(l-onnx-doc-Equal)=

# Equal


(l-onnx-op-equal-19)=

## Equal - 19

### Version

- **name**: [Equal (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Equal)
- **domain**: `main`
- **since_version**: `19`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 19**.

### Summary

Returns the tensor resulted from performing the `equal` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).

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

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

  First input operand for the logical operator.
- **B** (heterogeneous) - **T**:

  Second input operand for the logical operator.

### Outputs

- **C** (heterogeneous) - **T1**:

  Result tensor.

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(bool)`, `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 types to all (non-complex) tensors.
* **T1** in ( `tensor(bool)` ):

  Constrain output to boolean tensor.

```{toctree}
text_diff_Equal_13_19
```

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

## Equal - 13

### Version

- **name**: [Equal (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Equal)
- **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

Returns the tensor resulted from performing the `equal` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).

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

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

  First input operand for the logical operator.
- **B** (heterogeneous) - **T**:

  Second input operand for the logical operator.

### Outputs

- **C** (heterogeneous) - **T1**:

  Result tensor.

### Type Constraints

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

  Constrain input types to all numeric tensors.
* **T1** in ( `tensor(bool)` ):

  Constrain output to boolean tensor.

```{toctree}
text_diff_Equal_11_19
text_diff_Equal_11_13
```

(l-onnx-op-equal-11)=

## Equal - 11

### Version

- **name**: [Equal (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Equal)
- **domain**: `main`
- **since_version**: `11`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 11**.

### Summary

Returns the tensor resulted from performing the `equal` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).

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

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

  First input operand for the logical operator.
- **B** (heterogeneous) - **T**:

  Second input operand for the logical operator.

### Outputs

- **C** (heterogeneous) - **T1**:

  Result tensor.

### Type Constraints

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

  Constrain input types to all numeric tensors.
* **T1** in ( `tensor(bool)` ):

  Constrain output to boolean tensor.

```{toctree}
text_diff_Equal_7_19
text_diff_Equal_7_13
text_diff_Equal_7_11
```

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

## Equal - 7

### Version

- **name**: [Equal (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Equal)
- **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

Returns the tensor resulted from performing the `equal` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).

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

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

  First input operand for the logical operator.
- **B** (heterogeneous) - **T**:

  Second input operand for the logical operator.

### Outputs

- **C** (heterogeneous) - **T1**:

  Result tensor.

### Type Constraints

* **T** in ( `tensor(bool)`, `tensor(int32)`, `tensor(int64)` ):

  Constrain input to integral tensors.
* **T1** in ( `tensor(bool)` ):

  Constrain output to boolean tensor.

```{toctree}
text_diff_Equal_1_19
text_diff_Equal_1_13
text_diff_Equal_1_11
text_diff_Equal_1_7
```

(l-onnx-op-equal-1)=

## Equal - 1

### Version

- **name**: [Equal (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Equal)
- **domain**: `main`
- **since_version**: `1`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 1**.

### Summary

Returns the tensor resulted from performing the `equal` logical operation
elementwise on the input tensors `A` and `B`.

If broadcasting is enabled, the right-hand-side argument will be broadcasted
to match the shape of left-hand-side argument. See the doc of `Add` for a
detailed description of the broadcasting rules.

### Attributes

* **axis - INT** :

  If set, defines the broadcast dimensions.

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

  Enable broadcasting

### Inputs

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

  Left input tensor for the logical operator.
- **B** (heterogeneous) - **T**:

  Right input tensor for the logical operator.

### Outputs

- **C** (heterogeneous) - **T1**:

  Result tensor.

### Type Constraints

* **T** in ( `tensor(bool)`, `tensor(int32)`, `tensor(int64)` ):

  Constrain input to integral tensors.
* **T1** in ( `tensor(bool)` ):

  Constrain output to boolean tensor.