(l-onnx-doc-BitwiseNot)=

# BitwiseNot


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

## BitwiseNot - 18

### Version

- **name**: [BitwiseNot (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#BitwiseNot)
- **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 bitwise not of the input tensor element-wise.

### Inputs

- **X** (heterogeneous) - **T**:

  Input tensor

### Outputs

- **Y** (heterogeneous) - **T**:

  Output tensor

### Type Constraints

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

  Constrain input/output to integer tensors.

### Examples

#### default

```python
import numpy as np
import onnx

node = onnx.helper.make_node(
    "BitwiseNot",
    inputs=["x"],
    outputs=["bitwise_not"],
)

# 2d
x = create_random_int((3, 4), np.int32)
y = np.bitwise_not(x)
expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_2d")

# 3d
x = create_random_int((3, 4, 5), np.uint16)
y = np.bitwise_not(x)
expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_3d")

# 4d
x = create_random_int((3, 4, 5, 6), np.uint8)
y = np.bitwise_not(x)
expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_4d")
```