# any¶

`paddle.` `any` ( x, axis=None, keepdim=False, name=None ) [source]

Computes the the `logical or` of tensor elements over the given dimension.

Parameters
• x (Tensor) – An N-D Tensor, the input data type should be bool.

• axis (int|list|tuple, optional) – The dimensions along which the `logical or` is compute. If `None`, and all elements of `x` and return a Tensor with a single element, otherwise must be in the range \([-rank(x), rank(x))\). If \(axis[i] < 0\), the dimension to reduce is \(rank + axis[i]\).

• keepdim (bool, optional) – Whether to reserve the reduced dimension in the output Tensor. The result Tensor will have one fewer dimension than the `x` unless `keepdim` is true, default value is False.

• name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name

Returns

Results the `logical or` on the specified axis of input Tensor x, it’s data type is bool.

Return type

Tensor

Raises
• ValueError – If the data type of x is not bool.

• TypeError – The type of `axis` must be int, list or tuple.

Examples

```import paddle
import numpy as np

# x is a bool Tensor with following elements:
#    [[True, False]
#     [False, False]]
x = paddle.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
print(x)
x = paddle.cast(x, 'bool')

# out1 should be [True]
out1 = paddle.any(x)  # [True]
print(out1)

# out2 should be [True, False]
out2 = paddle.any(x, axis=0)  # [True, False]
print(out2)

# keep_dim=False, out3 should be [True, False], out.shape should be (2,)
out3 = paddle.any(x, axis=-1)  # [True, False]
print(out3)

# keep_dim=True, result should be [[True], [False]], out.shape should be (2,1)
out4 = paddle.any(x, axis=1, keepdim=True)
out4 = paddle.cast(out4, 'int32')  # [[True], [False]]
print(out4)
```