text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # Copyright 1999-2018 Alibaba Group Holding Ltd.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import numpy as np
  
  from .... import operands
  from ..utils import infer_dtype
  from .core import TensorUnaryOp
  from .utils import arithmetic_operand
  
  
  @arithmetic_operand(sparse_mode='unary')
  class TensorReciprocal(operands.Reciprocal, TensorUnaryOp):
      pass
  
  
  @infer_dtype(np.reciprocal)
  def reciprocal(x, out=None, where=None, **kwargs):
      """
      Return the reciprocal of the argument, element-wise.
  
      Calculates ``1/x``.
  
      Parameters
      ----------
      x : array_like
          Input tensor.
      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
          Return tensor.
  
      Notes
      -----
      .. note::
          This function is not designed to work with integers.
  
      For integer arguments with absolute value larger than 1 the result is
      always zero because of the way Python handles integer division.  For
      integer zero the result is an overflow.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.reciprocal(2.).execute()
      0.5
      >>> mt.reciprocal([1, 2., 3.33]).execute()
      array([ 1.       ,  0.5      ,  0.3003003])
      """
      op = TensorReciprocal(**kwargs)
      return op(x, out=out, where=where)
  