# logical_and¶

paddle. logical_and ( x, y, out=None, name=None ) [source]

Compute element-wise logical AND on `x` and `y`, and return `out`. `out` is N-dim boolean `Tensor`. Each element of `out` is calculated by

\[out = x \&\& y\]

Note

`paddle.logical_and` supports broadcasting. If you want know more about broadcasting, please refer to Introduction to Tensor .

Parameters
• x (Tensor) – the input tensor, it’s data type should be one of bool, int8, int16, in32, in64, float16, float32, float64, complex64, complex128.

• y (Tensor) – the input tensor, it’s data type should be one of bool, int8, int16, in32, in64, float16, float32, float64, complex64, complex128.

• out (Tensor, optional) – The `Tensor` that specifies the output of the operator, which can be any `Tensor` that has been created in the program. The default value is None, and a new `Tensor` will be created to save the output.

• name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

N-D Tensor. A location into which the result is stored. It’s dimension equals with `x`.

Examples

```>>> import paddle

>>> x = paddle.to_tensor([True])
>>> y = paddle.to_tensor([True, False, True, False])
>>> res = paddle.logical_and(x, y)
>>> print(res)
Tensor(shape=[4], dtype=bool, place=Place(cpu), stop_gradient=True,
[True , False, True , False])
```