# BitwiseOr#

## BitwiseOr - 18#

Version

• name: BitwiseOr (GitHub)

• 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 or operation elementwise on the input tensors A and B (with Numpy-style broadcasting support).

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

```node = onnx.helper.make_node(
"BitwiseOr",
inputs=["x", "y"],
outputs=["bitwiseor"],
)
# 2d
x = np.random.randn(3, 4).astype(np.int32)
y = np.random.randn(3, 4).astype(np.int32)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i32_2d")

# 4d
x = np.random.randn(3, 4, 5, 6).astype(np.int8)
y = np.random.randn(3, 4, 5, 6).astype(np.int8)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i16_4d")
```

```node = onnx.helper.make_node(
"BitwiseOr",
inputs=["x", "y"],
outputs=["bitwiseor"],
)

# 3d vs 1d
x = np.random.randn(3, 4, 5).astype(np.uint64)
y = np.random.randn(5).astype(np.uint64)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui64_bcast_3v1d")

# 4d vs 3d
x = np.random.randn(3, 4, 5, 6).astype(np.uint8)
y = np.random.randn(4, 5, 6).astype(np.uint8)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui8_bcast_4v3d")
```