(l-onnx-doc-Multinomial)=

# Multinomial


(l-onnx-op-multinomial-22)=

## Multinomial - 22

### Version

- **name**: [Multinomial (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Multinomial)
- **domain**: `main`
- **since_version**: `22`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 22**.

### Summary

Generate a tensor of samples from a multinomial distribution according to the probabilities
of each of the possible outcomes.

### Attributes

* **dtype - INT** (default is `'6'`):

  (Optional) The data type for the elements of the output tensor, if not specified, we will use int32.

* **sample_size - INT** (default is `'1'`):

  Number of times to sample.

* **seed - FLOAT** :

  (Optional) Seed to the random generator, if not specified we will auto generate one.

### Inputs

- **input** (heterogeneous) - **T1**:

  Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.

### Outputs

- **output** (heterogeneous) - **T2**:

  Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.

### Type Constraints

* **T1** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input types to float tensors.
* **T2** in ( `tensor(int32)`, `tensor(int64)` ):

  Constrain output types to integral tensors.

```{toctree}
text_diff_Multinomial_7_22
```

(l-onnx-op-multinomial-7)=

## Multinomial - 7

### Version

- **name**: [Multinomial (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Multinomial)
- **domain**: `main`
- **since_version**: `7`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 7**.

### Summary

Generate a tensor of samples from a multinomial distribution according to the probabilities
of each of the possible outcomes.

### Attributes

* **dtype - INT** (default is `'6'`):

  (Optional) The data type for the elements of the output tensor, if not specified, we will use int32.

* **sample_size - INT** (default is `'1'`):

  Number of times to sample.

* **seed - FLOAT** :

  (Optional) Seed to the random generator, if not specified we will auto generate one.

### Inputs

- **input** (heterogeneous) - **T1**:

  Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.

### Outputs

- **output** (heterogeneous) - **T2**:

  Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.

### Type Constraints

* **T1** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input types to float tensors.
* **T2** in ( `tensor(int32)`, `tensor(int64)` ):

  Constrain output types to integral tensors.