code: def _npx_reshape(a, newshape, reverse=False, order="C"):
      """
      Gives a new shape to an array without changing its data.
      This function always returns a copy of the input array if
      ``out`` is not provided.
  
      Parameters
      ----------
      a : ndarray
          Array to be reshaped.
      newshape : int or tuple of ints
          The new shape should be compatible with the original shape.
          If an integer, then the result will be a 1-D array of that length.
          One shape dimension can be -1. In this case, the value is inferred
          from the length of the array and remaining dimensions.
          -2 to -6 are used for data manipulation.
  
          - -2 copy this dimension from the input to the output shape.
          - -3 will skip current dimension if and only if the current dim size is one.
          - -4 copy all remain of the input dimensions to the output shape.
          - -5 use the product of two consecutive dimensions of the input
            shape as the output.
          - -6 split one dimension of the input into two dimensions passed
            subsequent to -6 in the new shape.
  
      reverse : bool, optional
          If set to true, the special values will be inferred from right to left.
      order : {'C'}, optional
          Read the elements of `a` using this index order, and place the
          elements into the reshaped array using this index order.  'C'
          means to read / write the elements using C-like index order,
          with the last axis index changing fastest, back to the first
          axis index changing slowest. Other order types such as 'F'/'A'
          may be added in the future.
  
      Returns
      -------
      reshaped_array : ndarray
          It will be always a copy of the original array. This behavior is different
          from the official NumPy ``reshape`` operator where views of the original array may be
          generated.
  
      Examples
      --------
      >>> x = np.ones((2, 3, 8))
      >>> npx.reshape(x, (-2, -2, 2, -1)).shape
      (2, 3, 2, 4)
      >>> x = np.ones((8, 3, 3, 3, 4, 4))
      >>> npx.reshape(x, (-6, 2, -1, -4)).shape
      (2, 4, 3, 3, 3, 4, 4)
      >>> x = np.ones((8, 3, 3, 3, 4, 4))
      >>> npx.reshape(x, (-5, -4)).shape
      (24, 3, 3, 4, 4)
      >>> x = np.ones((8, 1, 1, 1, 3))
      >>> npx.reshape(x, (-2, -3, -3, -3, -2)).shape
      (8, 3)
      >>> x = np.ones((8, 3, 3, 3, 3, 8))
      >>> npx.reshape(x, (-4, -5), reverse=True).shape
      (8, 3, 3, 3, 24)
      >>> x = np.ones((8, 3, 2, 4, 8))
      >>> npx.reshape(x, (-4, -1, 2, -6), reverse=True).shape
      (8, 3, 2, 4, 4, 2)
      """
      pass
  
file_path: mxnet/python/mxnet/_numpy_op_doc.py
label_desc: Use image node_id 13 for calling a global function with example usage: _npx_reshape(a, newshape, reverse, order) without return types