code: def 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 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 : Tensor
          The bases in `x1` raised to the exponents in `x2`.
  
      See Also
      --------
      power : power function that preserves type
  
      Examples
      --------
      Cube each element in a list.
  
      >>> import mars.tensor as mt
  
      >>> 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.
  
      >>> 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.
  
      >>> 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.]])
      """
      op = TensorFloatPower(**kwargs)
      return op(x1, x2, out=out, where=where)
  
file_path: mars/mars/tensor/arithmetic/float_power.py
imports: import numpy
  from ...None import opcodes
  from ..utils import inject_dtype
  from .core import TensorBinOp
  from .utils import arithmetic_operand
label_desc: Use image node_id 1 for calling a global function with example usage: float_power(x1, x2, out, where) and returns: op