(l-onnx-doc-Mod)=

# Mod


(l-onnx-op-mod-13)=

## Mod - 13

### Version

- **name**: [Mod (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Mod)
- **domain**: `main`
- **since_version**: `13`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 13**.

### Summary

Performs element-wise binary modulus (with Numpy-style broadcasting support).
The sign of the remainder is the same as that of the Divisor.

Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend
(in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided.
This attribute is set to 0 by default causing the behavior to be like integer mod.
Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().

If the input type is floating point, then `fmod` attribute must be set to 1.

In case of dividend being zero, the results will be platform dependent.

This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [Broadcasting in ONNX](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

### Attributes

* **fmod - INT** (default is `'0'`):

  Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment

### Inputs

- **A** (heterogeneous) - **T**:

  Dividend tensor
- **B** (heterogeneous) - **T**:

  Divisor tensor

### Outputs

- **C** (heterogeneous) - **T**:

  Remainder tensor

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input and output types to high-precision numeric tensors.

```{toctree}
text_diff_Mod_10_13
```

(l-onnx-op-mod-10)=

## Mod - 10

### Version

- **name**: [Mod (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Mod)
- **domain**: `main`
- **since_version**: `10`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 10**.

### Summary

Performs element-wise binary modulus (with Numpy-style broadcasting support).
  The sign of the remainder is the same as that of the Divisor.

  Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend
  (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided.
  This attribute is set to 0 by default causing the behavior to be like integer mod.
  Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().

  If the input type is floating point, then `fmod` attribute must be set to 1.

  In case of dividend being zero, the results will be platform dependent.

This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [Broadcasting in ONNX](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

### Attributes

* **fmod - INT** (default is `'0'`):

  Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment

### Inputs

- **A** (heterogeneous) - **T**:

  Dividend tensor
- **B** (heterogeneous) - **T**:

  Divisor tensor

### Outputs

- **C** (heterogeneous) - **T**:

  Remainder tensor

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input and output types to high-precision numeric tensors.