(l-onnx-doc-BitwiseXor)=

# BitwiseXor


(l-onnx-op-bitwisexor-18)=

## BitwiseXor - 18

### Version

- **name**: [BitwiseXor (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#BitwiseXor)
- **domain**: `main`
- **since_version**: `18`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 18**.

### Summary

Returns the tensor resulting from performing the bitwise `xor` 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 bitwise operator.
- **B** (heterogeneous) - **T**:

  Second input operand for the bitwise operator.

### Outputs

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

  Result tensor.

### Type Constraints

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

  Constrain input to integer tensors.

### Examples

#### default

```python
import numpy as np
import onnx

node = onnx.helper.make_node(
    "BitwiseXor",
    inputs=["x", "y"],
    outputs=["bitwisexor"],
)

# 2d
x = create_random_int((3, 4), np.int32)
y = create_random_int((3, 4), np.int32)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i32_2d")

# 3d
x = create_random_int((3, 4, 5), np.int16)
y = create_random_int((3, 4, 5), np.int16)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i16_3d")
```

#### _bitwiseor_broadcast

```python
import numpy as np
import onnx

node = onnx.helper.make_node(
    "BitwiseXor",
    inputs=["x", "y"],
    outputs=["bitwisexor"],
)

# 3d vs 1d
x = create_random_int((3, 4, 5), np.uint64)
y = create_random_int((5,), np.uint64)
z = np.bitwise_xor(x, y)
expect(
    node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui64_bcast_3v1d"
)

# 4d vs 3d
x = create_random_int((3, 4, 5, 6), np.uint8)
y = create_random_int((4, 5, 6), np.uint8)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui8_bcast_4v3d")
```