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# tfp.experimental.distributions.marginal_fns.ps.reduce_all

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Computes `tf.math.logical_and` of elements across dimensions of a tensor.

This is the reduction operation for the elementwise `tf.math.logical_and` op.

Reduces `input_tensor` along the dimensions given in `axis`. Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each of the entries in `axis`, which must be unique. If `keepdims` is true, the reduced dimensions are retained with length 1.

If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned.

``````>>> x = tf.constant([[True,  True], [False, False]])
>>> tf.math.reduce_all(x)
<tf.Tensor: shape=(), dtype=bool, numpy=False>
>>> tf.math.reduce_all(x, 0)
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([False, False])>
>>> tf.math.reduce_all(x, 1)
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, False])>
``````

`input_tensor` The boolean tensor to reduce.
`axis` The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range ```[-rank(input_tensor), rank(input_tensor))```.
`keepdims` If true, retains reduced dimensions with length 1.
`name` A name for the operation (optional).

The reduced tensor.

## numpy compatibility

Equivalent to np.all

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