code: def hybrid_forward(self, F, *states): # pylint: disable=arguments-differ
          
          # pylint: disable=unused-argument
          if self._beta != 0:
              if states:
                  means = [self._beta * (state[1:] - state[:-1]).__pow__(2).mean()
                           for state in states[-1:]]
                  return F.add_n(*means)
              else:
                  return F.zeros(1)
          return F.zeros(1)
docs: Parameters
          ----------
          states : list
              the stack outputs from RNN, which consists of output from each time step (TNC).
  
          Returns
          --------
          loss : NDArray
              loss tensor with shape (batch_size,). Dimensions other than batch_axis are averaged out.