code: def _np_reshape(a, newshape, order="C", out=None):
      """
      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.
      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.
  
      See Also
      --------
      ndarray.reshape : Equivalent method.
  
      Examples
      --------
      >>> a = np.arange(6).reshape((3, 2))
      >>> a
      array([[0., 1.],
             [2., 3.],
             [4., 5.]])
  
      >>> np.reshape(a, (2, 3)) # C-like index ordering
      array([[0., 1., 2.],
             [3., 4., 5.]])
  
      >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
      array([[0., 1., 2.],
             [3., 4., 5.]])
  
      >>> a = np.array([[1,2,3], [4,5,6]])
      >>> np.reshape(a, 6)
      array([1., 2., 3., 4., 5., 6.])
  
      >>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
      array([[1., 2.],
             [3., 4.],
             [5., 6.]])
      """
  
file_path: mxnet/python/mxnet/_numpy_op_doc.py
label_desc: Use image node_id 6 for calling a global function with example usage: _np_reshape(a, newshape, order, out) without return types