(l-onnx-doc-Where)=

# Where


(l-onnx-op-where-16)=

## Where - 16

### Version

- **name**: [Where (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Where)
- **domain**: `main`
- **since_version**: `16`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 16**.

### Summary

Return elements, either from X or Y, depending on condition.
Where behaves like
[numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html)
with three parameters.

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).

### Inputs

- **condition** (heterogeneous) - **B**:

  When True (nonzero), yield X, otherwise yield Y
- **X** (heterogeneous) - **T**:

  values selected at indices where condition is True
- **Y** (heterogeneous) - **T**:

  values selected at indices where condition is False

### Outputs

- **output** (heterogeneous) - **T**:

  Tensor of shape equal to the broadcasted shape of condition, X, and Y.

### Type Constraints

* **B** in ( `tensor(bool)` ):

  Constrain to boolean tensors.
* **T** in ( `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input and output types to all tensor types (including bfloat).

```{toctree}
text_diff_Where_9_16
```

(l-onnx-op-where-9)=

## Where - 9

### Version

- **name**: [Where (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Where)
- **domain**: `main`
- **since_version**: `9`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 9**.

### Summary

Return elements, either from X or Y, depending on condition.
Where behaves like
[numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html)
with three parameters.

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).

### Inputs

- **condition** (heterogeneous) - **B**:

  When True (nonzero), yield X, otherwise yield Y
- **X** (heterogeneous) - **T**:

  values selected at indices where condition is True
- **Y** (heterogeneous) - **T**:

  values selected at indices where condition is False

### Outputs

- **output** (heterogeneous) - **T**:

  Tensor of shape equal to the broadcasted shape of condition, X, and Y.

### Type Constraints

* **B** in ( `tensor(bool)` ):

  Constrain to boolean tensors.
* **T** in ( `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input and output types to all tensor types.