diff --git a/docs/articles_en/openvino_workflow/running_inference_with_openvino/dldt_deployment_optimization_guide/preprocessing_overview/torchvision_preprocessing_converter.rst b/docs/articles_en/openvino_workflow/running_inference_with_openvino/dldt_deployment_optimization_guide/preprocessing_overview/torchvision_preprocessing_converter.rst
index 264edda073b..5d6bd9c4633 100644
--- a/docs/articles_en/openvino_workflow/running_inference_with_openvino/dldt_deployment_optimization_guide/preprocessing_overview/torchvision_preprocessing_converter.rst
+++ b/docs/articles_en/openvino_workflow/running_inference_with_openvino/dldt_deployment_optimization_guide/preprocessing_overview/torchvision_preprocessing_converter.rst
@@ -37,35 +37,6 @@ and enabling additional performance optimizations.
 Example
 ###################
 
-.. code-block:: py
-
-   preprocess_pipeline = torchvision.transforms.Compose(
-       [
-           torchvision.transforms.Resize(256, interpolation=transforms.InterpolationMode.NEAREST),
-           torchvision.transforms.CenterCrop((216, 218)),
-           torchvision.transforms.Pad((2, 3, 4, 5), fill=3),
-           torchvision.transforms.ToTensor(),
-           torchvision.transforms.ConvertImageDtype(torch.float32),
-           torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-       ]
-   )
-
-   torch_model = SimpleConvnet(input_channels=3)
-
-   torch.onnx.export(torch_model, torch.randn(1, 3, 224, 224), "test_convnet.onnx", verbose=False, input_names=["input"], output_names=["output"])
-   core = Core()
-   ov_model = core.read_model(model="test_convnet.onnx")
-
-   test_input = np.random.randint(255, size=(260, 260, 3), dtype=np.uint16)
-   ov_model = PreprocessConverter.from_torchvision(
-       model=ov_model, transform=preprocess_pipeline, input_example=Image.fromarray(test_input.astype("uint8"), "RGB")
-   )
-   ov_model = core.compile_model(ov_model, "CPU")
-   ov_input = np.expand_dims(test_input, axis=0)
-   output = ov_model.output(0)
-   ov_result = ov_model(ov_input)[output]
-
-
-
-
-
+.. doxygensnippet:: docs/snippets/torchvision_preprocessing.py
+    :language: Python
+    :fragment: torchvision_preprocessing
diff --git a/docs/snippets/torchvision_preprocessing.py b/docs/snippets/torchvision_preprocessing.py
new file mode 100644
index 00000000000..45452059102
--- /dev/null
+++ b/docs/snippets/torchvision_preprocessing.py
@@ -0,0 +1,65 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+
+def main():
+
+    #! [torchvision_preprocessing]
+    import torch.nn.functional as f
+    import openvino as ov
+    import numpy as np
+    import torchvision
+    import torch
+    import os
+
+    from openvino.preprocess.torchvision import PreprocessConverter
+    from PIL import Image
+
+
+    # 1. Create a sample model
+    class Convnet(torch.nn.Module):
+        def __init__(self, input_channels):
+            super(Convnet, self).__init__()
+            self.conv1 = torch.nn.Conv2d(input_channels, 6, 5)
+            self.conv2 = torch.nn.Conv2d(6, 16, 3)
+
+        def forward(self, data):
+            data = f.max_pool2d(f.relu(self.conv1(data)), 2)
+            data = f.max_pool2d(f.relu(self.conv2(data)), 2)
+            return data
+
+
+    # 2. Define torchvision preprocessing pipeline
+    preprocess_pipeline = torchvision.transforms.Compose(
+       [
+           torchvision.transforms.Resize(256, interpolation=torchvision.transforms.InterpolationMode.NEAREST),
+           torchvision.transforms.CenterCrop((216, 218)),
+           torchvision.transforms.Pad((2, 3, 4, 5), fill=3),
+           torchvision.transforms.ToTensor(),
+           torchvision.transforms.ConvertImageDtype(torch.float32),
+           torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+       ]
+   )
+
+    # 3. Read the model into OpenVINO    
+    torch_model = Convnet(input_channels=3)
+    torch.onnx.export(torch_model, torch.randn(1, 3, 224, 224), "test_convnet.onnx", verbose=False, input_names=["input"], output_names=["output"])
+    core = ov.Core()
+    ov_model = core.read_model(model="test_convnet.onnx")
+    if os.path.exists("test_convnet.onnx"):
+        os.remove("test_convnet.onnx")
+    test_input = np.random.randint(255, size=(260, 260, 3), dtype=np.uint16)
+
+    # 4. Embed the torchvision preocessing into OpenVINO model
+    ov_model = PreprocessConverter.from_torchvision(
+        model=ov_model, transform=preprocess_pipeline, input_example=Image.fromarray(test_input.astype("uint8"), "RGB")
+    )
+    ov_model = core.compile_model(ov_model, "CPU")
+
+    # 5. Perform inference
+    ov_input = np.expand_dims(test_input, axis=0)
+    output = ov_model.output(0)
+    ov_result = ov_model(ov_input)[output]
+    #! [torchvision_preprocessing]
+ 
+    return 0