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pow:
description: |
Calculates the `exponent` power of each element in `input`.
When `exponent` is a Tensor, the shapes of `input` and `exponent` must be broadcastable.
.. math::
out_{i} = input_{i} ^{ exponent_{i}}
Args:
input (Union[Tensor, Number]): The first input is a Number or a tensor whose data type is
`number <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_ or
`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_.
exponent (Union[Tensor, Number]): The second input is a Number or a tensor whose data type is
`number <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_ or
`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_.
Returns:
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision or higher digits among the two inputs.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
>>> exponent = 3.0
>>> output = ops.pow(input, exponent)
>>> print(output)
[ 1. 8. 64.]
>>>
>>> input = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
>>> exponent = Tensor(np.array([2.0, 4.0, 3.0]), mindspore.float32)
>>> output = ops.pow(input, exponent)
>>> print(output)
[ 1. 16. 64.]
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