# tf.math.logical_and

Returns the truth value of x AND y element-wise.

Logical AND function.

Requires that `x` and `y` have the same shape or have broadcast-compatible shapes. For example, `x` and `y` can be:

• Two single elements of type `bool`.
• One `tf.Tensor` of type `bool` and one single `bool`, where the result will be calculated by applying logical AND with the single element to each element in the larger Tensor.
• Two `tf.Tensor` objects of type `bool` of the same shape. In this case, the result will be the element-wise logical AND of the two input tensors.

You can also use the `&` operator instead.

``````>>> a = tf.constant([True])
>>> b = tf.constant([False])
>>> tf.math.logical_and(a, b)
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([False])>
>>> a & b
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([False])>
``````
````c = tf.constant([True])`
`x = tf.constant([False, True, True, False])`
`tf.math.logical_and(c, x)`
`<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False,  True,  True, False])>`
`c & x`
`<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False,  True,  True, False])>`
```
````y = tf.constant([False, False, True, True])`
`z = tf.constant([False, True, False, True])`
`tf.math.logical_and(y, z)`
`<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, False, False, True])>`
`y & z`
`<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, False, False, True])>`
```

````tf.logical_and([[True, False]], [[True], [False]])`
`<tf.Tensor: shape=(2, 2), dtype=bool, numpy=`
`  array([[ True, False],`
`         [False, False]])>`
```

The reduction version of this elementwise operation is `tf.math.reduce_all`.

`x` A `tf.Tensor` of type bool.
`y` A `tf.Tensor` of type bool.
`name` A name for the operation (optional).

A `tf.Tensor` of type bool with the shape that `x` and `y` broadcast to.

`x` A `Tensor` of type `bool`.
`y` A `Tensor` of type `bool`.
`name` A name for the operation (optional).

A `Tensor` of type `bool`.

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{ "last_modified": "Last updated 2024-01-23 UTC.", "state": "" }