+    
         vae_encoder = VAEEncoderWrapper(vae)
         vae_encoder.eval()
         image = torch.zeros((1, 3, 512, 512))
         with torch.no_grad():
-            ov_model = ov.convert_model(vae_encoder, example_input=image, input=[((1,3,512,512),)])
+            ov_model = ov.convert_model(vae_encoder, example_input=image, input=[((1, 3, 512, 512),)])
         ov.save_model(ov_model, ir_path)
         del ov_model
-        print(f'VAE encoder successfully converted to IR and saved to {ir_path}')
+        print(f"VAE encoder successfully converted to IR and saved to {ir_path}")
     
     
     if not VAE_ENCODER_OV_PATH.exists():
@@ -364,19 +370,21 @@ of the pipeline, it will be better to convert them to separate models.
     else:
         print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH}")
     
-    VAE_DECODER_OV_PATH = Path('vae_decoder.xml')
+    VAE_DECODER_OV_PATH = Path("vae_decoder.xml")
+    
     
     def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path):
         """
-        Convert VAE model for decoding to IR format. 
-        Function accepts vae model, creates wrapper class for export only necessary for inference part, 
-        prepares example inputs for conversion, 
-        Parameters: 
+        Convert VAE model for decoding to IR format.
+        Function accepts vae model, creates wrapper class for export only necessary for inference part,
+        prepares example inputs for conversion,
+        Parameters:
             vae (torch.nn.Module): VAE model frm StableDiffusion pipeline
             ir_path (Path): File for storing model
         Returns:
             None
         """
+    
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
@@ -384,16 +392,16 @@ of the pipeline, it will be better to convert them to separate models.
     
             def forward(self, latents):
                 return self.vae.decode(latents)
-        
+    
         vae_decoder = VAEDecoderWrapper(vae)
         latents = torch.zeros((1, 4, 64, 64))
     
         vae_decoder.eval()
         with torch.no_grad():
-            ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1,4,64,64),)])
+            ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1, 4, 64, 64),)])
         ov.save_model(ov_model, ir_path)
         del ov_model
-        print(f'VAE decoder successfully converted to IR and saved to {ir_path}')
+        print(f"VAE decoder successfully converted to IR and saved to {ir_path}")
     
     
     if not VAE_DECODER_OV_PATH.exists():
@@ -483,11 +491,11 @@ of the variational auto encoder.
     from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
     
     
-    def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
+    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
-        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio 
+        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-        
+    
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -507,7 +515,7 @@ of the variational auto encoder.
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-        
+    
         Parameters:
           image (PIL.Image.Image): input image
         Returns:
@@ -515,10 +523,8 @@ of the variational auto encoder.
            meta (Dict): dictionary with preprocessing metadata info
         """
         src_width, src_height = image.size
-        dst_width, dst_height = scale_fit_to_window(
-            512, 512, src_width, src_height)
-        image = np.array(image.resize((dst_width, dst_height),
-                         resample=PIL.Image.Resampling.LANCZOS))[None, :]
+        dst_width, dst_height = scale_fit_to_window(512, 512, src_width, src_height)
+        image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
         pad_width = 512 - dst_width
         pad_height = 512 - dst_height
         pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
@@ -611,7 +617,7 @@ of the variational auto encoder.
                 gif (bool, *optional*, False):
                     Flag for storing all steps results or not.
             Returns:
-                Dictionary with keys: 
+                Dictionary with keys:
                     sample - the last generated image PIL.Image.Image or np.array
                     iterations - *optional* (if gif=True) images for all diffusion steps, List of PIL.Image.Image or np.array.
             """
@@ -621,8 +627,12 @@ of the variational auto encoder.
             img_buffer = []
             do_classifier_free_guidance = guidance_scale > 1.0
             # get prompt text embeddings
-            text_embeddings = self._encode_prompt(prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt)
-            
+            text_embeddings = self._encode_prompt(
+                prompt,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                negative_prompt=negative_prompt,
+            )
+    
             # set timesteps
             accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
             extra_set_kwargs = {}
@@ -658,7 +668,12 @@ of the variational auto encoder.
                     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     
                 # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs)["prev_sample"].numpy()
+                latents = self.scheduler.step(
+                    torch.from_numpy(noise_pred),
+                    t,
+                    torch.from_numpy(latents),
+                    **extra_step_kwargs,
+                )["prev_sample"].numpy()
                 if gif:
                     image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
                     image = self.postprocess_image(image, meta, output_type)
@@ -668,9 +683,15 @@ of the variational auto encoder.
             image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
     
             image = self.postprocess_image(image, meta, output_type)
-            return {"sample": image, 'iterations': img_buffer}
-        
-        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True, negative_prompt:Union[str, List[str]] = None):
+            return {"sample": image, "iterations": img_buffer}
+    
+        def _encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+            negative_prompt: Union[str, List[str]] = None,
+        ):
             """
             Encodes the prompt into text encoder hidden states.
     
@@ -694,16 +715,13 @@ of the variational auto encoder.
             )
             text_input_ids = text_inputs.input_ids
     
-            text_embeddings = self.text_encoder(
-                text_input_ids)[self._text_encoder_output]
+            text_embeddings = self.text_encoder(text_input_ids)[self._text_encoder_output]
     
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(
-                    text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(
-                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
+                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
+                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
     
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
@@ -737,11 +755,10 @@ of the variational auto encoder.
     
             return text_embeddings
     
-    
-        def prepare_latents(self, image:PIL.Image.Image = None, latent_timestep:torch.Tensor = None):
+        def prepare_latents(self, image: PIL.Image.Image = None, latent_timestep: torch.Tensor = None):
             """
             Function for getting initial latents for starting generation
-            
+    
             Parameters:
                 image (PIL.Image.Image, *optional*, None):
                     Input image for generation, if not provided randon noise will be used as starting point
@@ -763,11 +780,11 @@ of the variational auto encoder.
             latents = self.scheduler.add_noise(torch.from_numpy(latents), torch.from_numpy(noise), latent_timestep).numpy()
             return latents, meta
     
-        def postprocess_image(self, image:np.ndarray, meta:Dict, output_type:str = "pil"):
+        def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
             """
-            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required), 
+            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
             normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-            
+    
             Parameters:
                 image (np.ndarray):
                     Generated image
@@ -793,25 +810,23 @@ of the variational auto encoder.
                 image = self.numpy_to_pil(image)
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [img.resize((orig_width, orig_height),
-                                        PIL.Image.Resampling.LANCZOS) for img in image]
+                    image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
             else:
                 if "src_height" in meta:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [cv2.resize(img, (orig_width, orig_width))
-                             for img in image]
+                    image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
             return image
     
-        def get_timesteps(self, num_inference_steps:int, strength:float):
+        def get_timesteps(self, num_inference_steps: int, strength: float):
             """
             Helper function for getting scheduler timesteps for generation
             In case of image-to-image generation, it updates number of steps according to strength
-            
+    
             Parameters:
                num_inference_steps (int):
                   number of inference steps for generation
                strength (float):
-                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. 
+                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
                    Values that approach 1.0 enable lots of variations but will also produce images that are not semantically consistent with the input.
             """
             # get the original timestep using init_timestep
@@ -820,7 +835,7 @@ of the variational auto encoder.
             t_start = max(num_inference_steps - init_timestep, 0)
             timesteps = self.scheduler.timesteps[t_start:]
     
-            return timesteps, num_inference_steps - t_start 
+            return timesteps, num_inference_steps - t_start
 
 Configure Inference Pipeline
 ----------------------------
@@ -841,8 +856,8 @@ Select device from dropdown list for running inference using OpenVINO.
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -874,36 +889,36 @@ operations to be executed in full precision.
 .. code:: ipython3
 
     import pickle
-    import urllib.request
+    import requests
     import os
     
     # Fetch `model_upcast_utils` which helps to restore accuracy when inferred on GPU
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/model_upcast_utils.py',
-        filename='model_upcast_utils.py'
-    )
+    r = requests.get("https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/model_upcast_utils.py")
+    with open("model_upcast_utils.py", "w") as f:
+        f.write(r.text)
     
     # Fetch an example input for UNet model needed for upcasting calibration process
-    urllib.request.urlretrieve(
-        url='https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/pkl/unet_calibration_example_input.pkl',
-        filename='unet_calibration_example_input.pkl'
+    r = requests.get("https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/pkl/unet_calibration_example_input.pkl")
+    with open("unet_calibration_example_input.pkl", "wb") as f:
+        f.write(r.content)
+    
+    from model_upcast_utils import (
+        is_model_partially_upcasted,
+        partially_upcast_nodes_to_fp32,
     )
-    from model_upcast_utils import is_model_partially_upcasted, partially_upcast_nodes_to_fp32
     
     unet_model = core.read_model(UNET_OV_PATH)
-    if 'GPU' in core.available_devices and not is_model_partially_upcasted(unet_model):
+    if "GPU" in core.available_devices and not is_model_partially_upcasted(unet_model):
         with open("unet_calibration_example_input.pkl", "rb") as f:
             example_input = pickle.load(f)
-        unet_model = partially_upcast_nodes_to_fp32(unet_model, example_input, upcast_ratio=0.7,
-                                                    operation_types=["Convolution"])
+        unet_model = partially_upcast_nodes_to_fp32(unet_model, example_input, upcast_ratio=0.7, operation_types=["Convolution"])
     
         ov.save_model(unet_model, UNET_OV_PATH.with_suffix("._tmp.xml"))
         del unet_model
         os.remove(UNET_OV_PATH)
         os.remove(str(UNET_OV_PATH).replace(".xml", ".bin"))
         UNET_OV_PATH.with_suffix("._tmp.xml").rename(UNET_OV_PATH)
-        UNET_OV_PATH.with_suffix("._tmp.bin").rename(UNET_OV_PATH.with_suffix('.bin'))
-
+        UNET_OV_PATH.with_suffix("._tmp.bin").rename(UNET_OV_PATH.with_suffix(".bin"))
 
 .. code:: ipython3
 
@@ -924,12 +939,8 @@ Let us define them and put all components together
     from transformers import CLIPTokenizer
     from diffusers.schedulers import LMSDiscreteScheduler
     
-    lms = LMSDiscreteScheduler(
-        beta_start=0.00085, 
-        beta_end=0.012, 
-        beta_schedule="scaled_linear"
-    )
-    tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
+    lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
     
     ov_pipe = OVStableDiffusionPipeline(
         tokenizer=tokenizer,
@@ -937,7 +948,7 @@ Let us define them and put all components together
         unet=unet_model,
         vae_encoder=vae_encoder,
         vae_decoder=vae_decoder,
-        scheduler=lms
+        scheduler=lms,
     )
 
 Text-to-Image generation
@@ -949,16 +960,16 @@ Now, let’s see model in action
 
 .. code:: ipython3
 
-    text_prompt = 'RAW studio photo of An intricate forest minitown landscape trapped in a bottle, atmospheric oliva lighting, on the table, intricate details, dark shot, soothing tones, muted colors '
+    text_prompt = "RAW studio photo of An intricate forest minitown landscape trapped in a bottle, atmospheric oliva lighting, on the table, intricate details, dark shot, soothing tones, muted colors "
     seed = 431
     num_steps = 20
 
 .. code:: ipython3
 
-    print('Pipeline settings')
-    print(f'Input text: {text_prompt}')
-    print(f'Seed: {seed}')
-    print(f'Number of steps: {num_steps}')
+    print("Pipeline settings")
+    print(f"Input text: {text_prompt}")
+    print(f"Seed: {seed}")
+    print(f"Number of steps: {num_steps}")
 
 
 .. parsed-literal::
@@ -986,14 +997,14 @@ contains list of intermediate results for each step.
 
 .. code:: ipython3
 
-    final_image = result['sample'][0]
-    final_image.save('result.png')
+    final_image = result["sample"][0]
+    final_image.save("result.png")
 
 Now is show time!
 
 .. code:: ipython3
 
-    text = '\n\t'.join(text_prompt.split('.'))
+    text = "\n\t".join(text_prompt.split("."))
     print("Input text:")
     print("\t" + text)
     display(final_image)
@@ -1006,7 +1017,7 @@ Now is show time!
 
 
 
-.. image:: 251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_35_1.png
+.. image:: tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_35_1.png
 
 
 Nice. As you can see, the picture has quite a high definition 🔥.
@@ -1044,8 +1055,10 @@ found in this
 
 .. code:: ipython3
 
-    text_prompt_i2i = 'professional photo portrait of woman, highly detailed, hyper realistic, cinematic effects, soft lighting'
-    negative_prompt_i2i = "blurry, poor quality, low res, worst quality, cropped, ugly, poorly drawn face, without eyes, mutation, unreal, animate, poorly drawn eyes"
+    text_prompt_i2i = "professional photo portrait of woman, highly detailed, hyper realistic, cinematic effects, soft lighting"
+    negative_prompt_i2i = (
+        "blurry, poor quality, low res, worst quality, cropped, ugly, poorly drawn face, without eyes, mutation, unreal, animate, poorly drawn eyes"
+    )
     num_steps_i2i = 40
     seed_i2i = 82698152
     strength = 0.68
@@ -1057,15 +1070,22 @@ found in this
     default_image_url = "https://user-images.githubusercontent.com/29454499/260418860-69cc443a-9ee6-493c-a393-3a97af080be7.jpg"
     # read uploaded image
     image = load_image(default_image_url)
-    print('Pipeline settings')
-    print(f'Input positive prompt: \n\t{text_prompt_i2i}')
-    print(f'Input negative prompt: \n\t{negative_prompt_i2i}')
-    print(f'Seed: {seed_i2i}')
-    print(f'Number of steps: {num_steps_i2i}')
-    print(f'Strength: {strength}')
+    print("Pipeline settings")
+    print(f"Input positive prompt: \n\t{text_prompt_i2i}")
+    print(f"Input negative prompt: \n\t{negative_prompt_i2i}")
+    print(f"Seed: {seed_i2i}")
+    print(f"Number of steps: {num_steps_i2i}")
+    print(f"Strength: {strength}")
     print("Input image:")
     display(image)
-    processed_image = ov_pipe(text_prompt_i2i, image, negative_prompt=negative_prompt_i2i, num_inference_steps=num_steps_i2i, seed=seed_i2i, strength=strength)
+    processed_image = ov_pipe(
+        text_prompt_i2i,
+        image,
+        negative_prompt=negative_prompt_i2i,
+        num_inference_steps=num_steps_i2i,
+        seed=seed_i2i,
+        strength=strength,
+    )
 
 
 .. parsed-literal::
@@ -1082,7 +1102,7 @@ found in this
 
 
 
-.. image:: 251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_39_1.png
+.. image:: tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_39_1.png
 
 
 
@@ -1093,12 +1113,12 @@ found in this
 
 .. code:: ipython3
 
-    final_image_i2i = processed_image['sample'][0]
-    final_image_i2i.save('result_i2i.png')
+    final_image_i2i = processed_image["sample"][0]
+    final_image_i2i.save("result_i2i.png")
 
 .. code:: ipython3
 
-    text_i2i = '\n\t'.join(text_prompt_i2i.split('.'))
+    text_i2i = "\n\t".join(text_prompt_i2i.split("."))
     print("Input text:")
     print("\t" + text_i2i)
     display(final_image_i2i)
@@ -1111,7 +1131,7 @@ found in this
 
 
 
-.. image:: 251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_41_1.png
+.. image:: tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_41_1.png
 
 
 Interactive Demo
@@ -1127,13 +1147,21 @@ Interactive Demo
     
     img = load_image(sample_img_url).save("tower.jpg")
     
+    
     def generate_from_text(text, negative_text, seed, num_steps, _=gr.Progress(track_tqdm=True)):
         result = ov_pipe(text, negative_prompt=negative_text, num_inference_steps=num_steps, seed=seed)
         return result["sample"][0]
     
     
     def generate_from_image(img, text, negative_text, seed, num_steps, strength, _=gr.Progress(track_tqdm=True)):
-        result = ov_pipe(text, img, negative_prompt=negative_text, num_inference_steps=num_steps, seed=seed, strength=strength)
+        result = ov_pipe(
+            text,
+            img,
+            negative_prompt=negative_text,
+            num_inference_steps=num_steps,
+            seed=seed,
+            strength=strength,
+        )
         return result["sample"][0]
     
     
@@ -1146,13 +1174,25 @@ Interactive Demo
                     seed_input = gr.Slider(0, 10000000, value=751, label="Seed")
                     steps_input = gr.Slider(1, 50, value=20, step=1, label="Steps")
                 out = gr.Image(label="Result", type="pil")
-            sample_text = "futuristic synthwave city, retro sunset, crystals, spires, volumetric lighting, studio Ghibli style, rendered in unreal engine with clean details"
+            sample_text = (
+                "futuristic synthwave city, retro sunset, crystals, spires, volumetric lighting, studio Ghibli style, rendered in unreal engine with clean details"
+            )
             sample_text2 = "RAW studio photo of tiny cute happy  cat in a yellow raincoat in the woods, rain, a character portrait, soft lighting, high resolution, photo realistic, extremely detailed"
             negative_sample_text = ""
             negative_sample_text2 = "bad anatomy, blurry, noisy, jpeg artifacts, low quality, geometry, mutation, disgusting. ugly"
             btn = gr.Button()
-            btn.click(generate_from_text, [text_input, negative_text_input, seed_input, steps_input], out)
-            gr.Examples([[sample_text, negative_sample_text, 42, 20], [sample_text2, negative_sample_text2, 1561, 25]], [text_input, negative_text_input, seed_input, steps_input])
+            btn.click(
+                generate_from_text,
+                [text_input, negative_text_input, seed_input, steps_input],
+                out,
+            )
+            gr.Examples(
+                [
+                    [sample_text, negative_sample_text, 42, 20],
+                    [sample_text2, negative_sample_text2, 1561, 25],
+                ],
+                [text_input, negative_text_input, seed_input, steps_input],
+            )
         with gr.Tab("Image-to-Image generation"):
             with gr.Row():
                 with gr.Column():
@@ -1167,12 +1207,26 @@ Interactive Demo
             sample_i2i_text = "amazing watercolor painting"
             i2i_btn.click(
                 generate_from_image,
-                [i2i_input, i2i_text_input, i2i_negative_text_input, i2i_seed_input, i2i_steps_input, strength_input],
+                [
+                    i2i_input,
+                    i2i_text_input,
+                    i2i_negative_text_input,
+                    i2i_seed_input,
+                    i2i_steps_input,
+                    strength_input,
+                ],
                 i2i_out,
             )
             gr.Examples(
                 [["tower.jpg", sample_i2i_text, "", 6400023, 40, 0.3]],
-                [i2i_input, i2i_text_input, i2i_negative_text_input, i2i_seed_input, i2i_steps_input, strength_input],
+                [
+                    i2i_input,
+                    i2i_text_input,
+                    i2i_negative_text_input,
+                    i2i_seed_input,
+                    i2i_steps_input,
+                    strength_input,
+                ],
             )
     
     try:
diff --git a/docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_35_1.jpg b/docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_35_1.jpg
similarity index 100%
rename from docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_35_1.jpg
rename to docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_35_1.jpg
diff --git a/docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_35_1.png b/docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_35_1.png
similarity index 100%
rename from docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_35_1.png
rename to docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_35_1.png
diff --git a/docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_39_1.jpg b/docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_39_1.jpg
similarity index 100%
rename from docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_39_1.jpg
rename to docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_39_1.jpg
diff --git a/docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_39_1.png b/docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_39_1.png
similarity index 100%
rename from docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_39_1.png
rename to docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_39_1.png
diff --git a/docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_41_1.jpg b/docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_41_1.jpg
similarity index 100%
rename from docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_41_1.jpg
rename to docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_41_1.jpg
diff --git a/docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_41_1.png b/docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_41_1.png
similarity index 100%
rename from docs/notebooks/251-tiny-sd-image-generation-with-output_files/251-tiny-sd-image-generation-with-output_41_1.png
rename to docs/notebooks/tiny-sd-image-generation-with-output_files/tiny-sd-image-generation-with-output_41_1.png
diff --git a/docs/notebooks/triposr-3d-reconstruction-with-output.rst b/docs/notebooks/triposr-3d-reconstruction-with-output.rst
new file mode 100644
index 00000000000..4b3cfdaa5e5
--- /dev/null
+++ b/docs/notebooks/triposr-3d-reconstruction-with-output.rst
@@ -0,0 +1,962 @@
+TripoSR feedforward 3D reconstruction from a single image and OpenVINO
+======================================================================
+
+`TripoSR `__ is a
+state-of-the-art open-source model for fast feedforward 3D
+reconstruction from a single image, developed in collaboration between
+`Tripo AI `__ and `Stability
+AI `__.
+
+You can find `the source code on
+GitHub `__ and `demo on
+HuggingFace `__.
+Also, you can read the paper `TripoSR: Fast 3D Object Reconstruction
+from a Single Image `__.
+
+
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+-  `Get the original model <#get-the-original-model>`__
+-  `Convert the model to OpenVINO
+   IR <#convert-the-model-to-openvino-ir>`__
+-  `Compiling models and prepare
+   pipeline <#compiling-models-and-prepare-pipeline>`__
+-  `Interactive inference <#interactive-inference>`__
+
+Prerequisites
+-------------
+
+
+
+.. code:: ipython3
+
+    %pip install -q wheel setuptools pip --upgrade
+    %pip install -q "gradio>=4.19" torch rembg trimesh einops omegaconf "transformers>=4.35.0" "openvino>=2024.0.0" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "git+https://github.com/tatsy/torchmcubes.git"
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. code:: ipython3
+
+    import sys
+    from pathlib import Path
+
+    if not Path("TripoSR").exists():
+        !git clone https://huggingface.co/spaces/stabilityai/TripoSR
+
+    sys.path.append("TripoSR")
+
+
+.. parsed-literal::
+
+    Cloning into 'TripoSR'...
+
+
+.. parsed-literal::
+
+    remote: Enumerating objects: 117, done.
+    remote: Counting objects:   0% (1/117)
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+remote: Counting objects:  99% (116/117)
+remote: Counting objects: 100% (117/117)
+remote: Counting objects: 100% (117/117), done.
+    remote: Compressing objects:   1% (1/77)
+remote: Compressing objects:   2% (2/77)
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+remote: Compressing objects:  98% (76/77)
+remote: Compressing objects: 100% (77/77)
+remote: Compressing objects: 100% (77/77), done.
+
+
+.. parsed-literal::
+
+    Receiving objects:   0% (1/117)
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+
+.. parsed-literal::
+
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+
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+
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+
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+
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+
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+
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+remote: Total 117 (delta 38), reused 117 (delta 38), pack-reused 0
+    Receiving objects:  57% (67/117)
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+Receiving objects:  99% (116/117)
+Receiving objects: 100% (117/117)
+Receiving objects: 100% (117/117), 568.99 KiB | 2.54 MiB/s, done.
+    Resolving deltas:   0% (0/38)
+Resolving deltas:   5% (2/38)
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+Resolving deltas:  92% (35/38)
+Resolving deltas: 100% (38/38)
+Resolving deltas: 100% (38/38), done.
+
+
+Get the original model
+----------------------
+
+.. code:: ipython3
+
+    import os
+
+    from tsr.system import TSR
+
+
+    model = TSR.from_pretrained(
+        "stabilityai/TripoSR",
+        config_name="config.yaml",
+        weight_name="model.ckpt",
+    )
+    model.renderer.set_chunk_size(131072)
+    model.to("cpu")
+
+
+
+
+.. parsed-literal::
+
+    TSR(
+      (image_tokenizer): DINOSingleImageTokenizer(
+        (model): ViTModel(
+          (embeddings): ViTEmbeddings(
+            (patch_embeddings): ViTPatchEmbeddings(
+              (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
+            )
+            (dropout): Dropout(p=0.0, inplace=False)
+          )
+          (encoder): ViTEncoder(
+            (layer): ModuleList(
+              (0-11): 12 x ViTLayer(
+                (attention): ViTAttention(
+                  (attention): ViTSelfAttention(
+                    (query): Linear(in_features=768, out_features=768, bias=True)
+                    (key): Linear(in_features=768, out_features=768, bias=True)
+                    (value): Linear(in_features=768, out_features=768, bias=True)
+                    (dropout): Dropout(p=0.0, inplace=False)
+                  )
+                  (output): ViTSelfOutput(
+                    (dense): Linear(in_features=768, out_features=768, bias=True)
+                    (dropout): Dropout(p=0.0, inplace=False)
+                  )
+                )
+                (intermediate): ViTIntermediate(
+                  (dense): Linear(in_features=768, out_features=3072, bias=True)
+                  (intermediate_act_fn): GELUActivation()
+                )
+                (output): ViTOutput(
+                  (dense): Linear(in_features=3072, out_features=768, bias=True)
+                  (dropout): Dropout(p=0.0, inplace=False)
+                )
+                (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              )
+            )
+          )
+          (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+          (pooler): ViTPooler(
+            (dense): Linear(in_features=768, out_features=768, bias=True)
+            (activation): Tanh()
+          )
+        )
+      )
+      (tokenizer): Triplane1DTokenizer()
+      (backbone): Transformer1D(
+        (norm): GroupNorm(32, 1024, eps=1e-06, affine=True)
+        (proj_in): Linear(in_features=1024, out_features=1024, bias=True)
+        (transformer_blocks): ModuleList(
+          (0-15): 16 x BasicTransformerBlock(
+            (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+            (attn1): Attention(
+              (to_q): Linear(in_features=1024, out_features=1024, bias=False)
+              (to_k): Linear(in_features=1024, out_features=1024, bias=False)
+              (to_v): Linear(in_features=1024, out_features=1024, bias=False)
+              (to_out): ModuleList(
+                (0): Linear(in_features=1024, out_features=1024, bias=True)
+                (1): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+            (attn2): Attention(
+              (to_q): Linear(in_features=1024, out_features=1024, bias=False)
+              (to_k): Linear(in_features=768, out_features=1024, bias=False)
+              (to_v): Linear(in_features=768, out_features=1024, bias=False)
+              (to_out): ModuleList(
+                (0): Linear(in_features=1024, out_features=1024, bias=True)
+                (1): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (norm3): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+            (ff): FeedForward(
+              (net): ModuleList(
+                (0): GEGLU(
+                  (proj): Linear(in_features=1024, out_features=8192, bias=True)
+                )
+                (1): Dropout(p=0.0, inplace=False)
+                (2): Linear(in_features=4096, out_features=1024, bias=True)
+              )
+            )
+          )
+        )
+        (proj_out): Linear(in_features=1024, out_features=1024, bias=True)
+      )
+      (post_processor): TriplaneUpsampleNetwork(
+        (upsample): ConvTranspose2d(1024, 40, kernel_size=(2, 2), stride=(2, 2))
+      )
+      (decoder): NeRFMLP(
+        (layers): Sequential(
+          (0): Linear(in_features=120, out_features=64, bias=True)
+          (1): SiLU(inplace=True)
+          (2): Linear(in_features=64, out_features=64, bias=True)
+          (3): SiLU(inplace=True)
+          (4): Linear(in_features=64, out_features=64, bias=True)
+          (5): SiLU(inplace=True)
+          (6): Linear(in_features=64, out_features=64, bias=True)
+          (7): SiLU(inplace=True)
+          (8): Linear(in_features=64, out_features=64, bias=True)
+          (9): SiLU(inplace=True)
+          (10): Linear(in_features=64, out_features=64, bias=True)
+          (11): SiLU(inplace=True)
+          (12): Linear(in_features=64, out_features=64, bias=True)
+          (13): SiLU(inplace=True)
+          (14): Linear(in_features=64, out_features=64, bias=True)
+          (15): SiLU(inplace=True)
+          (16): Linear(in_features=64, out_features=64, bias=True)
+          (17): SiLU(inplace=True)
+          (18): Linear(in_features=64, out_features=4, bias=True)
+        )
+      )
+      (renderer): TriplaneNeRFRenderer()
+    )
+
+
+
+Convert the model to OpenVINO IR
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Define the conversion function for PyTorch modules. We use
+``ov.convert_model`` function to obtain OpenVINO Intermediate
+Representation object and ``ov.save_model`` function to save it as XML
+file.
+
+.. code:: ipython3
+
+    import torch
+
+    import openvino as ov
+
+
+    def convert(model: torch.nn.Module, xml_path: str, example_input):
+        xml_path = Path(xml_path)
+        if not xml_path.exists():
+            xml_path.parent.mkdir(parents=True, exist_ok=True)
+            with torch.no_grad():
+                converted_model = ov.convert_model(model, example_input=example_input)
+            ov.save_model(converted_model, xml_path, compress_to_fp16=False)
+
+            # cleanup memory
+            torch._C._jit_clear_class_registry()
+            torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
+            torch.jit._state._clear_class_state()
+
+The original model is a pipeline of several models. There are
+``image_tokenizer``, ``tokenizer``, ``backbone`` and ``post_processor``.
+``image_tokenizer`` contains ``ViTModel`` that consists of
+``ViTPatchEmbeddings``, ``ViTEncoder`` and ``ViTPooler``. ``tokenizer``
+is ``Triplane1DTokenizer``, ``backbone`` is ``Transformer1D``,
+``post_processor`` is ``TriplaneUpsampleNetwork``. Convert all internal
+models one by one.
+
+.. code:: ipython3
+
+    VIT_PATCH_EMBEDDINGS_OV_PATH = Path("models/vit_patch_embeddings_ir.xml")
+
+
+    class PatchEmbedingWrapper(torch.nn.Module):
+        def __init__(self, patch_embeddings):
+            super().__init__()
+            self.patch_embeddings = patch_embeddings
+
+        def forward(self, pixel_values, interpolate_pos_encoding=True):
+            outputs = self.patch_embeddings(pixel_values=pixel_values, interpolate_pos_encoding=True)
+            return outputs
+
+
+    example_input = {
+        "pixel_values": torch.rand([1, 3, 512, 512], dtype=torch.float32),
+    }
+
+    convert(
+        PatchEmbedingWrapper(model.image_tokenizer.model.embeddings.patch_embeddings),
+        VIT_PATCH_EMBEDDINGS_OV_PATH,
+        example_input,
+    )
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/vit/modeling_vit.py:170: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if num_channels != self.num_channels:
+
+
+.. code:: ipython3
+
+    VIT_ENCODER_OV_PATH = Path("models/vit_encoder_ir.xml")
+
+
+    class EncoderWrapper(torch.nn.Module):
+        def __init__(self, encoder):
+            super().__init__()
+            self.encoder = encoder
+
+        def forward(
+            self,
+            hidden_states=None,
+            head_mask=None,
+            output_attentions=False,
+            output_hidden_states=False,
+            return_dict=False,
+        ):
+            outputs = self.encoder(
+                hidden_states=hidden_states,
+            )
+
+            return outputs.last_hidden_state
+
+
+    example_input = {
+        "hidden_states": torch.rand([1, 1025, 768], dtype=torch.float32),
+    }
+
+    convert(
+        EncoderWrapper(model.image_tokenizer.model.encoder),
+        VIT_ENCODER_OV_PATH,
+        example_input,
+    )
+
+.. code:: ipython3
+
+    VIT_POOLER_OV_PATH = Path("models/vit_pooler_ir.xml")
+    convert(
+        model.image_tokenizer.model.pooler,
+        VIT_POOLER_OV_PATH,
+        torch.rand([1, 1025, 768], dtype=torch.float32),
+    )
+
+.. code:: ipython3
+
+    TOKENIZER_OV_PATH = Path("models/tokenizer_ir.xml")
+    convert(model.tokenizer, TOKENIZER_OV_PATH, torch.tensor(1))
+
+.. code:: ipython3
+
+    example_input = {
+        "hidden_states": torch.rand([1, 1024, 3072], dtype=torch.float32),
+        "encoder_hidden_states": torch.rand([1, 1025, 768], dtype=torch.float32),
+    }
+
+    BACKBONE_OV_PATH = Path("models/backbone_ir.xml")
+    convert(model.backbone, BACKBONE_OV_PATH, example_input)
+
+.. code:: ipython3
+
+    POST_PROCESSOR_OV_PATH = Path("models/post_processor_ir.xml")
+    convert(
+        model.post_processor,
+        POST_PROCESSOR_OV_PATH,
+        torch.rand([1, 3, 1024, 32, 32], dtype=torch.float32),
+    )
+
+Compiling models and prepare pipeline
+-------------------------------------
+
+
+
+Select device from dropdown list for running inference using OpenVINO.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+
+
+    core = ov.Core()
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+.. code:: ipython3
+
+    compiled_vit_patch_embeddings = core.compile_model(VIT_PATCH_EMBEDDINGS_OV_PATH, device.value)
+    compiled_vit_model_encoder = core.compile_model(VIT_ENCODER_OV_PATH, device.value)
+    compiled_vit_model_pooler = core.compile_model(VIT_POOLER_OV_PATH, device.value)
+
+    compiled_tokenizer = core.compile_model(TOKENIZER_OV_PATH, device.value)
+    compiled_backbone = core.compile_model(BACKBONE_OV_PATH, device.value)
+    compiled_post_processor = core.compile_model(POST_PROCESSOR_OV_PATH, device.value)
+
+Let’s create callable wrapper classes for compiled models to allow
+interaction with original ``TSR`` class. Note that all of wrapper
+classes return ``torch.Tensor``\ s instead of ``np.array``\ s.
+
+.. code:: ipython3
+
+    from collections import namedtuple
+
+
+    class VitPatchEmdeddingsWrapper(torch.nn.Module):
+        def __init__(self, vit_patch_embeddings, model):
+            super().__init__()
+            self.vit_patch_embeddings = vit_patch_embeddings
+            self.projection = model.projection
+
+        def forward(self, pixel_values, interpolate_pos_encoding=False):
+            inputs = {
+                "pixel_values": pixel_values,
+            }
+            outs = self.vit_patch_embeddings(inputs)[0]
+
+            return torch.from_numpy(outs)
+
+
+    class VitModelEncoderWrapper(torch.nn.Module):
+        def __init__(self, vit_model_encoder):
+            super().__init__()
+            self.vit_model_encoder = vit_model_encoder
+
+        def forward(
+            self,
+            hidden_states,
+            head_mask,
+            output_attentions=False,
+            output_hidden_states=False,
+            return_dict=False,
+        ):
+            inputs = {
+                "hidden_states": hidden_states.detach().numpy(),
+            }
+
+            outs = self.vit_model_encoder(inputs)
+            outputs = namedtuple("BaseModelOutput", ("last_hidden_state", "hidden_states", "attentions"))
+
+            return outputs(torch.from_numpy(outs[0]), None, None)
+
+
+    class VitModelPoolerWrapper(torch.nn.Module):
+        def __init__(self, vit_model_pooler):
+            super().__init__()
+            self.vit_model_pooler = vit_model_pooler
+
+        def forward(self, hidden_states):
+            outs = self.vit_model_pooler(hidden_states.detach().numpy())[0]
+
+            return torch.from_numpy(outs)
+
+
+    class TokenizerWrapper(torch.nn.Module):
+        def __init__(self, tokenizer, model):
+            super().__init__()
+            self.tokenizer = tokenizer
+            self.detokenize = model.detokenize
+
+        def forward(self, batch_size):
+            outs = self.tokenizer(batch_size)[0]
+
+            return torch.from_numpy(outs)
+
+
+    class BackboneWrapper(torch.nn.Module):
+        def __init__(self, backbone):
+            super().__init__()
+            self.backbone = backbone
+
+        def forward(self, hidden_states, encoder_hidden_states):
+            inputs = {
+                "hidden_states": hidden_states,
+                "encoder_hidden_states": encoder_hidden_states.detach().numpy(),
+            }
+
+            outs = self.backbone(inputs)[0]
+
+            return torch.from_numpy(outs)
+
+
+    class PostProcessorWrapper(torch.nn.Module):
+        def __init__(self, post_processor):
+            super().__init__()
+            self.post_processor = post_processor
+
+        def forward(self, triplanes):
+            outs = self.post_processor(triplanes)[0]
+
+            return torch.from_numpy(outs)
+
+Replace all models in the original model by wrappers instances:
+
+.. code:: ipython3
+
+    model.image_tokenizer.model.embeddings.patch_embeddings = VitPatchEmdeddingsWrapper(
+        compiled_vit_patch_embeddings,
+        model.image_tokenizer.model.embeddings.patch_embeddings,
+    )
+    model.image_tokenizer.model.encoder = VitModelEncoderWrapper(compiled_vit_model_encoder)
+    model.image_tokenizer.model.pooler = VitModelPoolerWrapper(compiled_vit_model_pooler)
+
+    model.tokenizer = TokenizerWrapper(compiled_tokenizer, model.tokenizer)
+    model.backbone = BackboneWrapper(compiled_backbone)
+    model.post_processor = PostProcessorWrapper(compiled_post_processor)
+
+Interactive inference
+---------------------
+
+
+
+.. code:: ipython3
+
+    import tempfile
+
+    import gradio as gr
+    import numpy as np
+    import rembg
+    from PIL import Image
+
+    from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation
+
+
+    rembg_session = rembg.new_session()
+
+
+    def check_input_image(input_image):
+        if input_image is None:
+            raise gr.Error("No image uploaded!")
+
+
+    def preprocess(input_image, do_remove_background, foreground_ratio):
+        def fill_background(image):
+            image = np.array(image).astype(np.float32) / 255.0
+            image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5
+            image = Image.fromarray((image * 255.0).astype(np.uint8))
+            return image
+
+        if do_remove_background:
+            image = input_image.convert("RGB")
+            image = remove_background(image, rembg_session)
+            image = resize_foreground(image, foreground_ratio)
+            image = fill_background(image)
+        else:
+            image = input_image
+            if image.mode == "RGBA":
+                image = fill_background(image)
+        return image
+
+
+    def generate(image):
+        scene_codes = model(image, "cpu")  # the device is provided for the image processor
+        mesh = model.extract_mesh(scene_codes)[0]
+        mesh = to_gradio_3d_orientation(mesh)
+        mesh_path = tempfile.NamedTemporaryFile(suffix=".obj", delete=False)
+        mesh.export(mesh_path.name)
+        return mesh_path.name
+
+
+    with gr.Blocks() as demo:
+        with gr.Row(variant="panel"):
+            with gr.Column():
+                with gr.Row():
+                    input_image = gr.Image(
+                        label="Input Image",
+                        image_mode="RGBA",
+                        sources="upload",
+                        type="pil",
+                        elem_id="content_image",
+                    )
+                    processed_image = gr.Image(label="Processed Image", interactive=False)
+                with gr.Row():
+                    with gr.Group():
+                        do_remove_background = gr.Checkbox(label="Remove Background", value=True)
+                        foreground_ratio = gr.Slider(
+                            label="Foreground Ratio",
+                            minimum=0.5,
+                            maximum=1.0,
+                            value=0.85,
+                            step=0.05,
+                        )
+                with gr.Row():
+                    submit = gr.Button("Generate", elem_id="generate", variant="primary")
+            with gr.Column():
+                with gr.Tab("Model"):
+                    output_model = gr.Model3D(
+                        label="Output Model",
+                        interactive=False,
+                    )
+        with gr.Row(variant="panel"):
+            gr.Examples(
+                examples=[os.path.join("TripoSR/examples", img_name) for img_name in sorted(os.listdir("TripoSR/examples"))],
+                inputs=[input_image],
+                outputs=[processed_image, output_model],
+                label="Examples",
+                examples_per_page=20,
+            )
+        submit.click(fn=check_input_image, inputs=[input_image]).success(
+            fn=preprocess,
+            inputs=[input_image, do_remove_background, foreground_ratio],
+            outputs=[processed_image],
+        ).success(
+            fn=generate,
+            inputs=[processed_image],
+            outputs=[output_model],
+        )
+
+    try:
+        demo.launch(debug=False, height=680)
+    except Exception:
+        demo.queue().launch(share=True, debug=False, height=680)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
diff --git a/docs/notebooks/245-typo-detector-with-output.rst b/docs/notebooks/typo-detector-with-output.rst
similarity index 93%
rename from docs/notebooks/245-typo-detector-with-output.rst
rename to docs/notebooks/typo-detector-with-output.rst
index 841a5378126..b96f108081b 100644
--- a/docs/notebooks/245-typo-detector-with-output.rst
+++ b/docs/notebooks/typo-detector-with-output.rst
@@ -62,14 +62,19 @@ Table of contents:
 
 .. code:: ipython3
 
-    %pip install -q "diffusers>=0.17.1" "openvino>=2023.1.0" "nncf>=2.5.0" "gradio" "onnx>=1.11.0" "transformers>=4.33.0" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "diffusers>=0.17.1" "openvino>=2023.1.0" "nncf>=2.5.0" "gradio>=4.19" "onnx>=1.11.0" "transformers>=4.33.0" "torch>=2.1" --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "git+https://github.com/huggingface/optimum-intel.git"
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    WARNING: typer 0.12.3 does not provide the extra 'all'
+    
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
 
 .. parsed-literal::
 
@@ -79,7 +84,7 @@ Table of contents:
 .. parsed-literal::
 
     DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-
+    
 
 .. parsed-literal::
 
@@ -93,7 +98,12 @@ Imports
 
 .. code:: ipython3
 
-    from transformers import AutoConfig, AutoTokenizer, AutoModelForTokenClassification, pipeline
+    from transformers import (
+        AutoConfig,
+        AutoTokenizer,
+        AutoModelForTokenClassification,
+        pipeline,
+    )
     from pathlib import Path
     import numpy as np
     import re
@@ -103,14 +113,14 @@ Imports
 
 .. parsed-literal::
 
-    2024-03-12 23:57:16.557513: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-12 23:57:16.591185: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 01:21:29.019297: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-04-18 01:21:29.053387: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-12 23:57:17.177770: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 01:21:29.651521: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Methods
@@ -178,16 +188,16 @@ select device from dropdown list for running inference using OpenVINO
 
     import ipywidgets as widgets
     import openvino as ov
-
+    
     core = ov.Core()
-
+    
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
-
+    
     device
 
 
@@ -235,7 +245,7 @@ Import required model class
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
 
 
@@ -253,9 +263,9 @@ your model.
 
     # The pretrained model we are using
     model_id = "m3hrdadfi/typo-detector-distilbert-en"
-
+    
     model_dir = Path("optimum_model")
-
+    
     # Save the model to the path if not existing
     if model_dir.exists():
         model = OVModelForTokenClassification.from_pretrained(model_dir, device=device.value)
@@ -271,13 +281,7 @@ your model.
 
 .. parsed-literal::
 
-    Using the export variant default. Available variants are:
-        - default: The default ONNX variant.
-
-
-.. parsed-literal::
-
-    Using framework PyTorch: 2.2.1+cpu
+    Using framework PyTorch: 2.2.2+cpu
 
 
 .. parsed-literal::
@@ -292,13 +296,9 @@ your model.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/dynamic_graph/wrappers.py:82: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/dynamic_graph/wrappers.py:82: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       op1 = operator(\*args, \*\*kwargs)
 
 
@@ -333,13 +333,12 @@ pipelines in this
 
 .. code:: ipython3
 
-    nlp = pipeline('token-classification', model=model, tokenizer=tokenizer, aggregation_strategy="average")
-
-
-.. parsed-literal::
-
-    device must be of type  but got  instead
-
+    nlp = pipeline(
+        "token-classification",
+        model=model,
+        tokenizer=tokenizer,
+        aggregation_strategy="average",
+    )
 
 Function to find typos in a sentence and write them to the terminal
 
@@ -349,17 +348,17 @@ Function to find typos in a sentence and write them to the terminal
         """
         Detect typos from the given sentence.
         Writes both the original input and typo-tagged version to the terminal.
-
+    
         Arguments:
         sentence -- Sentence to be evaluated (string)
         """
-
-        typos = [sentence[r["start"]: r["end"]] for r in nlp(sentence)]
-
+    
+        typos = [sentence[r["start"] : r["end"]] for r in nlp(sentence)]
+    
         detected = sentence
         for typo in typos:
-            detected = detected.replace(typo, f'{typo}')
-
+            detected = detected.replace(typo, f"{typo}")
+    
         print("[Input]: ", sentence)
         print("[Detected]: ", detected)
         print("-" * 130)
@@ -378,14 +377,14 @@ Let’s run a demo using the Hugging Face Optimum API.
         "My faorite restuarant servs the best spahgetti in the town, but they are always so buzy that you have to make a resrvation in advnace.",
         "I was goig to watch a mvoie on Netflx last night, but the straming was so slow that I decided to cancled my subscrpition.",
         "My freind and I went campign in the forest last weekend and saw a beutiful sunst that was so amzing it took our breth away.",
-        "I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber."
+        "I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber.",
     ]
-
+    
     start = time.time()
-
+    
     for sentence in sentences:
         show_typos(sentence)
-
+    
     print(f"Time elapsed: {time.time() - start}")
 
 
@@ -421,7 +420,7 @@ Let’s run a demo using the Hugging Face Optimum API.
     [Input]:  I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber.
     [Detected]:  I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber.
     ----------------------------------------------------------------------------------------------------------------------------------
-    Time elapsed: 0.16592812538146973
+    Time elapsed: 0.16799616813659668
 
 
 2. Converting the model to OpenVINO IR
@@ -441,10 +440,10 @@ pytorch model.
 
     model_id = "m3hrdadfi/typo-detector-distilbert-en"
     model_dir = Path("pytorch_model")
-
+    
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     config = AutoConfig.from_pretrained(model_id)
-
+    
     # Save the model to the path if not existing
     if model_dir.exists():
         model = AutoModelForTokenClassification.from_pretrained(model_dir)
@@ -460,7 +459,7 @@ Converting to OpenVINO IR
 .. code:: ipython3
 
     ov_model_path = Path(model_dir) / "typo_detect.xml"
-
+    
     dummy_model_input = tokenizer("This is a sample", return_tensors="pt")
     ov_model = ov.convert_model(model, example_input=dict(dummy_model_input))
     ov.save_model(ov_model, ov_model_path)
@@ -493,18 +492,18 @@ Helper Functions
         """
         Maps the list of tokens to words in the original text.
         Built on the feature that tokens starting with '##' is attached to the previous token as tokens derived from the same word.
-
+    
         Arguments:
         tokens -- List of tokens
-
+    
         Returns:
         map_to_words -- Dictionary mapping tokens to words in original text
         """
-
+    
         word_count = -1
         map_to_words = {}
         for token in tokens:
-            if token.startswith('##'):
+            if token.startswith("##"):
                 map_to_words[token] = word_count
                 continue
             word_count += 1
@@ -516,14 +515,14 @@ Helper Functions
     def infer(input_text: str) -> Dict[np.ndarray, np.ndarray]:
         """
         Creating a generic inference function to read the input and infer the result
-
+    
         Arguments:
         input_text -- The text to be infered (String)
-
+    
         Returns:
         result -- Resulting list from inference
         """
-
+    
         tokens = tokenizer(
             input_text,
             return_tensors="np",
@@ -534,18 +533,22 @@ Helper Functions
 
 .. code:: ipython3
 
-    def get_typo_indexes(result: Dict[np.ndarray, np.ndarray], map_to_words: Dict[str, int], tokens: List[str]) -> List[int]:
+    def get_typo_indexes(
+        result: Dict[np.ndarray, np.ndarray],
+        map_to_words: Dict[str, int],
+        tokens: List[str],
+    ) -> List[int]:
         """
         Given results from the inference and tokens-map-to-words, identifies the indexes of the words with typos.
-
+    
         Arguments:
         result -- Result from inference (tensor)
         map_to_words -- Dictionary mapping tokens to words (Dictionary)
-
+    
         Results:
         wrong_words -- List of indexes of words with typos
         """
-
+    
         wrong_words = []
         c = 0
         result_list = result[0][1:-1]
@@ -562,15 +565,15 @@ Helper Functions
     def sentence_split(sentence: str) -> List[str]:
         """
         Split the sentence into words and characters
-
+    
         Arguments:
         sentence - Sentence to be split (string)
-
+    
         Returns:
         splitted -- List of words and characters
         """
-
-        splitted = re.split("([',. ])",sentence)
+    
+        splitted = re.split("([',. ])", sentence)
         splitted = [x for x in splitted if x != " " and x != ""]
         return splitted
 
@@ -580,24 +583,24 @@ Helper Functions
         """
         Detect typos from the given sentence.
         Writes both the original input and typo-tagged version to the terminal.
-
+    
         Arguments:
         sentence -- Sentence to be evaluated (string)
         """
-
+    
         tokens = tokenizer.tokenize(sentence)
         map_to_words = token_to_words(tokens)
         result = infer(sentence)
-        typo_indexes = get_typo_indexes(result,map_to_words, tokens)
-
+        typo_indexes = get_typo_indexes(result, map_to_words, tokens)
+    
         sentence_words = sentence_split(sentence)
-
+    
         typos = [sentence_words[i] for i in typo_indexes]
-
+    
         detected = sentence
         for typo in typos:
-            detected = detected.replace(typo, f'{typo}')
-
+            detected = detected.replace(typo, f"{typo}")
+    
         print("   [Input]: ", sentence)
         print("[Detected]: ", detected)
         print("-" * 130)
@@ -616,14 +619,14 @@ Let’s run a demo using the converted OpenVINO IR model.
         "My faorite restuarant servs the best spahgetti in the town, but they are always so buzy that you have to make a resrvation in advnace.",
         "I was goig to watch a mvoie on Netflx last night, but the straming was so slow that I decided to cancled my subscrpition.",
         "My freind and I went campign in the forest last weekend and saw a beutiful sunst that was so amzing it took our breth away.",
-        "I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber."
+        "I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber.",
     ]
-
+    
     start = time.time()
-
+    
     for sentence in sentences:
         show_typos(sentence)
-
+    
     print(f"Time elapsed: {time.time() - start}")
 
 
@@ -659,5 +662,5 @@ Let’s run a demo using the converted OpenVINO IR model.
        [Input]:  I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber.
     [Detected]:  I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber.
     ----------------------------------------------------------------------------------------------------------------------------------
-    Time elapsed: 0.09940457344055176
+    Time elapsed: 0.09974002838134766
 
diff --git a/docs/notebooks/218-vehicle-detection-and-recognition-with-output.rst b/docs/notebooks/vehicle-detection-and-recognition-with-output.rst
similarity index 86%
rename from docs/notebooks/218-vehicle-detection-and-recognition-with-output.rst
rename to docs/notebooks/vehicle-detection-and-recognition-with-output.rst
index 4f7cd86ee90..c0b16cca826 100644
--- a/docs/notebooks/218-vehicle-detection-and-recognition-with-output.rst
+++ b/docs/notebooks/vehicle-detection-and-recognition-with-output.rst
@@ -51,7 +51,7 @@ Import the required modules.
 
     import platform
     
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" opencv-python tqdm
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -82,7 +82,6 @@ Import the required modules.
 .. code:: ipython3
 
     import os
-    import sys
     from pathlib import Path
     from typing import Tuple
     
@@ -91,7 +90,15 @@ Import the required modules.
     import matplotlib.pyplot as plt
     import openvino as ov
     
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
+    
     import notebook_utils as utils
 
 Download Models
@@ -127,23 +134,27 @@ model is already downloaded, this step is skipped.
     base_model_url = "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1"
     
     # Check if the model exists.
-    detection_model_url = (
-        f"{base_model_url}/{detection_model_name}/{precision}/{detection_model_name}.xml"
-    )
-    recognition_model_url = (
-        f"{base_model_url}/{recognition_model_name}/{precision}/{recognition_model_name}.xml"
-    )
-    detection_model_path = (base_model_dir / detection_model_name).with_suffix('.xml')
-    recognition_model_path = (base_model_dir / recognition_model_name).with_suffix('.xml')
+    detection_model_url = f"{base_model_url}/{detection_model_name}/{precision}/{detection_model_name}.xml"
+    recognition_model_url = f"{base_model_url}/{recognition_model_name}/{precision}/{recognition_model_name}.xml"
+    detection_model_path = (base_model_dir / detection_model_name).with_suffix(".xml")
+    recognition_model_path = (base_model_dir / recognition_model_name).with_suffix(".xml")
     
     # Download the detection model.
     if not detection_model_path.exists():
-        utils.download_file(detection_model_url, detection_model_name + '.xml', base_model_dir)
-        utils.download_file(detection_model_url.replace('.xml', '.bin'), detection_model_name + '.bin', base_model_dir)
+        utils.download_file(detection_model_url, detection_model_name + ".xml", base_model_dir)
+        utils.download_file(
+            detection_model_url.replace(".xml", ".bin"),
+            detection_model_name + ".bin",
+            base_model_dir,
+        )
     # Download the recognition model.
     if not os.path.exists(recognition_model_path):
-        utils.download_file(recognition_model_url, recognition_model_name + '.xml', base_model_dir)
-        utils.download_file(recognition_model_url.replace('.xml', '.bin'), recognition_model_name + '.bin', base_model_dir)
+        utils.download_file(recognition_model_url, recognition_model_name + ".xml", base_model_dir)
+        utils.download_file(
+            recognition_model_url.replace(".xml", ".bin"),
+            recognition_model_name + ".bin",
+            base_model_dir,
+        )
 
 
 
@@ -189,8 +200,8 @@ specified device.
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -289,7 +300,12 @@ channel with ``expand_dims`` function.
     filename = "cars.jpg"
     directory = "data"
     image_file = utils.download_file(
-        url, filename=filename, directory=directory, show_progress=False, silent=True,timeout=30
+        url,
+        filename=filename,
+        directory=directory,
+        show_progress=False,
+        silent=True,
+        timeout=30,
     )
     assert Path(image_file).exists()
     
@@ -304,7 +320,7 @@ channel with ``expand_dims`` function.
 
 
 
-.. image:: 218-vehicle-detection-and-recognition-with-output_files/218-vehicle-detection-and-recognition-with-output_14_0.png
+.. image:: vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_14_0.png
 
 
 Use the Detection Model to Detect Vehicles
@@ -354,7 +370,7 @@ the image and filter out low-confidence results.
     def crop_images(bgr_image, resized_image, boxes, threshold=0.6) -> np.ndarray:
         """
         Use bounding boxes from detection model to find the absolute car position
-        
+    
         :param: bgr_image: raw image
         :param: resized_image: resized image
         :param: boxes: detection model returns rectangle position
@@ -362,7 +378,10 @@ the image and filter out low-confidence results.
         :returns: car_position: car's absolute position
         """
         # Fetch image shapes to calculate ratio
-        (real_y, real_x), (resized_y, resized_x) = bgr_image.shape[:2], resized_image.shape[:2]
+        (real_y, real_x), (resized_y, resized_x) = (
+            bgr_image.shape[:2],
+            resized_image.shape[:2],
+        )
         ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
     
         # Find the boxes ratio
@@ -375,16 +394,15 @@ the image and filter out low-confidence results.
             conf = box[0]
             if conf > threshold:
                 # Convert float to int and multiply corner position of each box by x and y ratio
-                # In case that bounding box is found at the top of the image, 
-                # upper box  bar should be positioned a little bit lower to make it visible on image 
+                # In case that bounding box is found at the top of the image,
+                # upper box  bar should be positioned a little bit lower to make it visible on image
                 (x_min, y_min, x_max, y_max) = [
-                    int(max(corner_position * ratio_y * resized_y, 10)) if idx % 2 
-                    else int(corner_position * ratio_x * resized_x)
+                    (int(max(corner_position * ratio_y * resized_y, 10)) if idx % 2 else int(corner_position * ratio_x * resized_x))
                     for idx, corner_position in enumerate(box[1:])
                 ]
-                
+    
                 car_position.append([x_min, y_min, x_max, y_max])
-                
+    
         return car_position
 
 .. code:: ipython3
@@ -406,7 +424,7 @@ the input image and run inference.
     # Select a vehicle to recognize.
     pos = car_position[0]
     # Crop the image with [y_min:y_max, x_min:x_max].
-    test_car = image_de[pos[1]:pos[3], pos[0]:pos[2]]
+    test_car = image_de[pos[1] : pos[3], pos[0] : pos[2]]
     # Resize the image to input_size.
     resized_image_re = cv2.resize(test_car, (width_re, height_re))
     input_image_re = np.expand_dims(resized_image_re.transpose(2, 0, 1), 0)
@@ -414,7 +432,7 @@ the input image and run inference.
 
 
 
-.. image:: 218-vehicle-detection-and-recognition-with-output_files/218-vehicle-detection-and-recognition-with-output_21_0.png
+.. image:: vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_21_0.png
 
 
 Recognition processing
@@ -432,20 +450,20 @@ determine the maximum probability as the result.
     def vehicle_recognition(compiled_model_re, input_size, raw_image):
         """
         Vehicle attributes recognition, input a single vehicle, return attributes
-        :param: compiled_model_re: recognition net 
+        :param: compiled_model_re: recognition net
         :param: input_size: recognition input size
         :param: raw_image: single vehicle image
         :returns: attr_color: predicted color
                            attr_type: predicted type
         """
         # An attribute of a vehicle.
-        colors = ['White', 'Gray', 'Yellow', 'Red', 'Green', 'Blue', 'Black']
-        types = ['Car', 'Bus', 'Truck', 'Van']
-        
+        colors = ["White", "Gray", "Yellow", "Red", "Green", "Blue", "Black"]
+        types = ["Car", "Bus", "Truck", "Van"]
+    
         # Resize the image to input size.
         resized_image_re = cv2.resize(raw_image, input_size)
         input_image_re = np.expand_dims(resized_image_re.transpose(2, 0, 1), 0)
-        
+    
         # Run inference.
         # Predict result.
         predict_colors = compiled_model_re([input_image_re])[compiled_model_re.output(1)]
@@ -454,8 +472,10 @@ determine the maximum probability as the result.
         predict_types = compiled_model_re([input_image_re])[compiled_model_re.output(0)]
         predict_types = np.squeeze(predict_types, (2, 3))
     
-        attr_color, attr_type = (colors[np.argmax(predict_colors)],
-                                 types[np.argmax(predict_types)])
+        attr_color, attr_type = (
+            colors[np.argmax(predict_colors)],
+            types[np.argmax(predict_types)],
+        )
         return attr_color, attr_type
 
 .. code:: ipython3
@@ -481,7 +501,7 @@ image with a vehicle and recognize the attributes of a vehicle.
     def convert_result_to_image(compiled_model_re, bgr_image, resized_image, boxes, threshold=0.6):
         """
         Use Detection model boxes to draw rectangles and plot the result
-        
+    
         :param: compiled_model_re: recognition net
         :param: input_key_re: recognition input key
         :param: bgr_image: raw image
@@ -492,17 +512,16 @@ image with a vehicle and recognize the attributes of a vehicle.
         """
         # Define colors for boxes and descriptions.
         colors = {"red": (255, 0, 0), "green": (0, 255, 0)}
-        
+    
         # Convert the base image from BGR to RGB format.
         rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
-        
+    
         # Find positions of cars.
         car_position = crop_images(image_de, resized_image, boxes)
-        
+    
         for x_min, y_min, x_max, y_max in car_position:
             # Run vehicle recognition inference.
-            attr_color, attr_type = vehicle_recognition(compiled_model_re, (72, 72), 
-                                                        image_de[y_min:y_max, x_min:x_max])
+            attr_color, attr_type = vehicle_recognition(compiled_model_re, (72, 72), image_de[y_min:y_max, x_min:x_max])
     
             # Close the window with a vehicle.
             plt.close()
@@ -511,17 +530,17 @@ image with a vehicle and recognize the attributes of a vehicle.
             # Parameters in the `rectangle` function are: image, start_point, end_point, color, thickness.
             rgb_image = cv2.rectangle(rgb_image, (x_min, y_min), (x_max, y_max), colors["red"], 2)
     
-            # Print the attributes of a vehicle. 
+            # Print the attributes of a vehicle.
             # Parameters in the `putText` function are: img, text, org, fontFace, fontScale, color, thickness, lineType.
             rgb_image = cv2.putText(
-                rgb_image, 
+                rgb_image,
                 f"{attr_color} {attr_type}",
                 (x_min, y_min - 10),
                 cv2.FONT_HERSHEY_SIMPLEX,
                 2,
                 colors["green"],
                 10,
-                cv2.LINE_AA
+                cv2.LINE_AA,
             )
     
         return rgb_image
@@ -532,5 +551,5 @@ image with a vehicle and recognize the attributes of a vehicle.
 
 
 
-.. image:: 218-vehicle-detection-and-recognition-with-output_files/218-vehicle-detection-and-recognition-with-output_27_0.png
+.. image:: vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_27_0.png
 
diff --git a/docs/notebooks/vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_14_0.png b/docs/notebooks/vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_14_0.png
new file mode 100644
index 00000000000..38d0d6df487
--- /dev/null
+++ b/docs/notebooks/vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_14_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1945f98709c8b37ef7986a8e5fabb7612858f8805d2d93f2036584f2213a3e64
+size 172680
diff --git a/docs/notebooks/vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_21_0.png b/docs/notebooks/vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_21_0.png
new file mode 100644
index 00000000000..3991f58aa9f
--- /dev/null
+++ b/docs/notebooks/vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_21_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4039b630d77457354f8d1bd2529e09ef2883d214572fa0c184684411d066fca
+size 19599
diff --git a/docs/notebooks/vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_27_0.png b/docs/notebooks/vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_27_0.png
new file mode 100644
index 00000000000..8c74cbfe358
--- /dev/null
+++ b/docs/notebooks/vehicle-detection-and-recognition-with-output_files/vehicle-detection-and-recognition-with-output_27_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1343140ccf561cfa4c3839872f164ad481a6053854ca28beb8bff3083013ea4
+size 175941
diff --git a/docs/notebooks/257-videollava-multimodal-chatbot-with-output.rst b/docs/notebooks/videollava-multimodal-chatbot-with-output.rst
similarity index 99%
rename from docs/notebooks/257-videollava-multimodal-chatbot-with-output.rst
rename to docs/notebooks/videollava-multimodal-chatbot-with-output.rst
index e7a5819c2cf..a1f4cb07d7f 100644
--- a/docs/notebooks/257-videollava-multimodal-chatbot-with-output.rst
+++ b/docs/notebooks/videollava-multimodal-chatbot-with-output.rst
@@ -84,7 +84,7 @@ Install required dependencies
 .. code:: ipython3
 
     %pip install -q torch "torchvision<0.17.0" "transformers>=4.31.0,<4.35.0" "pytorchvideo" "einops" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
-    %pip install -q opencv_python decord sentencepiece protobuf "openvino>=2023.2.0" "nncf>=2.7.0" gradio
+    %pip install -q opencv_python decord sentencepiece protobuf "openvino>=2023.2.0" "nncf>=2.7.0" "gradio>=4.19"
 
 .. code:: ipython3
 
@@ -118,6 +118,7 @@ Install required dependencies
         DEFAULT_VID_END_TOKEN,
         DEFAULT_IMAGE_TOKEN,
     )
+    
     transformers.logging.set_verbosity_error()
     
     model_id = "LanguageBind/Video-LLaVA-7B"
@@ -297,10 +298,11 @@ on disk using ``ov.save_model``.
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
     
+    
     def convert_videollava(
         pt_model: torch.nn.Module,
         model_path: Path,
-        videollava_wc_parameters: Optional[dict] = None
+        videollava_wc_parameters: Optional[dict] = None,
     ):
         """
         Video-LLaVA model conversion function
@@ -325,7 +327,7 @@ on disk using ``ov.save_model``.
             return
         example_input_first_stage = {
             "inputs_embeds": torch.zeros((1, 307, 4096)),
-            "attention_mask": torch.ones((1, 307), dtype=torch.long)
+            "attention_mask": torch.ones((1, 307), dtype=torch.long),
         }
         outs = wrapped(**example_input_first_stage)
         input_names = ["input_ids", "attention_mask"]
@@ -335,9 +337,7 @@ on disk using ``ov.save_model``.
             output_names.extend([f"present.{idx}.key", f"present.{idx}.value"])
     
         if not first_stage_model_path.exists():
-            ov_model = ov.convert_model(
-                wrapped, example_input=example_input_first_stage
-            )
+            ov_model = ov.convert_model(wrapped, example_input=example_input_first_stage)
             set_node_names(ov_model, output_names=output_names)
             if videollava_wc_parameters is not None:
                 print("Applying weight compression to first stage Video-LLaVA model")
@@ -346,7 +346,6 @@ on disk using ``ov.save_model``.
             cleanup_torchscript_cache()
             del ov_model
             gc.collect()
-                
     
         if not second_stage_model_path.exists():
             example_input_second_stage = {
@@ -485,7 +484,7 @@ compression instead of INT8 weight compression.
         compressed_model_dir.mkdir(exist_ok=True, parents=True)
         model = LlavaLlamaForCausalLM.from_pretrained(model_id)
         model.resize_token_embeddings(len(tokenizer))
-        
+    
         if hasattr(config, "max_sequence_length"):
             context_len = config.max_sequence_length
         else:
@@ -496,13 +495,13 @@ compression instead of INT8 weight compression.
         video_tower = model.get_video_tower()
         if not video_tower.is_loaded:
             video_tower.load_model()
-            
+    
         model.eval()
         with torch.no_grad():
             convert_videollava(
                 model,
                 compressed_model_dir,
-                videollava_wc_parameters=videollava_wc_parameters
+                videollava_wc_parameters=videollava_wc_parameters,
             )
         del model
         gc.collect();
@@ -662,21 +661,11 @@ documentation  CausalLMOutputWithPast:
-            return self.forward(
-                input_ids, images, attention_mask, prefix_mask, past_key_values
-            )
+            return self.forward(input_ids, images, attention_mask, prefix_mask, past_key_values)
     
         def forward(
             self,
@@ -719,11 +706,7 @@ documentation  Tuple[Tuple[torch.Tensor]]:
+        def _reorder_cache(self, past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
             """
             This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
             [`~PreTrainedModel.beam_sample`] is called.
@@ -800,10 +779,7 @@ documentation `__ and the research paper:
+`U^2-Net: Going Deeper with Nested U-Structure for Salient Object
+Detection `__.
+
+The PyTorch U\ :math:`^2`-Net model is converted to OpenVINO IR format.
+The model source is available
+`here `__.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Preparation <#preparation>`__
+
+   -  `Install requirements <#install-requirements>`__
+   -  `Import the PyTorch Library and
+      U\ :math:`^2`-Net <#import-the-pytorch-library-and-u2-net>`__
+   -  `Settings <#settings>`__
+   -  `Load the U\ :math:`^2`-Net Model <#load-the-u2-net-model>`__
+
+-  `Convert PyTorch U\ :math:`^2`-Net model to OpenVINO
+   IR <#convert-pytorch-u2-net-model-to-openvino-ir>`__
+-  `Load and Pre-Process Input
+   Image <#load-and-pre-process-input-image>`__
+-  `Select inference device <#select-inference-device>`__
+-  `Do Inference on OpenVINO IR
+   Model <#do-inference-on-openvino-ir-model>`__
+-  `Visualize Results <#visualize-results>`__
+
+   -  `Add a Background Image <#add-a-background-image>`__
+
+-  `References <#references>`__
+
+Preparation
+-----------
+
+
+
+Install requirements
+~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import platform
+
+    %pip install -q "openvino>=2023.1.0"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "torch>=2.1" opencv-python
+    %pip install -q "gdown<4.6.4"
+
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Import the PyTorch Library and U\ :math:`^2`-Net
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import os
+    import time
+    from collections import namedtuple
+    from pathlib import Path
+
+    import cv2
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import openvino as ov
+    import torch
+    from IPython.display import HTML, FileLink, display
+
+.. code:: ipython3
+
+    # Import local modules
+
+    if not Path("./notebook_utils.py").exists():
+        # Fetch `notebook_utils` module
+        import requests
+
+        r = requests.get(
+            url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+        )
+
+        open("notebook_utils.py", "w").write(r.text)
+
+    from notebook_utils import load_image
+    from model.u2net import U2NET, U2NETP
+
+Settings
+~~~~~~~~
+
+
+
+This tutorial supports using the original U\ :math:`^2`-Net salient
+object detection model, as well as the smaller U2NETP version. Two sets
+of weights are supported for the original model: salient object
+detection and human segmentation.
+
+.. code:: ipython3
+
+    model_config = namedtuple("ModelConfig", ["name", "url", "model", "model_args"])
+
+    u2net_lite = model_config(
+        name="u2net_lite",
+        url="https://drive.google.com/uc?id=1W8E4FHIlTVstfRkYmNOjbr0VDXTZm0jD",
+        model=U2NETP,
+        model_args=(),
+    )
+    u2net = model_config(
+        name="u2net",
+        url="https://drive.google.com/uc?id=1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ",
+        model=U2NET,
+        model_args=(3, 1),
+    )
+    u2net_human_seg = model_config(
+        name="u2net_human_seg",
+        url="https://drive.google.com/uc?id=1m_Kgs91b21gayc2XLW0ou8yugAIadWVP",
+        model=U2NET,
+        model_args=(3, 1),
+    )
+
+    # Set u2net_model to one of the three configurations listed above.
+    u2net_model = u2net_lite
+
+.. code:: ipython3
+
+    # The filenames of the downloaded and converted models.
+    MODEL_DIR = "model"
+    model_path = Path(MODEL_DIR) / u2net_model.name / Path(u2net_model.name).with_suffix(".pth")
+
+Load the U\ :math:`^2`-Net Model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+The U\ :math:`^2`-Net human segmentation model weights are stored on
+Google Drive. They will be downloaded if they are not present yet. The
+next cell loads the model and the pre-trained weights.
+
+.. code:: ipython3
+
+    if not model_path.exists():
+        import gdown
+
+        os.makedirs(name=model_path.parent, exist_ok=True)
+        print("Start downloading model weights file... ")
+        with open(model_path, "wb") as model_file:
+            gdown.download(url=u2net_model.url, output=model_file)
+            print(f"Model weights have been downloaded to {model_path}")
+
+
+.. parsed-literal::
+
+    Start downloading model weights file...
+
+
+.. parsed-literal::
+
+    Downloading...
+    From: https://drive.google.com/uc?id=1W8E4FHIlTVstfRkYmNOjbr0VDXTZm0jD
+    To: <_io.BufferedWriter name='model/u2net_lite/u2net_lite.pth'>
+
+
+.. parsed-literal::
+
+
+  0%|          | 0.00/4.68M [00:00
+
+
+
+Convert PyTorch U\ :math:`^2`-Net model to OpenVINO IR
+------------------------------------------------------
+
+
+
+We use model conversion Python API to convert the Pytorch model to
+OpenVINO IR format. Executing the following command may take a while.
+
+.. code:: ipython3
+
+    model_ir = ov.convert_model(net, example_input=torch.zeros((1, 3, 512, 512)), input=([1, 3, 512, 512]))
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:3782: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
+      warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
+
+
+Load and Pre-Process Input Image
+--------------------------------
+
+
+
+While OpenCV reads images in ``BGR`` format, the OpenVINO IR model
+expects images in ``RGB``. Therefore, convert the images to ``RGB``,
+resize them to ``512 x 512``, and transpose the dimensions to the format
+the OpenVINO IR model expects.
+
+We add the mean values to the image tensor and scale the input with the
+standard deviation. It is called the input data normalization before
+propagating it through the network. The mean and standard deviation
+values can be found in the
+`dataloader `__
+file in the `U^2-Net
+repository `__ and multiplied by
+255 to support images with pixel values from 0-255.
+
+.. code:: ipython3
+
+    IMAGE_URI = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_hollywood.jpg"
+
+    input_mean = np.array([123.675, 116.28, 103.53]).reshape(1, 3, 1, 1)
+    input_scale = np.array([58.395, 57.12, 57.375]).reshape(1, 3, 1, 1)
+
+    image = cv2.cvtColor(
+        src=load_image(IMAGE_URI),
+        code=cv2.COLOR_BGR2RGB,
+    )
+
+    resized_image = cv2.resize(src=image, dsize=(512, 512))
+    # Convert the image shape to a shape and a data type expected by the network
+    # for OpenVINO IR model: (1, 3, 512, 512).
+    input_image = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0)
+
+    input_image = (input_image - input_mean) / input_scale
+
+Select inference device
+-----------------------
+
+
+
+select device from dropdown list for running inference using OpenVINO
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+
+    core = ov.Core()
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+Do Inference on OpenVINO IR Model
+---------------------------------
+
+
+
+Load the OpenVINO IR model to OpenVINO Runtime and do inference.
+
+.. code:: ipython3
+
+    core = ov.Core()
+    # Load the network to OpenVINO Runtime.
+    compiled_model_ir = core.compile_model(model=model_ir, device_name=device.value)
+    # Get the names of input and output layers.
+    input_layer_ir = compiled_model_ir.input(0)
+    output_layer_ir = compiled_model_ir.output(0)
+
+    # Do inference on the input image.
+    start_time = time.perf_counter()
+    result = compiled_model_ir([input_image])[output_layer_ir]
+    end_time = time.perf_counter()
+    print(f"Inference finished. Inference time: {end_time-start_time:.3f} seconds, " f"FPS: {1/(end_time-start_time):.2f}.")
+
+
+.. parsed-literal::
+
+    Inference finished. Inference time: 0.108 seconds, FPS: 9.25.
+
+
+Visualize Results
+-----------------
+
+
+
+Show the original image, the segmentation result, and the original image
+with the background removed.
+
+.. code:: ipython3
+
+    # Resize the network result to the image shape and round the values
+    # to 0 (background) and 1 (foreground).
+    # The network result has (1,1,512,512) shape. The `np.squeeze` function converts this to (512, 512).
+    resized_result = np.rint(cv2.resize(src=np.squeeze(result), dsize=(image.shape[1], image.shape[0]))).astype(np.uint8)
+
+    # Create a copy of the image and set all background values to 255 (white).
+    bg_removed_result = image.copy()
+    bg_removed_result[resized_result == 0] = 255
+
+    fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(20, 7))
+    ax[0].imshow(image)
+    ax[1].imshow(resized_result, cmap="gray")
+    ax[2].imshow(bg_removed_result)
+    for a in ax:
+        a.axis("off")
+
+
+
+.. image:: vision-background-removal-with-output_files/vision-background-removal-with-output_22_0.png
+
+
+Add a Background Image
+~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+In the segmentation result, all foreground pixels have a value of 1, all
+background pixels a value of 0. Replace the background image as follows:
+
+-  Load a new ``background_image``.
+-  Resize the image to the same size as the original image.
+-  In ``background_image``, set all the pixels, where the resized
+   segmentation result has a value of 1 - the foreground pixels in the
+   original image - to 0.
+-  Add ``bg_removed_result`` from the previous step - the part of the
+   original image that only contains foreground pixels - to
+   ``background_image``.
+
+.. code:: ipython3
+
+    BACKGROUND_FILE = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/wall.jpg"
+    OUTPUT_DIR = "output"
+
+    os.makedirs(name=OUTPUT_DIR, exist_ok=True)
+
+    background_image = cv2.cvtColor(src=load_image(BACKGROUND_FILE), code=cv2.COLOR_BGR2RGB)
+    background_image = cv2.resize(src=background_image, dsize=(image.shape[1], image.shape[0]))
+
+    # Set all the foreground pixels from the result to 0
+    # in the background image and add the image with the background removed.
+    background_image[resized_result == 1] = 0
+    new_image = background_image + bg_removed_result
+
+    # Save the generated image.
+    new_image_path = Path(f"{OUTPUT_DIR}/{Path(IMAGE_URI).stem}-{Path(BACKGROUND_FILE).stem}.jpg")
+    cv2.imwrite(filename=str(new_image_path), img=cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR))
+
+    # Display the original image and the image with the new background side by side
+    fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 7))
+    ax[0].imshow(image)
+    ax[1].imshow(new_image)
+    for a in ax:
+        a.axis("off")
+    plt.show()
+
+    # Create a link to download the image.
+    image_link = FileLink(new_image_path)
+    image_link.html_link_str = "%s"
+    display(
+        HTML(
+            f"The generated image {new_image_path.name} is saved in "
+            f"the directory {new_image_path.parent}. You can also "
+            "download the image by clicking on this link: "
+            f"{image_link._repr_html_()}"
+        )
+    )
+
+
+
+.. image:: vision-background-removal-with-output_files/vision-background-removal-with-output_24_0.png
+
+
+
+.. raw:: html
+
+    The generated image coco_hollywood-wall.jpg is saved in the directory output. You can also download the image by clicking on this link: output/coco_hollywood-wall.jpg

+
+
+References
+----------
+
+
+
+-  `PIP install
+   openvino-dev `__
+-  `Model Conversion
+   API `__
+-  `U^2-Net `__
+-  U^2-Net research paper: `U^2-Net: Going Deeper with Nested
+   U-Structure for Salient Object
+   Detection `__
diff --git a/docs/notebooks/vision-background-removal-with-output_files/vision-background-removal-with-output_22_0.png b/docs/notebooks/vision-background-removal-with-output_files/vision-background-removal-with-output_22_0.png
new file mode 100644
index 00000000000..d496b3facbe
--- /dev/null
+++ b/docs/notebooks/vision-background-removal-with-output_files/vision-background-removal-with-output_22_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46e22d5856ce8ff8c4e52afec585fb6e5ed0dd74afbd22abadc6b58c86049c09
+size 279572
diff --git a/docs/notebooks/vision-background-removal-with-output_files/vision-background-removal-with-output_24_0.png b/docs/notebooks/vision-background-removal-with-output_files/vision-background-removal-with-output_24_0.png
new file mode 100644
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+version https://git-lfs.github.com/spec/v1
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+size 927043
diff --git a/docs/notebooks/vision-image-colorization-with-output.rst b/docs/notebooks/vision-image-colorization-with-output.rst
new file mode 100644
index 00000000000..0f2863927be
--- /dev/null
+++ b/docs/notebooks/vision-image-colorization-with-output.rst
@@ -0,0 +1,4589 @@
+Image Colorization with OpenVINO
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This notebook demonstrates how to colorize images with OpenVINO using
+the Colorization model
+`colorization-v2 `__
+or
+`colorization-siggraph `__
+from `Open Model
+Zoo `__
+based on the paper `Colorful Image
+Colorization `__ models from Open
+Model Zoo.
+
+.. figure:: https://user-images.githubusercontent.com/18904157/180923280-9caefaf1-742b-4d2f-8943-5d4a6126e2fc.png
+   :alt: Let there be color
+
+   Let there be color
+
+Given a grayscale image as input, the model generates colorized version
+of the image as the output.
+
+About Colorization-v2
+^^^^^^^^^^^^^^^^^^^^^
+
+-  The colorization-v2 model is one of the colorization group of models
+   designed to perform image colorization.
+-  Model trained on the ImageNet dataset.
+-  Model consumes L-channel of LAB-image as input and produces predict
+   A- and B-channels of LAB-image as output.
+
+About Colorization-siggraph
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+-  The colorization-siggraph model is one of the colorization group of
+   models designed to real-time user-guided image colorization.
+-  Model trained on the ImageNet dataset with synthetically generated
+   user interaction.
+-  Model consumes L-channel of LAB-image as input and produces predict
+   A- and B-channels of LAB-image as output.
+
+See the `colorization `__
+repository for more details.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Imports <#imports>`__
+-  `Configurations <#configurations>`__
+
+   -  `Select inference device <#select-inference-device>`__
+
+-  `Download the model <#download-the-model>`__
+-  `Convert the model to OpenVINO
+   IR <#convert-the-model-to-openvino-ir>`__
+-  `Loading the Model <#loading-the-model>`__
+-  `Utility Functions <#utility-functions>`__
+-  `Load the Image <#load-the-image>`__
+-  `Display Colorized Image <#display-colorized-image>`__
+
+.. code:: ipython3
+
+    import platform
+
+    %pip install "openvino-dev>=2024.0.0" opencv-python tqdm
+
+    if platform.system() != "Windows":
+        %pip install -q "matplotlib>=3.4"
+    else:
+        %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+    Collecting openvino-dev>=2024.0.0
+      Using cached openvino_dev-2024.0.0-14509-py3-none-any.whl.metadata (16 kB)
+    Requirement already satisfied: opencv-python in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.9.0.80)
+    Requirement already satisfied: tqdm in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.66.2)
+
+
+.. parsed-literal::
+
+    Requirement already satisfied: defusedxml>=0.7.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (0.7.1)
+    Requirement already satisfied: networkx<=3.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (3.1)
+    Requirement already satisfied: numpy>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (1.23.5)
+    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (2024.1.0)
+    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (24.0)
+    Requirement already satisfied: pyyaml>=5.4.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (6.0.1)
+    Requirement already satisfied: requests>=2.25.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (2.31.0)
+    Requirement already satisfied: openvino==2024.0.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (2024.0.0)
+    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (3.3.2)
+    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (3.7)
+    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (2.2.1)
+    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (2024.2.2)
+
+
+.. parsed-literal::
+
+    Using cached openvino_dev-2024.0.0-14509-py3-none-any.whl (4.7 MB)
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    Installing collected packages: openvino-dev
+
+
+.. parsed-literal::
+
+    Successfully installed openvino-dev-2024.0.0
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Imports
+-------
+
+
+
+.. code:: ipython3
+
+    import os
+    from pathlib import Path
+
+    import cv2
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import openvino as ov
+
+    # Fetch `notebook_utils` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+
+    open("notebook_utils.py", "w").write(r.text)
+
+    import notebook_utils as utils
+
+Configurations
+--------------
+
+
+
+-  ``PRECISION`` - {FP16, FP32}, default: FP16.
+-  ``MODEL_DIR`` - directory where the model is to be stored, default:
+   public.
+-  ``MODEL_NAME`` - name of the model used for inference, default:
+   colorization-v2.
+-  ``DATA_DIR`` - directory where test images are stored, default: data.
+
+.. code:: ipython3
+
+    PRECISION = "FP16"
+    MODEL_DIR = "models"
+    MODEL_NAME = "colorization-v2"
+    # MODEL_NAME="colorization-siggraph"
+    MODEL_PATH = f"{MODEL_DIR}/public/{MODEL_NAME}/{PRECISION}/{MODEL_NAME}.xml"
+    DATA_DIR = "data"
+
+Select inference device
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+select device from dropdown list for running inference using OpenVINO
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+
+    core = ov.Core()
+
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+Download the model
+------------------
+
+
+
+``omz_downloader`` downloads model files from online sources and, if
+necessary, patches them to make them more usable with Model Converter.
+
+In this case, ``omz_downloader`` downloads the checkpoint and pytorch
+model of
+`colorization-v2 `__
+or
+`colorization-siggraph `__
+from `Open Model
+Zoo `__
+and saves it under ``MODEL_DIR``, as specified in the configuration
+above.
+
+.. code:: ipython3
+
+    download_command = f"omz_downloader " f"--name {MODEL_NAME} " f"--output_dir {MODEL_DIR} " f"--cache_dir {MODEL_DIR}"
+    ! $download_command
+
+
+.. parsed-literal::
+
+    ################|| Downloading colorization-v2 ||################
+
+    ========== Downloading models/public/colorization-v2/ckpt/colorization-v2-eccv16.pth
+
+
+.. parsed-literal::
+
+    ... 0%, 32 KB, 969 KB/s, 0 seconds passed
+
+.. parsed-literal::
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+
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+
+.. parsed-literal::
+
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+
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+... 18%, 22816 KB, 50505 KB/s, 0 seconds passed
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+... 18%, 23808 KB, 51717 KB/s, 0 seconds passed
+... 18%, 23840 KB, 51751 KB/s, 0 seconds passed
+... 18%, 23872 KB, 51792 KB/s, 0 seconds passed
+
+.. parsed-literal::
+
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+
+.. parsed-literal::
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+
+.. parsed-literal::
+
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+
+.. parsed-literal::
+
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 39%, 49600 KB, 51823 KB/s, 0 seconds passed
+... 39%, 49632 KB, 51843 KB/s, 0 seconds passed
+... 39%, 49664 KB, 51862 KB/s, 0 seconds passed
+... 39%, 49696 KB, 51881 KB/s, 0 seconds passed
+... 39%, 49728 KB, 51897 KB/s, 0 seconds passed
+... 39%, 49760 KB, 51914 KB/s, 0 seconds passed
+... 39%, 49792 KB, 51933 KB/s, 0 seconds passed
+... 39%, 49824 KB, 51952 KB/s, 0 seconds passed
+... 39%, 49856 KB, 51971 KB/s, 0 seconds passed
+... 39%, 49888 KB, 51990 KB/s, 0 seconds passed
+... 39%, 49920 KB, 52009 KB/s, 0 seconds passed
+... 39%, 49952 KB, 51919 KB/s, 0 seconds passed
+... 39%, 49984 KB, 51921 KB/s, 0 seconds passed
+... 39%, 50016 KB, 51940 KB/s, 0 seconds passed
+... 39%, 50048 KB, 51957 KB/s, 0 seconds passed
+... 39%, 50080 KB, 51976 KB/s, 0 seconds passed
+... 39%, 50112 KB, 51992 KB/s, 0 seconds passed
+... 39%, 50144 KB, 52011 KB/s, 0 seconds passed
+... 39%, 50176 KB, 52030 KB/s, 0 seconds passed
+... 39%, 50208 KB, 51982 KB/s, 0 seconds passed
+... 39%, 50240 KB, 51992 KB/s, 0 seconds passed
+... 39%, 50272 KB, 52005 KB/s, 0 seconds passed
+... 39%, 50304 KB, 52019 KB/s, 0 seconds passed
+... 39%, 50336 KB, 52033 KB/s, 0 seconds passed
+... 39%, 50368 KB, 52046 KB/s, 0 seconds passed
+... 40%, 50400 KB, 52059 KB/s, 0 seconds passed
+... 40%, 50432 KB, 52073 KB/s, 0 seconds passed
+... 40%, 50464 KB, 52086 KB/s, 0 seconds passed
+... 40%, 50496 KB, 52100 KB/s, 0 seconds passed
+... 40%, 50528 KB, 52113 KB/s, 0 seconds passed
+... 40%, 50560 KB, 52126 KB/s, 0 seconds passed
+... 40%, 50592 KB, 52139 KB/s, 0 seconds passed
+... 40%, 50624 KB, 52154 KB/s, 0 seconds passed
+... 40%, 50656 KB, 52167 KB/s, 0 seconds passed
+... 40%, 50688 KB, 52180 KB/s, 0 seconds passed
+... 40%, 50720 KB, 52194 KB/s, 0 seconds passed
+... 40%, 50752 KB, 52206 KB/s, 0 seconds passed
+... 40%, 50784 KB, 52219 KB/s, 0 seconds passed
+... 40%, 50816 KB, 52233 KB/s, 0 seconds passed
+... 40%, 50848 KB, 52246 KB/s, 0 seconds passed
+... 40%, 50880 KB, 52259 KB/s, 0 seconds passed
+... 40%, 50912 KB, 52273 KB/s, 0 seconds passed
+
+.. parsed-literal::
+
+    ... 40%, 50944 KB, 52286 KB/s, 0 seconds passed
+... 40%, 50976 KB, 52303 KB/s, 0 seconds passed
+... 40%, 51008 KB, 52320 KB/s, 0 seconds passed
+... 40%, 51040 KB, 52338 KB/s, 0 seconds passed
+... 40%, 51072 KB, 52356 KB/s, 0 seconds passed
+... 40%, 51104 KB, 52374 KB/s, 0 seconds passed
+... 40%, 51136 KB, 52391 KB/s, 0 seconds passed
+... 40%, 51168 KB, 52409 KB/s, 0 seconds passed
+... 40%, 51200 KB, 51274 KB/s, 0 seconds passed
+... 40%, 51232 KB, 51281 KB/s, 0 seconds passed
+... 40%, 51264 KB, 51293 KB/s, 0 seconds passed
+... 40%, 51296 KB, 51307 KB/s, 0 seconds passed
+... 40%, 51328 KB, 51300 KB/s, 1 seconds passed
+... 40%, 51360 KB, 51312 KB/s, 1 seconds passed
+... 40%, 51392 KB, 51323 KB/s, 1 seconds passed
+... 40%, 51424 KB, 51337 KB/s, 1 seconds passed
+... 40%, 51456 KB, 51352 KB/s, 1 seconds passed
+... 40%, 51488 KB, 51336 KB/s, 1 seconds passed
+... 40%, 51520 KB, 51228 KB/s, 1 seconds passed
+... 40%, 51552 KB, 51237 KB/s, 1 seconds passed
+... 40%, 51584 KB, 51248 KB/s, 1 seconds passed
+... 40%, 51616 KB, 51260 KB/s, 1 seconds passed
+... 41%, 51648 KB, 51273 KB/s, 1 seconds passed
+... 41%, 51680 KB, 51285 KB/s, 1 seconds passed
+... 41%, 51712 KB, 51297 KB/s, 1 seconds passed
+... 41%, 51744 KB, 51309 KB/s, 1 seconds passed
+... 41%, 51776 KB, 51321 KB/s, 1 seconds passed
+... 41%, 51808 KB, 51333 KB/s, 1 seconds passed
+... 41%, 51840 KB, 51346 KB/s, 1 seconds passed
+... 41%, 51872 KB, 51358 KB/s, 1 seconds passed
+... 41%, 51904 KB, 51372 KB/s, 1 seconds passed
+... 41%, 51936 KB, 51385 KB/s, 1 seconds passed
+... 41%, 51968 KB, 51396 KB/s, 1 seconds passed
+... 41%, 52000 KB, 51408 KB/s, 1 seconds passed
+... 41%, 52032 KB, 51421 KB/s, 1 seconds passed
+... 41%, 52064 KB, 51433 KB/s, 1 seconds passed
+... 41%, 52096 KB, 51446 KB/s, 1 seconds passed
+... 41%, 52128 KB, 51458 KB/s, 1 seconds passed
+... 41%, 52160 KB, 51470 KB/s, 1 seconds passed
+... 41%, 52192 KB, 51483 KB/s, 1 seconds passed
+... 41%, 52224 KB, 51495 KB/s, 1 seconds passed
+... 41%, 52256 KB, 51508 KB/s, 1 seconds passed
+... 41%, 52288 KB, 51520 KB/s, 1 seconds passed
+... 41%, 52320 KB, 51533 KB/s, 1 seconds passed
+... 41%, 52352 KB, 51546 KB/s, 1 seconds passed
+... 41%, 52384 KB, 51556 KB/s, 1 seconds passed
+... 41%, 52416 KB, 51569 KB/s, 1 seconds passed
+... 41%, 52448 KB, 51588 KB/s, 1 seconds passed
+... 41%, 52480 KB, 51607 KB/s, 1 seconds passed
+... 41%, 52512 KB, 51626 KB/s, 1 seconds passed
+... 41%, 52544 KB, 51645 KB/s, 1 seconds passed
+... 41%, 52576 KB, 51664 KB/s, 1 seconds passed
+... 41%, 52608 KB, 51682 KB/s, 1 seconds passed
+... 41%, 52640 KB, 51701 KB/s, 1 seconds passed
+... 41%, 52672 KB, 51720 KB/s, 1 seconds passed
+... 41%, 52704 KB, 51738 KB/s, 1 seconds passed
+... 41%, 52736 KB, 51757 KB/s, 1 seconds passed
+... 41%, 52768 KB, 51775 KB/s, 1 seconds passed
+... 41%, 52800 KB, 51794 KB/s, 1 seconds passed
+... 41%, 52832 KB, 51813 KB/s, 1 seconds passed
+... 41%, 52864 KB, 51833 KB/s, 1 seconds passed
+... 41%, 52896 KB, 51850 KB/s, 1 seconds passed
+... 42%, 52928 KB, 51870 KB/s, 1 seconds passed
+... 42%, 52960 KB, 51888 KB/s, 1 seconds passed
+... 42%, 52992 KB, 51907 KB/s, 1 seconds passed
+... 42%, 53024 KB, 51926 KB/s, 1 seconds passed
+... 42%, 53056 KB, 51945 KB/s, 1 seconds passed
+... 42%, 53088 KB, 51964 KB/s, 1 seconds passed
+... 42%, 53120 KB, 51983 KB/s, 1 seconds passed
+... 42%, 53152 KB, 52003 KB/s, 1 seconds passed
+... 42%, 53184 KB, 52021 KB/s, 1 seconds passed
+... 42%, 53216 KB, 52041 KB/s, 1 seconds passed
+... 42%, 53248 KB, 52060 KB/s, 1 seconds passed
+... 42%, 53280 KB, 52079 KB/s, 1 seconds passed
+... 42%, 53312 KB, 52098 KB/s, 1 seconds passed
+... 42%, 53344 KB, 52117 KB/s, 1 seconds passed
+... 42%, 53376 KB, 52136 KB/s, 1 seconds passed
+... 42%, 53408 KB, 52154 KB/s, 1 seconds passed
+... 42%, 53440 KB, 52173 KB/s, 1 seconds passed
+... 42%, 53472 KB, 52192 KB/s, 1 seconds passed
+... 42%, 53504 KB, 52210 KB/s, 1 seconds passed
+... 42%, 53536 KB, 52230 KB/s, 1 seconds passed
+... 42%, 53568 KB, 52249 KB/s, 1 seconds passed
+... 42%, 53600 KB, 52268 KB/s, 1 seconds passed
+
+.. parsed-literal::
+
+    ... 42%, 53632 KB, 52287 KB/s, 1 seconds passed
+... 42%, 53664 KB, 52306 KB/s, 1 seconds passed
+... 42%, 53696 KB, 52325 KB/s, 1 seconds passed
+... 42%, 53728 KB, 52348 KB/s, 1 seconds passed
+... 42%, 53760 KB, 52370 KB/s, 1 seconds passed
+... 42%, 53792 KB, 52392 KB/s, 1 seconds passed
+... 42%, 53824 KB, 52415 KB/s, 1 seconds passed
+... 42%, 53856 KB, 52438 KB/s, 1 seconds passed
+... 42%, 53888 KB, 52461 KB/s, 1 seconds passed
+... 42%, 53920 KB, 52483 KB/s, 1 seconds passed
+... 42%, 53952 KB, 52506 KB/s, 1 seconds passed
+... 42%, 53984 KB, 52528 KB/s, 1 seconds passed
+... 42%, 54016 KB, 52551 KB/s, 1 seconds passed
+... 42%, 54048 KB, 52572 KB/s, 1 seconds passed
+... 42%, 54080 KB, 52595 KB/s, 1 seconds passed
+... 42%, 54112 KB, 52617 KB/s, 1 seconds passed
+... 42%, 54144 KB, 52640 KB/s, 1 seconds passed
+... 43%, 54176 KB, 52658 KB/s, 1 seconds passed
+... 43%, 54208 KB, 52676 KB/s, 1 seconds passed
+... 43%, 54240 KB, 52693 KB/s, 1 seconds passed
+... 43%, 54272 KB, 52711 KB/s, 1 seconds passed
+... 43%, 54304 KB, 52725 KB/s, 1 seconds passed
+... 43%, 54336 KB, 52748 KB/s, 1 seconds passed
+... 43%, 54368 KB, 52764 KB/s, 1 seconds passed
+... 43%, 54400 KB, 52774 KB/s, 1 seconds passed
+... 43%, 54432 KB, 52784 KB/s, 1 seconds passed
+... 43%, 54464 KB, 52795 KB/s, 1 seconds passed
+... 43%, 54496 KB, 52817 KB/s, 1 seconds passed
+... 43%, 54528 KB, 52840 KB/s, 1 seconds passed
+... 43%, 54560 KB, 52859 KB/s, 1 seconds passed
+... 43%, 54592 KB, 52881 KB/s, 1 seconds passed
+... 43%, 54624 KB, 52899 KB/s, 1 seconds passed
+... 43%, 54656 KB, 52917 KB/s, 1 seconds passed
+... 43%, 54688 KB, 52934 KB/s, 1 seconds passed
+... 43%, 54720 KB, 52952 KB/s, 1 seconds passed
+... 43%, 54752 KB, 52966 KB/s, 1 seconds passed
+... 43%, 54784 KB, 52984 KB/s, 1 seconds passed
+... 43%, 54816 KB, 53001 KB/s, 1 seconds passed
+... 43%, 54848 KB, 53016 KB/s, 1 seconds passed
+... 43%, 54880 KB, 53034 KB/s, 1 seconds passed
+... 43%, 54912 KB, 53048 KB/s, 1 seconds passed
+... 43%, 54944 KB, 53066 KB/s, 1 seconds passed
+... 43%, 54976 KB, 53083 KB/s, 1 seconds passed
+... 43%, 55008 KB, 53098 KB/s, 1 seconds passed
+... 43%, 55040 KB, 53115 KB/s, 1 seconds passed
+... 43%, 55072 KB, 53130 KB/s, 1 seconds passed
+... 43%, 55104 KB, 53147 KB/s, 1 seconds passed
+... 43%, 55136 KB, 52987 KB/s, 1 seconds passed
+... 43%, 55168 KB, 52996 KB/s, 1 seconds passed
+... 43%, 55200 KB, 53007 KB/s, 1 seconds passed
+... 43%, 55232 KB, 53017 KB/s, 1 seconds passed
+... 43%, 55264 KB, 53041 KB/s, 1 seconds passed
+... 43%, 55296 KB, 53065 KB/s, 1 seconds passed
+... 43%, 55328 KB, 53082 KB/s, 1 seconds passed
+... 43%, 55360 KB, 53100 KB/s, 1 seconds passed
+... 43%, 55392 KB, 53117 KB/s, 1 seconds passed
+... 44%, 55424 KB, 53134 KB/s, 1 seconds passed
+... 44%, 55456 KB, 53152 KB/s, 1 seconds passed
+... 44%, 55488 KB, 51653 KB/s, 1 seconds passed
+... 44%, 55520 KB, 51658 KB/s, 1 seconds passed
+... 44%, 55552 KB, 51669 KB/s, 1 seconds passed
+... 44%, 55584 KB, 51682 KB/s, 1 seconds passed
+... 44%, 55616 KB, 51666 KB/s, 1 seconds passed
+
+.. parsed-literal::
+
+    ... 44%, 55648 KB, 51677 KB/s, 1 seconds passed
+... 44%, 55680 KB, 51689 KB/s, 1 seconds passed
+... 44%, 55712 KB, 51701 KB/s, 1 seconds passed
+... 44%, 55744 KB, 51712 KB/s, 1 seconds passed
+... 44%, 55776 KB, 51724 KB/s, 1 seconds passed
+... 44%, 55808 KB, 51736 KB/s, 1 seconds passed
+... 44%, 55840 KB, 51748 KB/s, 1 seconds passed
+... 44%, 55872 KB, 51760 KB/s, 1 seconds passed
+... 44%, 55904 KB, 51772 KB/s, 1 seconds passed
+... 44%, 55936 KB, 51779 KB/s, 1 seconds passed
+... 44%, 55968 KB, 51790 KB/s, 1 seconds passed
+... 44%, 56000 KB, 51802 KB/s, 1 seconds passed
+... 44%, 56032 KB, 51813 KB/s, 1 seconds passed
+... 44%, 56064 KB, 51824 KB/s, 1 seconds passed
+... 44%, 56096 KB, 51836 KB/s, 1 seconds passed
+... 44%, 56128 KB, 51847 KB/s, 1 seconds passed
+... 44%, 56160 KB, 51861 KB/s, 1 seconds passed
+... 44%, 56192 KB, 51877 KB/s, 1 seconds passed
+... 44%, 56224 KB, 51892 KB/s, 1 seconds passed
+... 44%, 56256 KB, 51909 KB/s, 1 seconds passed
+... 44%, 56288 KB, 51923 KB/s, 1 seconds passed
+
+.. parsed-literal::
+
+    ... 44%, 56320 KB, 49866 KB/s, 1 seconds passed
+... 44%, 56352 KB, 49873 KB/s, 1 seconds passed
+... 44%, 56384 KB, 49880 KB/s, 1 seconds passed
+... 44%, 56416 KB, 49891 KB/s, 1 seconds passed
+... 44%, 56448 KB, 49904 KB/s, 1 seconds passed
+... 44%, 56480 KB, 49917 KB/s, 1 seconds passed
+... 44%, 56512 KB, 49929 KB/s, 1 seconds passed
+... 44%, 56544 KB, 49939 KB/s, 1 seconds passed
+... 44%, 56576 KB, 49951 KB/s, 1 seconds passed
+... 44%, 56608 KB, 49962 KB/s, 1 seconds passed
+... 44%, 56640 KB, 49972 KB/s, 1 seconds passed
+... 44%, 56672 KB, 49984 KB/s, 1 seconds passed
+... 45%, 56704 KB, 49995 KB/s, 1 seconds passed
+... 45%, 56736 KB, 50006 KB/s, 1 seconds passed
+... 45%, 56768 KB, 50018 KB/s, 1 seconds passed
+... 45%, 56800 KB, 50029 KB/s, 1 seconds passed
+... 45%, 56832 KB, 50040 KB/s, 1 seconds passed
+... 45%, 56864 KB, 50050 KB/s, 1 seconds passed
+... 45%, 56896 KB, 50061 KB/s, 1 seconds passed
+... 45%, 56928 KB, 50073 KB/s, 1 seconds passed
+... 45%, 56960 KB, 50084 KB/s, 1 seconds passed
+... 45%, 56992 KB, 50095 KB/s, 1 seconds passed
+... 45%, 57024 KB, 50108 KB/s, 1 seconds passed
+... 45%, 57056 KB, 50121 KB/s, 1 seconds passed
+... 45%, 57088 KB, 50134 KB/s, 1 seconds passed
+... 45%, 57120 KB, 50146 KB/s, 1 seconds passed
+... 45%, 57152 KB, 50159 KB/s, 1 seconds passed
+... 45%, 57184 KB, 50171 KB/s, 1 seconds passed
+... 45%, 57216 KB, 50183 KB/s, 1 seconds passed
+... 45%, 57248 KB, 50194 KB/s, 1 seconds passed
+... 45%, 57280 KB, 50207 KB/s, 1 seconds passed
+... 45%, 57312 KB, 50220 KB/s, 1 seconds passed
+... 45%, 57344 KB, 50232 KB/s, 1 seconds passed
+... 45%, 57376 KB, 50244 KB/s, 1 seconds passed
+... 45%, 57408 KB, 50257 KB/s, 1 seconds passed
+... 45%, 57440 KB, 50270 KB/s, 1 seconds passed
+... 45%, 57472 KB, 50283 KB/s, 1 seconds passed
+... 45%, 57504 KB, 50296 KB/s, 1 seconds passed
+... 45%, 57536 KB, 50309 KB/s, 1 seconds passed
+... 45%, 57568 KB, 50321 KB/s, 1 seconds passed
+... 45%, 57600 KB, 50333 KB/s, 1 seconds passed
+... 45%, 57632 KB, 50347 KB/s, 1 seconds passed
+... 45%, 57664 KB, 50360 KB/s, 1 seconds passed
+... 45%, 57696 KB, 50373 KB/s, 1 seconds passed
+... 45%, 57728 KB, 50385 KB/s, 1 seconds passed
+... 45%, 57760 KB, 50398 KB/s, 1 seconds passed
+... 45%, 57792 KB, 50411 KB/s, 1 seconds passed
+... 45%, 57824 KB, 50424 KB/s, 1 seconds passed
+... 45%, 57856 KB, 50436 KB/s, 1 seconds passed
+... 45%, 57888 KB, 50449 KB/s, 1 seconds passed
+... 45%, 57920 KB, 50465 KB/s, 1 seconds passed
+... 46%, 57952 KB, 50482 KB/s, 1 seconds passed
+... 46%, 57984 KB, 50499 KB/s, 1 seconds passed
+... 46%, 58016 KB, 50517 KB/s, 1 seconds passed
+... 46%, 58048 KB, 50535 KB/s, 1 seconds passed
+... 46%, 58080 KB, 50553 KB/s, 1 seconds passed
+... 46%, 58112 KB, 50570 KB/s, 1 seconds passed
+... 46%, 58144 KB, 50588 KB/s, 1 seconds passed
+... 46%, 58176 KB, 50606 KB/s, 1 seconds passed
+... 46%, 58208 KB, 50624 KB/s, 1 seconds passed
+... 46%, 58240 KB, 50642 KB/s, 1 seconds passed
+... 46%, 58272 KB, 50660 KB/s, 1 seconds passed
+... 46%, 58304 KB, 50677 KB/s, 1 seconds passed
+... 46%, 58336 KB, 50695 KB/s, 1 seconds passed
+... 46%, 58368 KB, 50712 KB/s, 1 seconds passed
+... 46%, 58400 KB, 50729 KB/s, 1 seconds passed
+... 46%, 58432 KB, 50747 KB/s, 1 seconds passed
+... 46%, 58464 KB, 50765 KB/s, 1 seconds passed
+... 46%, 58496 KB, 50782 KB/s, 1 seconds passed
+... 46%, 58528 KB, 50799 KB/s, 1 seconds passed
+... 46%, 58560 KB, 50817 KB/s, 1 seconds passed
+... 46%, 58592 KB, 50834 KB/s, 1 seconds passed
+... 46%, 58624 KB, 50852 KB/s, 1 seconds passed
+... 46%, 58656 KB, 50869 KB/s, 1 seconds passed
+... 46%, 58688 KB, 50887 KB/s, 1 seconds passed
+... 46%, 58720 KB, 50905 KB/s, 1 seconds passed
+... 46%, 58752 KB, 50923 KB/s, 1 seconds passed
+... 46%, 58784 KB, 50940 KB/s, 1 seconds passed
+... 46%, 58816 KB, 50958 KB/s, 1 seconds passed
+... 46%, 58848 KB, 50974 KB/s, 1 seconds passed
+... 46%, 58880 KB, 50992 KB/s, 1 seconds passed
+... 46%, 58912 KB, 51010 KB/s, 1 seconds passed
+... 46%, 58944 KB, 51027 KB/s, 1 seconds passed
+... 46%, 58976 KB, 51045 KB/s, 1 seconds passed
+... 46%, 59008 KB, 51063 KB/s, 1 seconds passed
+... 46%, 59040 KB, 51080 KB/s, 1 seconds passed
+... 46%, 59072 KB, 51097 KB/s, 1 seconds passed
+... 46%, 59104 KB, 51114 KB/s, 1 seconds passed
+... 46%, 59136 KB, 51132 KB/s, 1 seconds passed
+... 46%, 59168 KB, 51149 KB/s, 1 seconds passed
+... 47%, 59200 KB, 51167 KB/s, 1 seconds passed
+... 47%, 59232 KB, 51185 KB/s, 1 seconds passed
+... 47%, 59264 KB, 51202 KB/s, 1 seconds passed
+... 47%, 59296 KB, 51218 KB/s, 1 seconds passed
+... 47%, 59328 KB, 51239 KB/s, 1 seconds passed
+... 47%, 59360 KB, 51260 KB/s, 1 seconds passed
+... 47%, 59392 KB, 51281 KB/s, 1 seconds passed
+... 47%, 59424 KB, 51302 KB/s, 1 seconds passed
+... 47%, 59456 KB, 51323 KB/s, 1 seconds passed
+... 47%, 59488 KB, 51344 KB/s, 1 seconds passed
+... 47%, 59520 KB, 51365 KB/s, 1 seconds passed
+... 47%, 59552 KB, 51385 KB/s, 1 seconds passed
+... 47%, 59584 KB, 51406 KB/s, 1 seconds passed
+... 47%, 59616 KB, 51427 KB/s, 1 seconds passed
+... 47%, 59648 KB, 51447 KB/s, 1 seconds passed
+... 47%, 59680 KB, 51468 KB/s, 1 seconds passed
+... 47%, 59712 KB, 51489 KB/s, 1 seconds passed
+... 47%, 59744 KB, 51509 KB/s, 1 seconds passed
+... 47%, 59776 KB, 51529 KB/s, 1 seconds passed
+... 47%, 59808 KB, 51546 KB/s, 1 seconds passed
+... 47%, 59840 KB, 51562 KB/s, 1 seconds passed
+... 47%, 59872 KB, 51577 KB/s, 1 seconds passed
+... 47%, 59904 KB, 51591 KB/s, 1 seconds passed
+... 47%, 59936 KB, 51602 KB/s, 1 seconds passed
+... 47%, 59968 KB, 51622 KB/s, 1 seconds passed
+... 47%, 60000 KB, 51636 KB/s, 1 seconds passed
+... 47%, 60032 KB, 51652 KB/s, 1 seconds passed
+... 47%, 60064 KB, 51663 KB/s, 1 seconds passed
+... 47%, 60096 KB, 51679 KB/s, 1 seconds passed
+... 47%, 60128 KB, 51697 KB/s, 1 seconds passed
+... 47%, 60160 KB, 51710 KB/s, 1 seconds passed
+... 47%, 60192 KB, 51729 KB/s, 1 seconds passed
+... 47%, 60224 KB, 51744 KB/s, 1 seconds passed
+... 47%, 60256 KB, 51621 KB/s, 1 seconds passed
+... 47%, 60288 KB, 51637 KB/s, 1 seconds passed
+... 47%, 60320 KB, 51651 KB/s, 1 seconds passed
+... 47%, 60352 KB, 51661 KB/s, 1 seconds passed
+... 47%, 60384 KB, 51671 KB/s, 1 seconds passed
+... 47%, 60416 KB, 51682 KB/s, 1 seconds passed
+... 47%, 60448 KB, 51694 KB/s, 1 seconds passed
+... 48%, 60480 KB, 51694 KB/s, 1 seconds passed
+... 48%, 60512 KB, 51706 KB/s, 1 seconds passed
+... 48%, 60544 KB, 51716 KB/s, 1 seconds passed
+... 48%, 60576 KB, 51727 KB/s, 1 seconds passed
+... 48%, 60608 KB, 51738 KB/s, 1 seconds passed
+... 48%, 60640 KB, 51749 KB/s, 1 seconds passed
+... 48%, 60672 KB, 51758 KB/s, 1 seconds passed
+... 48%, 60704 KB, 51769 KB/s, 1 seconds passed
+... 48%, 60736 KB, 51780 KB/s, 1 seconds passed
+... 48%, 60768 KB, 51791 KB/s, 1 seconds passed
+... 48%, 60800 KB, 51803 KB/s, 1 seconds passed
+... 48%, 60832 KB, 51814 KB/s, 1 seconds passed
+... 48%, 60864 KB, 51825 KB/s, 1 seconds passed
+... 48%, 60896 KB, 51836 KB/s, 1 seconds passed
+... 48%, 60928 KB, 51847 KB/s, 1 seconds passed
+... 48%, 60960 KB, 51857 KB/s, 1 seconds passed
+... 48%, 60992 KB, 51867 KB/s, 1 seconds passed
+... 48%, 61024 KB, 51877 KB/s, 1 seconds passed
+... 48%, 61056 KB, 51888 KB/s, 1 seconds passed
+... 48%, 61088 KB, 51898 KB/s, 1 seconds passed
+... 48%, 61120 KB, 51909 KB/s, 1 seconds passed
+... 48%, 61152 KB, 51921 KB/s, 1 seconds passed
+... 48%, 61184 KB, 51938 KB/s, 1 seconds passed
+... 48%, 61216 KB, 51954 KB/s, 1 seconds passed
+... 48%, 61248 KB, 51972 KB/s, 1 seconds passed
+... 48%, 61280 KB, 51990 KB/s, 1 seconds passed
+... 48%, 61312 KB, 52008 KB/s, 1 seconds passed
+... 48%, 61344 KB, 52027 KB/s, 1 seconds passed
+... 48%, 61376 KB, 52046 KB/s, 1 seconds passed
+
+.. parsed-literal::
+
+    ... 48%, 61408 KB, 52065 KB/s, 1 seconds passed
+... 48%, 61440 KB, 51476 KB/s, 1 seconds passed
+... 48%, 61472 KB, 51469 KB/s, 1 seconds passed
+... 48%, 61504 KB, 51477 KB/s, 1 seconds passed
+... 48%, 61536 KB, 51486 KB/s, 1 seconds passed
+... 48%, 61568 KB, 51495 KB/s, 1 seconds passed
+... 48%, 61600 KB, 51508 KB/s, 1 seconds passed
+... 48%, 61632 KB, 51495 KB/s, 1 seconds passed
+... 48%, 61664 KB, 51506 KB/s, 1 seconds passed
+... 48%, 61696 KB, 51517 KB/s, 1 seconds passed
+... 49%, 61728 KB, 51528 KB/s, 1 seconds passed
+... 49%, 61760 KB, 51538 KB/s, 1 seconds passed
+... 49%, 61792 KB, 51548 KB/s, 1 seconds passed
+... 49%, 61824 KB, 51558 KB/s, 1 seconds passed
+... 49%, 61856 KB, 51569 KB/s, 1 seconds passed
+... 49%, 61888 KB, 51579 KB/s, 1 seconds passed
+... 49%, 61920 KB, 51587 KB/s, 1 seconds passed
+... 49%, 61952 KB, 51597 KB/s, 1 seconds passed
+... 49%, 61984 KB, 51608 KB/s, 1 seconds passed
+... 49%, 62016 KB, 51618 KB/s, 1 seconds passed
+... 49%, 62048 KB, 51630 KB/s, 1 seconds passed
+... 49%, 62080 KB, 51639 KB/s, 1 seconds passed
+... 49%, 62112 KB, 51650 KB/s, 1 seconds passed
+... 49%, 62144 KB, 51660 KB/s, 1 seconds passed
+... 49%, 62176 KB, 51670 KB/s, 1 seconds passed
+... 49%, 62208 KB, 51682 KB/s, 1 seconds passed
+... 49%, 62240 KB, 51690 KB/s, 1 seconds passed
+... 49%, 62272 KB, 51700 KB/s, 1 seconds passed
+... 49%, 62304 KB, 51711 KB/s, 1 seconds passed
+... 49%, 62336 KB, 51721 KB/s, 1 seconds passed
+... 49%, 62368 KB, 51732 KB/s, 1 seconds passed
+... 49%, 62400 KB, 51743 KB/s, 1 seconds passed
+... 49%, 62432 KB, 51754 KB/s, 1 seconds passed
+... 49%, 62464 KB, 51764 KB/s, 1 seconds passed
+... 49%, 62496 KB, 51775 KB/s, 1 seconds passed
+... 49%, 62528 KB, 51785 KB/s, 1 seconds passed
+... 49%, 62560 KB, 51795 KB/s, 1 seconds passed
+... 49%, 62592 KB, 51804 KB/s, 1 seconds passed
+... 49%, 62624 KB, 51815 KB/s, 1 seconds passed
+... 49%, 62656 KB, 51829 KB/s, 1 seconds passed
+... 49%, 62688 KB, 51845 KB/s, 1 seconds passed
+... 49%, 62720 KB, 51861 KB/s, 1 seconds passed
+... 49%, 62752 KB, 51877 KB/s, 1 seconds passed
+... 49%, 62784 KB, 51893 KB/s, 1 seconds passed
+... 49%, 62816 KB, 51909 KB/s, 1 seconds passed
+... 49%, 62848 KB, 51925 KB/s, 1 seconds passed
+... 49%, 62880 KB, 51940 KB/s, 1 seconds passed
+... 49%, 62912 KB, 51956 KB/s, 1 seconds passed
+... 49%, 62944 KB, 51973 KB/s, 1 seconds passed
+... 49%, 62976 KB, 51989 KB/s, 1 seconds passed
+... 50%, 63008 KB, 52005 KB/s, 1 seconds passed
+... 50%, 63040 KB, 52022 KB/s, 1 seconds passed
+... 50%, 63072 KB, 52036 KB/s, 1 seconds passed
+... 50%, 63104 KB, 52052 KB/s, 1 seconds passed
+... 50%, 63136 KB, 52068 KB/s, 1 seconds passed
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 60%, 76352 KB, 52087 KB/s, 1 seconds passed
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+... 69%, 87040 KB, 53090 KB/s, 1 seconds passed
+... 69%, 87072 KB, 53088 KB/s, 1 seconds passed
+... 69%, 87104 KB, 53100 KB/s, 1 seconds passed
+
+.. parsed-literal::
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+... 70%, 89312 KB, 53778 KB/s, 1 seconds passed
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+... 71%, 89440 KB, 53819 KB/s, 1 seconds passed
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+... 71%, 89536 KB, 53850 KB/s, 1 seconds passed
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+
+.. parsed-literal::
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+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+.. parsed-literal::
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+.. parsed-literal::
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+
+.. parsed-literal::
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+
+.. parsed-literal::
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+.. parsed-literal::
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+
+.. parsed-literal::
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+
+
+
+.. parsed-literal::
+
+
+    ========== Downloading models/public/colorization-v2/model/__init__.py
+
+
+.. parsed-literal::
+
+    ... 100%, 0 KB, 288 KB/s, 0 seconds passed
+
+
+    ========== Downloading models/public/colorization-v2/model/base_color.py
+
+
+.. parsed-literal::
+
+    ... 100%, 0 KB, 1806 KB/s, 0 seconds passed
+
+
+    ========== Downloading models/public/colorization-v2/model/eccv16.py
+
+
+.. parsed-literal::
+
+    ... 100%, 4 KB, 17288 KB/s, 0 seconds passed
+
+
+    ========== Replacing text in models/public/colorization-v2/model/__init__.py
+    ========== Replacing text in models/public/colorization-v2/model/__init__.py
+    ========== Replacing text in models/public/colorization-v2/model/eccv16.py
+
+
+
+Convert the model to OpenVINO IR
+--------------------------------
+
+
+
+``omz_converter`` converts the models that are not in the OpenVINO™ IR
+format into that format using model conversion API.
+
+The downloaded pytorch model is not in OpenVINO IR format which is
+required for inference with OpenVINO runtime. ``omz_converter`` is used
+to convert the downloaded pytorch model into ONNX and OpenVINO IR format
+respectively
+
+.. code:: ipython3
+
+    if not os.path.exists(MODEL_PATH):
+        convert_command = f"omz_converter " f"--name {MODEL_NAME} " f"--download_dir {MODEL_DIR} " f"--precisions {PRECISION}"
+        ! $convert_command
+
+
+.. parsed-literal::
+
+    ========== Converting colorization-v2 to ONNX
+    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=models/public/colorization-v2 --model-name=ECCVGenerator --weights=models/public/colorization-v2/ckpt/colorization-v2-eccv16.pth --import-module=model --input-shape=1,1,256,256 --output-file=models/public/colorization-v2/colorization-v2-eccv16.onnx --input-names=data_l --output-names=color_ab
+
+
+
+.. parsed-literal::
+
+    ONNX check passed successfully.
+
+
+.. parsed-literal::
+
+
+    ========== Converting colorization-v2 to IR (FP16)
+    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=models/public/colorization-v2/FP16 --model_name=colorization-v2 --input=data_l --output=color_ab --input_model=models/public/colorization-v2/colorization-v2-eccv16.onnx '--layout=data_l(NCHW)' '--input_shape=[1, 1, 256, 256]' --compress_to_fp16=True
+
+
+
+.. parsed-literal::
+
+    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
+    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
+
+
+.. parsed-literal::
+
+    [ SUCCESS ] Generated IR version 11 model.
+    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/vision-image-colorization/models/public/colorization-v2/FP16/colorization-v2.xml
+    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/vision-image-colorization/models/public/colorization-v2/FP16/colorization-v2.bin
+
+
+
+
+
+Loading the Model
+-----------------
+
+
+
+Load the model in OpenVINO Runtime with ``ie.read_model`` and compile it
+for the specified device with ``ie.compile_model``.
+
+.. code:: ipython3
+
+    core = ov.Core()
+    model = core.read_model(model=MODEL_PATH)
+    compiled_model = core.compile_model(model=model, device_name=device.value)
+    input_layer = compiled_model.input(0)
+    output_layer = compiled_model.output(0)
+    N, C, H, W = list(input_layer.shape)
+
+Utility Functions
+-----------------
+
+
+
+.. code:: ipython3
+
+    def read_image(impath: str) -> np.ndarray:
+        """
+        Returns an image as ndarra, given path to an image reads the
+        (BGR) image using opencv's imread() API.
+
+            Parameter:
+                impath (string): Path of the image to be read and returned.
+
+            Returns:
+                image (ndarray): Numpy array representing the read image.
+        """
+
+        raw_image = cv2.imread(impath)
+        if raw_image.shape[2] > 1:
+            image = cv2.cvtColor(cv2.cvtColor(raw_image, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2RGB)
+        else:
+            image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
+
+        return image
+
+
+    def plot_image(image: np.ndarray, title: str = "") -> None:
+        """
+        Given a image as ndarray and title as string, display it using
+        matplotlib.
+
+            Parameters:
+                image (ndarray): Numpy array representing the image to be
+                                 displayed.
+                title (string): String representing the title of the plot.
+
+            Returns:
+                None
+
+        """
+
+        plt.imshow(image)
+        plt.title(title)
+        plt.axis("off")
+        plt.show()
+
+
+    def plot_output(gray_img: np.ndarray, color_img: np.ndarray) -> None:
+        """
+        Plots the original (bw or grayscale) image and colorized image
+        on different column axes for comparing side by side.
+
+            Parameters:
+                gray_image (ndarray): Numpy array representing the original image.
+                color_image (ndarray): Numpy array representing the model output.
+
+            Returns:
+                None
+        """
+
+        fig = plt.figure(figsize=(12, 12))
+
+        ax1 = fig.add_subplot(1, 2, 1)
+        plt.title("Input", fontsize=20)
+        ax1.axis("off")
+
+        ax2 = fig.add_subplot(1, 2, 2)
+        plt.title("Colorized", fontsize=20)
+        ax2.axis("off")
+
+        ax1.imshow(gray_img)
+        ax2.imshow(color_img)
+
+        plt.show()
+
+Load the Image
+--------------
+
+
+
+.. code:: ipython3
+
+    img_url_0 = "https://user-images.githubusercontent.com/18904157/180923287-20339d01-b1bf-493f-9a0d-55eff997aff1.jpg"
+    img_url_1 = "https://user-images.githubusercontent.com/18904157/180923289-0bb71e09-25e1-46a6-aaf1-e8f666b62d26.jpg"
+
+    image_file_0 = utils.download_file(
+        img_url_0,
+        filename="test_0.jpg",
+        directory="data",
+        show_progress=False,
+        silent=True,
+        timeout=30,
+    )
+    assert Path(image_file_0).exists()
+
+    image_file_1 = utils.download_file(
+        img_url_1,
+        filename="test_1.jpg",
+        directory="data",
+        show_progress=False,
+        silent=True,
+        timeout=30,
+    )
+    assert Path(image_file_1).exists()
+
+    test_img_0 = read_image("data/test_0.jpg")
+    test_img_1 = read_image("data/test_1.jpg")
+
+.. code:: ipython3
+
+    def colorize(gray_img: np.ndarray) -> np.ndarray:
+        """
+        Given an image as ndarray for inference convert the image into LAB image,
+        the model consumes as input L-Channel of LAB image and provides output
+        A & B - Channels of LAB image. i.e returns a colorized image
+
+            Parameters:
+                gray_img (ndarray): Numpy array representing the original
+                                    image.
+
+            Returns:
+                colorize_image (ndarray): Numpy arrray depicting the
+                                          colorized version of the original
+                                          image.
+        """
+
+        # Preprocess
+        h_in, w_in, _ = gray_img.shape
+        img_rgb = gray_img.astype(np.float32) / 255
+        img_lab = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2Lab)
+        img_l_rs = cv2.resize(img_lab.copy(), (W, H))[:, :, 0]
+
+        # Inference
+        inputs = np.expand_dims(img_l_rs, axis=[0, 1])
+        res = compiled_model([inputs])[output_layer]
+        update_res = np.squeeze(res)
+
+        # Post-process
+        out = update_res.transpose((1, 2, 0))
+        out = cv2.resize(out, (w_in, h_in))
+        img_lab_out = np.concatenate((img_lab[:, :, 0][:, :, np.newaxis], out), axis=2)
+        img_bgr_out = np.clip(cv2.cvtColor(img_lab_out, cv2.COLOR_Lab2RGB), 0, 1)
+        colorized_image = (cv2.resize(img_bgr_out, (w_in, h_in)) * 255).astype(np.uint8)
+        return colorized_image
+
+.. code:: ipython3
+
+    color_img_0 = colorize(test_img_0)
+    color_img_1 = colorize(test_img_1)
+
+Display Colorized Image
+-----------------------
+
+
+
+.. code:: ipython3
+
+    plot_output(test_img_0, color_img_0)
+
+
+
+.. image:: vision-image-colorization-with-output_files/vision-image-colorization-with-output_21_0.png
+
+
+.. code:: ipython3
+
+    plot_output(test_img_1, color_img_1)
+
+
+
+.. image:: vision-image-colorization-with-output_files/vision-image-colorization-with-output_22_0.png
+
diff --git a/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_21_0.png b/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_21_0.png
new file mode 100644
index 00000000000..4b8608e6f20
--- /dev/null
+++ b/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_21_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c278cf1e55ecf1ea7593ae094cbafee4fccef56951ba7b4de2b20d3bb6cb364
+size 415784
diff --git a/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_22_0.png b/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_22_0.png
new file mode 100644
index 00000000000..019f11ffd29
--- /dev/null
+++ b/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_22_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff4ab92d3df015fc56a10173b2192cb2026d64c12c03c734d775aa5189a85ebe
+size 284968
diff --git a/docs/notebooks/201-vision-monodepth-with-output.rst b/docs/notebooks/vision-monodepth-with-output.rst
similarity index 99%
rename from docs/notebooks/201-vision-monodepth-with-output.rst
rename to docs/notebooks/vision-monodepth-with-output.rst
index 0568f2d1b47..8fdc7523c3e 100644
--- a/docs/notebooks/201-vision-monodepth-with-output.rst
+++ b/docs/notebooks/vision-monodepth-with-output.rst
@@ -78,23 +78,40 @@ Install requirements
         %pip install -q "matplotlib>=3.4,<3.7"
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -104,7 +121,7 @@ Install requirements
 
 .. parsed-literal::
 
-    ('notebook_utils.py', )
+    21503
 
 
 
@@ -146,11 +163,11 @@ format.
 
 .. code:: ipython3
 
-    model_folder = Path('model')
+    model_folder = Path("model")
     
-    ir_model_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/depth-estimation-midas/FP32/'
-    ir_model_name_xml = 'MiDaS_small.xml'
-    ir_model_name_bin = 'MiDaS_small.bin'
+    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/depth-estimation-midas/FP32/"
+    ir_model_name_xml = "MiDaS_small.xml"
+    ir_model_name_bin = "MiDaS_small.bin"
     
     download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory=model_folder)
     download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory=model_folder)
@@ -219,8 +236,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -246,8 +263,12 @@ output keys and the expected input shape for the model.
 
 .. code:: ipython3
 
+    # Create cache folder
+    cache_folder = Path("cache")
+    cache_folder.mkdir(exist_ok=True)
+    
     core = ov.Core()
-    core.set_property({'CACHE_DIR': '../cache'})
+    core.set_property({"CACHE_DIR": cache_folder})
     model = core.read_model(model_xml_path)
     compiled_model = core.compile_model(model=model, device_name=device.value)
     
@@ -305,7 +326,7 @@ original image shape.
 
 .. parsed-literal::
 
-    /tmp/ipykernel_3058046/2076527990.py:15: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
+    /tmp/ipykernel_64442/2076527990.py:15: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
       cmap = matplotlib.cm.get_cmap(colormap)
 
 
@@ -322,7 +343,7 @@ Display monodepth image
 
 
 
-.. image:: 201-vision-monodepth-with-output_files/201-vision-monodepth-with-output_18_0.png
+.. image:: vision-monodepth-with-output_files/vision-monodepth-with-output_18_0.png
 
 
 Monodepth on Video
@@ -388,10 +409,7 @@ compute values for these properties for the monodepth video.
     target_frame_width = int(input_video_frame_width * SCALE_OUTPUT)
     
     cap.release()
-    print(
-        f"The input video has a frame width of {input_video_frame_width}, "
-        f"frame height of {input_video_frame_height} and runs at {input_fps:.2f} fps"
-    )
+    print(f"The input video has a frame width of {input_video_frame_width}, " f"frame height of {input_video_frame_height} and runs at {input_fps:.2f} fps")
     print(
         "The monodepth video will be scaled with a factor "
         f"{SCALE_OUTPUT}, have width {target_frame_width}, "
@@ -507,7 +525,7 @@ Do Inference on a Video and Create Monodepth Video
 
 .. parsed-literal::
 
-    Processed 60 frames in 26.30 seconds. Total FPS (including video processing): 2.28.Inference FPS: 43.50 
+    Processed 60 frames in 26.27 seconds. Total FPS (including video processing): 2.28.Inference FPS: 43.30 
     Monodepth Video saved to 'output/Coco%20Walking%20in%20Berkeley_monodepth.mp4'.
 
 
@@ -524,10 +542,7 @@ Display Monodepth Video
         raise ValueError("OpenCV was unable to write the video file. Showing one video frame.")
     else:
         print(f"Showing monodepth video saved at\n{result_video_path.resolve()}")
-        print(
-            "If you cannot see the video in your browser, please click on the "
-            "following link to download the video "
-        )
+        print("If you cannot see the video in your browser, please click on the " "following link to download the video ")
         video_link = FileLink(result_video_path)
         video_link.html_link_str = "%s"
         display(HTML(video_link._repr_html_()))
@@ -537,7 +552,7 @@ Display Monodepth Video
 .. parsed-literal::
 
     Showing monodepth video saved at
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/201-vision-monodepth/output/Coco%20Walking%20in%20Berkeley_monodepth.mp4
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/vision-monodepth/output/Coco%20Walking%20in%20Berkeley_monodepth.mp4
     If you cannot see the video in your browser, please click on the following link to download the video 
 
 
diff --git a/docs/notebooks/vision-monodepth-with-output_files/vision-monodepth-with-output_18_0.png b/docs/notebooks/vision-monodepth-with-output_files/vision-monodepth-with-output_18_0.png
new file mode 100644
index 00000000000..080c3d27e9f
--- /dev/null
+++ b/docs/notebooks/vision-monodepth-with-output_files/vision-monodepth-with-output_18_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb0ee9e50cfc68c5963f1f087c833d310e943e9c346390ce445b1328bb8ebb1c
+size 959858
diff --git a/docs/notebooks/206-vision-paddlegan-anime-with-output.rst b/docs/notebooks/vision-paddlegan-anime-with-output.rst
similarity index 83%
rename from docs/notebooks/206-vision-paddlegan-anime-with-output.rst
rename to docs/notebooks/vision-paddlegan-anime-with-output.rst
index 97380a91e59..925898fdcf3 100644
--- a/docs/notebooks/206-vision-paddlegan-anime-with-output.rst
+++ b/docs/notebooks/vision-paddlegan-anime-with-output.rst
@@ -72,15 +72,25 @@ Install requirements
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
     
-    %pip install -q opencv-python scikit-learn scikit-image
+    %pip install -q opencv-python scikit-learn "scikit-image>=0.19.2"
     %pip install -q "imageio==2.9.0" "imageio-ffmpeg" "numba>=0.53.1" easydict munch natsort
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -93,20 +103,35 @@ Install requirements
 
 .. parsed-literal::
 
-    Note: you may need to restart the kernel to use updated packages.
-
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
 
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    paddleclas 2.5.1 requires faiss-cpu==1.7.1.post2, but you have faiss-cpu 1.8.0 which is incompatible.
-    paddleclas 2.5.1 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.
-    ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.10.1 which is incompatible.
+    paddleclas 2.5.2 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.
+    paddleclas 2.5.2 requires opencv-python==4.6.0.66, but you have opencv-python 4.9.0.80 which is incompatible.
+    ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.
     ppgan 2.1.0 requires opencv-python<=4.6.0.66, but you have opencv-python 4.9.0.80 which is incompatible.
     scikit-image 0.21.0 requires imageio>=2.27, but you have imageio 2.9.0 which is incompatible.
     
@@ -127,7 +152,7 @@ Imports
     import time
     import os
     from pathlib import Path
-    import urllib.request
+    import requests
     
     import cv2
     import matplotlib.pyplot as plt
@@ -216,13 +241,13 @@ source of the function.
 
 .. parsed-literal::
 
-    [03/12 23:23:24] ppgan INFO: Found /opt/home/k8sworker/.cache/ppgan/animeganv2_hayao.pdparams
+    [04/18 01:23:57] ppgan INFO: Found /opt/home/k8sworker/.cache/ppgan/animeganv2_hayao.pdparams
 
 
 .. code:: ipython3
 
     # In a Jupyter Notebook, ?? shows the source and docstring
-    predictor.run??
+    ??predictor.run
 
 The ``AnimeGANPredictor.run()`` method works as follow:
 
@@ -252,11 +277,13 @@ cell.
     image_path = Path("./data/coco_bricks.png")
     # fetch the image from the web
     image_path.parent.mkdir(parents=True, exist_ok=True)
-    urllib.request.urlretrieve(
+    r = requests.get(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bricks.png",
-        image_path
     )
     
+    with image_path.open("wb") as f:
+        f.write(r.content)
+    
     image = cv2.cvtColor(cv2.imread(str(image_path), flags=cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)
     
     ## Inference takes a long time on large images. Resize to a max width of 600.
@@ -267,7 +294,7 @@ cell.
     input_tensor = paddle.to_tensor(transformed_image[None, ::])
     
     if PADDLEGAN_INFERENCE:
-        # Step 3. Do inference. 
+        # Step 3. Do inference.
         predictor.generator.eval()
         with paddle.no_grad():
             result = predictor.generator(input_tensor)
@@ -310,7 +337,7 @@ Show Inference Results on PaddleGAN model
 
 
 
-.. image:: 206-vision-paddlegan-anime-with-output_files/206-vision-paddlegan-anime-with-output_15_0.png
+.. image:: vision-paddlegan-anime-with-output_files/vision-paddlegan-anime-with-output_15_0.png
 
 
 Model Conversion to ONNX and OpenVINO IR
@@ -357,23 +384,23 @@ succeeds, the output of the next cell will include
 
 .. parsed-literal::
 
-    2024-03-12 23:23:32 [INFO]	Static PaddlePaddle model saved in model/paddle_model_static_onnx_temp_dir.
+    2024-04-18 01:24:06 [INFO]	Static PaddlePaddle model saved in model/paddle_model_static_onnx_temp_dir.
 
 
 .. parsed-literal::
 
     [Paddle2ONNX] Start to parse PaddlePaddle model...
     [Paddle2ONNX] Model file path: model/paddle_model_static_onnx_temp_dir/model.pdmodel
-    [Paddle2ONNX] Paramters file path: model/paddle_model_static_onnx_temp_dir/model.pdiparams
+    [Paddle2ONNX] Parameters file path: model/paddle_model_static_onnx_temp_dir/model.pdiparams
     [Paddle2ONNX] Start to parsing Paddle model...
     [Paddle2ONNX] Use opset_version = 11 for ONNX export.
     [Paddle2ONNX] PaddlePaddle model is exported as ONNX format now.
-    2024-03-12 23:23:32 [INFO]	ONNX model saved in model/paddlegan_anime.onnx.
+    2024-04-18 01:24:06 [INFO]	ONNX model saved in model/paddlegan_anime.onnx.
 
 
 .. parsed-literal::
 
-    I0312 23:23:32.235426 3060480 program_interpreter.cc:212] New Executor is Running.
+    I0418 01:24:06.033911 64719 program_interpreter.cc:212] New Executor is Running.
 
 
 Convert to OpenVINO IR
@@ -387,7 +414,7 @@ images manually. See the transforms that the ``.run()`` method used:
 
 .. code:: ipython3
 
-    predictor.__init__??
+    ??predictor.__init__
 
 .. code:: ipython3
 
@@ -462,11 +489,11 @@ Create Postprocessing Functions
 
 .. code:: ipython3
 
-    predictor.adjust_brightness??
+    ??predictor.adjust_brightness
 
 .. code:: ipython3
 
-    predictor.calc_avg_brightness??
+    ??predictor.calc_avg_brightness
 
 The average brightness is computed by a `standard
 formula `__. To adjust the
@@ -509,7 +536,7 @@ Do Inference on OpenVINO IR Model
 Load the OpenVINO IR model and do inference, following the same steps as
 for the PaddleGAN model. For more information about inference on
 OpenVINO IR models, see the `OpenVINO Runtime API
-notebook <002-openvino-api-with-output.html>`__.
+notebook `__.
 
 The OpenVINO IR model is generated with an input shape that is computed
 based on the input image. If you do inference on images with different
@@ -529,8 +556,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -566,8 +593,8 @@ select device from dropdown list for running inference using OpenVINO
     resized_image = cv2.resize(image, (target_width, target_height))
     input_image = resized_image.transpose(2, 0, 1)[None, :, :, :]
     # Normalize the image
-    input_mean = np.array([127.5,127.5,127.5]).reshape(1, 3, 1, 1)
-    input_scale = np.array([127.5,127.5,127.5]).reshape(1, 3, 1, 1)
+    input_mean = np.array([127.5, 127.5, 127.5]).reshape(1, 3, 1, 1)
+    input_scale = np.array([127.5, 127.5, 127.5]).reshape(1, 3, 1, 1)
     input_image = (input_image - input_mean) / input_scale
     
     # Step 3. Do inference.
@@ -607,7 +634,7 @@ select device from dropdown list for running inference using OpenVINO
 
 
 
-.. image:: 206-vision-paddlegan-anime-with-output_files/206-vision-paddlegan-anime-with-output_37_0.png
+.. image:: vision-paddlegan-anime-with-output_files/vision-paddlegan-anime-with-output_37_0.png
 
 
 Performance Comparison
@@ -619,7 +646,7 @@ Measure the time it takes to do inference on an image. This gives an
 indication of performance. It is not a perfect measure. Since the
 PaddleGAN model requires quite a bit of memory for inference, only
 measure inference on one image. For more accurate benchmarking, use
-`Benchmark Tool <104-model-tools-with-output.html>`__.
+`Benchmark Tool `__.
 
 .. code:: ipython3
 
@@ -629,14 +656,11 @@ measure inference on one image. For more accurate benchmarking, use
         compiled_model([input_image])
     end = time.perf_counter()
     time_ir = end - start
-    print(
-        f"OpenVINO IR model in OpenVINO Runtime/CPU: {time_ir/NUM_IMAGES:.3f} "
-        f"seconds per image, FPS: {NUM_IMAGES/time_ir:.2f}"
-    )
+    print(f"OpenVINO IR model in OpenVINO Runtime/CPU: {time_ir/NUM_IMAGES:.3f} " f"seconds per image, FPS: {NUM_IMAGES/time_ir:.2f}")
     
     ## `PADDLEGAN_INFERENCE` is defined in the "Inference on PaddleGAN model" section above.
     ## Uncomment the next line to enable a performance comparison with the PaddleGAN model
-    ## if you disabled it earlier. 
+    ## if you disabled it earlier.
     
     # PADDLEGAN_INFERENCE = True
     
@@ -647,20 +671,17 @@ measure inference on one image. For more accurate benchmarking, use
                 predictor.generator(input_tensor)
             end = time.perf_counter()
             time_paddle = end - start
-        print(
-            f"PaddleGAN model on CPU: {time_paddle/NUM_IMAGES:.3f} seconds per image, "
-            f"FPS: {NUM_IMAGES/time_paddle:.2f}"
-        )
+        print(f"PaddleGAN model on CPU: {time_paddle/NUM_IMAGES:.3f} seconds per image, " f"FPS: {NUM_IMAGES/time_paddle:.2f}")
 
 
 .. parsed-literal::
 
-    OpenVINO IR model in OpenVINO Runtime/CPU: 0.425 seconds per image, FPS: 2.36
+    OpenVINO IR model in OpenVINO Runtime/CPU: 0.416 seconds per image, FPS: 2.40
 
 
 .. parsed-literal::
 
-    PaddleGAN model on CPU: 6.184 seconds per image, FPS: 0.16
+    PaddleGAN model on CPU: 6.032 seconds per image, FPS: 0.17
 
 
 References
diff --git a/docs/notebooks/vision-paddlegan-anime-with-output_files/vision-paddlegan-anime-with-output_15_0.png b/docs/notebooks/vision-paddlegan-anime-with-output_files/vision-paddlegan-anime-with-output_15_0.png
new file mode 100644
index 00000000000..a5526c0ad84
--- /dev/null
+++ b/docs/notebooks/vision-paddlegan-anime-with-output_files/vision-paddlegan-anime-with-output_15_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9479be703d311d46b756a08fc1967fb6f62b89b90440661755f5ceee2d26f149
+size 1810982
diff --git a/docs/notebooks/vision-paddlegan-anime-with-output_files/vision-paddlegan-anime-with-output_37_0.png b/docs/notebooks/vision-paddlegan-anime-with-output_files/vision-paddlegan-anime-with-output_37_0.png
new file mode 100644
index 00000000000..f1ee632679e
--- /dev/null
+++ b/docs/notebooks/vision-paddlegan-anime-with-output_files/vision-paddlegan-anime-with-output_37_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:299c1d8f9aa88fd23a247712ae188d86ef085dc2a19e3947b5bfcd97f3dabb6d
+size 1931637
diff --git a/docs/notebooks/207-vision-paddlegan-superresolution-with-output.rst b/docs/notebooks/vision-paddlegan-superresolution-with-output.rst
similarity index 80%
rename from docs/notebooks/207-vision-paddlegan-superresolution-with-output.rst
rename to docs/notebooks/vision-paddlegan-superresolution-with-output.rst
index d0bedc6a43e..e6205e5a6a3 100644
--- a/docs/notebooks/207-vision-paddlegan-superresolution-with-output.rst
+++ b/docs/notebooks/vision-paddlegan-superresolution-with-output.rst
@@ -57,9 +57,9 @@ Imports
     
     %pip install -q "paddlepaddle>=2.5.1" "paddle2onnx>=0.6"
     
-    %pip install -q "imageio==2.9.0" "imageio-ffmpeg" "numba>=0.53.1" "easydict" "munch" "natsort"
+    %pip install -q "imageio==2.9.0" "imageio" "numba>=0.53.1" "easydict" "munch" "natsort" Pillow tqdm
     %pip install -q "git+https://github.com/PaddlePaddle/PaddleGAN.git" --no-deps
-    %pip install -q scikit-image
+    %pip install -q "scikit-image>=0.19.2"
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -67,11 +67,31 @@ Imports
         %pip install -q "matplotlib>=3.4,<3.7"
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -84,14 +104,14 @@ Imports
 
 .. parsed-literal::
 
-    Note: you may need to restart the kernel to use updated packages.
-
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
 
 .. parsed-literal::
 
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
     ppgan 2.1.0 requires imageio==2.9.0, but you have imageio 2.34.0 which is incompatible.
-    ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.10.1 which is incompatible.
+    ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.
     ppgan 2.1.0 requires opencv-python<=4.6.0.66, but you have opencv-python 4.9.0.80 which is incompatible.
     
 
@@ -100,6 +120,11 @@ Imports
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -123,11 +148,13 @@ Imports
     from ppgan.apps import RealSRPredictor
     
     # Fetch `notebook_utils` module
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
+    
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import NotebookAlert, download_file
 
 Settings
@@ -173,24 +200,24 @@ source code.
 
 .. parsed-literal::
 
-    [03/12 23:24:03] ppgan INFO: Found /opt/home/k8sworker/.cache/ppgan/DF2K_JPEG.pdparams
+    [04/18 01:24:39] ppgan INFO: Found /opt/home/k8sworker/.cache/ppgan/DF2K_JPEG.pdparams
 
 
 .. code:: ipython3
 
-    sr.run??
+    ??sr.run
 
 .. code:: ipython3
 
-    sr.run_image??
+    ??sr.run_image
 
 .. code:: ipython3
 
-    sr.norm??
+    ??sr.norm
 
 .. code:: ipython3
 
-    sr.denorm??
+    ??sr.denorm
 
 The run checks whether the input is an image or a video. For an image,
 it loads the image as an ``RGB`` image, normalizes it, and converts it
@@ -225,7 +252,7 @@ may take some time.
     # Load the image from openvino storage
     IMAGE_PATH = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_tulips.jpg",
-        directory="data"
+        directory="data",
     )
 
 
@@ -265,9 +292,7 @@ may take some time.
             result = sr.model(input_tensor)
         end_time = time.perf_counter()
         duration = end_time - start_time
-        result_image = (
-            (result.numpy().squeeze() * 255).clip(0, 255).astype("uint8").transpose((1, 2, 0))
-        )
+        result_image = (result.numpy().squeeze() * 255).clip(0, 255).astype("uint8").transpose((1, 2, 0))
         print(f"Superresolution image shape: {result_image.shape}")
         print(f"Inference duration: {duration:.2f} seconds")
         plt.imshow(result_image);
@@ -301,27 +326,27 @@ Convert PaddlePaddle Model to ONNX
 
 .. parsed-literal::
 
-    2024-03-12 23:24:10 [INFO]	Static PaddlePaddle model saved in model/paddle_model_static_onnx_temp_dir.
+    2024-04-18 01:24:46 [INFO]	Static PaddlePaddle model saved in model/paddle_model_static_onnx_temp_dir.
 
 
 .. parsed-literal::
 
-    I0312 23:24:10.040381 3060798 program_interpreter.cc:212] New Executor is Running.
+    I0418 01:24:46.227986 65088 program_interpreter.cc:212] New Executor is Running.
 
 
 .. parsed-literal::
 
     [Paddle2ONNX] Start to parse PaddlePaddle model...
     [Paddle2ONNX] Model file path: model/paddle_model_static_onnx_temp_dir/model.pdmodel
-    [Paddle2ONNX] Paramters file path: model/paddle_model_static_onnx_temp_dir/model.pdiparams
+    [Paddle2ONNX] Parameters file path: model/paddle_model_static_onnx_temp_dir/model.pdiparams
     [Paddle2ONNX] Start to parsing Paddle model...
-    [Paddle2ONNX] Use opset_version = 13 for ONNX export.
-    [Paddle2ONNX] PaddlePaddle model is exported as ONNX format now.
 
 
 .. parsed-literal::
 
-    2024-03-12 23:24:13 [INFO]	ONNX model saved in model/paddlegan_sr.onnx.
+    [Paddle2ONNX] Use opset_version = 13 for ONNX export.
+    [Paddle2ONNX] PaddlePaddle model is exported as ONNX format now.
+    2024-04-18 01:24:49 [INFO]	ONNX model saved in model/paddlegan_sr.onnx.
 
 
 Convert ONNX Model to OpenVINO IR with `Model Conversion Python API `__
@@ -333,10 +358,7 @@ Convert ONNX Model to OpenVINO IR with `Model Conversion Python API 
+    
 
 
 
 
-.. image:: 207-vision-paddlegan-superresolution-with-output_files/207-vision-paddlegan-superresolution-with-output_26_1.png
+.. image:: vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_26_1.png
 
 
 .. code:: ipython3
@@ -435,7 +457,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    Inference duration: 3.23 seconds
+    Inference duration: 3.22 seconds
 
 
 .. code:: ipython3
@@ -458,12 +480,12 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    
+    
 
 
 
 
-.. image:: 207-vision-paddlegan-superresolution-with-output_files/207-vision-paddlegan-superresolution-with-output_30_1.png
+.. image:: vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_30_1.png
 
 
 Show an Animated GIF
@@ -497,7 +519,7 @@ between both versions.
 
 
 
-.. image:: 207-vision-paddlegan-superresolution-with-output_files/207-vision-paddlegan-superresolution-with-output_32_0.png
+.. image:: vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_32_0.png
    :width: 960px
 
 
@@ -532,12 +554,8 @@ open it directly from the images directory, and play it locally.
         (video_target_width, video_target_height),
     )
     
-    resized_result_image = cv2.resize(image_super, (video_target_width, video_target_height))[
-        :, :, (2, 1, 0)
-    ]
-    resized_bicubic_image = cv2.resize(image_bicubic, (video_target_width, video_target_height))[
-        :, :, (2, 1, 0)
-    ]
+    resized_result_image = cv2.resize(image_super, (video_target_width, video_target_height))[:, :, (2, 1, 0)]
+    resized_bicubic_image = cv2.resize(image_bicubic, (video_target_width, video_target_height))[:, :, (2, 1, 0)]
     
     progress_bar = ProgressBar(total=video_target_width)
     progress_bar.display()
@@ -572,12 +590,12 @@ you use the Google Colab
 
 .. code:: ipython3
 
-    if 'google.colab' in str(get_ipython()):
+    if "google.colab" in str(get_ipython()):
         # Save a file
         from google.colab import files
     
         # Save the file to the local file system
-        with open(result_video_path, 'r') as f:
+        with open(result_video_path, "r") as f:
             files.download(result_video_path)
     else:
         video_link = FileLink(result_video_path)
diff --git a/docs/notebooks/vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_26_1.png b/docs/notebooks/vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_26_1.png
new file mode 100644
index 00000000000..d6f651c7f34
--- /dev/null
+++ b/docs/notebooks/vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_26_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff6ea7dc343f1463b8f50bf8874ba9acdcbd3db1c83641229531938078390331
+size 436999
diff --git a/docs/notebooks/vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_30_1.png b/docs/notebooks/vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_30_1.png
new file mode 100644
index 00000000000..b9c4fed382b
--- /dev/null
+++ b/docs/notebooks/vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_30_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:deb8bf91f95586f9f0906d4693b8072f2046be8b68dfa6de5a1ebd3f3dd802c8
+size 476190
diff --git a/docs/notebooks/207-vision-paddlegan-superresolution-with-output_files/207-vision-paddlegan-superresolution-with-output_32_0.png b/docs/notebooks/vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_32_0.png
similarity index 100%
rename from docs/notebooks/207-vision-paddlegan-superresolution-with-output_files/207-vision-paddlegan-superresolution-with-output_32_0.png
rename to docs/notebooks/vision-paddlegan-superresolution-with-output_files/vision-paddlegan-superresolution-with-output_32_0.png
diff --git a/docs/notebooks/202-vision-superresolution-image-with-output.rst b/docs/notebooks/vision-superresolution-image-with-output.rst
similarity index 83%
rename from docs/notebooks/202-vision-superresolution-image-with-output.rst
rename to docs/notebooks/vision-superresolution-image-with-output.rst
index 81210e18934..09e21372d11 100644
--- a/docs/notebooks/202-vision-superresolution-image-with-output.rst
+++ b/docs/notebooks/vision-superresolution-image-with-output.rst
@@ -69,7 +69,7 @@ Install requirements
     
     %pip install -q "openvino>=2023.1.0"
     %pip install -q opencv-python
-    %pip install -q pillow
+    %pip install -q pillow tqdm
     
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -79,19 +79,39 @@ Install requirements
 
 .. parsed-literal::
 
-    Note: you may need to restart the kernel to use updated packages.
-
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
 
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -122,9 +142,13 @@ Imports
     # Define a download file helper function
     def download_file(url: str, path: Path) -> None:
         """Download file."""
-        import urllib.request
+        import requests
+    
         path.parent.mkdir(parents=True, exist_ok=True)
-        urllib.request.urlretrieve(url, path)
+    
+        r = requests.get(url)
+        with path.open("wb") as f:
+            f.write(r.content)
 
 Settings
 ~~~~~~~~
@@ -145,8 +169,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -164,25 +188,25 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     # 1032: 4x superresolution, 1033: 3x superresolution
-    model_name = 'single-image-super-resolution-1032'
+    model_name = "single-image-super-resolution-1032"
     
     base_model_dir = Path("./model").expanduser()
     
-    model_xml_name = f'{model_name}.xml'
-    model_bin_name = f'{model_name}.bin'
+    model_xml_name = f"{model_name}.xml"
+    model_bin_name = f"{model_name}.bin"
     
     model_xml_path = base_model_dir / model_xml_name
     model_bin_path = base_model_dir / model_bin_name
     
     if not model_xml_path.exists():
-        base_url = f'https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/{model_name}/FP16/'
+        base_url = f"https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/{model_name}/FP16/"
         model_xml_url = base_url + model_xml_name
         model_bin_url = base_url + model_bin_name
     
         download_file(model_xml_url, model_xml_path)
         download_file(model_bin_url, model_bin_path)
     else:
-        print(f'{model_name} already downloaded to {base_model_dir}')
+        print(f"{model_name} already downloaded to {base_model_dir}")
 
 Functions
 ~~~~~~~~~
@@ -283,11 +307,7 @@ information about the network inputs and outputs.
     print(f"The network expects inputs with a width of {input_width}, " f"height of {input_height}")
     print(f"The network returns images with a width of {target_width}, " f"height of {target_height}")
     
-    print(
-        f"The image sides are upsampled by a factor of {upsample_factor}. "
-        f"The new image is {upsample_factor**2} times as large as the "
-        "original image"
-    )
+    print(f"The image sides are upsampled by a factor of {upsample_factor}. " f"The new image is {upsample_factor**2} times as large as the " "original image")
 
 
 .. parsed-literal::
@@ -313,7 +333,10 @@ Load and Show the Input Image
     
     os.makedirs(str(OUTPUT_PATH), exist_ok=True)
     
-    download_file('https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/tower.jpg', IMAGE_PATH)
+    download_file(
+        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/tower.jpg",
+        IMAGE_PATH,
+    )
     full_image = cv2.imread(str(IMAGE_PATH))
     
     # Uncomment these lines to load a raw image as BGR.
@@ -331,7 +354,7 @@ Load and Show the Input Image
 
 
 
-.. image:: 202-vision-superresolution-image-with-output_files/202-vision-superresolution-image-with-output_15_1.png
+.. image:: vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_15_1.png
 
 
 Superresolution on a Crop of the Image
@@ -387,7 +410,7 @@ as the crop size.
 
 
 
-.. image:: 202-vision-superresolution-image-with-output_files/202-vision-superresolution-image-with-output_17_1.png
+.. image:: vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_17_1.png
 
 
 Reshape/Resize Crop for Model Input
@@ -403,9 +426,7 @@ interpolation. This bicubic image is the second input to the network.
 .. code:: ipython3
 
     # Resize the image to the target shape with bicubic interpolation.
-    bicubic_image = cv2.resize(
-        src=image_crop, dsize=(target_width, target_height), interpolation=cv2.INTER_CUBIC
-    )
+    bicubic_image = cv2.resize(src=image_crop, dsize=(target_width, target_height), interpolation=cv2.INTER_CUBIC)
     
     # If required, resize the image to the input image shape.
     if CROP_FACTOR > 1:
@@ -459,7 +480,7 @@ Show the bicubic image and the enhanced superresolution image.
 
 
 
-.. image:: 202-vision-superresolution-image-with-output_files/202-vision-superresolution-image-with-output_23_1.png
+.. image:: vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_23_1.png
 
 
 Save Superresolution and Bicubic Image Crop
@@ -475,18 +496,22 @@ Save Superresolution and Bicubic Image Crop
     
     # Store the image and the results.
     crop_image_path = Path(f"{OUTPUT_PATH.stem}/{image_id}_{adjusted_upsample_factor}x_crop.png")
-    superres_image_path = Path(
-        f"{OUTPUT_PATH.stem}/{image_id}_{adjusted_upsample_factor}x_crop_superres.png"
-    )
-    bicubic_image_path = Path(
-        f"{OUTPUT_PATH.stem}/{image_id}_{adjusted_upsample_factor}x_crop_bicubic.png"
-    )
-    cv2.imwrite(filename=str(crop_image_path), img=image_crop, params=[cv2.IMWRITE_PNG_COMPRESSION, 0])
+    superres_image_path = Path(f"{OUTPUT_PATH.stem}/{image_id}_{adjusted_upsample_factor}x_crop_superres.png")
+    bicubic_image_path = Path(f"{OUTPUT_PATH.stem}/{image_id}_{adjusted_upsample_factor}x_crop_bicubic.png")
     cv2.imwrite(
-        filename=str(superres_image_path), img=image_super, params=[cv2.IMWRITE_PNG_COMPRESSION, 0]
+        filename=str(crop_image_path),
+        img=image_crop,
+        params=[cv2.IMWRITE_PNG_COMPRESSION, 0],
     )
     cv2.imwrite(
-        filename=str(bicubic_image_path), img=image_bicubic, params=[cv2.IMWRITE_PNG_COMPRESSION, 0]
+        filename=str(superres_image_path),
+        img=image_super,
+        params=[cv2.IMWRITE_PNG_COMPRESSION, 0],
+    )
+    cv2.imwrite(
+        filename=str(bicubic_image_path),
+        img=image_bicubic,
+        params=[cv2.IMWRITE_PNG_COMPRESSION, 0],
     )
     print(f"Images written to directory: {OUTPUT_PATH}")
 
@@ -531,7 +556,7 @@ Write Animated GIF with Bicubic/Superresolution Comparison
 
 
 
-.. image:: 202-vision-superresolution-image-with-output_files/202-vision-superresolution-image-with-output_27_1.png
+.. image:: vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_27_1.png
    :width: 960px
 
 
@@ -553,9 +578,7 @@ the ``Files`` tool.
 
     FOURCC = cv2.VideoWriter_fourcc(*"MJPG")
     
-    result_video_path = Path(
-        f"{OUTPUT_PATH.stem}/{image_id}_crop_comparison_{adjusted_upsample_factor}x.avi"
-    )
+    result_video_path = Path(f"{OUTPUT_PATH.stem}/{image_id}_crop_comparison_{adjusted_upsample_factor}x.avi")
     video_target_height, video_target_width = (
         result_image.shape[0] // 2,
         result_image.shape[1] // 2,
@@ -569,9 +592,7 @@ the ``Files`` tool.
     )
     
     resized_result_image = cv2.resize(src=result_image, dsize=(video_target_width, video_target_height))
-    resized_bicubic_image = cv2.resize(
-        src=bicubic_image, dsize=(video_target_width, video_target_height)
-    )
+    resized_bicubic_image = cv2.resize(src=bicubic_image, dsize=(video_target_width, video_target_height))
     
     progress_bar = ProgressBar(total=video_target_width)
     progress_bar.display()
@@ -648,16 +669,8 @@ Compute patches
     crop_height = y_coords[-1] + input_height * CROP_FACTOR
     
     # Compute the width and height of the target superresolution image.
-    new_width = (
-        x_coords[-1] * (upsample_factor // CROP_FACTOR)
-        + target_width
-        - CROPLINES * 2 * (upsample_factor // CROP_FACTOR)
-    )
-    new_height = (
-        y_coords[-1] * (upsample_factor // CROP_FACTOR)
-        + target_height
-        - CROPLINES * 2 * (upsample_factor // CROP_FACTOR)
-    )
+    new_width = x_coords[-1] * (upsample_factor // CROP_FACTOR) + target_width - CROPLINES * 2 * (upsample_factor // CROP_FACTOR)
+    new_height = y_coords[-1] * (upsample_factor // CROP_FACTOR) + target_height - CROPLINES * 2 * (upsample_factor // CROP_FACTOR)
     print(f"The output image will have a width of {new_width} " f"and a height of {new_height}")
 
 
@@ -766,11 +779,7 @@ as total time to process each patch.
                 clear_output(wait=True)
                 progress_bar.display()
                 display(
-                    Pretty(
-                        f"Processed patch {patch_nr}/{num_patches}. "
-                        f"Inference time: {inference_duration:.2f} seconds "
-                        f"({1/inference_duration:.2f} FPS)"
-                    )
+                    Pretty(f"Processed patch {patch_nr}/{num_patches}. " f"Inference time: {inference_duration:.2f} seconds " f"({1/inference_duration:.2f} FPS)")
                 )
     
     end_time = time.perf_counter()
@@ -786,8 +795,8 @@ as total time to process each patch.
 
 .. parsed-literal::
 
-    Processed 42 patches in 4.63 seconds. Total patches per second (including processing): 9.08.
-    Inference patches per second: 17.95 
+    Processed 42 patches in 4.57 seconds. Total patches per second (including processing): 9.19.
+    Inference patches per second: 18.33 
 
 
 Save superresolution image and the bicubic image
@@ -797,9 +806,7 @@ Save superresolution image and the bicubic image
 
 .. code:: ipython3
 
-    full_superresolution_image_path = Path(
-        f"{OUTPUT_PATH.stem}/full_superres_{adjusted_upsample_factor}x.jpg"
-    )
+    full_superresolution_image_path = Path(f"{OUTPUT_PATH.stem}/full_superres_{adjusted_upsample_factor}x.jpg")
     full_bicubic_image_path = Path(f"{OUTPUT_PATH.stem}/full_bicubic_{adjusted_upsample_factor}x.jpg")
     
     cv2.imwrite(str(full_superresolution_image_path), full_superresolution_image)
diff --git a/docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_15_1.png b/docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_15_1.png
new file mode 100644
index 00000000000..a9ec6addf5e
--- /dev/null
+++ b/docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_15_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3428dab756fda8428501e56a0539ad4d6580e7e10bf19c300375fcb31c09952
+size 272963
diff --git a/docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_17_1.png b/docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_17_1.png
new file mode 100644
index 00000000000..ff530904334
--- /dev/null
+++ b/docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_17_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a83f2639bb12fb675aff3be61cc0b86660fb0e758447a054723401b57c0a22c8
+size 356735
diff --git a/docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_23_1.png b/docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_23_1.png
new file mode 100644
index 00000000000..144fbf965a3
--- /dev/null
+++ b/docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_23_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd0a800cc2db5fbf13bc38b2194c2624218b83642138f7a24d76ec68b2f8ce21
+size 2896276
diff --git a/docs/notebooks/202-vision-superresolution-image-with-output_files/202-vision-superresolution-image-with-output_27_1.png b/docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_27_1.png
similarity index 100%
rename from docs/notebooks/202-vision-superresolution-image-with-output_files/202-vision-superresolution-image-with-output_27_1.png
rename to docs/notebooks/vision-superresolution-image-with-output_files/vision-superresolution-image-with-output_27_1.png
diff --git a/docs/notebooks/202-vision-superresolution-video-with-output.rst b/docs/notebooks/vision-superresolution-video-with-output.rst
similarity index 99%
rename from docs/notebooks/202-vision-superresolution-video-with-output.rst
rename to docs/notebooks/vision-superresolution-video-with-output.rst
index 27beed4cd7c..b3e2dcdec73 100644
--- a/docs/notebooks/202-vision-superresolution-video-with-output.rst
+++ b/docs/notebooks/vision-superresolution-video-with-output.rst
@@ -53,20 +53,35 @@ Install requirements
 .. code:: ipython3
 
     %pip install -q "openvino>=2023.1.0"
-    %pip install -q opencv-python
+    %pip install -q opencv-python tqdm
     %pip install -q "pytube>=12.1.0"
 
 
 .. parsed-literal::
 
-    Note: you may need to restart the kernel to use updated packages.
-
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
 
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
 
 
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. parsed-literal::
+
+    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    
+
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -101,9 +116,13 @@ Imports
     # Define a download file helper function
     def download_file(url: str, path: Path) -> None:
         """Download file."""
-        import urllib.request
+        import requests
+    
         path.parent.mkdir(parents=True, exist_ok=True)
-        urllib.request.urlretrieve(url, path)
+    
+        r = requests.get(url)
+        with path.open("wb") as f:
+            f.write(r.content)
 
 Settings
 ~~~~~~~~
@@ -124,8 +143,8 @@ select device from dropdown list for running inference using OpenVINO
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -143,25 +162,25 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     # 1032: 4x superresolution, 1033: 3x superresolution
-    model_name = 'single-image-super-resolution-1032'
+    model_name = "single-image-super-resolution-1032"
     
-    base_model_dir = Path('./model').expanduser()
+    base_model_dir = Path("./model").expanduser()
     
-    model_xml_name = f'{model_name}.xml'
-    model_bin_name = f'{model_name}.bin'
+    model_xml_name = f"{model_name}.xml"
+    model_bin_name = f"{model_name}.bin"
     
     model_xml_path = base_model_dir / model_xml_name
     model_bin_path = base_model_dir / model_bin_name
     
     if not model_xml_path.exists():
-        base_url = f'https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/{model_name}/FP16/'
+        base_url = f"https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/{model_name}/FP16/"
         model_xml_url = base_url + model_xml_name
         model_bin_url = base_url + model_bin_name
     
         download_file(model_xml_url, model_xml_path)
         download_file(model_bin_url, model_bin_path)
     else:
-        print(f'{model_name} already downloaded to {base_model_dir}')
+        print(f"{model_name} already downloaded to {base_model_dir}")
 
 
 .. parsed-literal::
@@ -227,11 +246,7 @@ resolution version of the image in 1920x1080.
     print(f"The network expects inputs with a width of {input_width}, " f"height of {input_height}")
     print(f"The network returns images with a width of {target_width}, " f"height of {target_height}")
     
-    print(
-        f"The image sides are upsampled by a factor of {upsample_factor}. "
-        f"The new image is {upsample_factor**2} times as large as the "
-        "original image"
-    )
+    print(f"The image sides are upsampled by a factor of {upsample_factor}. " f"The new image is {upsample_factor**2} times as large as the " "original image")
 
 
 .. parsed-literal::
@@ -323,10 +338,7 @@ Download and Prepare Video
     original_frame_height, original_frame_width = image.shape[:2]
     
     cap.release()
-    print(
-        f"The input video has a frame width of {original_frame_width}, "
-        f"frame height of {original_frame_height} and runs at {fps:.2f} fps"
-    )
+    print(f"The input video has a frame width of {original_frame_width}, " f"frame height of {original_frame_height} and runs at {fps:.2f} fps")
 
 
 .. parsed-literal::
@@ -412,7 +424,9 @@ video.
             # Resize and reshape the image to the target shape with bicubic
             # interpolation.
             bicubic_image = cv2.resize(
-                src=image, dsize=(target_width, target_height), interpolation=cv2.INTER_CUBIC
+                src=image,
+                dsize=(target_width, target_height),
+                interpolation=cv2.INTER_CUBIC,
             )
             input_image_bicubic = np.expand_dims(bicubic_image.transpose(2, 0, 1), axis=0)
     
@@ -446,13 +460,7 @@ video.
             if frame_nr % 10 == 0 or frame_nr == total_frames:
                 clear_output(wait=True)
                 progress_bar.display()
-                display(
-                    Pretty(
-                        f"Processed frame {frame_nr}. Inference time: "
-                        f"{inference_duration:.2f} seconds "
-                        f"({1/inference_duration:.2f} FPS)"
-                    )
-                )
+                display(Pretty(f"Processed frame {frame_nr}. Inference time: " f"{inference_duration:.2f} seconds " f"({1/inference_duration:.2f} FPS)"))
     
     
     except KeyboardInterrupt:
@@ -480,13 +488,13 @@ video.
 
 .. parsed-literal::
 
-    Processed frame 100. Inference time: 0.05 seconds (20.76 FPS)
+    Processed frame 100. Inference time: 0.06 seconds (17.97 FPS)
 
 
 .. parsed-literal::
 
     Video's saved to output directory.
-    Processed 100 frames in 242.92 seconds. Total FPS (including video processing): 0.41. Inference FPS: 18.04.
+    Processed 100 frames in 242.21 seconds. Total FPS (including video processing): 0.41. Inference FPS: 17.95.
 
 
 Show Side-by-Side Video of Bicubic and Superresolution Version
diff --git a/docs/notebooks/227-whisper-convert-with-output.rst b/docs/notebooks/whisper-convert-with-output.rst
similarity index 91%
rename from docs/notebooks/227-whisper-convert-with-output.rst
rename to docs/notebooks/whisper-convert-with-output.rst
index d3c6f442ad0..f06129b8269 100644
--- a/docs/notebooks/227-whisper-convert-with-output.rst
+++ b/docs/notebooks/whisper-convert-with-output.rst
@@ -57,7 +57,7 @@ Install dependencies.
     %pip install -q "openvino>=2023.1.0"
     %pip install -q "python-ffmpeg<=1.0.16" moviepy transformers --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "git+https://github.com/garywu007/pytube.git"
-    %pip install -q  gradio
+    %pip install -q  "gradio>=4.19"
     %pip install -q "openai-whisper==20231117" --extra-index-url https://download.pytorch.org/whl/cpu
 
 Instantiate model
@@ -90,14 +90,14 @@ Whisper family.
 
     from whisper import _MODELS
     import ipywidgets as widgets
-    
+
     model_id = widgets.Dropdown(
         options=list(_MODELS),
-        value='large-v2',
-        description='Model:',
+        value="large-v2",
+        description="Model:",
         disabled=False,
     )
-    
+
     model_id
 
 
@@ -112,7 +112,7 @@ Whisper family.
 .. code:: ipython3
 
     import whisper
-    
+
     model = whisper.load_model(model_id.value, "cpu")
     model.eval()
     pass
@@ -138,7 +138,7 @@ Convert Whisper Encoder to OpenVINO IR
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     WHISPER_ENCODER_OV = Path(f"whisper_{model_id.value}_encoder.xml")
     WHISPER_DECODER_OV = Path(f"whisper_{model_id.value}_decoder.xml")
 
@@ -146,8 +146,8 @@ Convert Whisper Encoder to OpenVINO IR
 
     import torch
     import openvino as ov
-    
-    mel = torch.zeros((1, 80 if 'v3' not in model_id.value else 128, 3000))
+
+    mel = torch.zeros((1, 80 if "v3" not in model_id.value else 128, 3000))
     audio_features = model.encoder(mel)
     if not WHISPER_ENCODER_OV.exists():
         encoder_model = ov.convert_model(model.encoder, example_input=mel)
@@ -167,14 +167,14 @@ modify this process for correct tracing.
     import torch
     from typing import Optional, Tuple
     from functools import partial
-    
-    
+
+
     def attention_forward(
-            attention_module,
-            x: torch.Tensor,
-            xa: Optional[torch.Tensor] = None,
-            mask: Optional[torch.Tensor] = None,
-            kv_cache: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        attention_module,
+        x: torch.Tensor,
+        xa: Optional[torch.Tensor] = None,
+        mask: Optional[torch.Tensor] = None,
+        kv_cache: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         """
         Override for forward method of decoder attention module with storing cache values explicitly.
@@ -190,7 +190,7 @@ modify this process for correct tracing.
           updated kv_cache
         """
         q = attention_module.query(x)
-    
+
         if xa is None:
             # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
             # otherwise, perform key/value projections for self- or cross-attention as usual.
@@ -205,11 +205,11 @@ modify this process for correct tracing.
             k = attention_module.key(xa)
             v = attention_module.value(xa)
             kv_cache_new = (None, None)
-    
+
         wv, qk = attention_module.qkv_attention(q, k, v, mask)
         return attention_module.out(wv), kv_cache_new
-    
-    
+
+
     def block_forward(
         residual_block,
         x: torch.Tensor,
@@ -228,59 +228,58 @@ modify this process for correct tracing.
           Returns:
             x: residual block output
             kv_cache: updated kv_cache
-    
+
         """
-        x0, kv_cache = residual_block.attn(residual_block.attn_ln(
-            x), mask=mask, kv_cache=kv_cache)
+        x0, kv_cache = residual_block.attn(residual_block.attn_ln(x), mask=mask, kv_cache=kv_cache)
         x = x + x0
         if residual_block.cross_attn:
-            x1, _ = residual_block.cross_attn(
-                residual_block.cross_attn_ln(x), xa)
+            x1, _ = residual_block.cross_attn(residual_block.cross_attn_ln(x), xa)
             x = x + x1
         x = x + residual_block.mlp(residual_block.mlp_ln(x))
         return x, kv_cache
-    
-    
-    
+
+
     # update forward functions
     for idx, block in enumerate(model.decoder.blocks):
         block.forward = partial(block_forward, block)
         block.attn.forward = partial(attention_forward, block.attn)
         if block.cross_attn:
             block.cross_attn.forward = partial(attention_forward, block.cross_attn)
-    
-    
-    def decoder_forward(decoder, x: torch.Tensor, xa: torch.Tensor, kv_cache: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor]]] = None):
+
+
+    def decoder_forward(
+        decoder,
+        x: torch.Tensor,
+        xa: torch.Tensor,
+        kv_cache: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor]]] = None,
+    ):
         """
         Override for decoder forward method.
         Parameters:
           x: torch.LongTensor, shape = (batch_size, <= n_ctx) the text tokens
           xa: torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
                the encoded audio features to be attended on
-          kv_cache: Dict[str, torch.Tensor], attention modules hidden states cache from previous steps 
+          kv_cache: Dict[str, torch.Tensor], attention modules hidden states cache from previous steps
         """
         if kv_cache is not None:
             offset = kv_cache[0][0].shape[1]
         else:
             offset = 0
             kv_cache = [None for _ in range(len(decoder.blocks))]
-        x = decoder.token_embedding(
-            x) + decoder.positional_embedding[offset: offset + x.shape[-1]]
+        x = decoder.token_embedding(x) + decoder.positional_embedding[offset : offset + x.shape[-1]]
         x = x.to(xa.dtype)
         kv_cache_upd = []
-    
+
         for block, kv_block_cache in zip(decoder.blocks, kv_cache):
             x, kv_block_cache_upd = block(x, xa, mask=decoder.mask, kv_cache=kv_block_cache)
             kv_cache_upd.append(tuple(kv_block_cache_upd))
-    
+
         x = decoder.ln(x)
-        logits = (
-            x @ torch.transpose(decoder.token_embedding.weight.to(x.dtype), 1, 0)).float()
-    
+        logits = (x @ torch.transpose(decoder.token_embedding.weight.to(x.dtype), 1, 0)).float()
+
         return logits, tuple(kv_cache_upd)
-    
-    
-    
+
+
     # override decoder forward
     model.decoder.forward = partial(decoder_forward, model.decoder)
 
@@ -288,9 +287,9 @@ modify this process for correct tracing.
 
     tokens = torch.ones((5, 3), dtype=torch.int64)
     logits, kv_cache = model.decoder(tokens, audio_features, kv_cache=None)
-    
+
     tokens = torch.ones((5, 1), dtype=torch.int64)
-    
+
     if not WHISPER_DECODER_OV.exists():
         decoder_model = ov.convert_model(model.decoder, example_input=(tokens, audio_features, kv_cache))
         ov.save_model(decoder_model, WHISPER_DECODER_OV)
@@ -320,7 +319,8 @@ To run the PyTorch Whisper model, we just need to call the
 original model pipeline for audio transcribing after replacing the
 original models with OpenVINO IR versions.
 
-### Select inference device 
+Select inference device
+~~~~~~~~~~~~~~~~~~~~~~~
 
 select device from dropdown list for running inference using OpenVINO
 
@@ -331,14 +331,14 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -352,10 +352,14 @@ select device from dropdown list for running inference using OpenVINO
 
 .. code:: ipython3
 
-    from utils import patch_whisper_for_ov_inference, OpenVINOAudioEncoder, OpenVINOTextDecoder
-    
+    from utils import (
+        patch_whisper_for_ov_inference,
+        OpenVINOAudioEncoder,
+        OpenVINOTextDecoder,
+    )
+
     patch_whisper_for_ov_inference(model)
-    
+
     model.encoder = OpenVINOAudioEncoder(core, WHISPER_ENCODER_OV, device=device.value)
     model.decoder = OpenVINOTextDecoder(core, WHISPER_DECODER_OV, device=device.value)
 
@@ -371,14 +375,15 @@ take some time.
 .. code:: ipython3
 
     import ipywidgets as widgets
+
     VIDEO_LINK = "https://youtu.be/kgL5LBM-hFI"
     link = widgets.Text(
         value=VIDEO_LINK,
         placeholder="Type link for video",
         description="Video:",
-        disabled=False
+        disabled=False,
     )
-    
+
     link
 
 
@@ -393,9 +398,9 @@ take some time.
 .. code:: ipython3
 
     from pytube import YouTube
-    
+
     print(f"Downloading video {link.value} started")
-    
+
     output_file = Path("downloaded_video.mp4")
     yt = YouTube(link.value)
     yt.streams.get_highest_resolution().download(filename=output_file)
@@ -411,7 +416,7 @@ take some time.
 .. code:: ipython3
 
     from utils import get_audio
-    
+
     audio, duration = get_audio(output_file)
 
 Select the task for the model:
@@ -427,7 +432,7 @@ Select the task for the model:
         options=["transcribe", "translate"],
         value="translate",
         description="Select task:",
-        disabled=False
+        disabled=False,
     )
     task
 
@@ -453,7 +458,7 @@ into video files using ``ffmpeg``.
 .. code:: ipython3
 
     from utils import prepare_srt
-    
+
     srt_lines = prepare_srt(transcription, filter_duration=duration)
     # save transcription
     with output_file.with_suffix(".srt").open("w") as f:
@@ -484,36 +489,36 @@ Now let us see the results.
     1
     00:00:00,000 --> 00:00:05,000
      What's that?
-    
+
     2
     00:00:05,000 --> 00:00:07,000
      Wow.
-    
+
     3
     00:00:07,000 --> 00:00:10,000
      Hello, humans.
-    
+
     4
     00:00:10,000 --> 00:00:15,000
      Focus on me.
-    
+
     5
     00:00:15,000 --> 00:00:16,000
      Focus on the guard.
-    
+
     6
     00:00:16,000 --> 00:00:20,000
      Don't tell anyone what you've seen in here.
-    
+
     7
     00:00:20,000 --> 00:00:24,000
      Have you seen what's in there?
-    
+
     8
     00:00:24,000 --> 00:00:30,000
      Intel. This is where it all changes.
-    
-    
+
+
 
 
 Interactive demo
@@ -524,8 +529,8 @@ Interactive demo
 .. code:: ipython3
 
     import gradio as gr
-    
-    
+
+
     def transcribe(url, task):
         output_file = Path("downloaded_video.mp4")
         yt = YouTube(url)
@@ -536,14 +541,17 @@ Interactive demo
         with output_file.with_suffix(".srt").open("w") as f:
             f.writelines(srt_lines)
         return [str(output_file), str(output_file.with_suffix(".srt"))]
-    
-    
+
+
     demo = gr.Interface(
         transcribe,
-        [gr.Textbox(label="YouTube URL"), gr.Radio(["Transcribe", "Translate"], value="Transcribe")],
+        [
+            gr.Textbox(label="YouTube URL"),
+            gr.Radio(["Transcribe", "Translate"], value="Transcribe"),
+        ],
         "video",
         examples=[["https://youtu.be/kgL5LBM-hFI", "Transcribe"]],
-        allow_flagging="never"
+        allow_flagging="never",
     )
     try:
         demo.launch(debug=False)
@@ -557,7 +565,7 @@ Interactive demo
 .. parsed-literal::
 
     Running on local URL:  http://127.0.0.1:7862
-    
+
     To create a public link, set `share=True` in `launch()`.
 
 
diff --git a/docs/notebooks/227-whisper-nncf-quantize-with-output.rst b/docs/notebooks/whisper-nncf-quantize-with-output.rst
similarity index 93%
rename from docs/notebooks/227-whisper-nncf-quantize-with-output.rst
rename to docs/notebooks/whisper-nncf-quantize-with-output.rst
index e98ef4ba779..78cd96c29c3 100644
--- a/docs/notebooks/227-whisper-nncf-quantize-with-output.rst
+++ b/docs/notebooks/whisper-nncf-quantize-with-output.rst
@@ -7,17 +7,17 @@ applying 8-bit post-training quantization from
 Compression Framework) and infer quantized model via OpenVINO™ Toolkit.
 The optimization process contains the following steps:
 
-1. Quantize the converted OpenVINO model from `227-whisper-convert
-   notebook <227-whisper-convert-with-output.html>`__ with NNCF.
+1. Quantize the converted OpenVINO model from `whisper-convert
+   notebook `__ with NNCF.
 2. Check model result for the demo video.
 3. Compare model size, performance and accuracy of FP32 and quantized
    INT8 models.
 
 ..
 
-   **NOTE**: you should run
-   `227-whisper-convert <227-whisper-convert-with-output.html>`__ notebook first to
-   generate OpenVINO IR model that is used for quantization.
+   **NOTE**: you should run `whisper-convert `__
+   notebook first to generate OpenVINO IR model that is used for
+   quantization.
 
 Table of contents:
 ^^^^^^^^^^^^^^^^^^
@@ -56,10 +56,12 @@ Select model for quantization
     from pathlib import Path
     import ipywidgets as widgets
 
+
     def get_model_id(model_path):
         return model_path.name.replace("whisper_", "").replace("encoder.xml", "").replace("_", "")
 
-    model_list = [get_model_id(model_path) for model_path in Path('.').glob("whisper_*encoder.xml")]
+
+    model_list = [get_model_id(model_path) for model_path in Path(".").glob("whisper_*encoder.xml")]
     model_list = [model_name for model_name in model_list if model_name]
 
     if not model_list:
@@ -68,7 +70,7 @@ Select model for quantization
     model_id = widgets.Dropdown(
         options=model_list,
         value=model_list[0],
-        description='Model:',
+        description="Model:",
         disabled=False,
     )
 
@@ -90,12 +92,13 @@ Select device from dropdown list for running inference using OpenVINO.
     import ipywidgets as widgets
 
     from openvino import Core
+
     core = Core()
 
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
 
@@ -123,7 +126,7 @@ Select the task for the model:
         options=["transcribe", "translate"],
         value="translate",
         description="Select task:",
-        disabled=False
+        disabled=False,
     )
     task
 
@@ -136,8 +139,9 @@ Select the task for the model:
 
 
 
-## Create and initialize quantization `⇑ <#0>`__ `back to top
-⬆️ <#table-of-contents>`__
+Create and initialize quantization
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 
 `NNCF `__ enables
 post-training quantization by adding the quantization layers into the
@@ -155,8 +159,8 @@ The optimization process contains the following steps:
    function.
 
 Set paths to the model converted in
-`227-whisper-convert <227-whisper-convert-with-output.html>`__ notebook and the
-paths where quantized models will be saved.
+`whisper-convert `__ notebook and the paths where
+quantized models will be saved.
 
 .. code:: ipython3
 
@@ -173,7 +177,11 @@ Load FP32 model IR.
 .. code:: ipython3
 
     import whisper
-    from utils import patch_whisper_for_ov_inference, OpenVINOAudioEncoder, OpenVINOTextDecoder
+    from utils import (
+        patch_whisper_for_ov_inference,
+        OpenVINOAudioEncoder,
+        OpenVINOTextDecoder,
+    )
 
     model_fp32 = whisper.load_model(model_id.value, "cpu").eval()
     patch_whisper_for_ov_inference(model_fp32)
@@ -204,6 +212,7 @@ calibration samples.
     encoder_calibration_data = []
     decoder_calibration_data = []
 
+
     @contextmanager
     def calibration_data_collection():
         global COLLECT_CALIBRATION_DATA
@@ -219,14 +228,16 @@ calibration samples.
             encoder_calibration_data.append(mel)
         return torch.from_numpy(self.compiled_model(mel)[self.output_blob])
 
+
     def decoder_forward(self, x: torch.Tensor, xa: torch.Tensor, kv_cache: Optional[dict] = None):
-        feed_dict = {'x': ov.Tensor(x.numpy()), 'xa': ov.Tensor(xa.numpy())}
-        feed_dict = (self.preprocess_kv_cache_inputs(feed_dict, kv_cache))
+        feed_dict = {"x": ov.Tensor(x.numpy()), "xa": ov.Tensor(xa.numpy())}
+        feed_dict = self.preprocess_kv_cache_inputs(feed_dict, kv_cache)
         if COLLECT_CALIBRATION_DATA:
             decoder_calibration_data.append(feed_dict)
         res = self.compiled_model(feed_dict)
         return self.postprocess_outputs(res)
 
+
     model_fp32.encoder.forward = partial(encoder_forward, model_fp32.encoder)
     model_fp32.decoder.forward = partial(decoder_forward, model_fp32.decoder)
 
@@ -244,7 +255,11 @@ dataset from Hugging Face as calibration data.
     calibration_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True).take(CALIBRATION_DATASET_SIZE)
 
     with calibration_data_collection():
-        for data_item in tqdm(calibration_dataset, desc="Collecting calibration data", total=CALIBRATION_DATASET_SIZE):
+        for data_item in tqdm(
+            calibration_dataset,
+            desc="Collecting calibration data",
+            total=CALIBRATION_DATASET_SIZE,
+        ):
             model_fp32.transcribe(data_item["audio"]["array"].astype("float32"), task=task.value)
 
 
@@ -274,8 +289,8 @@ and save the quantized IRs after that.
         subset_size=len(encoder_calibration_data),
         model_type=nncf.ModelType.TRANSFORMER,
         advanced_parameters=nncf.AdvancedQuantizationParameters(
-            smooth_quant_alpha=0.5      # Smooth Quant algorithm reduces activation quantization error; optimal alpha value was obtained through grid search
-        )
+            smooth_quant_alpha=0.5  # Smooth Quant algorithm reduces activation quantization error; optimal alpha value was obtained through grid search
+        ),
     )
     serialize(quantized_encoder, WHISPER_ENCODER_OV_INT8)
     print(f"Saved quantized encoder at ./{WHISPER_ENCODER_OV_INT8}")
@@ -287,8 +302,8 @@ and save the quantized IRs after that.
         subset_size=len(decoder_calibration_data),
         model_type=nncf.ModelType.TRANSFORMER,
         advanced_parameters=nncf.AdvancedQuantizationParameters(
-            smooth_quant_alpha=0.95     # Smooth Quant algorithm reduces activation quantization error; optimal alpha value was obtained through grid search
-        )
+            smooth_quant_alpha=0.95  # Smooth Quant algorithm reduces activation quantization error; optimal alpha value was obtained through grid search
+        ),
     )
     serialize(quantized_decoder, WHISPER_DECODER_OV_INT8)
     print(f"Saved quantized decoder at ./{WHISPER_DECODER_OV_INT8}")
@@ -361,7 +376,7 @@ Load ``INT8`` models saved above into a new instance of Whisper model.
     model_int8.decoder = OpenVINOTextDecoder(core, WHISPER_DECODER_OV_INT8, device=device.value)
 
 Select a video for transcription as in
-`227-whisper-convert <227-whisper-convert-with-output.html>`__ notebook.
+`whisper-convert `__ notebook.
 
 .. code:: ipython3
 
@@ -370,7 +385,7 @@ Select a video for transcription as in
         value=VIDEO_LINK,
         placeholder="Type link for video",
         description="Video:",
-        disabled=False
+        disabled=False,
     )
     link
 
@@ -502,6 +517,7 @@ Compare model file size.
         print(f"    * INT8 IR model size: {model_size_int8:.2f} KB")
         print(f"    * Model compression rate: {model_size_fp32 / model_size_int8:.3f}")
 
+
     calculate_compression_rate(WHISPER_ENCODER_OV, WHISPER_ENCODER_OV_INT8)
     calculate_compression_rate(WHISPER_DECODER_OV, WHISPER_DECODER_OV_INT8)
 
@@ -532,6 +548,7 @@ quantized models.
     import time
     import numpy as np
 
+
     def calculate_call_inference_time(model, dataset):
         inference_time = []
         for data_item in tqdm(dataset[:100], desc="Measuring performance"):
@@ -609,6 +626,7 @@ accuracy as ``(1 - WER)``.
     TEST_DATASET_SIZE = 100
     test_dataset = load_dataset("librispeech_asr", "clean", split="test", streaming=True).take(TEST_DATASET_SIZE)
 
+
     def calculate_transcription_time_and_accuracy(model, dataset):
         processor = WhisperProcessor.from_pretrained("openai/whisper-large")
 
@@ -633,6 +651,7 @@ accuracy as ``(1 - WER)``.
         mean_inference_time = np.mean(inference_time)
         return mean_inference_time, word_accuracy
 
+
     transcription_time_fp32, accuracy_fp32 = calculate_transcription_time_and_accuracy(model_fp32, test_dataset)
     transcription_time_int8, accuracy_int8 = calculate_transcription_time_and_accuracy(model_int8, test_dataset)
     print(f"Whisper transcription performance speedup: {transcription_time_fp32 / transcription_time_int8:.3f}")
diff --git a/docs/notebooks/265-wuerstchen-image-generation-with-output.rst b/docs/notebooks/wuerstchen-image-generation-with-output.rst
similarity index 95%
rename from docs/notebooks/265-wuerstchen-image-generation-with-output.rst
rename to docs/notebooks/wuerstchen-image-generation-with-output.rst
index aaecd5e3e18..f73a8de098f 100644
--- a/docs/notebooks/265-wuerstchen-image-generation-with-output.rst
+++ b/docs/notebooks/wuerstchen-image-generation-with-output.rst
@@ -1,7 +1,7 @@
 Image generation with Würstchen and OpenVINO
 ============================================
 
-.. image:: 265-wuerstchen-image-generation-with-output_files/499b779a-61d1-4e68-a1c3-437122622ba7.png
+.. image:: wuerstchen-image-generation-with-output_files/499b779a-61d1-4e68-a1c3-437122622ba7.png
 
 
 `Würstchen `__ is a diffusion model,
@@ -65,7 +65,7 @@ Prerequisites
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
 
-    %pip install -q  "diffusers>=0.21.0" transformers accelerate gradio "openvino>=2023.2.0" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q  "diffusers>=0.21.0" "torch>=2.1" transformers accelerate "gradio>=4.19" "openvino>=2023.2.0" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q datasets "nncf>=2.7.0"
 
 
@@ -150,7 +150,7 @@ Infer the original model
 
 
 
-.. image:: 265-wuerstchen-image-generation-with-output_files/265-wuerstchen-image-generation-with-output_11_0.png
+.. image:: wuerstchen-image-generation-with-output_files/wuerstchen-image-generation-with-output_11_0.png
 
 
 Convert the model to OpenVINO IR
@@ -247,7 +247,11 @@ timestep and encoder hidden states.
     convert(
         pipeline.prior_prior,
         PRIOR_PRIOR_PATH,
-        example_input=[torch.zeros(2, 16, 24, 24), torch.zeros(2), torch.zeros(2, 77, 1280)],
+        example_input=[
+            torch.zeros(2, 16, 24, 24),
+            torch.zeros(2),
+            torch.zeros(2, 77, 1280),
+        ],
         input=[((2, 16, 24, 24),), ((2),), ((2, 77, 1280),)],
     )
     del pipeline.prior_prior
@@ -383,8 +387,8 @@ Select device from dropdown list for running inference using OpenVINO.
 
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
 
@@ -438,9 +442,7 @@ that all of wrapper classes return ``torch.Tensor``\ s instead of
             self.text_encoder = text_encoder
 
         def __call__(self, input_ids, attention_mask):
-            output = self.text_encoder({"input_ids": input_ids, "attention_mask": attention_mask})[
-                "last_hidden_state"
-            ]
+            output = self.text_encoder({"input_ids": input_ids, "attention_mask": attention_mask})["last_hidden_state"]
             output = torch.tensor(output)
             return BaseModelOutputWithPooling(output)
 
@@ -525,7 +527,7 @@ Inference
 
 
 
-.. image:: 265-wuerstchen-image-generation-with-output_files/265-wuerstchen-image-generation-with-output_45_0.png
+.. image:: wuerstchen-image-generation-with-output_files/wuerstchen-image-generation-with-output_45_0.png
 
 
 Quantization
@@ -562,7 +564,7 @@ improve model inference speed.
 
     to_quantize = widgets.Checkbox(
         value=True,
-        description='Quantization',
+        description="Quantization",
         disabled=False,
     )
 
@@ -573,8 +575,13 @@ Let’s load ``skip magic`` extension to skip quantization if
 
 .. code:: ipython3
 
-    import sys
-    sys.path.append("../utils")
+    # Fetch `skip_kernel_extension` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
 
     int8_pipeline = None
 
@@ -807,7 +814,7 @@ pipelines.
 
 
 
-.. image:: 265-wuerstchen-image-generation-with-output_files/265-wuerstchen-image-generation-with-output_61_0.png
+.. image:: wuerstchen-image-generation-with-output_files/wuerstchen-image-generation-with-output_61_0.png
 
 
 Compare model file sizes
@@ -923,7 +930,7 @@ launch the interactive demo.
 
     use_quantized_model = widgets.Checkbox(
         value=quantized_model_present,
-        description='Use quantized model',
+        description="Use quantized model",
         disabled=not quantized_model_present,
     )
 
@@ -933,6 +940,7 @@ launch the interactive demo.
 
     pipe = int8_pipeline if use_quantized_model.value else pipeline
 
+
     def generate(caption, negative_prompt, prior_guidance_scale, seed):
         generator = torch.Generator().manual_seed(seed)
         image = pipe(
@@ -955,7 +963,7 @@ launch the interactive demo.
             gr.Textbox(label="Caption"),
             gr.Textbox(label="Negative prompt"),
             gr.Slider(2, 20, step=1, label="Prior guidance scale"),
-            gr.Slider(0, np.iinfo(np.int32).max, label="Seed")
+            gr.Slider(0, np.iinfo(np.int32).max, label="Seed"),
         ],
         "image",
         examples=[["Anthropomorphic cat dressed as a firefighter", "", 4, 0]],
diff --git a/docs/notebooks/265-wuerstchen-image-generation-with-output_files/499b779a-61d1-4e68-a1c3-437122622ba7.png b/docs/notebooks/wuerstchen-image-generation-with-output_files/499b779a-61d1-4e68-a1c3-437122622ba7.png
similarity index 100%
rename from docs/notebooks/265-wuerstchen-image-generation-with-output_files/499b779a-61d1-4e68-a1c3-437122622ba7.png
rename to docs/notebooks/wuerstchen-image-generation-with-output_files/499b779a-61d1-4e68-a1c3-437122622ba7.png
diff --git a/docs/notebooks/265-wuerstchen-image-generation-with-output_files/265-wuerstchen-image-generation-with-output_11_0.png b/docs/notebooks/wuerstchen-image-generation-with-output_files/wuerstchen-image-generation-with-output_11_0.png
similarity index 100%
rename from docs/notebooks/265-wuerstchen-image-generation-with-output_files/265-wuerstchen-image-generation-with-output_11_0.png
rename to docs/notebooks/wuerstchen-image-generation-with-output_files/wuerstchen-image-generation-with-output_11_0.png
diff --git a/docs/notebooks/265-wuerstchen-image-generation-with-output_files/265-wuerstchen-image-generation-with-output_45_0.png b/docs/notebooks/wuerstchen-image-generation-with-output_files/wuerstchen-image-generation-with-output_45_0.png
similarity index 100%
rename from docs/notebooks/265-wuerstchen-image-generation-with-output_files/265-wuerstchen-image-generation-with-output_45_0.png
rename to docs/notebooks/wuerstchen-image-generation-with-output_files/wuerstchen-image-generation-with-output_45_0.png
diff --git a/docs/notebooks/265-wuerstchen-image-generation-with-output_files/265-wuerstchen-image-generation-with-output_61_0.png b/docs/notebooks/wuerstchen-image-generation-with-output_files/wuerstchen-image-generation-with-output_61_0.png
similarity index 100%
rename from docs/notebooks/265-wuerstchen-image-generation-with-output_files/265-wuerstchen-image-generation-with-output_61_0.png
rename to docs/notebooks/wuerstchen-image-generation-with-output_files/wuerstchen-image-generation-with-output_61_0.png
diff --git a/docs/notebooks/226-yolov7-optimization-with-output.rst b/docs/notebooks/yolov7-optimization-with-output.rst
similarity index 69%
rename from docs/notebooks/226-yolov7-optimization-with-output.rst
rename to docs/notebooks/yolov7-optimization-with-output.rst
index a58ad437aa0..9b80d155a3c 100644
--- a/docs/notebooks/226-yolov7-optimization-with-output.rst
+++ b/docs/notebooks/yolov7-optimization-with-output.rst
@@ -97,7 +97,7 @@ Prerequisites
 
     import platform
 
-    %pip install -q "openvino>=2023.1.0" "nncf>=2.5.0" "opencv-python" "seaborn" "onnx"  "pandas" "scikit-learn" "torch" "torchvision" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2023.1.0" "nncf>=2.5.0" "opencv-python" "seaborn" "onnx" "Pillow" "pandas" "scikit-learn" "torch" "torchvision"  "PyYAML>=5.3.1" "tqdm" --extra-index-url https://download.pytorch.org/whl/cpu
 
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -110,14 +110,6 @@ Prerequisites
     DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
 
 
-.. parsed-literal::
-
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 4.25.3 which is incompatible.
-    tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.25.3 which is incompatible.
-    tf2onnx 1.16.1 requires protobuf~=3.20, but you have protobuf 4.25.3 which is incompatible.
-
-
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
@@ -135,15 +127,22 @@ Prerequisites
 
 .. code:: ipython3
 
-    import sys
-    from pathlib import Path
-    sys.path.append("../utils")
+    # Fetch `notebook_utils` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
 .. code:: ipython3
 
     # Clone YOLOv7 repo
-    if not Path('yolov7').exists():
+    from pathlib import Path
+
+    if not Path("yolov7").exists():
         !git clone https://github.com/WongKinYiu/yolov7
     %cd yolov7
 
@@ -161,10 +160,7 @@ Receiving objects:   1% (12/1197)
 Receiving objects:   2% (24/1197)
 Receiving objects:   3% (36/1197)
 Receiving objects:   4% (48/1197)
-
-.. parsed-literal::
-
-    Receiving objects:   5% (60/1197)
+Receiving objects:   5% (60/1197)
 Receiving objects:   6% (72/1197)
 Receiving objects:   7% (84/1197)
 Receiving objects:   8% (96/1197)
@@ -177,199 +173,206 @@ Receiving objects:  14% (168/1197)
 Receiving objects:  15% (180/1197)
 Receiving objects:  16% (192/1197)
 Receiving objects:  17% (204/1197)
-Receiving objects:  18% (216/1197)
+
+.. parsed-literal::
+
+    Receiving objects:  18% (216/1197)
 Receiving objects:  19% (228/1197)
 Receiving objects:  20% (240/1197)
 Receiving objects:  21% (252/1197)
 Receiving objects:  22% (264/1197)
-Receiving objects:  23% (276/1197)
-Receiving objects:  24% (288/1197)
-Receiving objects:  25% (300/1197)
 
 .. parsed-literal::
 
-    Receiving objects:  26% (312/1197)
+    Receiving objects:  23% (276/1197)
 
 .. parsed-literal::
 
-    Receiving objects:  27% (324/1197)
+    Receiving objects:  24% (288/1197)
 
 .. parsed-literal::
 
-    Receiving objects:  28% (336/1197)
+    Receiving objects:  25% (300/1197)
 
 .. parsed-literal::
 
-    Receiving objects:  28% (344/1197), 28.49 MiB | 28.49 MiB/s
+    Receiving objects:  25% (310/1197), 23.78 MiB | 22.88 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  29% (348/1197), 28.49 MiB | 28.49 MiB/s
+    Receiving objects:  26% (312/1197), 23.78 MiB | 22.88 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  30% (360/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  31% (372/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  32% (384/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  33% (396/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  34% (407/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  35% (419/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  36% (431/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  37% (443/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  38% (455/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  39% (467/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  40% (479/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  41% (491/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  42% (503/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  43% (515/1197), 28.49 MiB | 28.49 MiB/s
+    Receiving objects:  27% (324/1197), 34.91 MiB | 22.68 MiB/s
+Receiving objects:  28% (336/1197), 34.91 MiB | 22.68 MiB/s
+Receiving objects:  29% (348/1197), 34.91 MiB | 22.68 MiB/s
+Receiving objects:  30% (360/1197), 34.91 MiB | 22.68 MiB/s
+Receiving objects:  31% (372/1197), 34.91 MiB | 22.68 MiB/s
+Receiving objects:  32% (384/1197), 34.91 MiB | 22.68 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  44% (527/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  45% (539/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  46% (551/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  47% (563/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  48% (575/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  49% (587/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  50% (599/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  51% (611/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  52% (623/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  53% (635/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  54% (647/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  55% (659/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  56% (671/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  57% (683/1197), 28.49 MiB | 28.49 MiB/s
-Receiving objects:  58% (695/1197), 28.49 MiB | 28.49 MiB/s
+    Receiving objects:  32% (388/1197), 47.25 MiB | 23.17 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  59% (707/1197), 28.49 MiB | 28.49 MiB/s
+    Receiving objects:  33% (396/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  34% (407/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  35% (419/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  36% (431/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  37% (443/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  38% (455/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  39% (467/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  40% (479/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  41% (491/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  42% (503/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  43% (515/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  44% (527/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  45% (539/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  46% (551/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  47% (563/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  48% (575/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  49% (587/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  50% (599/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  51% (611/1197), 47.25 MiB | 23.17 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  60% (719/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  61% (731/1197), 44.43 MiB | 29.61 MiB/s
+    Receiving objects:  52% (623/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  53% (635/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  54% (647/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  55% (659/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  56% (671/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  57% (683/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  58% (695/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  59% (707/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  60% (719/1197), 47.25 MiB | 23.17 MiB/s
+Receiving objects:  61% (731/1197), 47.25 MiB | 23.17 MiB/s
 
 .. parsed-literal::
 
-    Receiving objects:  62% (743/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  63% (755/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  64% (767/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  65% (779/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  66% (791/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  67% (802/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  68% (814/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  69% (826/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  70% (838/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  71% (850/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  72% (862/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  73% (874/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  74% (886/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  75% (898/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  76% (910/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  77% (922/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  78% (934/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  79% (946/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  80% (958/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  81% (970/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  82% (982/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  83% (994/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  84% (1006/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  85% (1018/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  86% (1030/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  87% (1042/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  88% (1054/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  89% (1066/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  90% (1078/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  91% (1090/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  92% (1102/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  93% (1114/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  94% (1126/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  95% (1138/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  96% (1150/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  97% (1162/1197), 44.43 MiB | 29.61 MiB/s
-Receiving objects:  97% (1172/1197), 60.66 MiB | 30.33 MiB/s
+    Receiving objects:  62% (743/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  63% (755/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  64% (767/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  65% (779/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  66% (791/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  67% (802/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  68% (814/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  69% (826/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  70% (838/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  71% (850/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  72% (862/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  73% (874/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  74% (886/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  75% (898/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  76% (910/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  77% (922/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  78% (934/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  79% (946/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  80% (958/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  81% (970/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  82% (982/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  83% (994/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  84% (1006/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  85% (1018/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  86% (1030/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  87% (1042/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  88% (1054/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  89% (1066/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  90% (1078/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  91% (1090/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  92% (1102/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  93% (1114/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  94% (1126/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  95% (1138/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  96% (1150/1197), 59.09 MiB | 23.27 MiB/s
+Receiving objects:  97% (1162/1197), 59.09 MiB | 23.27 MiB/s
+
+.. parsed-literal::
+
+    Receiving objects:  97% (1172/1197), 71.33 MiB | 23.39 MiB/s
 
 .. parsed-literal::
 
     remote: Total 1197 (delta 0), reused 0 (delta 0), pack-reused 1197
-    Receiving objects:  98% (1174/1197), 60.66 MiB | 30.33 MiB/s
-Receiving objects:  99% (1186/1197), 60.66 MiB | 30.33 MiB/s
-Receiving objects: 100% (1197/1197), 60.66 MiB | 30.33 MiB/s
-Receiving objects: 100% (1197/1197), 74.23 MiB | 30.26 MiB/s, done.
-    Resolving deltas:   0% (0/519)
-Resolving deltas:   1% (8/519)
-Resolving deltas:   3% (16/519)
-Resolving deltas:   4% (21/519)
-Resolving deltas:   5% (29/519)
-Resolving deltas:   6% (32/519)
-Resolving deltas:   7% (37/519)
-Resolving deltas:   8% (45/519)
-Resolving deltas:   9% (50/519)
-Resolving deltas:  10% (56/519)
-Resolving deltas:  11% (62/519)
-Resolving deltas:  12% (64/519)
-Resolving deltas:  13% (69/519)
-Resolving deltas:  14% (75/519)
-Resolving deltas:  15% (78/519)
-Resolving deltas:  17% (89/519)
-Resolving deltas:  18% (94/519)
-Resolving deltas:  22% (115/519)
-Resolving deltas:  24% (125/519)
-Resolving deltas:  27% (141/519)
-Resolving deltas:  33% (172/519)
-Resolving deltas:  35% (182/519)
-Resolving deltas:  36% (189/519)
-
-.. parsed-literal::
-
-    Resolving deltas:  39% (203/519)
-Resolving deltas:  40% (212/519)
-Resolving deltas:  48% (253/519)
-Resolving deltas:  49% (255/519)
-Resolving deltas:  50% (260/519)
-Resolving deltas:  51% (268/519)
-Resolving deltas:  52% (271/519)
-Resolving deltas:  53% (279/519)
-Resolving deltas:  57% (301/519)
-Resolving deltas:  66% (346/519)
-Resolving deltas:  67% (350/519)
-Resolving deltas:  68% (354/519)
-Resolving deltas:  69% (361/519)
-Resolving deltas:  70% (364/519)
-Resolving deltas:  71% (369/519)
-Resolving deltas:  72% (374/519)
-Resolving deltas:  73% (381/519)
-Resolving deltas:  74% (385/519)
-Resolving deltas:  75% (394/519)
-Resolving deltas:  76% (396/519)
-Resolving deltas:  77% (400/519)
-Resolving deltas:  78% (406/519)
-Resolving deltas:  79% (411/519)
-Resolving deltas:  81% (422/519)
-Resolving deltas:  82% (427/519)
-Resolving deltas:  83% (431/519)
-Resolving deltas:  84% (438/519)
-Resolving deltas:  85% (446/519)
-Resolving deltas:  86% (447/519)
-Resolving deltas:  87% (454/519)
-Resolving deltas:  88% (461/519)
-Resolving deltas:  89% (464/519)
-Resolving deltas:  90% (468/519)
-Resolving deltas:  91% (476/519)
-Resolving deltas:  93% (487/519)
-Resolving deltas:  94% (488/519)
-Resolving deltas:  95% (497/519)
-Resolving deltas:  96% (499/519)
-Resolving deltas:  98% (511/519)
-Resolving deltas:  99% (517/519)
-Resolving deltas: 100% (519/519)
-Resolving deltas: 100% (519/519), done.
+    Receiving objects:  98% (1174/1197), 71.33 MiB | 23.39 MiB/s
+Receiving objects:  99% (1186/1197), 71.33 MiB | 23.39 MiB/s
+Receiving objects: 100% (1197/1197), 71.33 MiB | 23.39 MiB/s
+Receiving objects: 100% (1197/1197), 74.23 MiB | 23.41 MiB/s, done.
+    Resolving deltas:   0% (0/520)
+Resolving deltas:   2% (12/520)
+Resolving deltas:   4% (23/520)
+Resolving deltas:   5% (26/520)
+Resolving deltas:   6% (34/520)
+Resolving deltas:   7% (37/520)
+Resolving deltas:   8% (42/520)
+Resolving deltas:   9% (48/520)
+Resolving deltas:  10% (56/520)
+Resolving deltas:  11% (59/520)
+Resolving deltas:  12% (64/520)
+Resolving deltas:  13% (68/520)
+Resolving deltas:  14% (76/520)
+Resolving deltas:  15% (78/520)
+Resolving deltas:  16% (85/520)
+Resolving deltas:  17% (90/520)
+Resolving deltas:  18% (94/520)
+Resolving deltas:  19% (99/520)
+Resolving deltas:  20% (109/520)
+Resolving deltas:  21% (111/520)
+Resolving deltas:  22% (115/520)
+Resolving deltas:  23% (121/520)
+Resolving deltas:  24% (125/520)
+Resolving deltas:  25% (132/520)
+Resolving deltas:  26% (138/520)
+Resolving deltas:  29% (154/520)
+Resolving deltas:  31% (165/520)
+Resolving deltas:  32% (169/520)
+Resolving deltas:  36% (191/520)
+Resolving deltas:  37% (194/520)
+Resolving deltas:  38% (201/520)
+Resolving deltas:  44% (233/520)
+Resolving deltas:  45% (234/520)
+Resolving deltas:  46% (244/520)
+Resolving deltas:  47% (245/520)
+Resolving deltas:  48% (251/520)
+Resolving deltas:  50% (264/520)
+Resolving deltas:  51% (268/520)
+Resolving deltas:  52% (273/520)
+Resolving deltas:  60% (317/520)
+Resolving deltas:  61% (318/520)
+Resolving deltas:  62% (323/520)
+Resolving deltas:  63% (332/520)
+Resolving deltas:  64% (333/520)
+Resolving deltas:  65% (338/520)
+Resolving deltas:  70% (365/520)
+Resolving deltas:  71% (370/520)
+Resolving deltas:  72% (376/520)
+Resolving deltas:  73% (382/520)
+Resolving deltas:  77% (403/520)
+Resolving deltas:  81% (425/520)
+Resolving deltas:  82% (427/520)
+Resolving deltas:  83% (432/520)
+Resolving deltas:  84% (437/520)
+Resolving deltas:  86% (448/520)
+Resolving deltas:  88% (458/520)
+Resolving deltas:  89% (466/520)
+Resolving deltas:  90% (468/520)
+Resolving deltas:  91% (475/520)
+Resolving deltas:  92% (480/520)
+Resolving deltas:  93% (485/520)
+Resolving deltas:  94% (492/520)
+Resolving deltas:  95% (495/520)
+Resolving deltas:  97% (507/520)
+Resolving deltas:  98% (514/520)
+Resolving deltas:  99% (519/520)
+Resolving deltas: 100% (520/520)
+Resolving deltas: 100% (520/520), done.
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/226-yolov7-optimization/yolov7
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/yolov7-optimization/yolov7
 
 
 .. code:: ipython3
@@ -394,7 +397,7 @@ Resolving deltas: 100% (519/519), done.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/226-yolov7-optimization/yolov7/model/yolov7-tiny.pt')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/yolov7-optimization/yolov7/model/yolov7-tiny.pt')
 
 
 
@@ -414,7 +417,7 @@ result,
 .. parsed-literal::
 
     Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', no_trace=False, nosave=False, project='runs/detect', save_conf=False, save_txt=False, source='inference/images/horses.jpg', update=False, view_img=False, weights=['model/yolov7-tiny.pt'])
-    YOLOR 🚀 v0.1-128-ga207844 torch 1.13.1+cpu CPU
+    YOLOR 🚀 v0.1-128-ga207844 torch 2.2.2+cpu CPU
 
 
 
@@ -425,7 +428,7 @@ result,
 
 .. parsed-literal::
 
-    Model Summary: 200 layers, 6219709 parameters, 229245 gradients
+    Model Summary: 200 layers, 6219709 parameters, 229245 gradients, 13.7 GFLOPS
      Convert model to Traced-model...
 
 
@@ -438,21 +441,22 @@ result,
 
 .. parsed-literal::
 
-    5 horses, Done. (72.0ms) Inference, (0.8ms) NMS
+    5 horses, Done. (75.0ms) Inference, (0.9ms) NMS
      The image with the result is saved in: runs/detect/exp/horses.jpg
-    Done. (0.085s)
+    Done. (0.089s)
 
 
 .. code:: ipython3
 
     from PIL import Image
+
     # visualize prediction result
-    Image.open('runs/detect/exp/horses.jpg')
+    Image.open("runs/detect/exp/horses.jpg")
 
 
 
 
-.. image:: 226-yolov7-optimization-with-output_files/226-yolov7-optimization-with-output_10_0.png
+.. image:: yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.png
 
 
 
@@ -536,7 +540,7 @@ an end2end ONNX model, you can check this
 
     Import onnx_graphsurgeon failure: No module named 'onnx_graphsurgeon'
     Namespace(batch_size=1, conf_thres=0.25, device='cpu', dynamic=False, dynamic_batch=False, end2end=False, fp16=False, grid=True, img_size=[640, 640], include_nms=False, int8=False, iou_thres=0.45, max_wh=None, simplify=False, topk_all=100, weights='model/yolov7-tiny.pt')
-    YOLOR 🚀 v0.1-128-ga207844 torch 1.13.1+cpu CPU
+    YOLOR 🚀 v0.1-128-ga207844 torch 2.2.2+cpu CPU
 
 
 
@@ -547,13 +551,13 @@ an end2end ONNX model, you can check this
 
 .. parsed-literal::
 
-    Model Summary: 200 layers, 6219709 parameters, 6219709 gradients
+    Model Summary: 200 layers, 6219709 parameters, 6219709 gradients, 13.7 GFLOPS
 
 
 .. parsed-literal::
 
 
-    Starting TorchScript export with torch 1.13.1+cpu...
+    Starting TorchScript export with torch 2.2.2+cpu...
 
 
 .. parsed-literal::
@@ -561,30 +565,29 @@ an end2end ONNX model, you can check this
     TorchScript export success, saved as model/yolov7-tiny.torchscript.pt
     CoreML export failure: No module named 'coremltools'
 
-    Starting TorchScript-Lite export with torch 1.13.1+cpu...
+    Starting TorchScript-Lite export with torch 2.2.2+cpu...
 
 
 .. parsed-literal::
 
     TorchScript-Lite export success, saved as model/yolov7-tiny.torchscript.ptl
 
-    Starting ONNX export with onnx 1.15.0...
+    Starting ONNX export with onnx 1.16.0...
 
 
 .. parsed-literal::
 
     ONNX export success, saved as model/yolov7-tiny.onnx
 
-    Export complete (2.42s). Visualize with https://github.com/lutzroeder/netron.
+    Export complete (2.62s). Visualize with https://github.com/lutzroeder/netron.
 
 
-Convert ONNX Model to OpenVINO Intermediate Representation (IR)
----------------------------------------------------------------
-
-While ONNX models are directly
-supported by OpenVINO runtime, it can be useful to convert them to IR
-format to take the advantage of OpenVINO model conversion API features.
-The ``ov.convert_model`` python function of `model conversion
+If you have not installed all required dependencies## Convert ONNX Model
+to OpenVINO Intermediate Representation (IR)
+ While ONNX models are directly supported by
+OpenVINO runtime, it can be useful to convert them to IR format to take
+the advantage of OpenVINO model conversion API features. The
+``ov.convert_model`` python function of `model conversion
 API `__
 can be used for converting the model. The function returns instance of
 OpenVINO Model class, which is ready to use in Python interface.
@@ -595,9 +598,9 @@ However, it can also be save on device in OpenVINO IR format using
 
     import openvino as ov
 
-    model = ov.convert_model('model/yolov7-tiny.onnx')
+    model = ov.convert_model("model/yolov7-tiny.onnx")
     # serialize model for saving IR
-    ov.save_model(model, 'model/yolov7-tiny.xml')
+    ov.save_model(model, "model/yolov7-tiny.xml")
 
 Verify model inference
 ----------------------
@@ -676,15 +679,88 @@ To keep specific shape, preprocessing automatically enables padding.
 
 
     # label names for visualization
-    DEFAULT_NAMES = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
-                     'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
-                     'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
-                     'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
-                     'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
-                     'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
-                     'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
-                     'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
-                     'hair drier', 'toothbrush']
+    DEFAULT_NAMES = [
+        "person",
+        "bicycle",
+        "car",
+        "motorcycle",
+        "airplane",
+        "bus",
+        "train",
+        "truck",
+        "boat",
+        "traffic light",
+        "fire hydrant",
+        "stop sign",
+        "parking meter",
+        "bench",
+        "bird",
+        "cat",
+        "dog",
+        "horse",
+        "sheep",
+        "cow",
+        "elephant",
+        "bear",
+        "zebra",
+        "giraffe",
+        "backpack",
+        "umbrella",
+        "handbag",
+        "tie",
+        "suitcase",
+        "frisbee",
+        "skis",
+        "snowboard",
+        "sports ball",
+        "kite",
+        "baseball bat",
+        "baseball glove",
+        "skateboard",
+        "surfboard",
+        "tennis racket",
+        "bottle",
+        "wine glass",
+        "cup",
+        "fork",
+        "knife",
+        "spoon",
+        "bowl",
+        "banana",
+        "apple",
+        "sandwich",
+        "orange",
+        "broccoli",
+        "carrot",
+        "hot dog",
+        "pizza",
+        "donut",
+        "cake",
+        "chair",
+        "couch",
+        "potted plant",
+        "bed",
+        "dining table",
+        "toilet",
+        "tv",
+        "laptop",
+        "mouse",
+        "remote",
+        "keyboard",
+        "cell phone",
+        "microwave",
+        "oven",
+        "toaster",
+        "sink",
+        "refrigerator",
+        "book",
+        "clock",
+        "vase",
+        "scissors",
+        "teddy bear",
+        "hair drier",
+        "toothbrush",
+    ]
 
     # obtain class names from model checkpoint
     state_dict = torch.load("model/yolov7-tiny.pt", map_location="cpu")
@@ -696,8 +772,7 @@ To keep specific shape, preprocessing automatically enables padding.
     del state_dict
 
     # colors for visualization
-    COLORS = {name: [np.random.randint(0, 255) for _ in range(3)]
-              for i, name in enumerate(NAMES)}
+    COLORS = {name: [np.random.randint(0, 255) for _ in range(3)] for i, name in enumerate(NAMES)}
 
 Postprocessing
 ~~~~~~~~~~~~~~
@@ -728,7 +803,14 @@ algorithm and rescale boxes coordinates to original image size.
     from utils.general import scale_coords, non_max_suppression
 
 
-    def detect(model: ov.Model, image_path: Path, conf_thres: float = 0.25, iou_thres: float = 0.45, classes: List[int] = None, agnostic_nms: bool = False):
+    def detect(
+        model: ov.Model,
+        image_path: Path,
+        conf_thres: float = 0.25,
+        iou_thres: float = 0.45,
+        classes: List[int] = None,
+        agnostic_nms: bool = False,
+    ):
         """
         OpenVINO YOLOv7 model inference function. Reads image, preprocess it, runs model inference and postprocess results using NMS.
         Parameters:
@@ -752,7 +834,13 @@ algorithm and rescale boxes coordinates to original image size.
         return pred, orig_img, input_tensor.shape
 
 
-    def draw_boxes(predictions: np.ndarray, input_shape: Tuple[int], image: np.ndarray, names: List[str], colors: Dict[str, int]):
+    def draw_boxes(
+        predictions: np.ndarray,
+        input_shape: Tuple[int],
+        image: np.ndarray,
+        names: List[str],
+        colors: Dict[str, int],
+    ):
         """
         Utility function for drawing predicted bounding boxes on image
         Parameters:
@@ -770,7 +858,7 @@ algorithm and rescale boxes coordinates to original image size.
 
         # Write results
         for *xyxy, conf, cls in reversed(predictions):
-            label = f'{names[int(cls)]} {conf:.2f}'
+            label = f"{names[int(cls)]} {conf:.2f}"
             plot_one_box(xyxy, image, label=label, color=colors[names[int(cls)]], line_thickness=1)
         return image
 
@@ -778,7 +866,7 @@ algorithm and rescale boxes coordinates to original image size.
 
     core = ov.Core()
     # read converted model
-    model = core.read_model('model/yolov7-tiny.xml')
+    model = core.read_model("model/yolov7-tiny.xml")
 
 Select inference device
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -793,8 +881,8 @@ select device from dropdown list for running inference using OpenVINO
 
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
 
@@ -816,7 +904,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. code:: ipython3
 
-    boxes, image, input_shape = detect(compiled_model, 'inference/images/horses.jpg')
+    boxes, image, input_shape = detect(compiled_model, "inference/images/horses.jpg")
     image_with_boxes = draw_boxes(boxes[0], input_shape, image, NAMES, COLORS)
     # visualize results
     Image.fromarray(image_with_boxes)
@@ -824,7 +912,7 @@ select device from dropdown list for running inference using OpenVINO
 
 
 
-.. image:: 226-yolov7-optimization-with-output_files/226-yolov7-optimization-with-output_27_0.png
+.. image:: yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.png
 
 
 
@@ -848,22 +936,19 @@ the original model evaluation scripts.
 
     from zipfile import ZipFile
 
-    sys.path.append("../../utils")
-    from notebook_utils import download_file
-
     DATA_URL = "http://images.cocodataset.org/zips/val2017.zip"
     LABELS_URL = "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip"
 
-    OUT_DIR = Path('.')
+    OUT_DIR = Path(".")
 
     download_file(DATA_URL, directory=OUT_DIR, show_progress=True)
     download_file(LABELS_URL, directory=OUT_DIR, show_progress=True)
 
     if not (OUT_DIR / "coco/labels").exists():
-        with ZipFile('coco2017labels-segments.zip' , "r") as zip_ref:
+        with ZipFile("coco2017labels-segments.zip", "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
-        with ZipFile('val2017.zip' , "r") as zip_ref:
-            zip_ref.extractall(OUT_DIR / 'coco/images')
+        with ZipFile("val2017.zip", "r") as zip_ref:
+            zip_ref.extractall(OUT_DIR / "coco/images")
 
 
 
@@ -891,109 +976,117 @@ Create dataloader
     from utils.general import check_dataset, box_iou, xywh2xyxy, colorstr
 
     # read dataset config
-    DATA_CONFIG = 'data/coco.yaml'
+    DATA_CONFIG = "data/coco.yaml"
     with open(DATA_CONFIG) as f:
         data = yaml.load(f, Loader=yaml.SafeLoader)
 
     # Dataloader
-    TASK = 'val'  # path to train/val/test images
-    Option = namedtuple('Options', ['single_cls'])  # imitation of commandline provided options for single class evaluation
+    TASK = "val"  # path to train/val/test images
+    Option = namedtuple("Options", ["single_cls"])  # imitation of commandline provided options for single class evaluation
     opt = Option(False)
-    dataloader = create_dataloader(
-        data[TASK], 640, 1, 32, opt, pad=0.5,
-        prefix=colorstr(f'{TASK}: ')
-    )[0]
+    dataloader = create_dataloader(data[TASK], 640, 1, 32, opt, pad=0.5, prefix=colorstr(f"{TASK}: "))[0]
 
 
 .. parsed-literal::
 
 
-   Scanning images:   0%|          | 0/5000 [00:00
     [ INFO ]     KV_CACHE_PRECISION: 
     [ INFO ]     LOG_LEVEL: Level.NO
-    [ INFO ]     NETWORK_NAME: torch_jit
+    [ INFO ]     NETWORK_NAME: main_graph
     [ INFO ]     NUM_STREAMS: 6
     [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
     [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
@@ -1471,31 +1593,27 @@ models.
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
     [ INFO ] Fill input 'images' with random values
-
-
-.. parsed-literal::
-
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
 
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 46.06 ms
+    [ INFO ] First inference took 45.37 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            11574 iterations
-    [ INFO ] Duration:         120058.43 ms
+    [ INFO ] Count:            11616 iterations
+    [ INFO ] Duration:         120088.98 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        62.00 ms
-    [ INFO ]    Average:       62.10 ms
-    [ INFO ]    Min:           34.07 ms
-    [ INFO ]    Max:           83.34 ms
-    [ INFO ] Throughput:   96.40 FPS
+    [ INFO ]    Median:        61.78 ms
+    [ INFO ]    Average:       61.89 ms
+    [ INFO ]    Min:           52.70 ms
+    [ INFO ]    Max:           86.06 ms
+    [ INFO ] Throughput:   96.73 FPS
 
 
 .. code:: ipython3
@@ -1526,7 +1644,7 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] Read model took 22.43 ms
+    [ INFO ] Read model took 22.55 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     images (node: images) : f32 / [...] / [1,3,640,640]
@@ -1544,18 +1662,18 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 490.48 ms
+    [ INFO ] Compile model took 490.36 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: torch_jit
+    [ INFO ]   NETWORK_NAME: main_graph
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
+    [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
+    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
+    [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
 
 
 .. parsed-literal::
 
-    [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
-    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
-    [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
     [ INFO ]   CPU:
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
@@ -1569,7 +1687,7 @@ models.
     [ INFO ]     INFERENCE_PRECISION_HINT: 
     [ INFO ]     KV_CACHE_PRECISION: 
     [ INFO ]     LOG_LEVEL: Level.NO
-    [ INFO ]     NETWORK_NAME: torch_jit
+    [ INFO ]     NETWORK_NAME: main_graph
     [ INFO ]     NUM_STREAMS: 6
     [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
     [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
@@ -1587,19 +1705,19 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 24.27 ms
+    [ INFO ] First inference took 25.87 ms
 
 
 .. parsed-literal::
 
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            33084 iterations
-    [ INFO ] Duration:         120018.88 ms
+    [ INFO ] Count:            33138 iterations
+    [ INFO ] Duration:         120022.80 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        21.57 ms
-    [ INFO ]    Average:       21.65 ms
-    [ INFO ]    Min:           17.90 ms
-    [ INFO ]    Max:           43.37 ms
-    [ INFO ] Throughput:   275.66 FPS
+    [ INFO ]    Median:        21.54 ms
+    [ INFO ]    Average:       21.62 ms
+    [ INFO ]    Min:           16.58 ms
+    [ INFO ]    Max:           44.18 ms
+    [ INFO ] Throughput:   276.10 FPS
 
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.jpg b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.jpg
new file mode 100644
index 00000000000..20b27976437
--- /dev/null
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87ad41796d80a7476abe2468539278d48d57b2d00f6678e2d89f9f9f4c68d12c
+size 64027
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.png b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.png
new file mode 100644
index 00000000000..c91fba3e724
--- /dev/null
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9e7413af200393765221f9347b9b65c44e0ffa5ae151448253ab8ef76fdd825
+size 567624
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.jpg b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.jpg
new file mode 100644
index 00000000000..81522c0a30e
--- /dev/null
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19225f2fa8e9ec59a736b54a7d8ba8ab0b78585f7ba84b979e78ab170fe91dd9
+size 63300
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.png b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.png
new file mode 100644
index 00000000000..1540d858953
--- /dev/null
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62d04350cea388a70ba62e4a95ba864d857458c203c6e922c7fc0414ad616db7
+size 574231
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.jpg b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.jpg
new file mode 100644
index 00000000000..8ba3353f3a1
--- /dev/null
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c3c6eeb175f0d9538d5eca0c75083e3934c78c2d381cd7ad627d47883cccbf1
+size 63469
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.png b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.png
new file mode 100644
index 00000000000..2a7d874ea7b
--- /dev/null
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e2c8063e39f02a889050cf8c9df439e643d39ce8955a0ccfd7c3444f6f0d1c1
+size 574122
diff --git a/docs/notebooks/230-yolov8-instance-segmentation-with-output.rst b/docs/notebooks/yolov8-instance-segmentation-with-output.rst
similarity index 52%
rename from docs/notebooks/230-yolov8-instance-segmentation-with-output.rst
rename to docs/notebooks/yolov8-instance-segmentation-with-output.rst
index 05e8a0e4e81..9e9a7bc61ef 100644
--- a/docs/notebooks/230-yolov8-instance-segmentation-with-output.rst
+++ b/docs/notebooks/yolov8-instance-segmentation-with-output.rst
@@ -31,8 +31,6 @@ Table of contents:
 
    -  `Convert model to OpenVINO IR <#convert-model-to-openvino-ir>`__
    -  `Verify model inference <#verify-model-inference>`__
-   -  `Preprocessing <#preprocessing>`__
-   -  `Postprocessing <#postprocessing>`__
    -  `Select inference device <#select-inference-device>`__
    -  `Test on single image <#test-on-single-image>`__
 
@@ -95,8 +93,8 @@ Install necessary packages.
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0" "nncf>=2.5.0"
-    %pip install -q "torch>=2.1" "torchvision>=0.16" "ultralytics==8.0.43" onnx --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2024.0.0" "nncf>=2.9.0"
+    %pip install -q "torch>=2.1" "torchvision>=0.16" "ultralytics==8.1.42" onnx opencv-python tqdm --extra-index-url https://download.pytorch.org/whl/cpu
 
 Import required utility functions. The lower cell will download the
 ``notebook_utils`` Python module from GitHub.
@@ -105,95 +103,38 @@ Import required utility functions. The lower cell will download the
 
     from pathlib import Path
     
-    # Fetch the notebook utils script from the openvino_notebooks repo
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file, VideoPlayer
 
-Define utility functions for drawing results
-
-.. code:: ipython3
-
-    from typing import Tuple, Dict
-    import cv2
-    import numpy as np
-    from PIL import Image
-    from ultralytics.yolo.utils.plotting import colors
-    
-    
-    def plot_one_box(box:np.ndarray, img:np.ndarray, color:Tuple[int, int, int] = None, mask:np.ndarray = None, label:str = None, line_thickness:int = 5):
-        """
-        Helper function for drawing single bounding box on image
-        Parameters:
-            x (np.ndarray): bounding box coordinates in format [x1, y1, x2, y2]
-            img (no.ndarray): input image
-            color (Tuple[int, int, int], *optional*, None): color in BGR format for drawing box, if not specified will be selected randomly
-            mask (np.ndarray, *optional*, None): instance segmentation mask polygon in format [N, 2], where N - number of points in contour, if not provided, only box will be drawn
-            label (str, *optonal*, None): box label string, if not provided will not be provided as drowing result
-            line_thickness (int, *optional*, 5): thickness for box drawing lines
-        """
-        # Plots one bounding box on image img
-        tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
-        color = color or [random.randint(0, 255) for _ in range(3)]
-        c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
-        cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
-        if label:
-            tf = max(tl - 1, 1)  # font thickness
-            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
-            c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
-            cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
-            cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
-        if mask is not None:
-            image_with_mask = img.copy()
-            mask
-            cv2.fillPoly(image_with_mask, pts=[mask.astype(int)], color=color)
-            img = cv2.addWeighted(img, 0.5, image_with_mask, 0.5, 1)
-        return img
-    
-    
-    def draw_results(results:Dict, source_image:np.ndarray, label_map:Dict):
-        """
-        Helper function for drawing bounding boxes on image
-        Parameters:
-            image_res (np.ndarray): detection predictions in format [x1, y1, x2, y2, score, label_id]
-            source_image (np.ndarray): input image for drawing
-            label_map; (Dict[int, str]): label_id to class name mapping
-        Returns:
-            
-        """
-        boxes = results["det"]
-        masks = results.get("segment")
-        for idx, (*xyxy, conf, lbl) in enumerate(boxes):
-            label = f'{label_map[int(lbl)]} {conf:.2f}'
-            mask = masks[idx] if masks is not None else None
-            source_image = plot_one_box(xyxy, source_image, mask=mask, label=label, color=colors(int(lbl)), line_thickness=1)
-        return source_image
-
 .. code:: ipython3
 
     # Download a test sample
-    IMAGE_PATH = Path('./data/coco_bike.jpg')
+    IMAGE_PATH = Path("./data/coco_bike.jpg")
     download_file(
-        url='https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bike.jpg',
+        url="https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bike.jpg",
         filename=IMAGE_PATH.name,
-        directory=IMAGE_PATH.parent
-    ) 
+        directory=IMAGE_PATH.parent,
+    )
+
 
 
 .. parsed-literal::
 
-    'data/coco_bike.jpg' already exists.
+    data/coco_bike.jpg:   0%|          | 0.00/182k [00:00 1:
-            masks = result[model.output(1)]
-        input_hw = input_tensor.shape[2:]
-        detections = postprocess(pred_boxes=boxes, input_hw=input_hw, orig_img=image, pred_masks=masks)
-        return detections
+    def infer(*args):
+        result = seg_compiled_model(args)
+        return torch.from_numpy(result[0]), torch.from_numpy(result[1])
     
-    input_image = np.array(Image.open(IMAGE_PATH))
-    detections = detect(input_image, seg_compiled_model)[0]
-    image_with_masks = draw_results(detections, input_image, label_map)
     
-    Image.fromarray(image_with_masks)
+    seg_model.predictor.inference = infer
+    seg_model.predictor.model.pt = False
+
+.. code:: ipython3
+
+    res = seg_model(IMAGE_PATH)
+    Image.fromarray(res[0].plot()[:, :, ::-1])
+
+
+.. parsed-literal::
+
+    
+    image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 640x640 1 bicycle, 2 cars, 1 dog, 27.6ms
+    Speed: 3.5ms preprocess, 27.6ms inference, 4.5ms postprocess per image at shape (1, 3, 640, 640)
 
 
 
 
-.. image:: 230-yolov8-instance-segmentation-with-output_files/230-yolov8-instance-segmentation-with-output_22_0.png
+.. image:: yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_18_1.png
 
 
 
@@ -610,11 +351,14 @@ evaluation function.
 
     from zipfile import ZipFile
     
+    from ultralytics.data.utils import DATASETS_DIR
+    
+    
     DATA_URL = "http://images.cocodataset.org/zips/val2017.zip"
     LABELS_URL = "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip"
-    CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/8ebe94d1e928687feaa1fee6d5668987df5e43be/ultralytics/datasets/coco.yaml"  # last compatible format with ultralytics 8.0.43
+    CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/v8.1.0/ultralytics/cfg/datasets/coco.yaml"
     
-    OUT_DIR = Path('./datasets')
+    OUT_DIR = DATASETS_DIR
     
     DATA_PATH = OUT_DIR / "val2017.zip"
     LABELS_PATH = OUT_DIR / "coco2017labels-segments.zip"
@@ -625,23 +369,10 @@ evaluation function.
     download_file(CFG_URL, CFG_PATH.name, CFG_PATH.parent)
     
     if not (OUT_DIR / "coco/labels").exists():
-        with ZipFile(LABELS_PATH , "r") as zip_ref:
+        with ZipFile(LABELS_PATH, "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
-        with ZipFile(DATA_PATH , "r") as zip_ref:
-            zip_ref.extractall(OUT_DIR / 'coco/images')
-
-
-.. parsed-literal::
-
-    '/home/ea/work/openvino_notebooks/notebooks/230-yolov8-optimization/datasets/val2017.zip' already exists.
-    '/home/ea/work/openvino_notebooks/notebooks/230-yolov8-optimization/datasets/coco2017labels-segments.zip' already exists.
-
-
-
-.. parsed-literal::
-
-    /home/ea/work/openvino_notebooks/notebooks/230-yolov8-optimization/datasets/coco.yaml:   0%|          | 0.00/1…
-
+        with ZipFile(DATA_PATH, "r") as zip_ref:
+            zip_ref.extractall(OUT_DIR / "coco/images")
 
 Define validation function
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -650,11 +381,18 @@ Define validation function
 
 .. code:: ipython3
 
+    import numpy as np
     from tqdm.notebook import tqdm
-    from ultralytics.yolo.utils.metrics import ConfusionMatrix
+    from ultralytics.utils.metrics import ConfusionMatrix
     
     
-    def test(model:ov.Model, core:ov.Core, data_loader:torch.utils.data.DataLoader, validator, num_samples:int = None):
+    def test(
+        model: ov.Model,
+        core: ov.Core,
+        data_loader: torch.utils.data.DataLoader,
+        validator,
+        num_samples: int = None,
+    ):
         """
         OpenVINO YOLOv8 model accuracy validation function. Runs model validation on dataset and returns metrics
         Parameters:
@@ -667,7 +405,7 @@ Define validation function
         """
         validator.seen = 0
         validator.jdict = []
-        validator.stats = []
+        validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[])
         validator.batch_i = 1
         validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
         model.reshape({0: [1, 3, -1, -1]})
@@ -681,14 +419,17 @@ Define validation function
             if num_outputs == 1:
                 preds = torch.from_numpy(results[compiled_model.output(0)])
             else:
-                preds = [torch.from_numpy(results[compiled_model.output(0)]), torch.from_numpy(results[compiled_model.output(1)])]
+                preds = [
+                    torch.from_numpy(results[compiled_model.output(0)]),
+                    torch.from_numpy(results[compiled_model.output(1)]),
+                ]
             preds = validator.postprocess(preds)
             validator.update_metrics(preds, batch)
         stats = validator.get_stats()
         return stats
     
     
-    def print_stats(stats:np.ndarray, total_images:int, total_objects:int):
+    def print_stats(stats: np.ndarray, total_images: int, total_objects: int):
         """
         Helper function for printing accuracy statistic
         Parameters:
@@ -699,19 +440,47 @@ Define validation function
             None
         """
         print("Boxes:")
-        mp, mr, map50, mean_ap = stats['metrics/precision(B)'], stats['metrics/recall(B)'], stats['metrics/mAP50(B)'], stats['metrics/mAP50-95(B)']
+        mp, mr, map50, mean_ap = (
+            stats["metrics/precision(B)"],
+            stats["metrics/recall(B)"],
+            stats["metrics/mAP50(B)"],
+            stats["metrics/mAP50-95(B)"],
+        )
         # Print results
-        s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'Precision', 'Recall', 'mAP@.5', 'mAP@.5:.95')
+        print("    Best mean average:")
+        s = ("%20s" + "%12s" * 6) % (
+            "Class",
+            "Images",
+            "Labels",
+            "Precision",
+            "Recall",
+            "mAP@.5",
+            "mAP@.5:.95",
+        )
         print(s)
-        pf = '%20s' + '%12i' * 2 + '%12.3g' * 4  # print format
-        print(pf % ('all', total_images, total_objects, mp, mr, map50, mean_ap))
-        if 'metrics/precision(M)' in stats:
-            s_mp, s_mr, s_map50, s_mean_ap = stats['metrics/precision(M)'], stats['metrics/recall(M)'], stats['metrics/mAP50(M)'], stats['metrics/mAP50-95(M)']
+        pf = "%20s" + "%12i" * 2 + "%12.3g" * 4  # print format
+        print(pf % ("all", total_images, total_objects, mp, mr, map50, mean_ap))
+        if "metrics/precision(M)" in stats:
+            s_mp, s_mr, s_map50, s_mean_ap = (
+                stats["metrics/precision(M)"],
+                stats["metrics/recall(M)"],
+                stats["metrics/mAP50(M)"],
+                stats["metrics/mAP50-95(M)"],
+            )
             # Print results
-            s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'Precision', 'Recall', 'mAP@.5', 'mAP@.5:.95')
+            print("    Macro average mean:")
+            s = ("%20s" + "%12s" * 6) % (
+                "Class",
+                "Images",
+                "Labels",
+                "Precision",
+                "Recall",
+                "mAP@.5",
+                "mAP@.5:.95",
+            )
             print(s)
-            pf = '%20s' + '%12i' * 2 + '%12.3g' * 4  # print format
-            print(pf % ('all', total_images, total_objects, s_mp, s_mr, s_map50, s_mean_ap))
+            pf = "%20s" + "%12i" * 2 + "%12.3g" * 4  # print format
+            print(pf % ("all", total_images, total_objects, s_mp, s_mr, s_map50, s_mean_ap))
 
 Configure Validator helper and create DataLoader
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -730,21 +499,24 @@ validator class instance.
 
 .. code:: ipython3
 
-    from ultralytics.yolo.utils import DEFAULT_CFG
-    from ultralytics.yolo.cfg import get_cfg
-    from ultralytics.yolo.data.utils import check_det_dataset
+    from ultralytics.utils import DEFAULT_CFG
+    from ultralytics.cfg import get_cfg
+    from ultralytics.data.converter import coco80_to_coco91_class
+    from ultralytics.data.utils import check_det_dataset
+    from ultralytics.utils import ops
     
     args = get_cfg(cfg=DEFAULT_CFG)
     args.data = str(CFG_PATH)
 
 .. code:: ipython3
 
-    seg_validator = seg_model.ValidatorClass(args=args)
+    seg_validator = seg_model.task_map[seg_model.task]["validator"](args=args)
     seg_validator.data = check_det_dataset(args.data)
-    seg_data_loader = seg_validator.get_dataloader("datasets/coco/", 1)
+    seg_validator.stride = 32
+    seg_data_loader = seg_validator.get_dataloader(OUT_DIR / "coco/", 1)
     
     seg_validator.is_coco = True
-    seg_validator.class_map = ops.coco80_to_coco91_class()
+    seg_validator.class_map = coco80_to_coco91_class()
     seg_validator.names = seg_model.model.names
     seg_validator.metrics.names = seg_validator.names
     seg_validator.nc = seg_model.model.model[-1].nc
@@ -755,7 +527,7 @@ validator class instance.
 
 .. parsed-literal::
 
-    val: Scanning datasets/coco/labels/val2017.cache... 4952 images, 48 backgrounds, 0 corrupt: 100%|██████████| 5000/5000 [00:00
+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. parsed-literal::
+
+    /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/venv/lib/python3.10/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+      return Tensor(self.data * unwrap_tensor_data(other))
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
 
 
 .. code:: ipython3
 
-    from openvino.runtime import serialize
+    %%skip not $to_quantize.value
     
-    int8_model_seg_path = models_dir / f'{SEG_MODEL_NAME}_openvino_int8_model/{SEG_MODEL_NAME}.xml'
     print(f"Quantized segmentation model will be saved to {int8_model_seg_path}")
-    serialize(quantized_seg_model, str(int8_model_seg_path))
+    ov.save_model(quantized_seg_model, str(int8_model_seg_path))
 
 
 .. parsed-literal::
@@ -949,33 +893,50 @@ on the image.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     device
 
+.. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
+    ov_config = {}
+    if device.value != "CPU":
+        quantized_seg_model.reshape({0: [1, 3, 640, 640]})
+    if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
+        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
+    
+    quantized_seg_compiled_model = core.compile_model(quantized_seg_model, device.value, ov_config)
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    
+    def infer(*args):
+        result = quantized_seg_compiled_model(args)
+        return torch.from_numpy(result[0]), torch.from_numpy(result[1])
+    
+    seg_model.predictor.inference = infer
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    res = seg_model(IMAGE_PATH)
+    display(Image.fromarray(res[0].plot()[:, :, ::-1]))
 
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-    if device.value != "CPU":
-        quantized_seg_model.reshape({0: [1, 3, 640, 640]})
-    quantized_seg_compiled_model = core.compile_model(quantized_seg_model, device.value, ov_config)
-    input_image = np.array(Image.open(IMAGE_PATH))
-    detections = detect(input_image, quantized_seg_compiled_model)[0]
-    image_with_masks = draw_results(detections, input_image, label_map)
     
-    Image.fromarray(image_with_masks)
+    image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 640x640 1 bicycle, 2 cars, 2 dogs, 26.8ms
+    Speed: 2.8ms preprocess, 26.8ms inference, 3.4ms postprocess per image at shape (1, 3, 640, 640)
 
 
 
-
-.. image:: 230-yolov8-instance-segmentation-with-output_files/230-yolov8-instance-segmentation-with-output_44_0.png
-
+.. image:: yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_46_1.png
 
 
 Compare the Original and Quantized Models
@@ -986,7 +947,7 @@ Compare the Original and Quantized Models
 Compare performance of the Original and Quantized Models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Finally, use the OpenVINO
+ Finally, use the OpenVINO
 `Benchmark
 Tool `__
 to measure the inference performance of the ``FP32`` and ``INT8``
@@ -1003,20 +964,14 @@ models.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     device
 
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
 .. code:: ipython3
 
-    !benchmark_app -m $seg_model_path -d $device.value -api async -shape "[1,3,640,640]" -t 15
+    if int8_model_seg_path.exists():
+        !benchmark_app -m $seg_model_path -d $device.value -api async -shape "[1,3,640,640]" -t 15
 
 
 .. parsed-literal::
@@ -1025,39 +980,39 @@ models.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 20.21 ms
+    [ INFO ] Read model took 15.84 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : f32 / [...] / [?,3,?,?]
+    [ INFO ]     x (node: x) : f32 / [...] / [?,3,?,?]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [?,116,?]
-    [ INFO ]     output1 (node: output1) : f32 / [...] / [?,32,8..,8..]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_8) : f32 / [...] / [?,116,16..]
+    [ INFO ]     input.199 (node: __module.model.22.cv4.2.1.act/aten::silu_/Swish_37) : f32 / [...] / [?,32,8..,8..]
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: 'images': [1,3,640,640]
-    [ INFO ] Reshape model took 13.52 ms
+    [ INFO ] Reshaping model: 'x': [1,3,640,640]
+    [ INFO ] Reshape model took 9.23 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : u8 / [N,C,H,W] / [1,3,640,640]
+    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,116,8400]
-    [ INFO ]     output1 (node: output1) : f32 / [...] / [1,32,160,160]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_8) : f32 / [...] / [1,116,8400]
+    [ INFO ]     input.199 (node: __module.model.22.cv4.2.1.act/aten::silu_/Swish_37) : f32 / [...] / [1,32,160,160]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 457.49 ms
+    [ INFO ] Compile model took 304.42 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: torch_jit
+    [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
     [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
@@ -1066,42 +1021,46 @@ models.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
     [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
     [ INFO ]     INFERENCE_NUM_THREADS: 36
     [ INFO ]     INFERENCE_PRECISION_HINT: 
-    [ INFO ]     NETWORK_NAME: torch_jit
+    [ INFO ]     KV_CACHE_PRECISION: 
+    [ INFO ]     LOG_LEVEL: Level.NO
+    [ INFO ]     NETWORK_NAME: Model0
     [ INFO ]     NUM_STREAMS: 12
     [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
-    [ INFO ]     PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
+    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
     [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
-    [ INFO ]     PERF_COUNT: False
+    [ INFO ]     PERF_COUNT: NO
     [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
     [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
+    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
+    [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 42.16 ms
+    [ INFO ] First inference took 50.67 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            1860 iterations
-    [ INFO ] Duration:         15069.12 ms
+    [ INFO ] Count:            2124 iterations
+    [ INFO ] Duration:         15076.69 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        92.86 ms
-    [ INFO ]    Average:       96.95 ms
-    [ INFO ]    Min:           53.68 ms
-    [ INFO ]    Max:           181.23 ms
-    [ INFO ] Throughput:   123.43 FPS
+    [ INFO ]    Median:        84.69 ms
+    [ INFO ]    Average:       84.95 ms
+    [ INFO ]    Min:           43.23 ms
+    [ INFO ]    Max:           184.81 ms
+    [ INFO ] Throughput:   140.88 FPS
 
 
 .. code:: ipython3
 
-    !benchmark_app -m $int8_model_seg_path -d $device.value -api async -shape "[1,3,640,640]" -t 15
+    if int8_model_seg_path.exists():
+        !benchmark_app -m $int8_model_seg_path -d $device.value -api async -shape "[1,3,640,640]" -t 15
 
 
 .. parsed-literal::
@@ -1110,39 +1069,39 @@ models.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 31.10 ms
+    [ INFO ] Read model took 24.33 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : f32 / [...] / [1,3,?,?]
+    [ INFO ]     x (node: x) : f32 / [...] / [1,3,?,?]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,116,21..]
-    [ INFO ]     output1 (node: output1) : f32 / [...] / [1,32,8..,8..]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_8) : f32 / [...] / [1,116,21..]
+    [ INFO ]     input.199 (node: __module.model.22.cv4.2.1.act/aten::silu_/Swish_37) : f32 / [...] / [1,32,8..,8..]
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: 'images': [1,3,640,640]
-    [ INFO ] Reshape model took 17.80 ms
+    [ INFO ] Reshaping model: 'x': [1,3,640,640]
+    [ INFO ] Reshape model took 13.01 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : u8 / [N,C,H,W] / [1,3,640,640]
+    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,116,8400]
-    [ INFO ]     output1 (node: output1) : f32 / [...] / [1,32,160,160]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_8) : f32 / [...] / [1,116,8400]
+    [ INFO ]     input.199 (node: __module.model.22.cv4.2.1.act/aten::silu_/Swish_37) : f32 / [...] / [1,32,160,160]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 679.71 ms
+    [ INFO ] Compile model took 574.36 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: torch_jit
+    [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
     [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
@@ -1151,37 +1110,40 @@ models.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
     [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
     [ INFO ]     INFERENCE_NUM_THREADS: 36
     [ INFO ]     INFERENCE_PRECISION_HINT: 
-    [ INFO ]     NETWORK_NAME: torch_jit
+    [ INFO ]     KV_CACHE_PRECISION: 
+    [ INFO ]     LOG_LEVEL: Level.NO
+    [ INFO ]     NETWORK_NAME: Model0
     [ INFO ]     NUM_STREAMS: 12
     [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
-    [ INFO ]     PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
+    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
     [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
-    [ INFO ]     PERF_COUNT: False
+    [ INFO ]     PERF_COUNT: NO
     [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
     [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
+    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
+    [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 24.87 ms
+    [ INFO ] First inference took 41.26 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            4416 iterations
-    [ INFO ] Duration:         15063.93 ms
+    [ INFO ] Count:            3048 iterations
+    [ INFO ] Duration:         15096.50 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        38.93 ms
-    [ INFO ]    Average:       40.76 ms
-    [ INFO ]    Min:           24.40 ms
-    [ INFO ]    Max:           83.87 ms
-    [ INFO ] Throughput:   293.15 FPS
+    [ INFO ]    Median:        58.82 ms
+    [ INFO ]    Average:       59.20 ms
+    [ INFO ]    Min:           33.17 ms
+    [ INFO ]    Max:           120.39 ms
+    [ INFO ] Throughput:   201.90 FPS
 
 
 Validate quantized model accuracy
@@ -1196,6 +1158,8 @@ accuracy on a dataset.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     int8_seg_stats = test(quantized_seg_model, core, seg_data_loader, seg_validator, num_samples=NUM_TEST_SAMPLES)
 
 
@@ -1207,6 +1171,8 @@ accuracy on a dataset.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     print("FP32 model accuracy")
     print_stats(fp_seg_stats, seg_validator.seen, seg_validator.nt_per_class.sum())
     
@@ -1218,16 +1184,20 @@ accuracy on a dataset.
 
     FP32 model accuracy
     Boxes:
+        Best mean average:
                    Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
-                     all         300        2145       0.609       0.524       0.579       0.416
+                     all         300        2153       0.609       0.521        0.58       0.416
+        Macro average mean:
                    Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
-                     all         300        2145       0.602       0.501       0.557       0.354
+                     all         300        2153       0.605       0.501       0.558       0.353
     INT8 model accuracy
     Boxes:
+        Best mean average:
                    Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
-                     all         300        2145       0.604       0.505        0.57       0.407
+                     all         300        2153       0.522       0.538       0.555       0.376
+        Macro average mean:
                    Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
-                     all         300        2145       0.653       0.465       0.554       0.349
+                     all         300        2153       0.631       0.463       0.529       0.344
 
 
 Great! Looks like accuracy was changed, but not significantly and it
@@ -1247,7 +1217,7 @@ inference, the application can perform other tasks in parallel (for
 example, populating inputs or scheduling other requests) rather than
 wait for the current inference to complete first. To understand how to
 perform async inference using openvino, refer to `Async API
-tutorial <115-async-api-with-output.html>`__
+tutorial `__
 
 Preprocessing API enables making preprocessing a part of the model
 reducing application code and dependency on additional image processing
@@ -1257,10 +1227,10 @@ on a selected device (CPU/GPU etc.) rather than always being executed on
 CPU as part of an application. This will also improve selected device
 utilization. For more information, refer to the overview of
 `Preprocessing API
-tutorial <118-optimize-preprocessing-with-output.html>`__.
-To see, how it could be used with YOLOV8 object detection model ,
-please, see `Convert and Optimize YOLOv8 real-time object detection with
-OpenVINO tutorial <230-yolov8-object-detection-with-output.html>`__
+tutorial `__. To
+see, how it could be used with YOLOV8 object detection model , please,
+see `Convert and Optimize YOLOv8 real-time object detection with
+OpenVINO tutorial <./yolov8-object-detection.ipynb>`__
 
 Live demo
 ---------
@@ -1273,29 +1243,41 @@ The following code runs model inference on a video:
 
     import collections
     import time
+    import cv2
     from IPython import display
     
     
-    def run_instance_segmentation(source=0, flip=False, use_popup=False, skip_first_frames=0, model=seg_model, device=device.value):
+    def run_instance_segmentation(
+        source=0,
+        flip=False,
+        use_popup=False,
+        skip_first_frames=0,
+        model=seg_model,
+        device=device.value,
+    ):
         player = None
+    
+        ov_config = {}
         if device != "CPU":
             model.reshape({0: [1, 3, 640, 640]})
-        ov_config = {}
         if "GPU" in device or ("AUTO" in device and "GPU" in core.available_devices):
             ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
         compiled_model = core.compile_model(model, device, ov_config)
+    
+        def infer(*args):
+            result = compiled_model(args)
+            return torch.from_numpy(result[0]), torch.from_numpy(result[1])
+    
+        seg_model.predictor.inference = infer
+    
         try:
             # Create a video player to play with target fps.
-            player = VideoPlayer(
-                source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames
-            )
+            player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
             # Start capturing.
             player.start()
             if use_popup:
                 title = "Press ESC to Exit"
-                cv2.namedWindow(
-                    winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE
-                )
+                cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
     
             processing_times = collections.deque()
             while True:
@@ -1316,15 +1298,12 @@ The following code runs model inference on a video:
                     )
                 # Get the results.
                 input_image = np.array(frame)
-               
+    
                 start_time = time.time()
-                # model expects RGB image, while video capturing in BGR
-                detections = detect(input_image[:, :, ::-1], compiled_model)[0]
+                detections = seg_model(input_image)
                 stop_time = time.time()
-                
-                image_with_boxes = draw_results(detections, input_image, label_map)
-                frame = image_with_boxes
-               
+                frame = detections[0].plot()
+    
                 processing_times.append(stop_time - start_time)
                 # Use processing times from last 200 frames.
                 if len(processing_times) > 200:
@@ -1353,9 +1332,7 @@ The following code runs model inference on a video:
                         break
                 else:
                     # Encode numpy array to jpg.
-                    _, encoded_img = cv2.imencode(
-                        ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100]
-                    )
+                    _, encoded_img = cv2.imencode(ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                     # Create an IPython image.
                     i = display.Image(data=encoded_img)
                     # Display the image in this notebook.
@@ -1400,7 +1377,7 @@ set \ ``use_popup=True``.
     if WEBCAM_INFERENCE:
         VIDEO_SOURCE = 0  # Webcam
     else:
-        VIDEO_SOURCE = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4'
+        VIDEO_SOURCE = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4"
 
 .. code:: ipython3
 
@@ -1411,17 +1388,23 @@ set \ ``use_popup=True``.
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
 
 
 
 .. code:: ipython3
 
-    run_instance_segmentation(source=VIDEO_SOURCE, flip=True, use_popup=False, model=seg_ov_model, device=device.value)
+    run_instance_segmentation(
+        source=VIDEO_SOURCE,
+        flip=True,
+        use_popup=False,
+        model=seg_ov_model,
+        device=device.value,
+    )
 
 
 
-.. image:: 230-yolov8-instance-segmentation-with-output_files/230-yolov8-instance-segmentation-with-output_60_0.png
+.. image:: yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_62_0.png
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_18_1.png b/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_18_1.png
new file mode 100644
index 00000000000..bd478dd5c5c
--- /dev/null
+++ b/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_18_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:afdf904a57375654a2530bb56687c453fef87fbfa50aebcc7072810c33704c94
+size 789874
diff --git a/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_46_1.png b/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_46_1.png
new file mode 100644
index 00000000000..5e2d7f1d36c
--- /dev/null
+++ b/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_46_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6eb2408306c57279e2f19bd089e995ddb52cc782731756bcc7a665a7e1c4ea2d
+size 775225
diff --git a/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_62_0.png b/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_62_0.png
new file mode 100644
index 00000000000..4351f698a09
--- /dev/null
+++ b/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_62_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8bd8704f3f9ba9f5f678513872962d9531628f14ea828b3eea21d484c6ef5ec5
+size 473431
diff --git a/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_9_3.png b/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_9_3.png
new file mode 100644
index 00000000000..caa062d3eaa
--- /dev/null
+++ b/docs/notebooks/yolov8-instance-segmentation-with-output_files/yolov8-instance-segmentation-with-output_9_3.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8a0d5dff3c62904d70334d3cb2294de27b90a1f6ace402b4fd6aa35c7fad5e7
+size 789347
diff --git a/docs/notebooks/230-yolov8-keypoint-detection-with-output.rst b/docs/notebooks/yolov8-keypoint-detection-with-output.rst
similarity index 54%
rename from docs/notebooks/230-yolov8-keypoint-detection-with-output.rst
rename to docs/notebooks/yolov8-keypoint-detection-with-output.rst
index f30cacf8219..34da767702d 100644
--- a/docs/notebooks/230-yolov8-keypoint-detection-with-output.rst
+++ b/docs/notebooks/yolov8-keypoint-detection-with-output.rst
@@ -30,8 +30,6 @@ Table of contents:
 
    -  `Convert model to OpenVINO IR <#convert-model-to-openvino-ir>`__
    -  `Verify model inference <#verify-model-inference>`__
-   -  `Preprocessing <#preprocessing>`__
-   -  `Postprocessing <#postprocessing>`__
    -  `Select inference device <#select-inference-device>`__
    -  `Test on single image <#test-on-single-image>`__
 
@@ -94,7 +92,8 @@ Install necessary packages.
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0" "nncf>=2.5.0" "protobuf==3.20.*" "torch>=2.1" "torchvision>=0.16" "ultralytics==8.0.159" "onnx" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2024.0.0" "nncf>=2.9.0"
+    %pip install -q "protobuf==3.20.*" "torch>=2.1" "torchvision>=0.16" "ultralytics==8.1.42" "onnx" tqdm opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
 
 Import required utility functions. The lower cell will download the
 ``notebook_utils`` Python module from GitHub.
@@ -103,122 +102,38 @@ Import required utility functions. The lower cell will download the
 
     from pathlib import Path
     
-    # Fetch the notebook utils script from the openvino_notebooks repo
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file, VideoPlayer
 
-Define utility functions for drawing results
-
-.. code:: ipython3
-
-    from typing import Tuple, Dict
-    import cv2
-    import numpy as np
-    from PIL import Image
-    from ultralytics.utils.plotting import colors
-    
-    
-    def plot_one_box(box:np.ndarray, img:np.ndarray, color:Tuple[int, int, int] = None, keypoints:np.ndarray = None, label:str = None, line_thickness:int = 5):
-        """
-        Helper function for drawing single bounding box on image
-        Parameters:
-            box (np.ndarray): bounding box coordinates in format [x1, y1, x2, y2]
-            img (no.ndarray): input image
-            color (Tuple[int, int, int], *optional*, None): color in BGR format for drawing box, if not specified will be selected randomly
-            keypoints (np.ndarray, *optional*, None): keypoints in format [x1, y1, s], x1, y1 - keypoint coordinates, s - the confidence scores,
-                                                      if not provided, only box will be drawn
-            label (str, *optonal*, None): box label string, if not provided will not be provided as drowing result
-            line_thickness (int, *optional*, 5): thickness for box drawing lines
-        """
-        # Plots one bounding box on image img
-        tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
-        color = color or [random.randint(0, 255) for _ in range(3)]
-        c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
-        cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
-        if label:
-            tf = max(tl - 1, 1)  # font thickness
-            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
-            c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
-            cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
-            cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
-        if keypoints is not None:
-            kpt_color = colors.pose_palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
-            skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8],
-                        [7, 9], [8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
-            limb_color = colors.pose_palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
-            shape = img.shape[:2]
-            for i, k in enumerate(keypoints):
-                color_k = [int(x) for x in kpt_color[i]]
-                x_coord, y_coord = k[0], k[1]
-                if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
-                    if len(k) == 3:
-                        if k[2] < 0.5:
-                            continue
-                    cv2.circle(img, (int(x_coord), int(y_coord)), 5, color_k, -1, lineType=cv2.LINE_AA)
-    
-            ndim = keypoints.shape[-1]
-            for i, sk in enumerate(skeleton):
-                pos1 = (int(keypoints[(sk[0] - 1), 0]), int(keypoints[(sk[0] - 1), 1]))
-                pos2 = (int(keypoints[(sk[1] - 1), 0]), int(keypoints[(sk[1] - 1), 1]))
-                if ndim == 3:
-                    conf1 = keypoints[(sk[0] - 1), 2]
-                    conf2 = keypoints[(sk[1] - 1), 2]
-                    if conf1 < 0.5 or conf2 < 0.5:
-                        continue
-                if pos1[0] % shape[1] == 0 or pos1[1] % shape[0] == 0 or pos1[0] < 0 or pos1[1] < 0:
-                    continue
-                if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
-                    continue
-                cv2.line(img, pos1, pos2, [int(x) for x in limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
-    
-        return img
-    
-    
-    def draw_results(results:Dict, source_image:np.ndarray, label_map:Dict):
-        """
-        Helper function for drawing bounding boxes on image
-        Parameters:
-            image_res (np.ndarray): detection predictions in format [x1, y1, x2, y2, score, label_id]
-            source_image (np.ndarray): input image for drawing
-            label_map; (Dict[int, str]): label_id to class name mapping
-        """
-        boxes = results["box"]
-        keypoints = results.get("kpt")
-        h, w = source_image.shape[:2]
-        for idx, (*xyxy, conf, lbl) in enumerate(boxes):
-            if conf < 0.4:
-                continue
-            label = f'{label_map[0]} {conf:.2f}'
-            kp = keypoints[idx] if keypoints is not None else None
-            source_image = plot_one_box(xyxy, source_image, keypoints=kp, label=label, color=colors(int(lbl)), line_thickness=1)
-        return source_image
-
 .. code:: ipython3
 
     # Download a test sample
-    IMAGE_PATH = Path('./data/intel_rnb.jpg')
+    IMAGE_PATH = Path("./data/intel_rnb.jpg")
     download_file(
-        url='https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg',
+        url="https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg",
         filename=IMAGE_PATH.name,
-        directory=IMAGE_PATH.parent
-    ) 
+        directory=IMAGE_PATH.parent,
+    )
+
 
 
 .. parsed-literal::
 
-    'data/intel_rnb.jpg' already exists.
+    data/intel_rnb.jpg:   0%|          | 0.00/288k [00:00
+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. parsed-literal::
+
+    /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/venv/lib/python3.10/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+      return Tensor(self.data * unwrap_tensor_data(other))
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
 
 
 .. code:: ipython3
 
-    from openvino.runtime import serialize
-    int8_model_pose_path = models_dir / f'{POSE_MODEL_NAME}_openvino_int8_model/{POSE_MODEL_NAME}.xml'
+    %%skip not $to_quantize.value
+    
     print(f"Quantized keypoint detection model will be saved to {int8_model_pose_path}")
-    serialize(quantized_pose_model, str(int8_model_pose_path))
+    ov.save_model(quantized_pose_model, str(int8_model_pose_path))
 
 
 .. parsed-literal::
@@ -943,36 +886,40 @@ on the image.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     device
 
+.. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
+    ov_config = {}
+    if device.value != "CPU":
+        quantized_pose_model.reshape({0: [1, 3, 640, 640]})
+    if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
+        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
+    quantized_pose_compiled_model = core.compile_model(quantized_pose_model, device.value, ov_config)
+    
+    def infer(*args):
+        result = quantized_pose_compiled_model(args)
+        return torch.from_numpy(result[0])
+    
+    pose_model.predictor.inference = infer
+    
+    res = pose_model(IMAGE_PATH)
+    display(Image.fromarray(res[0].plot()[:, :, ::-1]))
 
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-    if device.value != "CPU":
-        quantized_pose_model.reshape({0: [1, 3, 640, 640]})
-    ov_config = {}
-    if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
-        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
-    quantized_pose_compiled_model = core.compile_model(quantized_pose_model, device.value, ov_config)
-    input_image = np.array(Image.open(IMAGE_PATH))
-    detections = detect(input_image, quantized_pose_compiled_model)[0]
-    image_with_boxes = draw_results(detections, input_image, label_map)
     
-    Image.fromarray(image_with_boxes)
+    image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/intel_rnb.jpg: 640x640 2 persons, 20.0ms
+    Speed: 3.5ms preprocess, 20.0ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)
 
 
 
-
-.. image:: 230-yolov8-keypoint-detection-with-output_files/230-yolov8-keypoint-detection-with-output_46_0.png
-
+.. image:: yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_44_1.png
 
 
 Compare the Original and Quantized Models
@@ -983,7 +930,7 @@ Compare the Original and Quantized Models
 Compare performance of the Original and Quantized Models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Finally, use the OpenVINO
+ Finally, use the OpenVINO
 `Benchmark
 Tool `__
 to measure the inference performance of the ``FP32`` and ``INT8``
@@ -1000,21 +947,15 @@ models.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     device
 
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
 .. code:: ipython3
 
-    # Inference FP32 model (OpenVINO IR)
-    !benchmark_app -m $pose_model_path -d $device.value -api async -shape "[1,3,640,640]"
+    if int8_model_pose_path.exists():
+        # Inference FP32 model (OpenVINO IR)
+        !benchmark_app -m $pose_model_path -d $device.value -api async -shape "[1,3,640,640]"
 
 
 .. parsed-literal::
@@ -1024,37 +965,37 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 17.85 ms
+    [ INFO ] Read model took 16.24 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : f32 / [...] / [?,3,?,?]
+    [ INFO ]     x (node: x) : f32 / [...] / [?,3,?,?]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [?,56,?]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_9) : f32 / [...] / [?,56,16..]
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: 'images': [1,3,640,640]
-    [ INFO ] Reshape model took 11.94 ms
+    [ INFO ] Reshaping model: 'x': [1,3,640,640]
+    [ INFO ] Reshape model took 9.32 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : u8 / [N,C,H,W] / [1,3,640,640]
+    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,56,8400]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_9) : f32 / [...] / [1,56,8400]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 410.27 ms
+    [ INFO ] Compile model took 313.69 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: torch_jit
+    [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
     [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
@@ -1063,43 +1004,47 @@ models.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
     [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
     [ INFO ]     INFERENCE_NUM_THREADS: 36
     [ INFO ]     INFERENCE_PRECISION_HINT: 
-    [ INFO ]     NETWORK_NAME: torch_jit
+    [ INFO ]     KV_CACHE_PRECISION: 
+    [ INFO ]     LOG_LEVEL: Level.NO
+    [ INFO ]     NETWORK_NAME: Model0
     [ INFO ]     NUM_STREAMS: 12
     [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
-    [ INFO ]     PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
+    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
     [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
-    [ INFO ]     PERF_COUNT: False
+    [ INFO ]     PERF_COUNT: NO
     [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
     [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
+    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
+    [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 33.91 ms
+    [ INFO ] First inference took 42.40 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            18420 iterations
-    [ INFO ] Duration:         120067.97 ms
+    [ INFO ] Count:            21612 iterations
+    [ INFO ] Duration:         120076.97 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        74.24 ms
-    [ INFO ]    Average:       78.05 ms
-    [ INFO ]    Min:           39.74 ms
-    [ INFO ]    Max:           165.06 ms
-    [ INFO ] Throughput:   153.41 FPS
+    [ INFO ]    Median:        66.18 ms
+    [ INFO ]    Average:       66.51 ms
+    [ INFO ]    Min:           32.58 ms
+    [ INFO ]    Max:           123.83 ms
+    [ INFO ] Throughput:   179.98 FPS
 
 
 .. code:: ipython3
 
-    # Inference INT8 model (OpenVINO IR)
-    !benchmark_app -m $int8_model_pose_path -d $device.value -api async -shape "[1,3,640,640]" -t 15
+    if int8_model_pose_path.exists():
+        # Inference INT8 model (OpenVINO IR)
+        !benchmark_app -m $int8_model_pose_path -d $device.value -api async -shape "[1,3,640,640]" -t 15
 
 
 .. parsed-literal::
@@ -1108,76 +1053,79 @@ models.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 29.51 ms
+    [ INFO ] Read model took 23.83 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : f32 / [...] / [1,3,?,?]
+    [ INFO ]     x (node: x) : f32 / [...] / [1,3,?,?]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,56,21..]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_9) : f32 / [...] / [1,56,21..]
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: 'images': [1,3,640,640]
-    [ INFO ] Reshape model took 16.46 ms
+    [ INFO ] Reshaping model: 'x': [1,3,640,640]
+    [ INFO ] Reshape model took 13.45 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : u8 / [N,C,H,W] / [1,3,640,640]
+    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,56,8400]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_9) : f32 / [...] / [1,56,8400]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 732.13 ms
+    [ INFO ] Compile model took 540.15 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: torch_jit
+    [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
-    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 18
+    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
     [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
     [ INFO ]   CPU:
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
     [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
     [ INFO ]     INFERENCE_NUM_THREADS: 36
     [ INFO ]     INFERENCE_PRECISION_HINT: 
-    [ INFO ]     NETWORK_NAME: torch_jit
-    [ INFO ]     NUM_STREAMS: 18
-    [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 18
-    [ INFO ]     PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
+    [ INFO ]     KV_CACHE_PRECISION: 
+    [ INFO ]     LOG_LEVEL: Level.NO
+    [ INFO ]     NETWORK_NAME: Model0
+    [ INFO ]     NUM_STREAMS: 12
+    [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
+    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
     [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
-    [ INFO ]     PERF_COUNT: False
+    [ INFO ]     PERF_COUNT: NO
     [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
     [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
-    [Step 10/11] Measuring performance (Start inference asynchronously, 18 inference requests, limits: 15000 ms duration)
+    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
+    [ INFO ] Fill input 'x' with random values 
+    [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 26.46 ms
+    [ INFO ] First inference took 34.70 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            6426 iterations
-    [ INFO ] Duration:         15072.05 ms
+    [ INFO ] Count:            4248 iterations
+    [ INFO ] Duration:         15064.81 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        40.12 ms
-    [ INFO ]    Average:       42.00 ms
-    [ INFO ]    Min:           27.49 ms
-    [ INFO ]    Max:           121.32 ms
-    [ INFO ] Throughput:   426.35 FPS
+    [ INFO ]    Median:        42.11 ms
+    [ INFO ]    Average:       42.37 ms
+    [ INFO ]    Min:           24.13 ms
+    [ INFO ]    Max:           69.95 ms
+    [ INFO ] Throughput:   281.98 FPS
 
 
 Compare accuracy of the Original and Quantized Models
@@ -1192,6 +1140,8 @@ accuracy on a dataset.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     int8_pose_stats = test(quantized_pose_model, core, pose_data_loader, pose_validator, num_samples=NUM_TEST_SAMPLES)
 
 
@@ -1203,6 +1153,8 @@ accuracy on a dataset.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     print("FP32 model accuracy")
     print_stats(fp_pose_stats, pose_validator.seen, pose_validator.nt_per_class.sum())
     
@@ -1214,12 +1166,14 @@ accuracy on a dataset.
 
     FP32 model accuracy
     Boxes:
+        Best mean average:
                    Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
-                     all           8          21           1         0.9       0.955       0.736
+                     all           8          21           1       0.899       0.955       0.736
     INT8 model accuracy
     Boxes:
+        Best mean average:
                    Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
-                     all           8          21       0.905       0.909       0.979       0.703
+                     all           8          21       0.952       0.938       0.953       0.638
 
 
 Great! Looks like accuracy was changed, but not significantly and it
@@ -1239,7 +1193,7 @@ inference, the application can perform other tasks in parallel (for
 example, populating inputs or scheduling other requests) rather than
 wait for the current inference to complete first. To understand how to
 perform async inference using openvino, refer to `Async API
-tutorial <115-async-api-with-output.html>`__
+tutorial `__
 
 Preprocessing API enables making preprocessing a part of the model
 reducing application code and dependency on additional image processing
@@ -1249,10 +1203,10 @@ on a selected device (CPU/GPU etc.) rather than always being executed on
 CPU as part of an application. This will also improve selected device
 utilization. For more information, refer to the overview of
 `Preprocessing API
-tutorial <118-optimize-preprocessing-with-output.html>`__.
-To see, how it could be used with YOLOV8 object detection model ,
-please, see `Convert and Optimize YOLOv8 real-time object detection with
-OpenVINO tutorial <230-yolov8-object-detection-with-output.html>`__
+tutorial `__. To
+see, how it could be used with YOLOV8 object detection model , please,
+see `Convert and Optimize YOLOv8 real-time object detection with
+OpenVINO tutorial <./yolov8-object-detection.ipynb>`__
 
 Live demo
 ---------
@@ -1266,28 +1220,40 @@ The following code runs model inference on a video:
     import collections
     import time
     from IPython import display
+    import cv2
     
     
-    def run_keypoint_detection(source=0, flip=False, use_popup=False, skip_first_frames=0, model=pose_model, device=device.value):
+    def run_keypoint_detection(
+        source=0,
+        flip=False,
+        use_popup=False,
+        skip_first_frames=0,
+        model=pose_model,
+        device=device.value,
+    ):
         player = None
+    
+        ov_config = {}
         if device != "CPU":
             model.reshape({0: [1, 3, 640, 640]})
-        ov_config = {}
         if "GPU" in device or ("AUTO" in device and "GPU" in core.available_devices):
             ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
         compiled_model = core.compile_model(model, device, ov_config)
+    
+        def infer(*args):
+            result = compiled_model(args)
+            return torch.from_numpy(result[0])
+    
+        pose_model.predictor.inference = infer
+    
         try:
             # Create a video player to play with target fps.
-            player = VideoPlayer(
-                source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames
-            )
+            player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
             # Start capturing.
             player.start()
             if use_popup:
                 title = "Press ESC to Exit"
-                cv2.namedWindow(
-                    winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE
-                )
+                cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
     
             processing_times = collections.deque()
             while True:
@@ -1306,17 +1272,15 @@ The following code runs model inference on a video:
                         fy=scale,
                         interpolation=cv2.INTER_AREA,
                     )
-                # Get the results.
+                # Get the results
                 input_image = np.array(frame)
-               
+    
                 start_time = time.time()
-                # model expects RGB image, while video capturing in BGR
-                detections = detect(input_image[:, :, ::-1], compiled_model)[0]
+    
+                detections = pose_model(input_image)
                 stop_time = time.time()
-                
-                image_with_boxes = draw_results(detections, input_image, label_map)
-                frame = image_with_boxes
-               
+                frame = detections[0].plot()
+    
                 processing_times.append(stop_time - start_time)
                 # Use processing times from last 200 frames.
                 if len(processing_times) > 200:
@@ -1345,9 +1309,7 @@ The following code runs model inference on a video:
                         break
                 else:
                     # Encode numpy array to jpg.
-                    _, encoded_img = cv2.imencode(
-                        ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100]
-                    )
+                    _, encoded_img = cv2.imencode(ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                     # Create an IPython image.
                     i = display.Image(data=encoded_img)
                     # Display the image in this notebook.
@@ -1373,7 +1335,7 @@ Run Keypoint Detection on video
 
 .. code:: ipython3
 
-    VIDEO_SOURCE = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4'
+    VIDEO_SOURCE = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4"
 
 .. code:: ipython3
 
@@ -1384,17 +1346,23 @@ Run Keypoint Detection on video
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
 
 
 
 .. code:: ipython3
 
-    run_keypoint_detection(source=VIDEO_SOURCE, flip=True, use_popup=False, model=pose_ov_model, device=device.value)
+    run_keypoint_detection(
+        source=VIDEO_SOURCE,
+        flip=True,
+        use_popup=False,
+        model=pose_ov_model,
+        device=device.value,
+    )
 
 
 
-.. image:: 230-yolov8-keypoint-detection-with-output_files/230-yolov8-keypoint-detection-with-output_62_0.png
+.. image:: yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_60_0.png
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_16_1.jpg b/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_16_1.jpg
new file mode 100644
index 00000000000..9c3a311e877
--- /dev/null
+++ b/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_16_1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:254000dd7415e7fe9d8352583701bfd61b9aeca783791ae5b5b17c9b6b90a12a
+size 60096
diff --git a/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_16_1.png b/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_16_1.png
new file mode 100644
index 00000000000..2798035922b
--- /dev/null
+++ b/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_16_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8056c2a3c3f6ce357cc5fca0af66fe1784b636ffbd457ef386dc70cf04b57b84
+size 579271
diff --git a/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_44_1.jpg b/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_44_1.jpg
new file mode 100644
index 00000000000..6f6c3098579
--- /dev/null
+++ b/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_44_1.jpg
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diff --git a/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_44_1.png b/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_44_1.png
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diff --git a/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_60_0.png b/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_60_0.png
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diff --git a/docs/notebooks/230-yolov8-keypoint-detection-with-output_files/230-yolov8-keypoint-detection-with-output_11_1.jpg b/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_9_3.jpg
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diff --git a/docs/notebooks/230-yolov8-keypoint-detection-with-output_files/230-yolov8-keypoint-detection-with-output_11_1.png b/docs/notebooks/yolov8-keypoint-detection-with-output_files/yolov8-keypoint-detection-with-output_9_3.png
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diff --git a/docs/notebooks/yolov8-obb-with-output.rst b/docs/notebooks/yolov8-obb-with-output.rst
new file mode 100644
index 00000000000..2ecfebe2acd
--- /dev/null
+++ b/docs/notebooks/yolov8-obb-with-output.rst
@@ -0,0 +1,724 @@
+YOLOv8 Oriented Bounding Boxes Object Detection with OpenVINO™
+==============================================================
+
+`YOLOv8-OBB `__ is introduced
+by Ultralytics.
+
+Oriented object detection goes a step further than object detection and
+introduce an extra angle to locate objects more accurate in an image.
+
+The output of an oriented object detector is a set of rotated bounding
+boxes that exactly enclose the objects in the image, along with class
+labels and confidence scores for each box. Object detection is a good
+choice when you need to identify objects of interest in a scene, but
+don’t need to know exactly where the object is or its exact shape.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+-  `Prerequisites <#prerequisites>`__
+-  `Get PyTorch model <#get-pytorch-model>`__
+-  `Prepare dataset and dataloader <#prepare-dataset-and-dataloader>`__
+-  `Run inference <#run-inference>`__
+-  `Convert PyTorch model to OpenVINO
+   IR <#convert-pytorch-model-to-openvino-ir>`__
+
+   -  `Select inference device <#select-inference-device>`__
+   -  `Compile model <#compile-model>`__
+   -  `Prepare the model for
+      inference <#prepare-the-model-for-inference>`__
+   -  `Run inference <#run-inference>`__
+
+-  `Quantization <#quantization>`__
+-  `Compare inference time and model
+   sizes. <#compare-inference-time-and-model-sizes>`__
+
+Prerequisites
+~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    %pip install -q "ultralytics==8.1.42" "openvino>=2024.0.0" "nncf>=2.9.0" tqdm
+
+
+.. parsed-literal::
+
+    DEPRECATION: torchsde 0.2.5 has a non-standard dependency specifier numpy>=1.19.*; python_version >= "3.7". pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of torchsde or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Import required utility functions. The lower cell will download the
+notebook_utils Python module from GitHub.
+
+.. code:: ipython3
+
+    from pathlib import Path
+
+    # Fetch `notebook_utils` module
+    import requests
+
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+
+    open("notebook_utils.py", "w").write(r.text)
+
+    from notebook_utils import download_file
+
+Get PyTorch model
+~~~~~~~~~~~~~~~~~
+
+
+
+Generally, PyTorch models represent an instance of the
+`torch.nn.Module `__
+class, initialized by a state dictionary with model weights. We will use
+the YOLOv8 pretrained OBB large model (also known as ``yolov8l-obbn``)
+pre-trained on a DOTAv1 dataset, which is available in this
+`repo `__. Similar steps are
+also applicable to other YOLOv8 models.
+
+.. code:: ipython3
+
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8l-obb.pt")
+
+Prepare dataset and dataloader
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+YOLOv8-obb is pre-trained on the DOTA dataset. Also, Ultralytics
+provides DOTA8 dataset. It is a small, but versatile oriented object
+detection dataset composed of the first 8 images of 8 images of the
+split DOTAv1 set, 4 for training and 4 for validation. This dataset is
+ideal for testing and debugging object detection models, or for
+experimenting with new detection approaches. With 8 images, it is small
+enough to be easily manageable, yet diverse enough to test training
+pipelines for errors and act as a sanity check before training larger
+datasets.
+
+The original model repository uses a Validator wrapper, which represents
+the accuracy validation pipeline. It creates dataloader and evaluation
+metrics and updates metrics on each data batch produced by the
+dataloader. Besides that, it is responsible for data preprocessing and
+results postprocessing. For class initialization, the configuration
+should be provided. We will use the default setup, but it can be
+replaced with some parameters overriding to test on custom data. The
+model has connected the task_map, which allows to get a validator class
+instance.
+
+.. code:: ipython3
+
+    from ultralytics.cfg import get_cfg
+    from ultralytics.data.utils import check_det_dataset
+    from ultralytics.utils import DEFAULT_CFG, DATASETS_DIR
+
+
+    CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/datasets/dota8.yaml"
+    OUT_DIR = Path("./datasets")
+    CFG_PATH = OUT_DIR / "dota8.yaml"
+
+    download_file(CFG_URL, CFG_PATH.name, CFG_PATH.parent)
+
+    args = get_cfg(cfg=DEFAULT_CFG)
+    args.data = CFG_PATH
+    args.task = model.task
+
+    validator = model.task_map[model.task]["validator"](args=args)
+
+    validator.stride = 32
+    validator.data = check_det_dataset(str(args.data))
+    data_loader = validator.get_dataloader(DATASETS_DIR / "dota8", 1)
+    example_image_path = list(data_loader)[1]["im_file"][0]
+
+
+
+.. parsed-literal::
+
+    datasets/dota8.yaml:   0%|          | 0.00/608 [00:00`__ enables
+post-training quantization by adding quantization layers into model
+graph and then using a subset of the training dataset to initialize the
+parameters of these additional quantization layers. Quantized operations
+are executed in ``INT8`` instead of ``FP32``/``FP16`` making model
+inference faster.
+
+The optimization process contains the following steps:
+
+1. Create a calibration dataset for quantization.
+2. Run ``nncf.quantize()`` to obtain quantized model.
+3. Save the ``INT8`` model using ``openvino.save_model()`` function.
+
+Please select below whether you would like to run quantization to
+improve model inference speed.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+
+    INT8_OV_PATH = Path("model/int8_model.xml")
+
+    to_quantize = widgets.Checkbox(
+        value=True,
+        description="Quantization",
+        disabled=False,
+    )
+
+    to_quantize
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Quantization')
+
+
+
+Let’s load ``skip magic`` extension to skip quantization if
+``to_quantize`` is not selected
+
+.. code:: ipython3
+
+    # Fetch skip_kernel_extension module
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
+
+    %load_ext skip_kernel_extension
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+
+    from typing import Dict
+
+    import nncf
+
+
+    def transform_fn(data_item: Dict):
+        input_tensor = validator.preprocess(data_item)["img"].numpy()
+        return input_tensor
+
+
+    quantization_dataset = nncf.Dataset(data_loader, transform_fn)
+
+
+.. parsed-literal::
+
+    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
+
+
+Create a quantized model from the pre-trained converted OpenVINO model.
+
+   **NOTE**: Quantization is time and memory consuming operation.
+   Running quantization code below may take some time.
+
+..
+
+   **NOTE**: We use the tiny DOTA8 dataset as a calibration dataset. It
+   gives a good enough result for tutorial purpose. For batter results,
+   use a bigger dataset. Usually 300 examples are enough.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+
+    if INT8_OV_PATH.exists():
+        print("Loading quantized model")
+        quantized_model = core.read_model(INT8_OV_PATH)
+    else:
+        ov_model.reshape({0: [1, 3, -1, -1]})
+        quantized_model = nncf.quantize(
+            ov_model,
+            quantization_dataset,
+            preset=nncf.QuantizationPreset.MIXED,
+        )
+        ov.save_model(quantized_model, INT8_OV_PATH)
+
+
+    ov_config = {}
+    if device.value != "CPU":
+        quantized_model.reshape({0: [1, 3, 1024, 1024]})
+    if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
+        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
+
+    model_optimized = core.compile_model(quantized_model, device.value, ov_config)
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+We can reuse the base model pipeline in the same way as for IR model.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+
+    def infer(*args):
+        result = model_optimized(args)[0]
+        return torch.from_numpy(result)
+
+    model.predictor.inference = infer
+
+Run inference
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+
+    res = model(example_image_path, device='cpu')
+    Image.fromarray(res[0].plot()[:, :, ::-1])
+
+
+.. parsed-literal::
+
+
+    image 1/1 /home/ea/work/openvino_notebooks/notebooks/fast-segment-anything/datasets/dota8/images/train/P1053__1024__0___90.jpg: 1024x1024 240.5ms
+    Speed: 3.2ms preprocess, 240.5ms inference, 4.2ms postprocess per image at shape (1, 3, 1024, 1024)
+
+
+You can see that the result is almost the same but it has a small
+difference. One small vehicle was recognized as two vehicles. But one
+large car was also identified, unlike the original model.
+
+Compare inference time and model sizes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+
+    fp16_ir_model_size = OV_MODEL_PATH.with_suffix(".bin").stat().st_size / 1024
+    quantized_model_size = INT8_OV_PATH.with_suffix(".bin").stat().st_size / 1024
+
+    print(f"FP16 model size: {fp16_ir_model_size:.2f} KB")
+    print(f"INT8 model size: {quantized_model_size:.2f} KB")
+    print(f"Model compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
+
+
+.. parsed-literal::
+
+    FP16 model size: 86849.05 KB
+    INT8 model size: 43494.78 KB
+    Model compression rate: 1.997
+
+
+.. code:: ipython3
+
+    # Inference FP32 model (OpenVINO IR)
+    !benchmark_app -m $OV_MODEL_PATH -d $device.value -api async -shape "[1,3,640,640]"
+
+
+.. parsed-literal::
+
+    [Step 1/11] Parsing and validating input arguments
+    [ INFO ] Parsing input parameters
+    [Step 2/11] Loading OpenVINO Runtime
+    [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
+    [ INFO ] OpenVINO:
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
+    [ INFO ]
+    [ INFO ] Device info:
+    [ INFO ] AUTO
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
+    [ INFO ]
+    [ INFO ]
+    [Step 3/11] Setting device configuration
+    [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
+    [Step 4/11] Reading model files
+    [ INFO ] Loading model files
+    [ INFO ] Read model took 25.07 ms
+    [ INFO ] Original model I/O parameters:
+    [ INFO ] Model inputs:
+    [ INFO ]     x (node: x) : f32 / [...] / [?,3,?,?]
+    [ INFO ] Model outputs:
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_9) : f32 / [...] / [?,20,16..]
+    [Step 5/11] Resizing model to match image sizes and given batch
+    [ INFO ] Model batch size: 1
+    [ INFO ] Reshaping model: 'x': [1,3,640,640]
+    [ INFO ] Reshape model took 10.42 ms
+    [Step 6/11] Configuring input of the model
+    [ INFO ] Model inputs:
+    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
+    [ INFO ] Model outputs:
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_9) : f32 / [...] / [1,20,8400]
+    [Step 7/11] Loading the model to the device
+    [ INFO ] Compile model took 645.51 ms
+    [Step 8/11] Querying optimal runtime parameters
+    [ INFO ] Model:
+    [ INFO ]   NETWORK_NAME: Model0
+    [ INFO ]   EXECUTION_DEVICES: ['CPU']
+    [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
+    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
+    [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
+    [ INFO ]   CPU:
+    [ INFO ]     AFFINITY: Affinity.CORE
+    [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
+    [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     ENABLE_CPU_PINNING: True
+    [ INFO ]     ENABLE_HYPER_THREADING: True
+    [ INFO ]     EXECUTION_DEVICES: ['CPU']
+    [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
+    [ INFO ]     INFERENCE_NUM_THREADS: 36
+    [ INFO ]     INFERENCE_PRECISION_HINT: 
+    [ INFO ]     KV_CACHE_PRECISION: 
+    [ INFO ]     LOG_LEVEL: Level.NO
+    [ INFO ]     NETWORK_NAME: Model0
+    [ INFO ]     NUM_STREAMS: 12
+    [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
+    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
+    [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
+    [ INFO ]     PERF_COUNT: NO
+    [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
+    [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
+    [ INFO ]   LOADED_FROM_CACHE: False
+    [Step 9/11] Creating infer requests and preparing input tensors
+    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
+    [ INFO ] Fill input 'x' with random values
+    [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
+    [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
+    [ INFO ] First inference took 362.70 ms
+    [Step 11/11] Dumping statistics report
+    [ INFO ] Execution Devices:['CPU']
+    [ INFO ] Count:            1620 iterations
+    [ INFO ] Duration:         121527.01 ms
+    [ INFO ] Latency:
+    [ INFO ]    Median:        884.92 ms
+    [ INFO ]    Average:       897.13 ms
+    [ INFO ]    Min:           599.38 ms
+    [ INFO ]    Max:           1131.46 ms
+    [ INFO ] Throughput:   13.33 FPS
+
+
+.. code:: ipython3
+
+    if INT8_OV_PATH.exists():
+        # Inference INT8 model (Quantized model)
+        !benchmark_app -m $INT8_OV_PATH -d $device.value -api async -shape "[1,3,640,640]" -t 15
+
+
+.. parsed-literal::
+
+    [Step 1/11] Parsing and validating input arguments
+    [ INFO ] Parsing input parameters
+    [Step 2/11] Loading OpenVINO Runtime
+    [ INFO ] OpenVINO:
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
+    [ INFO ]
+    [ INFO ] Device info:
+    [ INFO ] AUTO
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
+    [ INFO ]
+    [ INFO ]
+    [Step 3/11] Setting device configuration
+    [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
+    [Step 4/11] Reading model files
+    [ INFO ] Loading model files
+    [ INFO ] Read model took 46.47 ms
+    [ INFO ] Original model I/O parameters:
+    [ INFO ] Model inputs:
+    [ INFO ]     x (node: x) : f32 / [...] / [?,3,?,?]
+    [ INFO ] Model outputs:
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_9) : f32 / [...] / [?,20,16..]
+    [Step 5/11] Resizing model to match image sizes and given batch
+    [ INFO ] Model batch size: 1
+    [ INFO ] Reshaping model: 'x': [1,3,640,640]
+    [ INFO ] Reshape model took 20.10 ms
+    [Step 6/11] Configuring input of the model
+    [ INFO ] Model inputs:
+    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
+    [ INFO ] Model outputs:
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_9) : f32 / [...] / [1,20,8400]
+    [Step 7/11] Loading the model to the device
+    [ INFO ] Compile model took 1201.42 ms
+    [Step 8/11] Querying optimal runtime parameters
+    [ INFO ] Model:
+    [ INFO ]   NETWORK_NAME: Model0
+    [ INFO ]   EXECUTION_DEVICES: ['CPU']
+    [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
+    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
+    [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
+    [ INFO ]   CPU:
+    [ INFO ]     AFFINITY: Affinity.CORE
+    [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
+    [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     ENABLE_CPU_PINNING: True
+    [ INFO ]     ENABLE_HYPER_THREADING: True
+    [ INFO ]     EXECUTION_DEVICES: ['CPU']
+    [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
+    [ INFO ]     INFERENCE_NUM_THREADS: 36
+    [ INFO ]     INFERENCE_PRECISION_HINT: 
+    [ INFO ]     KV_CACHE_PRECISION: 
+    [ INFO ]     LOG_LEVEL: Level.NO
+    [ INFO ]     NETWORK_NAME: Model0
+    [ INFO ]     NUM_STREAMS: 12
+    [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
+    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
+    [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
+    [ INFO ]     PERF_COUNT: NO
+    [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
+    [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
+    [ INFO ]   LOADED_FROM_CACHE: False
+    [Step 9/11] Creating infer requests and preparing input tensors
+    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
+    [ INFO ] Fill input 'x' with random values
+    [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
+    [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
+    [ INFO ] First inference took 124.20 ms
+    [Step 11/11] Dumping statistics report
+    [ INFO ] Execution Devices:['CPU']
+    [ INFO ] Count:            708 iterations
+    [ INFO ] Duration:         15216.46 ms
+    [ INFO ] Latency:
+    [ INFO ]    Median:        252.23 ms
+    [ INFO ]    Average:       255.76 ms
+    [ INFO ]    Min:           176.97 ms
+    [ INFO ]    Max:           344.41 ms
+    [ INFO ] Throughput:   46.53 FPS
+
diff --git a/docs/notebooks/yolov8-obb-with-output_files/yolov8-obb-with-output_10_1.png b/docs/notebooks/yolov8-obb-with-output_files/yolov8-obb-with-output_10_1.png
new file mode 100644
index 00000000000..50aaf319c9a
--- /dev/null
+++ b/docs/notebooks/yolov8-obb-with-output_files/yolov8-obb-with-output_10_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:119895531f8d84d19c1532048e112156f80b57512e0353b52a48c5899a3d5b3f
+size 1421077
diff --git a/docs/notebooks/yolov8-obb-with-output_files/yolov8-obb-with-output_20_1.png b/docs/notebooks/yolov8-obb-with-output_files/yolov8-obb-with-output_20_1.png
new file mode 100644
index 00000000000..50aaf319c9a
--- /dev/null
+++ b/docs/notebooks/yolov8-obb-with-output_files/yolov8-obb-with-output_20_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:119895531f8d84d19c1532048e112156f80b57512e0353b52a48c5899a3d5b3f
+size 1421077
diff --git a/docs/notebooks/230-yolov8-object-detection-with-output.rst b/docs/notebooks/yolov8-object-detection-with-output.rst
similarity index 64%
rename from docs/notebooks/230-yolov8-object-detection-with-output.rst
rename to docs/notebooks/yolov8-object-detection-with-output.rst
index 02ddc6b09ed..e2bc325421a 100644
--- a/docs/notebooks/230-yolov8-object-detection-with-output.rst
+++ b/docs/notebooks/yolov8-object-detection-with-output.rst
@@ -29,8 +29,6 @@ Table of contents:
 
    -  `Convert model to OpenVINO IR <#convert-model-to-openvino-ir>`__
    -  `Verify model inference <#verify-model-inference>`__
-   -  `Preprocessing <#preprocessing>`__
-   -  `Postprocessing <#postprocessing>`__
    -  `Select inference device <#select-inference-device>`__
    -  `Test on single image <#test-on-single-image>`__
 
@@ -70,6 +68,7 @@ Table of contents:
          steps <#describe-preprocessing-steps>`__
       -  `Integrating Steps into a
          Model <#integrating-steps-into-a-model>`__
+      -  `Postprocessing <#postprocessing>`__
 
 -  `Live demo <#live-demo>`__
 
@@ -105,8 +104,8 @@ Install necessary packages.
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0" "nncf>=2.5.0"
-    %pip install -q "torch>=2.1" "torchvision>=0.16" "ultralytics==8.0.43" onnx --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino>=2024.0.0" "nncf>=2.9.0"
+    %pip install -q "torch>=2.1" "torchvision>=0.16" "ultralytics==8.1.42" onnx tqdm opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
 
 Import required utility functions. The lower cell will download the
 ``notebook_utils`` Python module from GitHub.
@@ -115,91 +114,26 @@ Import required utility functions. The lower cell will download the
 
     from pathlib import Path
     
-    # Fetch the notebook utils script from the openvino_notebooks repo
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file, VideoPlayer
 
-Define utility functions for drawing results
-
-.. code:: ipython3
-
-    from typing import Tuple, Dict
-    import cv2
-    import numpy as np
-    from ultralytics.yolo.utils.plotting import colors
-    
-    
-    def plot_one_box(box:np.ndarray, img:np.ndarray,
-                     color:Tuple[int, int, int] = None,
-                     label:str = None, line_thickness:int = 5):
-        """
-        Helper function for drawing single bounding box on image
-        Parameters:
-            x (np.ndarray): bounding box coordinates in format [x1, y1, x2, y2]
-            img (no.ndarray): input image
-            color (Tuple[int, int, int], *optional*, None): color in BGR format for drawing box, if not specified will be selected randomly
-            label (str, *optonal*, None): box label string, if not provided will not be provided as drowing result
-            line_thickness (int, *optional*, 5): thickness for box drawing lines
-        """
-        # Plots one bounding box on image img
-        tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
-        color = color or [random.randint(0, 255) for _ in range(3)]
-        c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
-        cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
-        if label:
-            tf = max(tl - 1, 1)  # font thickness
-            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
-            c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
-            cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
-            cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
-    
-        return img
-    
-    
-    def draw_results(results:Dict, source_image:np.ndarray, label_map:Dict):
-        """
-        Helper function for drawing bounding boxes on image
-        Parameters:
-            image_res (np.ndarray): detection predictions in format [x1, y1, x2, y2, score, label_id]
-            source_image (np.ndarray): input image for drawing
-            label_map; (Dict[int, str]): label_id to class name mapping
-        Returns:
-            Image with boxes
-        """
-        boxes = results["det"]
-        for idx, (*xyxy, conf, lbl) in enumerate(boxes):
-            label = f'{label_map[int(lbl)]} {conf:.2f}'
-            source_image = plot_one_box(xyxy, source_image, label=label, color=colors(int(lbl)), line_thickness=1)
-        return source_image
-
 .. code:: ipython3
 
     # Download a test sample
-    IMAGE_PATH = Path('./data/coco_bike.jpg')
+    IMAGE_PATH = Path("./data/coco_bike.jpg")
     download_file(
-        url='https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bike.jpg',
+        url="https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bike.jpg",
         filename=IMAGE_PATH.name,
-        directory=IMAGE_PATH.parent
-    ) 
-
-
-.. parsed-literal::
-
-    'data/coco_bike.jpg' already exists.
-
-
-
-
-.. parsed-literal::
-
-    PosixPath('/home/ea/work/openvino_notebooks/notebooks/230-yolov8-optimization/data/coco_bike.jpg')
-
-
+        directory=IMAGE_PATH.parent,
+    )
 
 Instantiate model
 -----------------
@@ -222,7 +156,7 @@ Let us consider the examples:
 
 .. code:: ipython3
 
-    models_dir = Path('./models')
+    models_dir = Path("./models")
     models_dir.mkdir(exist_ok=True)
 
 .. code:: ipython3
@@ -232,7 +166,7 @@ Let us consider the examples:
     
     DET_MODEL_NAME = "yolov8n"
     
-    det_model = YOLO(models_dir / f'{DET_MODEL_NAME}.pt')
+    det_model = YOLO(models_dir / f"{DET_MODEL_NAME}.pt")
     label_map = det_model.model.names
     
     res = det_model(IMAGE_PATH)
@@ -241,20 +175,24 @@ Let us consider the examples:
 
 .. parsed-literal::
 
-    2023-10-05 19:15:51.230030: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2023-10-05 19:15:51.269549: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2023-10-05 19:15:51.909328: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    Ultralytics YOLOv8.0.43 🚀 Python-3.8.10 torch-2.0.1+cpu CPU
-    YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs
+    Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt to 'models/yolov8n.pt'...
+
+
+.. parsed-literal::
+
+    100%|██████████████████████████████████████████████████████████████████████████████| 6.23M/6.23M [00:01<00:00, 3.73MB/s]
+
+
+.. parsed-literal::
+
     
-    image 1/1 /home/ea/work/openvino_notebooks/notebooks/230-yolov8-optimization/data/coco_bike.jpg: 480x640 2 bicycles, 2 cars, 1 dog, 48.7ms
-    Speed: 2.6ms preprocess, 48.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)
+    image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 480x640 2 bicycles, 2 cars, 1 dog, 43.2ms
+    Speed: 1.9ms preprocess, 43.2ms inference, 0.9ms postprocess per image at shape (1, 3, 480, 640)
 
 
 
 
-.. image:: 230-yolov8-object-detection-with-output_files/230-yolov8-object-detection-with-output_11_1.png
+.. image:: yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_9_3.png
 
 
 
@@ -273,199 +211,33 @@ preserve dynamic shapes in the model.
     # object detection model
     det_model_path = models_dir / f"{DET_MODEL_NAME}_openvino_model/{DET_MODEL_NAME}.xml"
     if not det_model_path.exists():
-        det_model.export(format="openvino", dynamic=True, half=False)
+        det_model.export(format="openvino", dynamic=True, half=True)
+
+
+.. parsed-literal::
+
+    Ultralytics YOLOv8.1.42 🚀 Python-3.10.12 torch-2.2.2+cpu CPU (Intel Core(TM) i9-10980XE 3.00GHz)
+    
+    PyTorch: starting from 'models/yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)
+    
+    OpenVINO: starting export with openvino 2024.0.0-14509-34caeefd078-releases/2024/0...
+    OpenVINO: export success ✅ 1.8s, saved as 'models/yolov8n_openvino_model/' (6.4 MB)
+    
+    Export complete (3.0s)
+    Results saved to /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/models
+    Predict:         yolo predict task=detect model=models/yolov8n_openvino_model imgsz=640 half 
+    Validate:        yolo val task=detect model=models/yolov8n_openvino_model imgsz=640 data=coco.yaml half 
+    Visualize:       https://netron.app
+
 
 Verify model inference
 ~~~~~~~~~~~~~~~~~~~~~~
 
 
 
-To test model work, we create inference pipeline similar to
-``model.predict`` method. The pipeline consists of preprocessing step,
-inference of OpenVINO model and results post-processing to get results.
-
-Preprocessing
-~~~~~~~~~~~~~
-
-
-
-Model input is a tensor with the ``[-1, 3, -1, -1]`` shape in the
-``N, C, H, W`` format, where \* ``N`` - number of images in batch (batch
-size) \* ``C`` - image channels \* ``H`` - image height \* ``W`` - image
-width
-
-The model expects images in RGB channels format and normalized in [0, 1]
-range. Although the model supports dynamic input shape with preserving
-input divisibility to 32, it is recommended to use static shapes, for
-example, 640x640 for better efficiency. To resize images to fit model
-size ``letterbox``, resize approach is used, where the aspect ratio of
-width and height is preserved.
-
-To keep a specific shape, preprocessing automatically enables padding.
-
-.. code:: ipython3
-
-    from typing import Tuple
-    from ultralytics.yolo.utils import ops
-    import torch
-    import numpy as np
-    
-    
-    def letterbox(img: np.ndarray, new_shape:Tuple[int, int] = (640, 640), color:Tuple[int, int, int] = (114, 114, 114), auto:bool = False, scale_fill:bool = False, scaleup:bool = False, stride:int = 32):
-        """
-        Resize image and padding for detection. Takes image as input, 
-        resizes image to fit into new shape with saving original aspect ratio and pads it to meet stride-multiple constraints
-        
-        Parameters:
-          img (np.ndarray): image for preprocessing
-          new_shape (Tuple(int, int)): image size after preprocessing in format [height, width]
-          color (Tuple(int, int, int)): color for filling padded area
-          auto (bool): use dynamic input size, only padding for stride constrins applied
-          scale_fill (bool): scale image to fill new_shape
-          scaleup (bool): allow scale image if it is lower then desired input size, can affect model accuracy
-          stride (int): input padding stride
-        Returns:
-          img (np.ndarray): image after preprocessing
-          ratio (Tuple(float, float)): hight and width scaling ratio
-          padding_size (Tuple(int, int)): height and width padding size
-        
-        
-        """
-        # Resize and pad image while meeting stride-multiple constraints
-        shape = img.shape[:2]  # current shape [height, width]
-        if isinstance(new_shape, int):
-            new_shape = (new_shape, new_shape)
-    
-        # Scale ratio (new / old)
-        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-        if not scaleup:  # only scale down, do not scale up (for better test mAP)
-            r = min(r, 1.0)
-    
-        # Compute padding
-        ratio = r, r  # width, height ratios
-        new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
-        dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
-        if auto:  # minimum rectangle
-            dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
-        elif scale_fill:  # stretch
-            dw, dh = 0.0, 0.0
-            new_unpad = (new_shape[1], new_shape[0])
-            ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
-    
-        dw /= 2  # divide padding into 2 sides
-        dh /= 2
-    
-        if shape[::-1] != new_unpad:  # resize
-            img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
-        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-        return img, ratio, (dw, dh)
-    
-    
-    def preprocess_image(img0: np.ndarray):
-        """
-        Preprocess image according to YOLOv8 input requirements. 
-        Takes image in np.array format, resizes it to specific size using letterbox resize and changes data layout from HWC to CHW.
-        
-        Parameters:
-          img0 (np.ndarray): image for preprocessing
-        Returns:
-          img (np.ndarray): image after preprocessing
-        """
-        # resize
-        img = letterbox(img0)[0]
-        
-        # Convert HWC to CHW
-        img = img.transpose(2, 0, 1)
-        img = np.ascontiguousarray(img)
-        return img
-    
-    
-    def image_to_tensor(image:np.ndarray):
-        """
-        Preprocess image according to YOLOv8 input requirements. 
-        Takes image in np.array format, resizes it to specific size using letterbox resize and changes data layout from HWC to CHW.
-        
-        Parameters:
-          img (np.ndarray): image for preprocessing
-        Returns:
-          input_tensor (np.ndarray): input tensor in NCHW format with float32 values in [0, 1] range 
-        """
-        input_tensor = image.astype(np.float32)  # uint8 to fp32
-        input_tensor /= 255.0  # 0 - 255 to 0.0 - 1.0
-        
-        # add batch dimension
-        if input_tensor.ndim == 3:
-            input_tensor = np.expand_dims(input_tensor, 0)
-        return input_tensor
-
-Postprocessing
-~~~~~~~~~~~~~~
-
-
-
-The model output contains detection boxes candidates, it is a tensor
-with the ``[-1,84,-1]`` shape in the ``B,84,N`` format, where:
-
--  ``B`` - batch size
--  ``N`` - number of detection boxes
-
-For getting the final prediction, we need to apply a non-maximum
-suppression algorithm and rescale box coordinates to the original image
-size.
-
-Finally, detection box has the [``x``, ``y``, ``h``, ``w``,
-``class_no_1``, …, ``class_no_80``] format, where:
-
--  (``x``, ``y``) - raw coordinates of box center
--  ``h``, ``w`` - raw height and width of the box
--  ``class_no_1``, …, ``class_no_80`` - probability distribution over
-   the classes.
-
-.. code:: ipython3
-
-    def postprocess(
-        pred_boxes:np.ndarray, 
-        input_hw:Tuple[int, int], 
-        orig_img:np.ndarray, 
-        min_conf_threshold:float = 0.25, 
-        nms_iou_threshold:float = 0.7, 
-        agnosting_nms:bool = False, 
-        max_detections:int = 300,
-    ):
-        """
-        YOLOv8 model postprocessing function. Applied non maximum supression algorithm to detections and rescale boxes to original image size
-        Parameters:
-            pred_boxes (np.ndarray): model output prediction boxes
-            input_hw (np.ndarray): preprocessed image
-            orig_image (np.ndarray): image before preprocessing
-            min_conf_threshold (float, *optional*, 0.25): minimal accepted confidence for object filtering
-            nms_iou_threshold (float, *optional*, 0.45): minimal overlap score for removing objects duplicates in NMS
-            agnostic_nms (bool, *optiona*, False): apply class agnostinc NMS approach or not
-            max_detections (int, *optional*, 300):  maximum detections after NMS
-        Returns:
-           pred (List[Dict[str, np.ndarray]]): list of dictionary with det - detected boxes in format [x1, y1, x2, y2, score, label]
-        """
-        nms_kwargs = {"agnostic": agnosting_nms, "max_det":max_detections}
-        preds = ops.non_max_suppression(
-            torch.from_numpy(pred_boxes),
-            min_conf_threshold,
-            nms_iou_threshold,
-            nc=80,
-            **nms_kwargs
-        )
-    
-        results = []
-        for i, pred in enumerate(preds):
-            shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
-            if not len(pred):
-                results.append({"det": [], "segment": []})
-                continue
-            pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()
-            results.append({"det": pred})
-    
-        return results
+We can reuse the base model pipeline for pre- and postprocessing just
+replacing the inference method where we will use the IR model for
+inference.
 
 Select inference device
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -483,8 +255,8 @@ Select device from dropdown list for running inference using OpenVINO
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -495,7 +267,7 @@ Select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
 
 
 
@@ -509,44 +281,42 @@ ready to check model prediction for object detection.
 
 .. code:: ipython3
 
+    import torch
+    
     core = ov.Core()
     
     det_ov_model = core.read_model(det_model_path)
+    
+    ov_config = {}
     if device.value != "CPU":
         det_ov_model.reshape({0: [1, 3, 640, 640]})
-    ov_config = {}
     if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
         ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
     det_compiled_model = core.compile_model(det_ov_model, device.value, ov_config)
     
     
-    def detect(image:np.ndarray, model:ov.Model):
-        """
-        OpenVINO YOLOv8 model inference function. Preprocess image, runs model inference and postprocess results using NMS.
-        Parameters:
-            image (np.ndarray): input image.
-            model (Model): OpenVINO compiled model.
-        Returns:
-            detections (np.ndarray): detected boxes in format [x1, y1, x2, y2, score, label]
-        """
-        preprocessed_image = preprocess_image(image)
-        input_tensor = image_to_tensor(preprocessed_image)
-        result = model(input_tensor)
-        boxes = result[model.output(0)]
-        input_hw = input_tensor.shape[2:]
-        detections = postprocess(pred_boxes=boxes, input_hw=input_hw, orig_img=image)
-        return detections
+    def infer(*args):
+        result = det_compiled_model(args)
+        return torch.from_numpy(result[0])
     
-    input_image = np.array(Image.open(IMAGE_PATH))
-    detections = detect(input_image, det_compiled_model)[0]
-    image_with_boxes = draw_results(detections, input_image, label_map)
     
-    Image.fromarray(image_with_boxes)
+    det_model.predictor.inference = infer
+    det_model.predictor.model.pt = False
+    
+    res = det_model(IMAGE_PATH)
+    Image.fromarray(res[0].plot()[:, :, ::-1])
+
+
+.. parsed-literal::
+
+    
+    image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 640x640 2 bicycles, 2 cars, 1 dog, 27.5ms
+    Speed: 3.2ms preprocess, 27.5ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)
 
 
 
 
-.. image:: 230-yolov8-object-detection-with-output_files/230-yolov8-object-detection-with-output_22_0.png
+.. image:: yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_16_1.png
 
 
 
@@ -578,11 +348,14 @@ evaluation function.
 
     from zipfile import ZipFile
     
+    from ultralytics.data.utils import DATASETS_DIR
+    
+    
     DATA_URL = "http://images.cocodataset.org/zips/val2017.zip"
     LABELS_URL = "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip"
-    CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/8ebe94d1e928687feaa1fee6d5668987df5e43be/ultralytics/datasets/coco.yaml"
+    CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/v8.1.0/ultralytics/cfg/datasets/coco.yaml"
     
-    OUT_DIR = Path('./datasets')
+    OUT_DIR = DATASETS_DIR
     
     DATA_PATH = OUT_DIR / "val2017.zip"
     LABELS_PATH = OUT_DIR / "coco2017labels-segments.zip"
@@ -593,23 +366,10 @@ evaluation function.
     download_file(CFG_URL, CFG_PATH.name, CFG_PATH.parent)
     
     if not (OUT_DIR / "coco/labels").exists():
-        with ZipFile(LABELS_PATH , "r") as zip_ref:
+        with ZipFile(LABELS_PATH, "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
-        with ZipFile(DATA_PATH , "r") as zip_ref:
-            zip_ref.extractall(OUT_DIR / 'coco/images')
-
-
-.. parsed-literal::
-
-    'datasets/val2017.zip' already exists.
-    'datasets/coco2017labels-segments.zip' already exists.
-
-
-
-.. parsed-literal::
-
-    datasets/coco.yaml:   0%|          | 0.00/1.25k [00:00
+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+.. parsed-literal::
+
+    /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/venv/lib/python3.10/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+      return Tensor(self.data * unwrap_tensor_data(other))
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
 
 
 .. code:: ipython3
 
-    from openvino.runtime import serialize
-    int8_model_det_path = models_dir / f'{DET_MODEL_NAME}_openvino_int8_model/{DET_MODEL_NAME}.xml'
+    %%skip not $to_quantize.value
+    
     print(f"Quantized detection model will be saved to {int8_model_det_path}")
-    serialize(quantized_det_model, str(int8_model_det_path))
+    ov.save_model(quantized_det_model, str(int8_model_det_path))
 
 
 .. parsed-literal::
@@ -912,36 +866,41 @@ on the image.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     device
 
+.. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
+    ov_config = {}
+    if device.value != "CPU":
+        quantized_det_model.reshape({0: [1, 3, 640, 640]})
+    if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
+        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
+    quantized_det_compiled_model = core.compile_model(quantized_det_model, device.value, ov_config)
+    
+    
+    def infer(*args):
+        result = quantized_det_compiled_model(args)
+        return torch.from_numpy(result[0])
+    
+    det_model.predictor.inference = infer
+    
+    res = det_model(IMAGE_PATH)
+    display(Image.fromarray(res[0].plot()[:, :, ::-1]))
 
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-    if device.value != "CPU":
-        quantized_det_model.reshape({0: [1, 3, 640, 640]})
-    ov_config = {}
-    if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
-        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
-    quantized_det_compiled_model = core.compile_model(quantized_det_model, device.value, ov_config)
-    input_image = np.array(Image.open(IMAGE_PATH))
-    detections = detect(input_image, quantized_det_compiled_model)[0]
-    image_with_boxes = draw_results(detections, input_image, label_map)
     
-    Image.fromarray(image_with_boxes)
+    image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 640x640 2 bicycles, 2 cars, 1 dog, 18.4ms
+    Speed: 2.1ms preprocess, 18.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)
 
 
 
-
-.. image:: 230-yolov8-object-detection-with-output_files/230-yolov8-object-detection-with-output_45_0.png
-
+.. image:: yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_43_1.png
 
 
 Compare the Original and Quantized Models
@@ -970,21 +929,15 @@ models.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     device
 
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
 .. code:: ipython3
 
-    # Inference FP32 model (OpenVINO IR)
-    !benchmark_app -m $det_model_path -d $device.value -api async -shape "[1,3,640,640]"
+    if int8_model_det_path.exists():
+        # Inference FP32 model (OpenVINO IR)
+        !benchmark_app -m $det_model_path -d $device.value -api async -shape "[1,3,640,640]"
 
 
 .. parsed-literal::
@@ -994,37 +947,37 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 16.72 ms
+    [ INFO ] Read model took 14.59 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : f32 / [...] / [?,3,?,?]
+    [ INFO ]     x (node: x) : f32 / [...] / [?,3,?,?]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [?,84,?]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_7) : f32 / [...] / [?,84,16..]
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: 'images': [1,3,640,640]
-    [ INFO ] Reshape model took 11.83 ms
+    [ INFO ] Reshaping model: 'x': [1,3,640,640]
+    [ INFO ] Reshape model took 8.72 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : u8 / [N,C,H,W] / [1,3,640,640]
+    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,84,8400]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_7) : f32 / [...] / [1,84,8400]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 424.62 ms
+    [ INFO ] Compile model took 272.15 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: torch_jit
+    [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
     [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
@@ -1033,43 +986,47 @@ models.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
     [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
     [ INFO ]     INFERENCE_NUM_THREADS: 36
     [ INFO ]     INFERENCE_PRECISION_HINT: 
-    [ INFO ]     NETWORK_NAME: torch_jit
+    [ INFO ]     KV_CACHE_PRECISION: 
+    [ INFO ]     LOG_LEVEL: Level.NO
+    [ INFO ]     NETWORK_NAME: Model0
     [ INFO ]     NUM_STREAMS: 12
     [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
-    [ INFO ]     PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
+    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
     [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
-    [ INFO ]     PERF_COUNT: False
+    [ INFO ]     PERF_COUNT: NO
     [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
     [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
+    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
+    [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 34.66 ms
+    [ INFO ] First inference took 41.00 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            15024 iterations
-    [ INFO ] Duration:         120272.02 ms
+    [ INFO ] Count:            21300 iterations
+    [ INFO ] Duration:         120060.45 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        77.25 ms
-    [ INFO ]    Average:       95.83 ms
-    [ INFO ]    Min:           60.23 ms
-    [ INFO ]    Max:           270.56 ms
-    [ INFO ] Throughput:   124.92 FPS
+    [ INFO ]    Median:        67.21 ms
+    [ INFO ]    Average:       67.48 ms
+    [ INFO ]    Min:           31.90 ms
+    [ INFO ]    Max:           143.04 ms
+    [ INFO ] Throughput:   177.41 FPS
 
 
 .. code:: ipython3
 
-    # Inference INT8 model (OpenVINO IR)
-    !benchmark_app -m $int8_model_det_path -d $device.value -api async -shape "[1,3,640,640]" -t 15
+    if int8_model_det_path.exists():
+        # Inference INT8 model (OpenVINO IR)
+        !benchmark_app -m $int8_model_det_path -d $device.value -api async -shape "[1,3,640,640]" -t 15
 
 
 .. parsed-literal::
@@ -1078,76 +1035,79 @@ models.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2023.2.0-12690-0ee0b4d9561
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 73.93 ms
+    [ INFO ] Read model took 21.34 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : f32 / [...] / [1,3,?,?]
+    [ INFO ]     x (node: x) : f32 / [...] / [1,3,?,?]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,84,21..]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_7) : f32 / [...] / [1,84,21..]
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: 'images': [1,3,640,640]
-    [ INFO ] Reshape model took 56.37 ms
+    [ INFO ] Reshaping model: 'x': [1,3,640,640]
+    [ INFO ] Reshape model took 11.86 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : u8 / [N,C,H,W] / [1,3,640,640]
+    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,84,8400]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_7) : f32 / [...] / [1,84,8400]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 1742.92 ms
+    [ INFO ] Compile model took 478.52 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: torch_jit
+    [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
-    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 18
+    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
     [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
     [ INFO ]   CPU:
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
     [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
     [ INFO ]     INFERENCE_NUM_THREADS: 36
     [ INFO ]     INFERENCE_PRECISION_HINT: 
-    [ INFO ]     NETWORK_NAME: torch_jit
-    [ INFO ]     NUM_STREAMS: 18
-    [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 18
-    [ INFO ]     PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
+    [ INFO ]     KV_CACHE_PRECISION: 
+    [ INFO ]     LOG_LEVEL: Level.NO
+    [ INFO ]     NETWORK_NAME: Model0
+    [ INFO ]     NUM_STREAMS: 12
+    [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
+    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
     [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
-    [ INFO ]     PERF_COUNT: False
+    [ INFO ]     PERF_COUNT: NO
     [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
     [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
-    [Step 10/11] Measuring performance (Start inference asynchronously, 18 inference requests, limits: 15000 ms duration)
+    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
+    [ INFO ] Fill input 'x' with random values 
+    [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 58.19 ms
+    [ INFO ] First inference took 35.17 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            3150 iterations
-    [ INFO ] Duration:         15116.07 ms
+    [ INFO ] Count:            4104 iterations
+    [ INFO ] Duration:         15062.51 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        79.96 ms
-    [ INFO ]    Average:       85.97 ms
-    [ INFO ]    Min:           56.29 ms
-    [ INFO ]    Max:           154.33 ms
-    [ INFO ] Throughput:   208.39 FPS
+    [ INFO ]    Median:        43.53 ms
+    [ INFO ]    Average:       43.85 ms
+    [ INFO ]    Min:           24.58 ms
+    [ INFO ]    Max:           70.57 ms
+    [ INFO ] Throughput:   272.46 FPS
 
 
 Validate quantized model accuracy
@@ -1162,6 +1122,8 @@ accuracy on a dataset.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     int8_det_stats = test(quantized_det_model, core, det_data_loader, det_validator, num_samples=NUM_TEST_SAMPLES)
 
 
@@ -1173,6 +1135,8 @@ accuracy on a dataset.
 
 .. code:: ipython3
 
+    %%skip not $to_quantize.value
+    
     print("FP32 model accuracy")
     print_stats(fp_det_stats, det_validator.seen, det_validator.nt_per_class.sum())
     
@@ -1184,12 +1148,14 @@ accuracy on a dataset.
 
     FP32 model accuracy
     Boxes:
+        Best mean average:
                    Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
-                     all         300        2145       0.594       0.543       0.579       0.417
+                     all         300        2153       0.594       0.542       0.579       0.417
     INT8 model accuracy
     Boxes:
+        Best mean average:
                    Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
-                     all         300        2145       0.623       0.517       0.572       0.406
+                     all         300        2153       0.597       0.509       0.562       0.389
 
 
 Great! Looks like accuracy was changed, but not significantly and it
@@ -1198,20 +1164,19 @@ meets passing criteria.
 Next steps
 ----------
 
-This section contains
+ This section contains
 suggestions on how to additionally improve the performance of your
 application using OpenVINO.
 
 Async inference pipeline
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-The key advantage of the Async
+ The key advantage of the Async
 API is that when a device is busy with inference, the application can
 perform other tasks in parallel (for example, populating inputs or
 scheduling other requests) rather than wait for the current inference to
 complete first. To understand how to perform async inference using
-openvino, refer to `Async API
-tutorial <115-async-api-with-output.html>`__
+openvino, refer to `Async API tutorial `__
 
 Integration preprocessing to model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1265,7 +1230,7 @@ provide this to the tensor description.
 
 .. code:: ipython3
 
-    ppp.input(0).tensor().set_shape([1, 640, 640, 3]).set_element_type(ov.Type.u8).set_layout(ov.Layout('NHWC'))
+    ppp.input(0).tensor().set_shape([1, 640, 640, 3]).set_element_type(ov.Type.u8).set_layout(ov.Layout("NHWC"))
     pass
 
 To perform layout conversion, we also should provide information about
@@ -1286,14 +1251,14 @@ preprocessing steps:
 
 .. code:: ipython3
 
-    ppp.input(0).preprocess().convert_element_type(ov.Type.f32).convert_layout(ov.Layout('NCHW')).scale([255., 255., 255.])
+    ppp.input(0).preprocess().convert_element_type(ov.Type.f32).convert_layout(ov.Layout("NCHW")).scale([255.0, 255.0, 255.0])
     
     print(ppp)
 
 
 .. parsed-literal::
 
-    Input "images":
+    Input "x":
         User's input tensor: [1,640,640,3], [N,H,W,C], u8
         Model's expected tensor: [1,3,?,?], [N,C,H,W], f32
         Pre-processing steps (3):
@@ -1315,14 +1280,206 @@ IR, using ``openvino.runtime.serialize``.
 .. code:: ipython3
 
     quantized_model_with_preprocess = ppp.build()
-    serialize(quantized_model_with_preprocess, str(int8_model_det_path.with_name(f"{DET_MODEL_NAME}_with_preprocess.xml")))
+    ov.save_model(
+        quantized_model_with_preprocess,
+        str(int8_model_det_path.with_name(f"{DET_MODEL_NAME}_with_preprocess.xml")),
+    )
 
 The model with integrated preprocessing is ready for loading to a
-device. Now, we can skip these preprocessing steps in detect function:
+device.
 
 .. code:: ipython3
 
-    def detect_without_preprocess(image:np.ndarray, model:ov.Model):
+    from typing import Tuple, Dict
+    import cv2
+    import numpy as np
+    from ultralytics.utils.plotting import colors
+    
+    
+    def plot_one_box(
+        box: np.ndarray,
+        img: np.ndarray,
+        color: Tuple[int, int, int] = None,
+        label: str = None,
+        line_thickness: int = 5,
+    ):
+        """
+        Helper function for drawing single bounding box on image
+        Parameters:
+            x (np.ndarray): bounding box coordinates in format [x1, y1, x2, y2]
+            img (no.ndarray): input image
+            color (Tuple[int, int, int], *optional*, None): color in BGR format for drawing box, if not specified will be selected randomly
+            label (str, *optonal*, None): box label string, if not provided will not be provided as drowing result
+            line_thickness (int, *optional*, 5): thickness for box drawing lines
+        """
+        # Plots one bounding box on image img
+        tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
+        color = color or [random.randint(0, 255) for _ in range(3)]
+        c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
+        cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
+        if label:
+            tf = max(tl - 1, 1)  # font thickness
+            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
+            c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
+            cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
+            cv2.putText(
+                img,
+                label,
+                (c1[0], c1[1] - 2),
+                0,
+                tl / 3,
+                [225, 255, 255],
+                thickness=tf,
+                lineType=cv2.LINE_AA,
+            )
+    
+        return img
+    
+    
+    def draw_results(results: Dict, source_image: np.ndarray, label_map: Dict):
+        """
+        Helper function for drawing bounding boxes on image
+        Parameters:
+            image_res (np.ndarray): detection predictions in format [x1, y1, x2, y2, score, label_id]
+            source_image (np.ndarray): input image for drawing
+            label_map; (Dict[int, str]): label_id to class name mapping
+        Returns:
+            Image with boxes
+        """
+        boxes = results["det"]
+        for idx, (*xyxy, conf, lbl) in enumerate(boxes):
+            label = f"{label_map[int(lbl)]} {conf:.2f}"
+            source_image = plot_one_box(xyxy, source_image, label=label, color=colors(int(lbl)), line_thickness=1)
+        return source_image
+
+Postprocessing
+''''''''''''''
+
+
+
+The model output contains detection boxes candidates, it is a tensor
+with the [-1,84,-1] shape in the B,84,N format, where:
+
+B - batch size N - number of detection boxes For getting the final
+prediction, we need to apply a non-maximum suppression algorithm and
+rescale box coordinates to the original image size.
+
+Finally, detection box has the [x, y, h, w, class_no_1, …, class_no_80]
+format, where:
+
+(x, y) - raw coordinates of box center h, w - raw height and width of
+the box class_no_1, …, class_no_80 - probability distribution over the
+classes.
+
+.. code:: ipython3
+
+    from typing import Tuple
+    from ultralytics.utils import ops
+    import torch
+    import numpy as np
+    
+    
+    def letterbox(
+        img: np.ndarray,
+        new_shape: Tuple[int, int] = (640, 640),
+        color: Tuple[int, int, int] = (114, 114, 114),
+        auto: bool = False,
+        scale_fill: bool = False,
+        scaleup: bool = False,
+        stride: int = 32,
+    ):
+        """
+        Resize image and padding for detection. Takes image as input,
+        resizes image to fit into new shape with saving original aspect ratio and pads it to meet stride-multiple constraints
+    
+        Parameters:
+          img (np.ndarray): image for preprocessing
+          new_shape (Tuple(int, int)): image size after preprocessing in format [height, width]
+          color (Tuple(int, int, int)): color for filling padded area
+          auto (bool): use dynamic input size, only padding for stride constrins applied
+          scale_fill (bool): scale image to fill new_shape
+          scaleup (bool): allow scale image if it is lower then desired input size, can affect model accuracy
+          stride (int): input padding stride
+        Returns:
+          img (np.ndarray): image after preprocessing
+          ratio (Tuple(float, float)): hight and width scaling ratio
+          padding_size (Tuple(int, int)): height and width padding size
+    
+    
+        """
+        # Resize and pad image while meeting stride-multiple constraints
+        shape = img.shape[:2]  # current shape [height, width]
+        if isinstance(new_shape, int):
+            new_shape = (new_shape, new_shape)
+    
+        # Scale ratio (new / old)
+        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+        if not scaleup:  # only scale down, do not scale up (for better test mAP)
+            r = min(r, 1.0)
+    
+        # Compute padding
+        ratio = r, r  # width, height ratios
+        new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+        dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+        if auto:  # minimum rectangle
+            dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
+        elif scale_fill:  # stretch
+            dw, dh = 0.0, 0.0
+            new_unpad = (new_shape[1], new_shape[0])
+            ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
+    
+        dw /= 2  # divide padding into 2 sides
+        dh /= 2
+    
+        if shape[::-1] != new_unpad:  # resize
+            img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+        return img, ratio, (dw, dh)
+    
+    
+    def postprocess(
+        pred_boxes: np.ndarray,
+        input_hw: Tuple[int, int],
+        orig_img: np.ndarray,
+        min_conf_threshold: float = 0.25,
+        nms_iou_threshold: float = 0.7,
+        agnosting_nms: bool = False,
+        max_detections: int = 300,
+    ):
+        """
+        YOLOv8 model postprocessing function. Applied non maximum supression algorithm to detections and rescale boxes to original image size
+        Parameters:
+            pred_boxes (np.ndarray): model output prediction boxes
+            input_hw (np.ndarray): preprocessed image
+            orig_image (np.ndarray): image before preprocessing
+            min_conf_threshold (float, *optional*, 0.25): minimal accepted confidence for object filtering
+            nms_iou_threshold (float, *optional*, 0.45): minimal overlap score for removing objects duplicates in NMS
+            agnostic_nms (bool, *optiona*, False): apply class agnostinc NMS approach or not
+            max_detections (int, *optional*, 300):  maximum detections after NMS
+        Returns:
+           pred (List[Dict[str, np.ndarray]]): list of dictionary with det - detected boxes in format [x1, y1, x2, y2, score, label]
+        """
+        nms_kwargs = {"agnostic": agnosting_nms, "max_det": max_detections}
+        preds = ops.non_max_suppression(torch.from_numpy(pred_boxes), min_conf_threshold, nms_iou_threshold, nc=80, **nms_kwargs)
+    
+        results = []
+        for i, pred in enumerate(preds):
+            shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
+            if not len(pred):
+                results.append({"det": [], "segment": []})
+                continue
+            pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()
+            results.append({"det": pred})
+    
+        return results
+
+Now, we can skip these preprocessing steps in detect function:
+
+.. code:: ipython3
+
+    def detect_without_preprocess(image: np.ndarray, model: ov.Model):
         """
         OpenVINO YOLOv8 model with integrated preprocessing inference function. Preprocess image, runs model inference and postprocess results using NMS.
         Parameters:
@@ -1350,7 +1507,7 @@ device. Now, we can skip these preprocessing steps in detect function:
 
 
 
-.. image:: 230-yolov8-object-detection-with-output_files/230-yolov8-object-detection-with-output_68_0.png
+.. image:: yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_70_0.png
 
 
 
@@ -1369,26 +1526,36 @@ The following code runs model inference on a video:
     
     
     # Main processing function to run object detection.
-    def run_object_detection(source=0, flip=False, use_popup=False, skip_first_frames=0, model=det_model, device=device.value):
+    def run_object_detection(
+        source=0,
+        flip=False,
+        use_popup=False,
+        skip_first_frames=0,
+        model=det_model,
+        device=device.value,
+    ):
         player = None
+        ov_config = {}
         if device != "CPU":
             model.reshape({0: [1, 3, 640, 640]})
-        ov_config = {}
         if "GPU" in device or ("AUTO" in device and "GPU" in core.available_devices):
             ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
         compiled_model = core.compile_model(model, device, ov_config)
+    
+        def infer(*args):
+            result = compiled_model(args)
+            return torch.from_numpy(result[0])
+    
+        det_model.predictor.inference = infer
+    
         try:
             # Create a video player to play with target fps.
-            player = VideoPlayer(
-                source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames
-            )
+            player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
             # Start capturing.
             player.start()
             if use_popup:
                 title = "Press ESC to Exit"
-                cv2.namedWindow(
-                    winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE
-                )
+                cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
     
             processing_times = collections.deque()
             while True:
@@ -1409,15 +1576,12 @@ The following code runs model inference on a video:
                     )
                 # Get the results.
                 input_image = np.array(frame)
-               
+    
                 start_time = time.time()
-                # model expects RGB image, while video capturing in BGR
-                detections = detect(input_image[:, :, ::-1], compiled_model)[0]
+                detections = det_model(input_image)
                 stop_time = time.time()
-                
-                image_with_boxes = draw_results(detections, input_image, label_map)
-                frame = image_with_boxes
-               
+                frame = detections[0].plot()
+    
                 processing_times.append(stop_time - start_time)
                 # Use processing times from last 200 frames.
                 if len(processing_times) > 200:
@@ -1446,9 +1610,7 @@ The following code runs model inference on a video:
                         break
                 else:
                     # Encode numpy array to jpg.
-                    _, encoded_img = cv2.imencode(
-                        ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100]
-                    )
+                    _, encoded_img = cv2.imencode(ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                     # Create an IPython image.
                     i = display.Image(data=encoded_img)
                     # Display the image in this notebook.
@@ -1495,7 +1657,7 @@ Run the object detection:
     if WEBCAM_INFERENCE:
         VIDEO_SOURCE = 0  # Webcam
     else:
-        VIDEO_SOURCE = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4'
+        VIDEO_SOURCE = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4"
 
 .. code:: ipython3
 
@@ -1506,17 +1668,23 @@ Run the object detection:
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
 
 
 
 .. code:: ipython3
 
-    run_object_detection(source=VIDEO_SOURCE, flip=True, use_popup=False, model=det_ov_model, device=device.value)
+    run_object_detection(
+        source=VIDEO_SOURCE,
+        flip=True,
+        use_popup=False,
+        model=det_ov_model,
+        device=device.value,
+    )
 
 
 
-.. image:: 230-yolov8-object-detection-with-output_files/230-yolov8-object-detection-with-output_74_0.png
+.. image:: yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_76_0.png
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_16_1.jpg b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_16_1.jpg
new file mode 100644
index 00000000000..2b4b65964f1
--- /dev/null
+++ b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_16_1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d6901acdfbfd273c0a7970318f59ba9514dbee953dd870d71a5de412e0cc246
+size 111417
diff --git a/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_16_1.png b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_16_1.png
new file mode 100644
index 00000000000..d42aab88446
--- /dev/null
+++ b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_16_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40fa74aecca1fa02a15db2a8fc43c257f172db88e21b6bba1f67819a167f7449
+size 908462
diff --git a/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_43_1.jpg b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_43_1.jpg
new file mode 100644
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+++ b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_43_1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 111335
diff --git a/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_43_1.png b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_43_1.png
new file mode 100644
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+++ b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_43_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9211888d2a74fcb67271b1c560fb4e2efedcefd37ac44481b05c75734908f8e
+size 908423
diff --git a/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_70_0.jpg b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_70_0.jpg
new file mode 100644
index 00000000000..35b6ae18fe9
--- /dev/null
+++ b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_70_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:914205f2366830f620cc88c5c2a1e88b15e69f0afe385c481154e3e153480e0d
+size 109877
diff --git a/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_70_0.png b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_70_0.png
new file mode 100644
index 00000000000..f14dab1a70f
--- /dev/null
+++ b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_70_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9483b93e0c62b097c25dcc88646c392fe1445f91d7e11581ccbd430ca1ae95d
+size 929860
diff --git a/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_76_0.png b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_76_0.png
new file mode 100644
index 00000000000..8ff867ed34e
--- /dev/null
+++ b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_76_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14eee81cc9c0befa33164309f8520d262db19d12e88a991d26f4cc07fb2e03ed
+size 534156
diff --git a/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_9_3.jpg b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_9_3.jpg
new file mode 100644
index 00000000000..83bcf44d9d7
--- /dev/null
+++ b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_9_3.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:81fe23909563b71b50f1ae60c9ae80a4c2cd01a4ba5b9e706a65bd03c0275fe1
+size 111189
diff --git a/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_9_3.png b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_9_3.png
new file mode 100644
index 00000000000..d4e0d69e39c
--- /dev/null
+++ b/docs/notebooks/yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_9_3.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:930dcd05aa18f0ee65ce5b6db12e1732901a052d6f71aa5955ebdf322a854e3c
+size 908490
diff --git a/docs/notebooks/122-yolov8-quantization-with-accuracy-control-with-output.rst b/docs/notebooks/yolov8-quantization-with-accuracy-control-with-output.rst
similarity index 67%
rename from docs/notebooks/122-yolov8-quantization-with-accuracy-control-with-output.rst
rename to docs/notebooks/yolov8-quantization-with-accuracy-control-with-output.rst
index b7a5f01792d..0606f49713e 100644
--- a/docs/notebooks/122-yolov8-quantization-with-accuracy-control-with-output.rst
+++ b/docs/notebooks/yolov8-quantization-with-accuracy-control-with-output.rst
@@ -65,17 +65,9 @@ Install necessary packages.
 
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0"
-    %pip install -q "nncf>=2.6.0"
-    %pip install -q "ultralytics==8.0.43" --extra-index-url https://download.pytorch.org/whl/cpu
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-    Note: you may need to restart the kernel to use updated packages.
-    Note: you may need to restart the kernel to use updated packages.
-
+    %pip install -q "openvino>=2024.0.0"
+    %pip install -q "nncf>=2.9.0"
+    %pip install -q "ultralytics==8.1.42" tqdm --extra-index-url https://download.pytorch.org/whl/cpu
 
 Get Pytorch model and OpenVINO IR model
 ---------------------------------------
@@ -105,14 +97,14 @@ we do not need to do these steps manually.
     from pathlib import Path
     
     from ultralytics import YOLO
-    from ultralytics.yolo.cfg import get_cfg
-    from ultralytics.yolo.data.utils import check_det_dataset
-    from ultralytics.yolo.engine.validator import BaseValidator as Validator
-    from ultralytics.yolo.utils import DEFAULT_CFG
-    from ultralytics.yolo.utils import ops
-    from ultralytics.yolo.utils.metrics import ConfusionMatrix
+    from ultralytics.cfg import get_cfg
+    from ultralytics.data.utils import check_det_dataset
+    from ultralytics.engine.validator import BaseValidator as Validator
+    from ultralytics.utils import DEFAULT_CFG
+    from ultralytics.utils import ops
+    from ultralytics.utils.metrics import ConfusionMatrix
     
-    ROOT = os.path.abspath('')
+    ROOT = os.path.abspath("")
     
     MODEL_NAME = "yolov8n-seg"
     
@@ -123,22 +115,26 @@ we do not need to do these steps manually.
 .. code:: ipython3
 
     # Fetch the notebook utils script from the openvino_notebooks repo
-    import urllib.request
-    urllib.request.urlretrieve(
-        url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
-        filename='notebook_utils.py'
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     
+    open("notebook_utils.py", "w").write(r.text)
+    
     from notebook_utils import download_file
 
 .. code:: ipython3
 
     from zipfile import ZipFile
     
+    from ultralytics.data.utils import DATASETS_DIR
+    
     DATA_URL = "https://www.ultralytics.com/assets/coco128-seg.zip"
     CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/8ebe94d1e928687feaa1fee6d5668987df5e43be/ultralytics/datasets/coco128-seg.yaml"  # last compatible format with ultralytics 8.0.43
     
-    OUT_DIR = Path('./datasets')
+    OUT_DIR = DATASETS_DIR
     
     DATA_PATH = OUT_DIR / "coco128-seg.zip"
     CFG_PATH = OUT_DIR / "coco128-seg.yaml"
@@ -147,19 +143,19 @@ we do not need to do these steps manually.
     download_file(CFG_URL, CFG_PATH.name, CFG_PATH.parent)
     
     if not (OUT_DIR / "coco128/labels").exists():
-        with ZipFile(DATA_PATH , "r") as zip_ref:
+        with ZipFile(DATA_PATH, "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
 
 
 .. parsed-literal::
 
-    'datasets/coco128-seg.zip' already exists.
+    '/home/maleksandr/test_notebooks/ultrali/datasets/coco128-seg.zip' already exists.
 
 
 
 .. parsed-literal::
 
-    datasets/coco128-seg.yaml:   0%|          | 0.00/0.98k [00:00 float:
         validator.seen = 0
         validator.jdict = []
-        validator.stats = []
+        validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[])
         validator.batch_i = 1
         validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
         num_outputs = len(compiled_model.outputs)
@@ -290,7 +283,7 @@ Prepare validation function
             stats_metrics = stats["metrics/mAP50-95(M)"]
         if log:
             print(f"Validate: dataset length = {counter}, metric value = {stats_metrics:.3f}")
-        
+    
         return stats_metrics
     
     
@@ -327,21 +320,10 @@ value 25 to speed up the execution.
         max_drop=0.01,
         preset=nncf.QuantizationPreset.MIXED,
         subset_size=128,
-        advanced_accuracy_restorer_parameters=AdvancedAccuracyRestorerParameters(
-            ranking_subset_size=25
-        ),
+        advanced_accuracy_restorer_parameters=AdvancedAccuracyRestorerParameters(ranking_subset_size=25),
     )
 
 
-.. parsed-literal::
-
-    2024-02-28 13:33:46.187903: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-02-28 13:33:46.189894: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-02-28 13:33:46.226943: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-02-28 13:33:46.942396: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
 
 .. parsed-literal::
 
@@ -363,6 +345,12 @@ value 25 to speed up the execution.
 
 
 
+.. parsed-literal::
+
+    /home/maleksandr/test_notebooks/ultrali/openvino_notebooks/notebooks/quantizing-model-with-accuracy-control/venv/lib/python3.10/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+      return Tensor(self.data * unwrap_tensor_data(other))
+
+
 
 .. parsed-literal::
 
@@ -388,19 +376,19 @@ value 25 to speed up the execution.
 
     INFO:nncf:Validation of initial model was started
     INFO:nncf:Elapsed Time: 00:00:00
-    INFO:nncf:Elapsed Time: 00:00:05
-    INFO:nncf:Metric of initial model: 0.36611468358574506
+    INFO:nncf:Elapsed Time: 00:00:03
+    INFO:nncf:Metric of initial model: 0.3651327608484117
     INFO:nncf:Collecting values for each data item using the initial model
-    INFO:nncf:Elapsed Time: 00:00:06
+    INFO:nncf:Elapsed Time: 00:00:04
     INFO:nncf:Validation of quantized model was started
     INFO:nncf:Elapsed Time: 00:00:00
-    INFO:nncf:Elapsed Time: 00:00:05
-    INFO:nncf:Metric of quantized model: 0.3406029678292
+    INFO:nncf:Elapsed Time: 00:00:03
+    INFO:nncf:Metric of quantized model: 0.34040251506886543
     INFO:nncf:Collecting values for each data item using the quantized model
-    INFO:nncf:Elapsed Time: 00:00:06
-    INFO:nncf:Accuracy drop: 0.02551171575654504 (absolute)
-    INFO:nncf:Accuracy drop: 0.02551171575654504 (absolute)
-    INFO:nncf:Total number of quantized operations in the model: 91
+    INFO:nncf:Elapsed Time: 00:00:04
+    INFO:nncf:Accuracy drop: 0.024730245779546245 (absolute)
+    INFO:nncf:Accuracy drop: 0.024730245779546245 (absolute)
+    INFO:nncf:Total number of quantized operations in the model: 92
     INFO:nncf:Number of parallel workers to rank quantized operations: 1
     INFO:nncf:ORIGINAL metric is used to rank quantizers
 
@@ -428,23 +416,100 @@ value 25 to speed up the execution.
 
 .. parsed-literal::
 
-    INFO:nncf:Elapsed Time: 00:02:25
+    INFO:nncf:Elapsed Time: 00:01:38
     INFO:nncf:Changing the scope of quantizer nodes was started
+    INFO:nncf:Reverted 1 operations to the floating-point precision: 
+    	__module.model.4.m.0.cv2.conv/aten::_convolution/Convolution
+    INFO:nncf:Accuracy drop with the new quantization scope is 0.023408466397916217 (absolute)
+    INFO:nncf:Reverted 1 operations to the floating-point precision: 
+    	__module.model.18.m.0.cv2.conv/aten::_convolution/Convolution
+    INFO:nncf:Accuracy drop with the new quantization scope is 0.024749654890442174 (absolute)
+    INFO:nncf:Re-calculating ranking scores for remaining groups
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. parsed-literal::
+
+    INFO:nncf:Elapsed Time: 00:01:36
+    INFO:nncf:Reverted 1 operations to the floating-point precision: 
+    	__module.model.22.proto.cv3.conv/aten::_convolution/Convolution
+    INFO:nncf:Accuracy drop with the new quantization scope is 0.023229513575966754 (absolute)
     INFO:nncf:Reverted 2 operations to the floating-point precision: 
-    	/model.22/Add_11
-    	/model.22/Sub_1
-    INFO:nncf:Accuracy drop with the new quantization scope is 0.013524778006655136 (absolute)
+    	__module.model.22/aten::add/Add_6
+    	__module.model.22/aten::sub/Subtract
+    INFO:nncf:Accuracy drop with the new quantization scope is 0.02425608378963906 (absolute)
+    INFO:nncf:Re-calculating ranking scores for remaining groups
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. raw:: html
+
+    
+    

+
+
+
+.. parsed-literal::
+
+    INFO:nncf:Elapsed Time: 00:01:35
     INFO:nncf:Reverted 1 operations to the floating-point precision: 
-    	/model.22/Mul_5
-    INFO:nncf:Accuracy drop with the new quantization scope is 0.011937545450662279 (absolute)
-    INFO:nncf:Reverted 1 operations to the floating-point precision: 
-    	/model.2/cv1/conv/Conv/WithoutBiases
-    INFO:nncf:Algorithm completed: achieved required accuracy drop 0.00905169821338292 (absolute)
-    INFO:nncf:4 out of 91 were reverted back to the floating-point precision:
-    	/model.22/Add_11
-    	/model.22/Sub_1
-    	/model.22/Mul_5
-    	/model.2/cv1/conv/Conv/WithoutBiases
+    	__module.model.6.m.0.cv2.conv/aten::_convolution/Convolution
+    INFO:nncf:Accuracy drop with the new quantization scope is 0.023297881500256024 (absolute)
+    INFO:nncf:Reverted 2 operations to the floating-point precision: 
+    	__module.model.12.cv2.conv/aten::_convolution/Convolution
+    	__module.model.12.m.0.cv1.conv/aten::_convolution/Convolution
+    INFO:nncf:Accuracy drop with the new quantization scope is 0.021779128052922092 (absolute)
+    INFO:nncf:Reverted 2 operations to the floating-point precision: 
+    	__module.model.7.conv/aten::_convolution/Convolution
+    	__module.model.12.cv1.conv/aten::_convolution/Convolution
+    INFO:nncf:Accuracy drop with the new quantization scope is 0.01696486517685941 (absolute)
+    INFO:nncf:Reverted 2 operations to the floating-point precision: 
+    	__module.model.22/aten::add/Add_7
+    	__module.model.22/aten::sub/Subtract_1
+    INFO:nncf:Algorithm completed: achieved required accuracy drop 0.005923437521415831 (absolute)
+    INFO:nncf:9 out of 92 were reverted back to the floating-point precision:
+    	__module.model.4.m.0.cv2.conv/aten::_convolution/Convolution
+    	__module.model.22.proto.cv3.conv/aten::_convolution/Convolution
+    	__module.model.6.m.0.cv2.conv/aten::_convolution/Convolution
+    	__module.model.12.cv2.conv/aten::_convolution/Convolution
+    	__module.model.12.m.0.cv1.conv/aten::_convolution/Convolution
+    	__module.model.7.conv/aten::_convolution/Convolution
+    	__module.model.12.cv1.conv/aten::_convolution/Convolution
+    	__module.model.22/aten::add/Add_7
+    	__module.model.22/aten::sub/Subtract_1
 
 
 Compare Accuracy and Performance of the Original and Quantized Models
@@ -464,8 +529,8 @@ is not exceeded.
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -476,7 +541,7 @@ is not exceeded.
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+    Dropdown(description='Device:', index=4, options=('CPU', 'GPU.0', 'GPU.1', 'GPU.2', 'AUTO'), value='AUTO')
 
 
 
@@ -484,17 +549,19 @@ is not exceeded.
 
     core = ov.Core()
     ov_config = {}
+    if device.value != "CPU":
+        quantized_model.reshape({0: [1, 3, 640, 640]})
     if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
         ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
-    quantized_compiled_model = core.compile_model(model=quantized_model, device_name=device.value, ov_config)
-    compiled_ov_model = core.compile_model(model=ov_model, device_name=device.value, ov_config)
+    quantized_compiled_model = core.compile_model(quantized_model, device.value, ov_config)
+    compiled_ov_model = core.compile_model(ov_model, device.value, ov_config)
     
     pt_result = validation_ac(compiled_ov_model, data_loader, validator)
     quantized_result = validation_ac(quantized_compiled_model, data_loader, validator)
     
     
-    print(f'[Original OpenVINO]: {pt_result:.4f}')
-    print(f'[Quantized OpenVINO]: {quantized_result:.4f}')
+    print(f"[Original OpenVINO]: {pt_result:.4f}")
+    print(f"[Quantized OpenVINO]: {quantized_result:.4f}")
 
 
 .. parsed-literal::
@@ -510,12 +577,13 @@ And compare performance.
 .. code:: ipython3
 
     from pathlib import Path
+    
     # Set model directory
     MODEL_DIR = Path("model")
     MODEL_DIR.mkdir(exist_ok=True)
     
-    ir_model_path = MODEL_DIR / 'ir_model.xml'
-    quantized_model_path = MODEL_DIR / 'quantized_model.xml'
+    ir_model_path = MODEL_DIR / "ir_model.xml"
+    quantized_model_path = MODEL_DIR / "quantized_model.xml"
     
     # Save models to use them in the commandline banchmark app
     ov.save_model(ov_model, ir_model_path, compress_to_fp16=False)
@@ -534,39 +602,39 @@ And compare performance.
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-14589-0ef2fab3490
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-14589-0ef2fab3490
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 17.83 ms
+    [ INFO ] Read model took 13.54 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : f32 / [...] / [?,3,?,?]
+    [ INFO ]     x (node: x) : f32 / [...] / [?,3,?,?]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [?,116,?]
-    [ INFO ]     output1 (node: output1) : f32 / [...] / [?,32,8..,8..]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_8) : f32 / [...] / [?,116,16..]
+    [ INFO ]     input.199 (node: __module.model.22.cv4.2.1.act/aten::silu_/Swish_37) : f32 / [...] / [?,32,8..,8..]
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: 'images': [1,3,640,640]
-    [ INFO ] Reshape model took 13.50 ms
+    [ INFO ] Reshaping model: 'x': [1,3,640,640]
+    [ INFO ] Reshape model took 8.56 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : u8 / [N,C,H,W] / [1,3,640,640]
+    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,116,8400]
-    [ INFO ]     output1 (node: output1) : f32 / [...] / [1,32,160,160]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_8) : f32 / [...] / [1,116,8400]
+    [ INFO ]     input.199 (node: __module.model.22.cv4.2.1.act/aten::silu_/Swish_37) : f32 / [...] / [1,32,160,160]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 320.32 ms
+    [ INFO ] Compile model took 437.16 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: torch_jit
+    [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
     [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
@@ -584,7 +652,7 @@ And compare performance.
     [ INFO ]     INFERENCE_PRECISION_HINT: 
     [ INFO ]     KV_CACHE_PRECISION: 
     [ INFO ]     LOG_LEVEL: Level.NO
-    [ INFO ]     NETWORK_NAME: torch_jit
+    [ INFO ]     NETWORK_NAME: Model0
     [ INFO ]     NUM_STREAMS: 12
     [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
     [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
@@ -594,21 +662,21 @@ And compare performance.
     [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
+    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
+    [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 44.16 ms
+    [ INFO ] First inference took 46.51 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            14820 iterations
-    [ INFO ] Duration:         120139.10 ms
+    [ INFO ] Count:            16872 iterations
+    [ INFO ] Duration:         120117.37 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        95.27 ms
-    [ INFO ]    Average:       97.10 ms
-    [ INFO ]    Min:           72.93 ms
-    [ INFO ]    Max:           164.81 ms
-    [ INFO ] Throughput:   123.36 FPS
+    [ INFO ]    Median:        85.10 ms
+    [ INFO ]    Average:       85.27 ms
+    [ INFO ]    Min:           53.55 ms
+    [ INFO ]    Max:           108.50 ms
+    [ INFO ] Throughput:   140.46 FPS
 
 
 .. code:: ipython3
@@ -624,39 +692,39 @@ And compare performance.
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-14589-0ef2fab3490
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-14589-0ef2fab3490
+    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 28.33 ms
+    [ INFO ] Read model took 20.52 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : f32 / [...] / [?,3,?,?]
+    [ INFO ]     x (node: x) : f32 / [...] / [?,3,?,?]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [?,116,?]
-    [ INFO ]     output1 (node: output1) : f32 / [...] / [?,32,8..,8..]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_8) : f32 / [...] / [?,116,16..]
+    [ INFO ]     input.199 (node: __module.model.22.cv4.2.1.act/aten::silu_/Swish_37) : f32 / [...] / [?,32,8..,8..]
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: 'images': [1,3,640,640]
-    [ INFO ] Reshape model took 17.60 ms
+    [ INFO ] Reshaping model: 'x': [1,3,640,640]
+    [ INFO ] Reshape model took 11.74 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
-    [ INFO ]     images (node: images) : u8 / [N,C,H,W] / [1,3,640,640]
+    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
     [ INFO ] Model outputs:
-    [ INFO ]     output0 (node: output0) : f32 / [...] / [1,116,8400]
-    [ INFO ]     output1 (node: output1) : f32 / [...] / [1,32,160,160]
+    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_8) : f32 / [...] / [1,116,8400]
+    [ INFO ]     input.199 (node: __module.model.22.cv4.2.1.act/aten::silu_/Swish_37) : f32 / [...] / [1,32,160,160]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 605.73 ms
+    [ INFO ] Compile model took 711.53 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: torch_jit
+    [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
     [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
@@ -674,7 +742,7 @@ And compare performance.
     [ INFO ]     INFERENCE_PRECISION_HINT: 
     [ INFO ]     KV_CACHE_PRECISION: 
     [ INFO ]     LOG_LEVEL: Level.NO
-    [ INFO ]     NETWORK_NAME: torch_jit
+    [ INFO ]     NETWORK_NAME: Model0
     [ INFO ]     NUM_STREAMS: 12
     [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
     [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
@@ -684,19 +752,19 @@ And compare performance.
     [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
+    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
+    [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 22.24 ms
+    [ INFO ] First inference took 35.64 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            33624 iterations
-    [ INFO ] Duration:         120057.46 ms
+    [ INFO ] Count:            33564 iterations
+    [ INFO ] Duration:         120059.16 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        41.97 ms
-    [ INFO ]    Average:       42.70 ms
-    [ INFO ]    Min:           31.11 ms
-    [ INFO ]    Max:           86.51 ms
-    [ INFO ] Throughput:   280.07 FPS
+    [ INFO ]    Median:        42.72 ms
+    [ INFO ]    Average:       42.76 ms
+    [ INFO ]    Min:           23.29 ms
+    [ INFO ]    Max:           67.71 ms
+    [ INFO ] Throughput:   279.56 FPS
 
diff --git a/docs/notebooks/287-yolov9-optimization-with-output.rst b/docs/notebooks/yolov9-optimization-with-output.rst
similarity index 65%
rename from docs/notebooks/287-yolov9-optimization-with-output.rst
rename to docs/notebooks/yolov9-optimization-with-output.rst
index bad963a330a..7d8cd68d6d7 100644
--- a/docs/notebooks/287-yolov9-optimization-with-output.rst
+++ b/docs/notebooks/yolov9-optimization-with-output.rst
@@ -45,14 +45,14 @@ The tutorial consists of the following steps:
 Prerequisites
 -------------
 
-## Prerequisites
+ ## Prerequisites
 
 .. code:: ipython3
 
     import platform
-
-    %pip install -q "openvino>=2023.3.0" "nncf>=2.8.1" "opencv-python" "seaborn" "pandas" "scikit-learn" "torch" "torchvision"  --extra-index-url https://download.pytorch.org/whl/cpu
-
+    
+    %pip install -q "openvino>=2023.3.0" "nncf>=2.8.1" "opencv-python" "seaborn" "pandas" "scikit-learn" "torch" "torchvision" "tqdm"  --extra-index-url https://download.pytorch.org/whl/cpu
+    
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -62,7 +62,7 @@ Prerequisites
 .. parsed-literal::
 
     DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-
+    
 
 .. parsed-literal::
 
@@ -72,7 +72,7 @@ Prerequisites
 .. parsed-literal::
 
     DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-
+    
 
 .. parsed-literal::
 
@@ -81,12 +81,19 @@ Prerequisites
 
 .. code:: ipython3
 
-    import sys
     from pathlib import Path
-    sys.path.append("../utils")
-    from notebook_utils import download_file
-
-    if not Path('yolov9').exists():
+    
+    # Fetch `notebook_utils` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
+    from notebook_utils import download_file, VideoPlayer
+    
+    if not Path("yolov9").exists():
         !git clone https://github.com/WongKinYiu/yolov9
     %cd yolov9
 
@@ -98,358 +105,256 @@ Prerequisites
 
 .. parsed-literal::
 
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-remote: Counting objects: 100% (192/192), done.
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-remote: Compressing objects:   2% (2/84)
-remote: Compressing objects:   3% (3/84)
-remote: Compressing objects:   4% (4/84)
-remote: Compressing objects:   5% (5/84)
-remote: Compressing objects:   7% (6/84)
-remote: Compressing objects:   8% (7/84)
-remote: Compressing objects:   9% (8/84)
-remote: Compressing objects:  10% (9/84)
-remote: Compressing objects:  11% (10/84)
-remote: Compressing objects:  13% (11/84)
-remote: Compressing objects:  14% (12/84)
-remote: Compressing objects:  15% (13/84)
-remote: Compressing objects:  16% (14/84)
-remote: Compressing objects:  17% (15/84)
-remote: Compressing objects:  19% (16/84)
-remote: Compressing objects:  20% (17/84)
-remote: Compressing objects:  21% (18/84)
-remote: Compressing objects:  22% (19/84)
-remote: Compressing objects:  23% (20/84)
-remote: Compressing objects:  25% (21/84)
-remote: Compressing objects:  26% (22/84)
-remote: Compressing objects:  27% (23/84)
-remote: Compressing objects:  28% (24/84)
-remote: Compressing objects:  29% (25/84)
-remote: Compressing objects:  30% (26/84)
-remote: Compressing objects:  32% (27/84)
-remote: Compressing objects:  33% (28/84)
-remote: Compressing objects:  34% (29/84)
-remote: Compressing objects:  35% (30/84)
-remote: Compressing objects:  36% (31/84)
-remote: Compressing objects:  38% (32/84)
-remote: Compressing objects:  39% (33/84)
-remote: Compressing objects:  40% (34/84)
-remote: Compressing objects:  41% (35/84)
-remote: Compressing objects:  42% (36/84)
-remote: Compressing objects:  44% (37/84)
-remote: Compressing objects:  45% (38/84)
-remote: Compressing objects:  46% (39/84)
-remote: Compressing objects:  47% (40/84)
-remote: Compressing objects:  48% (41/84)
-remote: Compressing objects:  50% (42/84)
-remote: Compressing objects:  51% (43/84)
-remote: Compressing objects:  52% (44/84)
-remote: Compressing objects:  53% (45/84)
-remote: Compressing objects:  54% (46/84)
-remote: Compressing objects:  55% (47/84)
-remote: Compressing objects:  57% (48/84)
-remote: Compressing objects:  58% (49/84)
-remote: Compressing objects:  59% (50/84)
-remote: Compressing objects:  60% (51/84)
-remote: Compressing objects:  61% (52/84)
-remote: Compressing objects:  63% (53/84)
-remote: Compressing objects:  64% (54/84)
-remote: Compressing objects:  65% (55/84)
-remote: Compressing objects:  66% (56/84)
-remote: Compressing objects:  67% (57/84)
-remote: Compressing objects:  69% (58/84)
-remote: Compressing objects:  70% (59/84)
-remote: Compressing objects:  71% (60/84)
-remote: Compressing objects:  72% (61/84)
-remote: Compressing objects:  73% (62/84)
-remote: Compressing objects:  75% (63/84)
-remote: Compressing objects:  76% (64/84)
-remote: Compressing objects:  77% (65/84)
-remote: Compressing objects:  78% (66/84)
-remote: Compressing objects:  79% (67/84)
-remote: Compressing objects:  80% (68/84)
-remote: Compressing objects:  82% (69/84)
-remote: Compressing objects:  83% (70/84)
-remote: Compressing objects:  84% (71/84)
-remote: Compressing objects:  85% (72/84)
-remote: Compressing objects:  86% (73/84)
-remote: Compressing objects:  88% (74/84)
-remote: Compressing objects:  89% (75/84)
-remote: Compressing objects:  90% (76/84)
-remote: Compressing objects:  91% (77/84)
-remote: Compressing objects:  92% (78/84)
-remote: Compressing objects:  94% (79/84)
-remote: Compressing objects:  95% (80/84)
-remote: Compressing objects:  96% (81/84)
-remote: Compressing objects:  97% (82/84)
-remote: Compressing objects:  98% (83/84)
-remote: Compressing objects: 100% (84/84)
-remote: Compressing objects: 100% (84/84), done.
-    Receiving objects:   0% (1/572)
-Receiving objects:   1% (6/572)
+    remote: Enumerating objects: 665, done.
+    remote: Counting objects:   2% (1/44)
+remote: Counting objects:   4% (2/44)
+remote: Counting objects:   6% (3/44)
+remote: Counting objects:   9% (4/44)
+remote: Counting objects:  11% (5/44)
+remote: Counting objects:  13% (6/44)
+remote: Counting objects:  15% (7/44)
+remote: Counting objects:  18% (8/44)
+remote: Counting objects:  20% (9/44)
+remote: Counting objects:  22% (10/44)
+remote: Counting objects:  25% (11/44)
+remote: Counting objects:  27% (12/44)
+remote: Counting objects:  29% (13/44)
+remote: Counting objects:  31% (14/44)
+remote: Counting objects:  34% (15/44)
+remote: Counting objects:  36% (16/44)
+remote: Counting objects:  38% (17/44)
+remote: Counting objects:  40% (18/44)
+remote: Counting objects:  43% (19/44)
+remote: Counting objects:  45% (20/44)
+remote: Counting objects:  47% (21/44)
+remote: Counting objects:  50% (22/44)
+remote: Counting objects:  52% (23/44)
+remote: Counting objects:  54% (24/44)
+remote: Counting objects:  56% (25/44)
+remote: Counting objects:  59% (26/44)
+remote: Counting objects:  61% (27/44)
+remote: Counting objects:  63% (28/44)
+remote: Counting objects:  65% (29/44)
+remote: Counting objects:  68% (30/44)
+remote: Counting objects:  70% (31/44)
+remote: Counting objects:  72% (32/44)
+remote: Counting objects:  75% (33/44)
+remote: Counting objects:  77% (34/44)
+remote: Counting objects:  79% (35/44)
+remote: Counting objects:  81% (36/44)
+remote: Counting objects:  84% (37/44)
+remote: Counting objects:  86% (38/44)
+remote: Counting objects:  88% (39/44)
+remote: Counting objects:  90% (40/44)
+remote: Counting objects:  93% (41/44)
+remote: Counting objects:  95% (42/44)
+remote: Counting objects:  97% (43/44)
+remote: Counting objects: 100% (44/44)
+remote: Counting objects: 100% (44/44), done.
+    remote: Compressing objects:   2% (1/37)
+remote: Compressing objects:   5% (2/37)
+remote: Compressing objects:   8% (3/37)
+remote: Compressing objects:  10% (4/37)
+remote: Compressing objects:  13% (5/37)
+remote: Compressing objects:  16% (6/37)
+remote: Compressing objects:  18% (7/37)
+remote: Compressing objects:  21% (8/37)
+remote: Compressing objects:  24% (9/37)
+remote: Compressing objects:  27% (10/37)
+remote: Compressing objects:  29% (11/37)
+remote: Compressing objects:  32% (12/37)
+remote: Compressing objects:  35% (13/37)
+remote: Compressing objects:  37% (14/37)
+remote: Compressing objects:  40% (15/37)
+remote: Compressing objects:  43% (16/37)
+remote: Compressing objects:  45% (17/37)
+remote: Compressing objects:  48% (18/37)
+remote: Compressing objects:  51% (19/37)
+remote: Compressing objects:  54% (20/37)
+remote: Compressing objects:  56% (21/37)
+remote: Compressing objects:  59% (22/37)
+remote: Compressing objects:  62% (23/37)
+remote: Compressing objects:  64% (24/37)
+remote: Compressing objects:  67% (25/37)
+remote: Compressing objects:  70% (26/37)
+remote: Compressing objects:  72% (27/37)
+remote: Compressing objects:  75% (28/37)
+remote: Compressing objects:  78% (29/37)
+remote: Compressing objects:  81% (30/37)
+remote: Compressing objects:  83% (31/37)
+remote: Compressing objects:  86% (32/37)
+remote: Compressing objects:  89% (33/37)
+remote: Compressing objects:  91% (34/37)
+remote: Compressing objects:  94% (35/37)
+remote: Compressing objects:  97% (36/37)
+remote: Compressing objects: 100% (37/37)
+remote: Compressing objects: 100% (37/37), done.
+    Receiving objects:   0% (1/665)
+Receiving objects:   1% (7/665)
 
 .. parsed-literal::
 
-    Receiving objects:   2% (12/572)
-Receiving objects:   3% (18/572)
-Receiving objects:   4% (23/572)
-Receiving objects:   5% (29/572)
-Receiving objects:   6% (35/572)
-Receiving objects:   7% (41/572)
-Receiving objects:   8% (46/572)
+    Receiving objects:   2% (14/665)
+Receiving objects:   3% (20/665)
+Receiving objects:   4% (27/665)
+Receiving objects:   5% (34/665)
+Receiving objects:   6% (40/665)
+Receiving objects:   7% (47/665)
+Receiving objects:   8% (54/665)
+Receiving objects:   9% (60/665)
+Receiving objects:  10% (67/665)
+Receiving objects:  11% (74/665)
+Receiving objects:  12% (80/665)
+Receiving objects:  13% (87/665)
+Receiving objects:  14% (94/665)
+Receiving objects:  15% (100/665)
 
 .. parsed-literal::
 
-    Receiving objects:   9% (52/572)
-Receiving objects:  10% (58/572)
-Receiving objects:  11% (63/572)
-Receiving objects:  12% (69/572)
+    Receiving objects:  16% (107/665)
+Receiving objects:  17% (114/665)
+Receiving objects:  18% (120/665)
+Receiving objects:  19% (127/665)
+Receiving objects:  20% (133/665)
+Receiving objects:  21% (140/665)
+Receiving objects:  22% (147/665)
+Receiving objects:  23% (153/665)
+Receiving objects:  24% (160/665)
+Receiving objects:  25% (167/665)
+Receiving objects:  26% (173/665)
+Receiving objects:  27% (180/665)
+Receiving objects:  28% (187/665)
+Receiving objects:  29% (193/665)
 
 .. parsed-literal::
 
-    Receiving objects:  13% (75/572)
-Receiving objects:  14% (81/572)
-Receiving objects:  15% (86/572)
-Receiving objects:  16% (92/572)
-Receiving objects:  17% (98/572)
-Receiving objects:  18% (103/572)
-Receiving objects:  19% (109/572)
-Receiving objects:  20% (115/572)
-Receiving objects:  21% (121/572)
-Receiving objects:  22% (126/572)
-Receiving objects:  23% (132/572)
-Receiving objects:  24% (138/572)
-Receiving objects:  25% (143/572)
-Receiving objects:  26% (149/572)
-Receiving objects:  27% (155/572)
-Receiving objects:  28% (161/572)
-Receiving objects:  29% (166/572)
-Receiving objects:  30% (172/572)
-Receiving objects:  31% (178/572)
-Receiving objects:  32% (184/572)
-Receiving objects:  33% (189/572)
-Receiving objects:  34% (195/572)
+    Receiving objects:  30% (200/665)
+Receiving objects:  31% (207/665)
+Receiving objects:  32% (213/665)
+Receiving objects:  33% (220/665)
+Receiving objects:  34% (227/665)
+Receiving objects:  35% (233/665)
+Receiving objects:  36% (240/665)
+Receiving objects:  37% (247/665)
+Receiving objects:  38% (253/665)
+Receiving objects:  39% (260/665)
+Receiving objects:  40% (266/665)
+Receiving objects:  41% (273/665)
+Receiving objects:  42% (280/665)
+Receiving objects:  43% (286/665)
+Receiving objects:  44% (293/665)
+Receiving objects:  45% (300/665)
+Receiving objects:  46% (306/665)
+Receiving objects:  47% (313/665)
+Receiving objects:  48% (320/665)
+Receiving objects:  49% (326/665)
+Receiving objects:  50% (333/665)
+Receiving objects:  51% (340/665)
+Receiving objects:  52% (346/665)
+Receiving objects:  53% (353/665)
+Receiving objects:  54% (360/665)
+Receiving objects:  55% (366/665)
+Receiving objects:  56% (373/665)
+Receiving objects:  57% (380/665)
+Receiving objects:  58% (386/665)
+Receiving objects:  59% (393/665)
+Receiving objects:  60% (399/665)
+Receiving objects:  61% (406/665)
+Receiving objects:  62% (413/665)
+Receiving objects:  63% (419/665)
+Receiving objects:  64% (426/665)
+Receiving objects:  65% (433/665)
+Receiving objects:  66% (439/665)
+Receiving objects:  67% (446/665)
+Receiving objects:  68% (453/665)
+Receiving objects:  69% (459/665)
+Receiving objects:  70% (466/665)
+Receiving objects:  71% (473/665)
+Receiving objects:  72% (479/665)
+Receiving objects:  73% (486/665)
+Receiving objects:  74% (493/665)
+Receiving objects:  75% (499/665)
+Receiving objects:  76% (506/665)
+Receiving objects:  77% (513/665)
+Receiving objects:  78% (519/665)
+Receiving objects:  79% (526/665)
+Receiving objects:  80% (532/665)
+Receiving objects:  81% (539/665)
+Receiving objects:  82% (546/665)
+Receiving objects:  83% (552/665)
+remote: Total 665 (delta 21), reused 18 (delta 7), pack-reused 621
+    Receiving objects:  84% (559/665)
+Receiving objects:  85% (566/665)
+Receiving objects:  86% (572/665)
+Receiving objects:  87% (579/665)
+Receiving objects:  88% (586/665)
+Receiving objects:  89% (592/665)
+Receiving objects:  90% (599/665)
+Receiving objects:  91% (606/665)
+Receiving objects:  92% (612/665)
+Receiving objects:  93% (619/665)
+Receiving objects:  94% (626/665)
+Receiving objects:  95% (632/665)
+Receiving objects:  96% (639/665)
+Receiving objects:  97% (646/665)
+Receiving objects:  98% (652/665)
+Receiving objects:  99% (659/665)
+Receiving objects: 100% (665/665)
+Receiving objects: 100% (665/665), 3.24 MiB | 14.69 MiB/s, done.
+    Resolving deltas:   0% (0/260)
+Resolving deltas:   2% (6/260)
+Resolving deltas:   3% (8/260)
+Resolving deltas:   5% (15/260)
+Resolving deltas:   6% (18/260)
+Resolving deltas:   7% (20/260)
+Resolving deltas:  10% (26/260)
+Resolving deltas:  11% (29/260)
+Resolving deltas:  12% (33/260)
+Resolving deltas:  15% (41/260)
+Resolving deltas:  18% (47/260)
+Resolving deltas:  28% (74/260)
+Resolving deltas:  30% (80/260)
+Resolving deltas:  31% (82/260)
+Resolving deltas:  32% (84/260)
+Resolving deltas:  33% (86/260)
+Resolving deltas:  34% (90/260)
+Resolving deltas:  36% (95/260)
+Resolving deltas:  37% (97/260)
+Resolving deltas:  38% (99/260)
+Resolving deltas:  39% (102/260)
 
 .. parsed-literal::
 
-    Receiving objects:  35% (201/572)
-Receiving objects:  36% (206/572)
-Receiving objects:  37% (212/572)
-Receiving objects:  38% (218/572)
-Receiving objects:  39% (224/572)
-Receiving objects:  40% (229/572)
-Receiving objects:  41% (235/572)
-Receiving objects:  42% (241/572)
-Receiving objects:  43% (246/572)
-Receiving objects:  44% (252/572)
-Receiving objects:  45% (258/572)
-Receiving objects:  46% (264/572)
-Receiving objects:  47% (269/572)
-Receiving objects:  48% (275/572)
-Receiving objects:  49% (281/572)
-Receiving objects:  50% (286/572)
-Receiving objects:  51% (292/572)
-Receiving objects:  52% (298/572)
-Receiving objects:  53% (304/572)
-Receiving objects:  54% (309/572)
-Receiving objects:  55% (315/572)
-Receiving objects:  56% (321/572)
-Receiving objects:  57% (327/572)
-Receiving objects:  58% (332/572)
-Receiving objects:  59% (338/572)
-Receiving objects:  60% (344/572)
-Receiving objects:  61% (349/572)
-Receiving objects:  62% (355/572)
-Receiving objects:  63% (361/572)
-
-.. parsed-literal::
-
-    Receiving objects:  64% (367/572)
-Receiving objects:  65% (372/572)
-Receiving objects:  66% (378/572)
-Receiving objects:  67% (384/572)
-Receiving objects:  68% (389/572)
-Receiving objects:  69% (395/572)
-Receiving objects:  70% (401/572)
-Receiving objects:  71% (407/572)
-Receiving objects:  72% (412/572)
-Receiving objects:  73% (418/572)
-Receiving objects:  74% (424/572)
-Receiving objects:  75% (429/572)
-Receiving objects:  76% (435/572)
-Receiving objects:  77% (441/572)
-Receiving objects:  78% (447/572)
-Receiving objects:  79% (452/572)
-Receiving objects:  80% (458/572)
-Receiving objects:  81% (464/572)
-Receiving objects:  82% (470/572)
-Receiving objects:  83% (475/572)
-Receiving objects:  84% (481/572)
-Receiving objects:  85% (487/572)
-Receiving objects:  86% (492/572)
-Receiving objects:  87% (498/572)
-Receiving objects:  88% (504/572)
-Receiving objects:  89% (510/572)
-Receiving objects:  90% (515/572)
-Receiving objects:  91% (521/572)
-Receiving objects:  92% (527/572)
-Receiving objects:  93% (532/572)
-Receiving objects:  94% (538/572)
-Receiving objects:  95% (544/572)
-Receiving objects:  96% (550/572)
-Receiving objects:  97% (555/572)
-Receiving objects:  98% (561/572)
-Receiving objects:  99% (567/572)
-remote: Total 572 (delta 143), reused 118 (delta 107), pack-reused 380
-    Receiving objects: 100% (572/572)
-Receiving objects: 100% (572/572), 3.20 MiB | 13.83 MiB/s, done.
-    Resolving deltas:   0% (0/204)
-Resolving deltas:   2% (6/204)
-Resolving deltas:   3% (7/204)
-Resolving deltas:   4% (9/204)
-Resolving deltas:   6% (13/204)
-Resolving deltas:   7% (15/204)
-Resolving deltas:   8% (17/204)
-Resolving deltas:   9% (20/204)
-Resolving deltas:  10% (21/204)
-Resolving deltas:  11% (23/204)
-Resolving deltas:  12% (25/204)
-Resolving deltas:  13% (27/204)
-Resolving deltas:  14% (30/204)
-Resolving deltas:  15% (31/204)
-Resolving deltas:  18% (37/204)
-Resolving deltas:  19% (40/204)
-Resolving deltas:  20% (41/204)
-Resolving deltas:  21% (43/204)
-Resolving deltas:  22% (45/204)
-Resolving deltas:  25% (53/204)
-Resolving deltas:  27% (57/204)
-Resolving deltas:  40% (83/204)
-Resolving deltas:  43% (88/204)
-Resolving deltas:  44% (91/204)
-Resolving deltas:  46% (94/204)
-Resolving deltas:  65% (134/204)
-Resolving deltas:  66% (136/204)
-Resolving deltas:  67% (137/204)
-Resolving deltas:  68% (140/204)
-Resolving deltas:  82% (169/204)
-Resolving deltas:  83% (171/204)
-Resolving deltas:  84% (173/204)
-Resolving deltas:  86% (177/204)
-Resolving deltas:  87% (178/204)
-Resolving deltas:  89% (183/204)
-Resolving deltas:  93% (190/204)
-Resolving deltas:  97% (199/204)
-Resolving deltas:  98% (200/204)
-Resolving deltas:  99% (203/204)
-Resolving deltas: 100% (204/204)
-Resolving deltas: 100% (204/204), done.
+    Resolving deltas:  44% (116/260)
+Resolving deltas:  52% (136/260)
+Resolving deltas:  58% (151/260)
+Resolving deltas:  59% (154/260)
+Resolving deltas:  60% (157/260)
+Resolving deltas:  61% (160/260)
+Resolving deltas:  62% (163/260)
+Resolving deltas:  63% (164/260)
+Resolving deltas:  65% (171/260)
+Resolving deltas:  70% (183/260)
+Resolving deltas:  72% (189/260)
+Resolving deltas:  73% (190/260)
+Resolving deltas:  75% (197/260)
+Resolving deltas:  76% (198/260)
+Resolving deltas:  79% (207/260)
+Resolving deltas:  80% (210/260)
+Resolving deltas:  82% (215/260)
+Resolving deltas:  85% (223/260)
+Resolving deltas:  88% (229/260)
+Resolving deltas:  95% (247/260)
+Resolving deltas:  96% (250/260)
+Resolving deltas:  98% (256/260)
+Resolving deltas:  99% (259/260)
+Resolving deltas: 100% (260/260)
+Resolving deltas: 100% (260/260), done.
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/287-yolov9-optimization/yolov9
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/yolov9-optimization/yolov9
 
 
 Get PyTorch model
@@ -473,7 +378,7 @@ applicable for other models from YOLO V9 family.
     MODEL_DIR = Path("model/")
     MODEL_DIR.mkdir(exist_ok=True)
     DATA_DIR.mkdir(exist_ok=True)
-
+    
     download_file(MODEL_LINK, directory=MODEL_DIR, show_progress=True)
 
 
@@ -487,7 +392,7 @@ applicable for other models from YOLO V9 family.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/287-yolov9-optimization/yolov9/model/gelan-c.pt')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/yolov9-optimization/yolov9/model/gelan-c.pt')
 
 
 
@@ -510,27 +415,26 @@ using ``ov.save_model``.
     import openvino as ov
     from models.yolo import Detect, DualDDetect
     from utils.general import yaml_save, yaml_load
-
+    
     weights = MODEL_DIR / "gelan-c.pt"
     ov_model_path = MODEL_DIR / weights.name.replace(".pt", "_openvino_model") / weights.name.replace(".pt", ".xml")
-
+    
     if not ov_model_path.exists():
         model = attempt_load(weights, device="cpu", inplace=True, fuse=True)
-        metadata = {'stride': int(max(model.stride)), 'names': model.names}
-
+        metadata = {"stride": int(max(model.stride)), "names": model.names}
+    
         model.eval()
         for k, m in model.named_modules():
             if isinstance(m, (Detect, DualDDetect)):
                 m.inplace = False
                 m.dynamic = True
                 m.export = True
-
-
+    
         example_input = torch.zeros((1, 3, 640, 640))
         model(example_input)
-
+    
         ov_model = ov.convert_model(model, example_input=example_input)
-
+    
         # specify input and output names for compatibility with yolov9 repo interface
         ov_model.outputs[0].get_tensor().set_names({"output0"})
         ov_model.inputs[0].get_tensor().set_names({"images"})
@@ -543,7 +447,7 @@ using ``ov.save_model``.
 
 .. parsed-literal::
 
-    Fusing layers...
+    Fusing layers... 
 
 
 .. parsed-literal::
@@ -553,7 +457,7 @@ using ``ov.save_model``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/287-yolov9-optimization/yolov9/models/yolo.py:108: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/notebooks/yolov9-optimization/yolov9/models/yolo.py:108: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       elif self.dynamic or self.shape != shape:
 
 
@@ -592,15 +496,16 @@ To keep specific shape, preprocessing automatically enables padding.
     import torch
     from PIL import Image
     from utils.augmentations import letterbox
-
+    
     image_url = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/7b6af406-4ccb-4ded-a13d-62b7c0e42e96"
     download_file(image_url, directory=DATA_DIR, filename="test_image.jpg", show_progress=True)
-
+    
+    
     def preprocess_image(img0: np.ndarray):
         """
         Preprocess image according to YOLOv9 input requirements.
         Takes image in np.array format, resizes it to specific size using letterbox resize, converts color space from BGR (default in OpenCV) to RGB and changes data layout from HWC to CHW.
-
+    
         Parameters:
           img0 (np.ndarray): image for preprocessing
         Returns:
@@ -609,18 +514,18 @@ To keep specific shape, preprocessing automatically enables padding.
         """
         # resize
         img = letterbox(img0, auto=False)[0]
-
+    
         # Convert
         img = img.transpose(2, 0, 1)
         img = np.ascontiguousarray(img)
         return img, img0
-
-
+    
+    
     def prepare_input_tensor(image: np.ndarray):
         """
         Converts preprocessed image to tensor format according to YOLOv9 input requirements.
         Takes image in np.array format with unit8 data in [0, 255] range and converts it to torch.Tensor object with float data in [0, 1] range
-
+    
         Parameters:
           image (np.ndarray): image for conversion to tensor
         Returns:
@@ -628,11 +533,12 @@ To keep specific shape, preprocessing automatically enables padding.
         """
         input_tensor = image.astype(np.float32)  # uint8 to fp16/32
         input_tensor /= 255.0  # 0 - 255 to 0.0 - 1.0
-
+    
         if input_tensor.ndim == 3:
             input_tensor = np.expand_dims(input_tensor, 0)
         return input_tensor
-
+    
+    
     NAMES = metadata["names"]
 
 
@@ -668,12 +574,19 @@ algorithm and rescale boxes coordinates to original image size.
 .. code:: ipython3
 
     from utils.plots import Annotator, colors
-
+    
     from typing import List, Tuple
     from utils.general import scale_boxes, non_max_suppression
-
-
-    def detect(model: ov.Model, image_path: Path, conf_thres: float = 0.25, iou_thres: float = 0.45, classes: List[int] = None, agnostic_nms: bool = False):
+    
+    
+    def detect(
+        model: ov.Model,
+        image_path: Path,
+        conf_thres: float = 0.25,
+        iou_thres: float = 0.45,
+        classes: List[int] = None,
+        agnostic_nms: bool = False,
+    ):
         """
         OpenVINO YOLOv9 model inference function. Reads image, preprocess it, runs model inference and postprocess results using NMS.
         Parameters:
@@ -697,9 +610,14 @@ algorithm and rescale boxes coordinates to original image size.
         predictions = torch.from_numpy(model(input_tensor)[0])
         pred = non_max_suppression(predictions, conf_thres, iou_thres, classes=classes, agnostic=agnostic_nms)
         return pred, orig_img, input_tensor.shape
-
-
-    def draw_boxes(predictions: np.ndarray, input_shape: Tuple[int], image: np.ndarray, names: List[str]):
+    
+    
+    def draw_boxes(
+        predictions: np.ndarray,
+        input_shape: Tuple[int],
+        image: np.ndarray,
+        names: List[str],
+    ):
         """
         Utility function for drawing predicted bounding boxes on image
         Parameters:
@@ -712,14 +630,14 @@ algorithm and rescale boxes coordinates to original image size.
         """
         if not len(predictions):
             return image
-
+    
         annotator = Annotator(image, line_width=1, example=str(names))
         # Rescale boxes from input size to original image size
         predictions[:, :4] = scale_boxes(input_shape[2:], predictions[:, :4], image.shape).round()
-
+    
         # Write results
         for *xyxy, conf, cls in reversed(predictions):
-            label = f'{names[int(cls)]} {conf:.2f}'
+            label = f"{names[int(cls)]} {conf:.2f}"
             annotator.box_label(xyxy, label, color=colors(int(cls), True))
         return image
 
@@ -739,14 +657,14 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-
+    
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
-
+    
     device
 
 
@@ -775,7 +693,7 @@ select device from dropdown list for running inference using OpenVINO
 
 
 
-.. image:: 287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_16_0.png
+.. image:: yolov9-optimization-with-output_files/yolov9-optimization-with-output_16_0.png
 
 
 
@@ -806,24 +724,21 @@ quantization.
 .. code:: ipython3
 
     from zipfile import ZipFile
-
-    sys.path.append("../../utils")
-    from notebook_utils import download_file
-
-
+    
+    
     DATA_URL = "http://images.cocodataset.org/zips/val2017.zip"
     LABELS_URL = "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip"
-
-    OUT_DIR = Path('.')
-
+    
+    OUT_DIR = Path(".")
+    
     download_file(DATA_URL, directory=OUT_DIR, show_progress=True)
     download_file(LABELS_URL, directory=OUT_DIR, show_progress=True)
-
+    
     if not (OUT_DIR / "coco/labels").exists():
-        with ZipFile('coco2017labels-segments.zip' , "r") as zip_ref:
+        with ZipFile("coco2017labels-segments.zip", "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
-        with ZipFile('val2017.zip' , "r") as zip_ref:
-            zip_ref.extractall(OUT_DIR / 'coco/images')
+        with ZipFile("val2017.zip", "r") as zip_ref:
+            zip_ref.extractall(OUT_DIR / "coco/images")
 
 
 
@@ -844,55 +759,60 @@ quantization.
     import yaml
     from utils.dataloaders import create_dataloader
     from utils.general import colorstr
-
+    
     # read dataset config
-    DATA_CONFIG = 'data/coco.yaml'
+    DATA_CONFIG = "data/coco.yaml"
     with open(DATA_CONFIG) as f:
         data = yaml.load(f, Loader=yaml.SafeLoader)
-
+    
     # Dataloader
-    TASK = 'val'  # path to train/val/test images
-    Option = namedtuple('Options', ['single_cls'])  # imitation of commandline provided options for single class evaluation
+    TASK = "val"  # path to train/val/test images
+    Option = namedtuple("Options", ["single_cls"])  # imitation of commandline provided options for single class evaluation
     opt = Option(False)
     dataloader = create_dataloader(
-        str(Path("coco") / data[TASK]), 640, 1, 32, opt, pad=0.5,
-        prefix=colorstr(f'{TASK}: ')
+        str(Path("coco") / data[TASK]),
+        640,
+        1,
+        32,
+        opt,
+        pad=0.5,
+        prefix=colorstr(f"{TASK}: "),
     )[0]
 
 
 .. parsed-literal::
 
-
-   val: Scanning coco/val2017...:   0%|          | 0/5000 00:00
+    
+    val: Scanning coco/val2017...:   0%|          | 0/5000 00:00
 
 .. parsed-literal::
 
-
-   val: Scanning coco/val2017... 985 images, 8 backgrounds, 0 corrupt:  20%|█▉        | 993/5000 00:00
+    
+    val: Scanning coco/val2017... 1093 images, 10 backgrounds, 0 corrupt:  22%|██▏       | 1103/5000 00:00
 
 .. parsed-literal::
 
-
-   val: Scanning coco/val2017... 2174 images, 17 backgrounds, 0 corrupt:  44%|████▍     | 2191/5000 00:00
+    
+    val: Scanning coco/val2017... 2351 images, 22 backgrounds, 0 corrupt:  47%|████▋     | 2373/5000 00:00
 
 .. parsed-literal::
 
-
-   val: Scanning coco/val2017... 3257 images, 31 backgrounds, 0 corrupt:  66%|██████▌   | 3288/5000 00:00
+    
+    val: Scanning coco/val2017... 3536 images, 34 backgrounds, 0 corrupt:  71%|███████▏  | 3570/5000 00:00
 
 .. parsed-literal::
 
-
-   val: Scanning coco/val2017... 4334 images, 41 backgrounds, 0 corrupt:  88%|████████▊ | 4375/5000 00:00
+    
+    val: Scanning coco/val2017... 4594 images, 44 backgrounds, 0 corrupt:  93%|█████████▎| 4638/5000 00:00
 
 .. parsed-literal::
 
-
-   val: Scanning coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100%|██████████| 5000/5000 00:00
-
+    
+    val: Scanning coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100%|██████████| 5000/5000 00:00
 
 
 
+    
 
 
 .. parsed-literal::
@@ -908,8 +828,8 @@ expected format.
 .. code:: ipython3
 
     import nncf
-
-
+    
+    
     def transform_fn(data_item):
         """
         Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
@@ -921,8 +841,8 @@ expected format.
         img = data_item[0].numpy()
         input_tensor = prepare_input_tensor(img)
         return input_tensor
-
-
+    
+    
     quantization_dataset = nncf.Dataset(dataloader, transform_fn)
 
 
@@ -948,25 +868,29 @@ asymmetric quantization of activations.
 
 .. code:: ipython3
 
-    ov_int8_model_path = MODEL_DIR / weights.name.replace(".pt","_int8_openvino_model") / weights.name.replace(".pt", "_int8.xml")
-
+    ov_int8_model_path = MODEL_DIR / weights.name.replace(".pt", "_int8_openvino_model") / weights.name.replace(".pt", "_int8.xml")
+    
     if not ov_int8_model_path.exists():
         quantized_model = nncf.quantize(ov_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED)
-
+    
         ov.save_model(quantized_model, ov_int8_model_path)
         yaml_save(ov_int8_model_path.parent / weights.name.replace(".pt", "_int8.yaml"), metadata)
 
 
 .. parsed-literal::
 
-    2024-03-13 00:53:25.630509: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-03-13 00:53:25.674983: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-04-18 01:42:45.719609: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+
+
+.. parsed-literal::
+
+    2024-04-18 01:42:45.755102: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
 
 .. parsed-literal::
 
-    2024-03-13 00:53:26.269021: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-04-18 01:42:46.331038: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -992,7 +916,7 @@ asymmetric quantization of activations.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-661/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
       return Tensor(self.data * unwrap_tensor_data(other))
 
 
@@ -1028,10 +952,10 @@ check the model work on the previously used image.
 .. code:: ipython3
 
     quantized_model = core.read_model(ov_int8_model_path)
-
+    
     if device.value != "CPU":
         quantized_model.reshape({0: [1, 3, 640, 640]})
-
+    
     compiled_model = core.compile_model(quantized_model, device.value)
 
 .. code:: ipython3
@@ -1044,7 +968,7 @@ check the model work on the previously used image.
 
 
 
-.. image:: 287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_27_0.png
+.. image:: yolov9-optimization-with-output_files/yolov9-optimization-with-output_27_0.png
 
 
 
@@ -1077,17 +1001,17 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ]
+    [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ]
-    [ INFO ]
+    [ INFO ] 
+    [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 27.34 ms
+    [ INFO ] Read model took 28.00 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     images (node: x) : f32 / [...] / [?,3,?,?]
@@ -1099,7 +1023,7 @@ models.
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
     [ INFO ] Reshaping model: 'images': [1,3,640,640]
-    [ INFO ] Reshape model took 8.24 ms
+    [ INFO ] Reshape model took 8.52 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     images (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
@@ -1113,7 +1037,7 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 590.72 ms
+    [ INFO ] Compile model took 570.57 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -1145,18 +1069,14 @@ models.
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values
-
-
-.. parsed-literal::
-
+    [ INFO ] Fill input 'images' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
 
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 190.30 ms
+    [ INFO ] First inference took 189.94 ms
 
 
 .. parsed-literal::
@@ -1164,13 +1084,13 @@ models.
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
     [ INFO ] Count:            222 iterations
-    [ INFO ] Duration:         15451.04 ms
+    [ INFO ] Duration:         15362.34 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        417.09 ms
-    [ INFO ]    Average:       414.67 ms
-    [ INFO ]    Min:           298.90 ms
-    [ INFO ]    Max:           436.39 ms
-    [ INFO ] Throughput:   14.37 FPS
+    [ INFO ]    Median:        413.17 ms
+    [ INFO ]    Average:       412.86 ms
+    [ INFO ]    Min:           202.77 ms
+    [ INFO ]    Max:           695.36 ms
+    [ INFO ] Throughput:   14.45 FPS
 
 
 .. code:: ipython3
@@ -1185,12 +1105,12 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ]
+    [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ]
-    [ INFO ]
+    [ INFO ] 
+    [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
@@ -1199,7 +1119,7 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] Read model took 52.40 ms
+    [ INFO ] Read model took 51.88 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     images (node: x) : f32 / [...] / [1,3,640,640]
@@ -1225,18 +1145,18 @@ models.
 
 .. parsed-literal::
 
-    [ INFO ] Compile model took 1163.81 ms
+    [ INFO ] Compile model took 1174.80 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   EXECUTION_DEVICES: ['CPU']
     [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
-    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
-    [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
 
 
 .. parsed-literal::
 
+    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
+    [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
     [ INFO ]   CPU:
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
@@ -1261,14 +1181,14 @@ models.
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values
+    [ INFO ] Fill input 'images' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
 
 
 .. parsed-literal::
 
-    [ INFO ] First inference took 75.25 ms
+    [ INFO ] First inference took 78.86 ms
 
 
 .. parsed-literal::
@@ -1276,13 +1196,13 @@ models.
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
     [ INFO ] Count:            750 iterations
-    [ INFO ] Duration:         15106.68 ms
+    [ INFO ] Duration:         15191.54 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        120.85 ms
-    [ INFO ]    Average:       120.49 ms
-    [ INFO ]    Min:           103.28 ms
-    [ INFO ]    Max:           138.46 ms
-    [ INFO ] Throughput:   49.65 FPS
+    [ INFO ]    Median:        121.65 ms
+    [ INFO ]    Average:       121.13 ms
+    [ INFO ]    Min:           73.78 ms
+    [ INFO ]    Max:           139.99 ms
+    [ INFO ] Throughput:   49.37 FPS
 
 
 Run Live Object Detection
@@ -1295,27 +1215,29 @@ Run Live Object Detection
     import collections
     import time
     from IPython import display
-    from notebook_utils import VideoPlayer
     import cv2
-
-
+    
+    
     # Main processing function to run object detection.
-    def run_object_detection(source=0, flip=False, use_popup=False, skip_first_frames=0, model=ov_model, device=device.value):
+    def run_object_detection(
+        source=0,
+        flip=False,
+        use_popup=False,
+        skip_first_frames=0,
+        model=ov_model,
+        device=device.value,
+    ):
         player = None
         compiled_model = core.compile_model(model, device)
         try:
             # Create a video player to play with target fps.
-            player = VideoPlayer(
-                source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames
-            )
+            player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
             # Start capturing.
             player.start()
             if use_popup:
                 title = "Press ESC to Exit"
-                cv2.namedWindow(
-                    winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE
-                )
-
+                cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
+    
             processing_times = collections.deque()
             while True:
                 # Grab the frame.
@@ -1335,20 +1257,20 @@ Run Live Object Detection
                     )
                 # Get the results.
                 input_image = np.array(frame)
-
+    
                 start_time = time.time()
                 # model expects RGB image, while video capturing in BGR
                 detections, _, input_shape = detect(compiled_model, input_image[:, :, ::-1])
                 stop_time = time.time()
-
+    
                 image_with_boxes = draw_boxes(detections[0], input_shape, input_image, NAMES)
                 frame = image_with_boxes
-
+    
                 processing_times.append(stop_time - start_time)
                 # Use processing times from last 200 frames.
                 if len(processing_times) > 200:
                     processing_times.popleft()
-
+    
                 _, f_width = frame.shape[:2]
                 # Mean processing time [ms].
                 processing_time = np.mean(processing_times) * 1000
@@ -1372,9 +1294,7 @@ Run Live Object Detection
                         break
                 else:
                     # Encode numpy array to jpg.
-                    _, encoded_img = cv2.imencode(
-                        ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100]
-                    )
+                    _, encoded_img = cv2.imencode(ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                     # Create an IPython image.⬆️
                     i = display.Image(data=encoded_img)
                     # Display the image in this notebook.
@@ -1412,7 +1332,7 @@ Run the object detection:
 .. code:: ipython3
 
     WEBCAM_INFERENCE = False
-
+    
     if WEBCAM_INFERENCE:
         VIDEO_SOURCE = 0  # Webcam
     else:
@@ -1434,12 +1354,18 @@ Run the object detection:
 .. code:: ipython3
 
     quantized_model = core.read_model(ov_int8_model_path)
-
-    run_object_detection(source=VIDEO_SOURCE, flip=True, use_popup=False, model=quantized_model, device=device.value)
+    
+    run_object_detection(
+        source=VIDEO_SOURCE,
+        flip=True,
+        use_popup=False,
+        model=quantized_model,
+        device=device.value,
+    )
 
 
 
-.. image:: 287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_36_0.png
+.. image:: yolov9-optimization-with-output_files/yolov9-optimization-with-output_36_0.png
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_16_0.jpg b/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_16_0.jpg
similarity index 100%
rename from docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_16_0.jpg
rename to docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_16_0.jpg
diff --git a/docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_16_0.png b/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_16_0.png
similarity index 100%
rename from docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_16_0.png
rename to docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_16_0.png
diff --git a/docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_27_0.jpg b/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_27_0.jpg
similarity index 100%
rename from docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_27_0.jpg
rename to docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_27_0.jpg
diff --git a/docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_27_0.png b/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_27_0.png
similarity index 100%
rename from docs/notebooks/287-yolov9-optimization-with-output_files/287-yolov9-optimization-with-output_27_0.png
rename to docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_27_0.png
diff --git a/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_36_0.png b/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_36_0.png
new file mode 100644
index 00000000000..f37245d461f
--- /dev/null
+++ b/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_36_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf18440131a6efa4ac70b72167eb50c602fc705dc4f9a4dbce860b916b67e633
+size 495625
diff --git a/docs/notebooks/253-zeroscope-text2video-with-output.rst b/docs/notebooks/zeroscope-text2video-with-output.rst
similarity index 99%
rename from docs/notebooks/253-zeroscope-text2video-with-output.rst
rename to docs/notebooks/zeroscope-text2video-with-output.rst
index 6c7d82f64cd..28e28d50415 100644
--- a/docs/notebooks/253-zeroscope-text2video-with-output.rst
+++ b/docs/notebooks/zeroscope-text2video-with-output.rst
@@ -72,7 +72,7 @@ provides already pretrained model from ``cerspense``.
 
 .. code:: ipython3
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  "diffusers[torch]>=0.18.0" transformers "openvino>=2023.1.0" numpy gradio
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  "diffusers>=0.18.0" "torch>=2.1" transformers "openvino>=2023.1.0" numpy "gradio>=4.19"
 
 .. code:: ipython3
 
@@ -122,7 +122,7 @@ of ``diffusers.DiffusionPipeline``.
 
 .. code:: ipython3
 
-    pipe = diffusers.DiffusionPipeline.from_pretrained('cerspense/zeroscope_v2_576w')
+    pipe = diffusers.DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w")
 
 
 .. parsed-literal::
@@ -189,6 +189,7 @@ function to serialize the result of conversion.
 
     from pathlib import Path
     
+    
     def convert(model: torch.nn.Module, xml_path: str, **convert_kwargs) -> Path:
         xml_path = Path(xml_path)
         if not xml_path.exists():
@@ -247,7 +248,7 @@ inference, we need only decoder part.
         def __init__(self, vae):
             super().__init__()
             self.vae = vae
-            
+    
         def forward(self, z: torch.FloatTensor):
             return self.vae.decode(z)
 
@@ -298,9 +299,7 @@ Build a pipeline
         video.clamp_(0, 1)
         # prepare the final outputs
         i, c, f, h, w = video.shape
-        images = video.permute(2, 3, 0, 4, 1).reshape(
-            f, h, i * w, c
-        )  # 1st (frames, h, batch_size, w, c) 2nd (frames, h, batch_size * w, c)
+        images = video.permute(2, 3, 0, 4, 1).reshape(f, h, i * w, c)  # 1st (frames, h, batch_size, w, c) 2nd (frames, h, batch_size * w, c)
         images = images.unbind(dim=0)  # prepare a list of indvidual (consecutive frames)
         images = [(image.cpu().numpy() * 255).astype("uint8") for image in images]  # f h w c
         return images
@@ -313,7 +312,6 @@ Build a pipeline
         from diffusers.utils.torch_utils import randn_tensor
     
     
-    
     class OVTextToVideoSDPipeline(diffusers.DiffusionPipeline):
         def __init__(
             self,
@@ -462,9 +460,7 @@ Build a pipeline
             with self.progress_bar(total=num_inference_steps) as progress_bar:
                 for i, t in enumerate(timesteps):
                     # expand the latents if we are doing classifier free guidance
-                    latent_model_input = (
-                        torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                    )
+                    latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
     
                     # predict the noise residual
@@ -480,35 +476,21 @@ Build a pipeline
                     # perform guidance
                     if do_classifier_free_guidance:
                         noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                        noise_pred = noise_pred_uncond + guidance_scale * (
-                            noise_pred_text - noise_pred_uncond
-                        )
+                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     
                     # reshape latents
                     bsz, channel, frames, width, height = latents.shape
-                    latents = latents.permute(0, 2, 1, 3, 4).reshape(
-                        bsz * frames, channel, width, height
-                    )
-                    noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(
-                        bsz * frames, channel, width, height
-                    )
+                    latents = latents.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)
+                    noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)
     
                     # compute the previous noisy sample x_t -> x_t-1
-                    latents = self.scheduler.step(
-                        noise_pred, t, latents, **extra_step_kwargs
-                    ).prev_sample
+                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
     
                     # reshape latents back
-                    latents = (
-                        latents[None, :]
-                        .reshape(bsz, frames, channel, width, height)
-                        .permute(0, 2, 1, 3, 4)
-                    )
+                    latents = latents[None, :].reshape(bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4)
     
                     # call the callback, if provided
-                    if i == len(timesteps) - 1 or (
-                        (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
-                    ):
+                    if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                         progress_bar.update()
                         if callback is not None and i % callback_steps == 0:
                             callback(i, t, latents)
@@ -573,16 +555,10 @@ Build a pipeline
                     return_tensors="pt",
                 )
                 text_input_ids = text_inputs.input_ids
-                untruncated_ids = self.tokenizer(
-                    prompt, padding="longest", return_tensors="pt"
-                ).input_ids
+                untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
     
-                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                    text_input_ids, untruncated_ids
-                ):
-                    removed_text = self.tokenizer.batch_decode(
-                        untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                    )
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+                    removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
                     print(
                         "The following part of your input was truncated because CLIP can only handle sequences up to"
                         f" {self.tokenizer.model_max_length} tokens: {removed_text}"
@@ -603,10 +579,7 @@ Build a pipeline
                 if negative_prompt is None:
                     uncond_tokens = [""] * batch_size
                 elif type(prompt) is not type(negative_prompt):
-                    raise TypeError(
-                        f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                        f" {type(prompt)}."
-                    )
+                    raise TypeError(f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}.")
                 elif isinstance(negative_prompt, str):
                     uncond_tokens = [negative_prompt]
                 elif batch_size != len(negative_prompt):
@@ -636,9 +609,7 @@ Build a pipeline
                 seq_len = negative_prompt_embeds.shape[1]
     
                 negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-                negative_prompt_embeds = negative_prompt_embeds.view(
-                    batch_size * num_images_per_prompt, seq_len, -1
-                )
+                negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
     
                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
@@ -684,28 +655,17 @@ Build a pipeline
             negative_prompt_embeds=None,
         ):
             if self.height % 8 != 0 or self.width % 8 != 0:
-                raise ValueError(
-                    f"`height` and `width` have to be divisible by 8 but are {self.height} and {self.width}."
-                )
+                raise ValueError(f"`height` and `width` have to be divisible by 8 but are {self.height} and {self.width}.")
     
-            if (callback_steps is None) or (
-                callback_steps is not None
-                and (not isinstance(callback_steps, int) or callback_steps <= 0)
-            ):
-                raise ValueError(
-                    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                    f" {type(callback_steps)}."
-                )
+            if (callback_steps is None) or (callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)):
+                raise ValueError(f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}.")
     
             if prompt is not None and prompt_embeds is not None:
                 raise ValueError(
-                    f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                    " only forward one of the two."
+                    f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two."
                 )
             elif prompt is None and prompt_embeds is None:
-                raise ValueError(
-                    "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-                )
+                raise ValueError("Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.")
             elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
                 raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
     
@@ -728,9 +688,7 @@ Build a pipeline
             latents = 1 / scale_factor * latents
     
             batch_size, channels, num_frames, height, width = latents.shape
-            latents = latents.permute(0, 2, 1, 3, 4).reshape(
-                batch_size * num_frames, channels, height, width
-            )
+            latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
             image = self.vae_decoder(latents)[0]
             image = torch.tensor(image)
             video = (
@@ -769,8 +727,8 @@ select device from dropdown list for running inference using OpenVINO
 
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value='AUTO',
-        description='Device:',
+        value="AUTO",
+        description="Device:",
         disabled=False,
     )
     
@@ -849,7 +807,7 @@ Video generation
 
 .. code:: ipython3
 
-    frames = ov_pipe(prompt, num_inference_steps=25)['frames']
+    frames = ov_pipe(prompt, num_inference_steps=25)["frames"]
 
 
 
@@ -863,8 +821,8 @@ Video generation
     images = [PIL.Image.fromarray(frame) for frame in frames]
     images[0].save("output.gif", save_all=True, append_images=images[1:], duration=125, loop=0)
     with open("output.gif", "rb") as gif_file:
-        b64 = f'data:image/gif;base64,{base64.b64encode(gif_file.read()).decode()}'
-    IPython.display.HTML(f"")
+        b64 = f"data:image/gif;base64,{base64.b64encode(gif_file.read()).decode()}"
+    IPython.display.HTML(f'')
 
 
 
@@ -882,9 +840,7 @@ Interactive demo
 
 .. code:: ipython3
 
-    def generate(
-        prompt, seed, num_inference_steps, _=gr.Progress(track_tqdm=True)
-    ):
+    def generate(prompt, seed, num_inference_steps, _=gr.Progress(track_tqdm=True)):
         generator = torch.Generator().manual_seed(seed)
         frames = ov_pipe(
             prompt,
@@ -893,9 +849,7 @@ Interactive demo
         )["frames"]
         out_file = tempfile.NamedTemporaryFile(suffix=".gif", delete=False)
         images = [PIL.Image.fromarray(frame) for frame in frames]
-        images[0].save(
-            out_file, save_all=True, append_images=images[1:], duration=125, loop=0
-        )
+        images[0].save(out_file, save_all=True, append_images=images[1:], duration=125, loop=0)
         return out_file.name
     
     
@@ -912,7 +866,7 @@ Interactive demo
             ["A panda eating bamboo on a rock.", 0, 25],
             ["Spiderman is surfing.", 0, 25],
         ],
-        allow_flagging="never"
+        allow_flagging="never",
     )
     
     try:
diff --git a/samples/js/node/notebooks/001-hello-world.nnb b/samples/js/node/notebooks/001-hello-world.nnb
index dcf861b8d69..dbc46d19634 100644
--- a/samples/js/node/notebooks/001-hello-world.nnb
+++ b/samples/js/node/notebooks/001-hello-world.nnb
@@ -3,7 +3,7 @@
         {
             "language": "markdown",
             "source": [
-                "# Hello Image Classification\n\nThis basic introduction to OpenVINO™ shows how to do inference with an image classification model.\n\n A pre-trained [MobileNetV3 model](https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used in this tutorial. For more information about how OpenVINO IR models are created, refer to the [TensorFlow to OpenVINO](../101-tensorflow-to-openvino/101-tensorflow-to-openvino.ipynb) tutorial.\n   "
+                "# Hello Image Classification\n\nThis basic introduction to OpenVINO™ shows how to do inference with an image classification model.\n\n A pre-trained [MobileNetV3 model](https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used in this tutorial. For more information about how OpenVINO IR models are created, refer to the [TensorFlow to OpenVINO](../tensorflow-to-openvino/tensorflow-to-openvino.ipynb) tutorial.\n   "
             ],
             "outputs": []
         },