86 lines
2.0 KiB
Markdown
86 lines
2.0 KiB
Markdown
## Mod <a name="Mod"></a> {#openvino_docs_ops_arithmetic_Mod_1}
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**Versioned name**: *Mod-1*
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**Category**: Arithmetic binary operation
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**Short description**: *Mod* returns an element-wise division reminder with two given tensors applying multi-directional broadcast rules.
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The result here is consistent with a truncated divide (like in C programming language): `truncated(x / y) * y + truncated_mod(x, y) = x`.
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The sign of the result is equal to a sign of a dividend.
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**Attributes**:
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* *auto_broadcast*
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* **Description**: specifies rules used for auto-broadcasting of input tensors.
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* **Range of values**:
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* *none* - no auto-broadcasting is allowed, all input shapes should match
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* *numpy* - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in <a href="https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md">ONNX docs</a>.
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* **Type**: string
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* **Default value**: "numpy"
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* **Required**: *no*
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**Inputs**
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* **1**: A tensor of type T. Required.
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* **2**: A tensor of type T. Required.
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**Outputs**
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* **1**: The element-wise division reminder. A tensor of type T.
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**Types**
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* *T*: any numeric type.
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**Examples**
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*Example 1*
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```xml
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<layer ... type="FloorMod">
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<input>
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<port id="0">
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<dim>256</dim>
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<dim>56</dim>
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</port>
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<port id="1">
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<dim>256</dim>
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<dim>56</dim>
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</port>
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</input>
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<output>
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<port id="2">
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<dim>256</dim>
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<dim>56</dim>
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</port>
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</output>
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</layer>
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```
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*Example 2: broadcast*
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```xml
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<layer ... type="FloorMod">
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<input>
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<port id="0">
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<dim>8</dim>
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<dim>1</dim>
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<dim>6</dim>
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<dim>1</dim>
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</port>
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<port id="1">
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<dim>7</dim>
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<dim>1</dim>
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<dim>5</dim>
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</port>
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</input>
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<output>
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<port id="2">
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<dim>8</dim>
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<dim>7</dim>
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<dim>6</dim>
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<dim>5</dim>
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</port>
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</output>
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</layer>
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``` |