docstr: ivy.Array instance method variant of ivy.float_power. This method
  simply wraps the function, and so the docstring for ivy.float_power
  also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Array-like with elements to raise in power.
  x2
      Array-like of exponents. If x1.shape != x2.shape,
      they must be broadcastable to a common shape
      (which becomes the shape of the output).
  out
      optional output array, for writing the result to.
  
  Returns
  -------
  ret
      The bases in x1 raised to the exponents in x2.
      This is a scalar if both x1 and x2 are scalars
  
  Examples
  --------
  >>> x1 = ivy.array([1, 2, 3, 4, 5])
  >>> x1.float_power(3)
  ivy.array([1.,    8.,   27.,   64.,  125.])
  >>> x1 = ivy.array([1, 2, 3, 4, 5])
  >>> x2 = ivy.array([2, 3, 3, 2, 1])
  >>> x1.float_power(x2)
  ivy.array([1.,   8.,  27.,  16.,   5.])
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code: def float_power(
      self: Union[ivy.Array, float, list, tuple],
      x2: Union[ivy.Array, float, list, tuple],
      /,
      *,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.float_power. This method
      simply wraps the function, and so the docstring for ivy.float_power
      also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Array-like with elements to raise in power.
      x2
          Array-like of exponents. If x1.shape != x2.shape,
          they must be broadcastable to a common shape
          (which becomes the shape of the output).
      out
          optional output array, for writing the result to.
  
      Returns
      -------
      ret
          The bases in x1 raised to the exponents in x2.
          This is a scalar if both x1 and x2 are scalars
  
      Examples
      --------
      >>> x1 = ivy.array([1, 2, 3, 4, 5])
      >>> x1.float_power(3)
      ivy.array([1.,    8.,   27.,   64.,  125.])
      >>> x1 = ivy.array([1, 2, 3, 4, 5])
      >>> x2 = ivy.array([2, 3, 3, 2, 1])
      >>> x1.float_power(x2)
      ivy.array([1.,   8.,  27.,  16.,   5.])
      """
      return ivy.float_power(self._data, x2, out=out)
  
imports: import abc
  from typing import Optional, Union, Tuple, List, Sequence
  from numbers import Number
  import ivy
label_desc: Use image node_id 7 for calling the _ArrayWithElementWiseExperimental obj's underlying member method code with example usage: obj.float_power() and returns: ivy