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# And


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## And - 7

### Version

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

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

  Constrain output to boolean tensor.

### Examples

#### default

```python
import numpy as np
import onnx

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

# 2d
x = (np.random.randn(3, 4) > 0).astype(bool)
y = (np.random.randn(3, 4) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and2d")

# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(3, 4, 5) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and3d")

# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and4d")
```

#### _and_broadcast

```python
import numpy as np
import onnx

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

# 3d vs 1d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(5) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v1d")

# 3d vs 2d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(4, 5) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v2d")

# 4d vs 2d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(5, 6) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v2d")

# 4d vs 3d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(4, 5, 6) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v3d")

# 4d vs 4d
x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v4d")
```

```{toctree}
text_diff_And_1_7
```

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

## And - 1

### Version

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

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

  Constrain output to boolean tensor.