code: def unpack_grad_tuple(gv, gpt):
      
      elt_widths = [x.num_elements() for x in gpt.shapes]
      with tf.device(gv[0][0].device):
          with tf.name_scope("unpack"):
              splits = tf.split(gv[0], elt_widths)
              unpacked_gv = []
              for idx, s in enumerate(splits):
                  unpacked_gv.append((tf.reshape(s, gpt.shapes[idx]),
                                      gpt.vars[idx]))
      return unpacked_gv
docs: Unpack a previously packed collection of gradient tensors.
  
    Args:
      gv: A (grad, var) pair to be unpacked.
      gpt: A GradPackTuple describing the packing operation that produced gv.
  
    Returns:
      A list of (grad, var) pairs corresponding to the values that were
       originally packed into gv, maybe following subsequent operations like
       reduction.