# maxframe.tensor.float_power

### maxframe.tensor.float_power(x1, x2, out=None, where=None, \*\*kwargs)

First tensor elements raised to powers from second array, element-wise.

Raise each base in x1 to the positionally-corresponding power in x2.
x1 and x2 must be broadcastable to the same shape. This differs from
the power function in that integers, float16, and float32  are promoted to
floats with a minimum precision of float64 so that the result is always
inexact.  The intent is that the function will return a usable result for
negative powers and seldom overflow for positive powers.

* **Parameters:**
  * **x1** (*array_like*) – The bases.
  * **x2** (*array_like*) – The exponents.
  * **out** (*Tensor* *,* *None* *, or* [*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple) *of* *Tensor and None* *,* *optional*) – A location into which the result is stored. If provided, it must have
    a shape that the inputs broadcast to. If not provided or None,
    a freshly-allocated tensor is returned. A tuple (possible only as a
    keyword argument) must have length equal to the number of outputs.
  * **where** (*array_like* *,* *optional*) – Values of True indicate to calculate the ufunc at that position, values
    of False indicate to leave the value in the output alone.
  * **\*\*kwargs**
* **Returns:**
  **y** – The bases in x1 raised to the exponents in x2.
* **Return type:**
  Tensor

#### SEE ALSO
[`power`](maxframe.tensor.power.md#maxframe.tensor.power)
: power function that preserves type

### Examples

Cube each element in a list.

```pycon
>>> import maxframe.tensor as mt
```

```pycon
>>> x1 = range(6)
>>> x1
[0, 1, 2, 3, 4, 5]
>>> mt.float_power(x1, 3).execute()
array([   0.,    1.,    8.,   27.,   64.,  125.])
```

Raise the bases to different exponents.

```pycon
>>> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]
>>> mt.float_power(x1, x2).execute()
array([  0.,   1.,   8.,  27.,  16.,   5.])
```

The effect of broadcasting.

```pycon
>>> x2 = mt.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
>>> x2.execute()
array([[1, 2, 3, 3, 2, 1],
       [1, 2, 3, 3, 2, 1]])
>>> mt.float_power(x1, x2).execute()
array([[  0.,   1.,   8.,  27.,  16.,   5.],
       [  0.,   1.,   8.,  27.,  16.,   5.]])
```
