# tensorplay.functional.pow

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.pow.html

```python
tensorplay.functional.pow(input, exponent, *, out=None) → Tensor
```

Takes the power of each element in input with exponent and
returns a tensor with the result.

exponent can be either a single float number or a Tensor
with the same number of elements as input.

When exponent is a scalar value, the operation applied is:

$$\text{out}_i = x_i ^ \text{exponent}$$

When exponent is a tensor, the operation applied is:

$$\text{out}_i = x_i ^ {\text{exponent}_i}$$

When exponent is a tensor, the shapes of input
and exponent must be [broadcastable](/docs/notes/broadcasting.html#broadcasting-semantics).

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the input tensor.

- exponent ([float](https://docs.python.org/3/builtins/functions.html#float) or tensor) – the exponent value

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

Example:

```
>>> a = tensorplay.randn(4)
>>> a
tensor([ 0.4331,  1.2475,  0.6834, -0.2791])
>>> tensorplay.pow(a, 2)
tensor([ 0.1875,  1.5561,  0.4670,  0.0779])
>>> exp = tensorplay.arange(1., 5.)

>>> a = tensorplay.arange(1., 5.)
>>> a
tensor([ 1.,  2.,  3.,  4.])
>>> exp
tensor([ 1.,  2.,  3.,  4.])
>>> tensorplay.pow(a, exp)
tensor([   1.,    4.,   27.,  256.])
```

```python
tensorplay.functional.pow(self, exponent, *, out=None) → Tensor
```

self is a scalar float value, and exponent is a tensor.
The returned tensor out is of the same shape as exponent

The operation applied is:

$$\text{out}_i = \text{self} ^ {\text{exponent}_i}$$

Parameters:

- self ([float](https://docs.python.org/3/builtins/functions.html#float)) – the scalar base value for the power operation

- exponent ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the exponent tensor

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

Example:

```
>>> exp = tensorplay.arange(1., 5.)
>>> base = 2
>>> tensorplay.pow(base, exp)
tensor([  2.,   4.,   8.,  16.])
```