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openvino/docs/ops/arithmetic/Mod_1.md

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## Mod <a name="Mod"></a> {#openvino_docs_ops_arithmetic_Mod_1}
2020-06-19 14:39:57 +03:00
**Versioned name**: *Mod-1*
**Category**: Arithmetic binary operation
**Short description**: *Mod* returns an element-wise division reminder with two given tensors applying multi-directional broadcast rules.
The result here is consistent with a truncated divide (like in C programming language): `truncated(x / y) * y + truncated_mod(x, y) = x`.
The sign of the result is equal to a sign of a dividend.
**Attributes**:
* *auto_broadcast*
* **Description**: specifies rules used for auto-broadcasting of input tensors.
* **Range of values**:
* *none* - no auto-broadcasting is allowed, all input shapes should match
* *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>.
* **Type**: string
* **Default value**: "numpy"
* **Required**: *no*
**Inputs**
* **1**: A tensor of type T. Required.
* **2**: A tensor of type T. Required.
**Outputs**
* **1**: The element-wise division reminder. A tensor of type T.
**Types**
* *T*: any numeric type.
**Examples**
*Example 1*
```xml
<layer ... type="FloorMod">
<input>
<port id="0">
<dim>256</dim>
<dim>56</dim>
</port>
<port id="1">
<dim>256</dim>
<dim>56</dim>
</port>
</input>
<output>
<port id="2">
<dim>256</dim>
<dim>56</dim>
</port>
</output>
</layer>
```
*Example 2: broadcast*
```xml
<layer ... type="FloorMod">
<input>
<port id="0">
<dim>8</dim>
<dim>1</dim>
<dim>6</dim>
<dim>1</dim>
</port>
<port id="1">
<dim>7</dim>
<dim>1</dim>
<dim>5</dim>
</port>
</input>
<output>
<port id="2">
<dim>8</dim>
<dim>7</dim>
<dim>6</dim>
<dim>5</dim>
</port>
</output>
</layer>
```