openvino/docs/ops/arithmetic/Mod_1.md

## Mod <a name="Mod"></a> {#openvino_docs_ops_arithmetic_Mod_1}

**Versioned name**: *Mod-1*

**Category**: Arithmetic binary operation

**Short description**: *Mod* performs an element-wise modulo operation with two given tensors applying broadcasting rule specified in the *auto_broadcast* attribute.

**Detailed description**
As a first step input tensors *a* and *b* are broadcasted if their shapes differ. Broadcasting is performed according to `auto_broadcast` attribute specification. As a second step *Mod* operation is computed element-wise on the input tensors *a* and *b* according to the formula below:

\f[
o_{i} = a_{i} \mod b_{i}
\f]

*Mod* operation computes a reminder of a truncated division. It is the same behaviour 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. The result of division by zero is undefined.

**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 must match
    * *numpy* - numpy broadcasting rules, description is available in [Broadcast Rules For Elementwise Operations](../broadcast_rules.md)
  * **Type**: string
  * **Default value**: "numpy"
  * **Required**: *no*


**Inputs**

* **1**: A tensor of type *T* and arbitrary shape. **Required.**
* **2**: A tensor of type *T* and arbitrary shape. **Required.**

**Outputs**

* **1**: The result of element-wise modulo operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs.

**Types**

* *T*: any numeric type.

**Examples**

*Example 1 - no broadcasting*

```xml
<layer ... type="Mod">
    <data auto_broadcast="none"/>
    <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: numpy broadcasting*
```xml
<layer ... type="Mod">
    <data auto_broadcast="numpy"/>
    <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>
```