parameters: (x1, x2, dtype=None)
return_statement: return _apply_tensor_op(F.tensor_pow, x1, x2, dtype=dtype)
docstring: First array 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 float64 are promoted to floats with
      a minimum precision of float32 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.
  
      Note:
          Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
          not supported.
          Integers and floats are promoted to float32 instead of float64.
  
      Args:
          x1 (Tensor): the bases.
          x2 (Tensor): the exponents.
          dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
              output Tensor.
  
      Returns:
          Tensor or scalar, the bases in `x1` raised to the exponents in `x2`. This
          is a scalar if both `x1` and `x2` are scalars.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> x1 = np.arange(6)
          >>> x2 = np.array(3)
          >>> output = np.float_power(x1, x2)
          >>> print(output)
          [  0.   1.   8.  27.  64. 125.]
docstring_summary: First array elements raised to powers from second array, element-wise.
docstring_tokens: First array elements raised to powers from second array element - wise .
function: def float_power(x1, x2, dtype=None):
      """
      First array 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 float64 are promoted to floats with
      a minimum precision of float32 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.
  
      Note:
          Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
          not supported.
          Integers and floats are promoted to float32 instead of float64.
  
      Args:
          x1 (Tensor): the bases.
          x2 (Tensor): the exponents.
          dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
              output Tensor.
  
      Returns:
          Tensor or scalar, the bases in `x1` raised to the exponents in `x2`. This
          is a scalar if both `x1` and `x2` are scalars.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> x1 = np.arange(6)
          >>> x2 = np.array(3)
          >>> output = np.float_power(x1, x2)
          >>> print(output)
          [  0.   1.   8.  27.  64. 125.]
      """
      if not _check_same_type(F.dtype(x1), mstype.float32):
          x1 = F.cast(x1, mstype.float32)
      if not _check_same_type(F.dtype(x2), mstype.float32):
          x2 = F.cast(x2, mstype.float32)
  
      return _apply_tensor_op(F.tensor_pow, x1, x2, dtype=dtype)
function_tokens: def float_power ( x1 , x2 , dtype = None ) : if not _check_same_type ( F . dtype ( x1 ) , mstype . float32 ) : x1 = F . cast ( x1 , mstype . float32 ) if not _check_same_type ( F . dtype ( x2 ) , mstype . float32 ) : x2 = F . cast ( x2 , mstype . float32 ) return _apply_tensor_op ( F . tensor_pow , x1 , x2 , dtype = dtype )