def hybrid_forward(self, F, *states): 
<EOL>if self._beta != <NUM_LIT:0>:<EOL><INDENT>if states:<EOL><INDENT>means = [self._beta * (state[<NUM_LIT:1>:] - state[:-<NUM_LIT:1>]).__pow__(<NUM_LIT:2>).mean()<EOL>for state in states[-<NUM_LIT:1>:]]<EOL>return F.add_n(*means)<EOL><DEDENT>else:<EOL><INDENT>return F.zeros(<NUM_LIT:1>)<EOL><DEDENT><DEDENT>return F.zeros(<NUM_LIT:1>)<EOL>
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.