code: def forward(
      self, input: Tensor, states: List[Tuple[Tensor, Tensor]]
  ) -> Tuple[Tensor, List[Tuple[Tensor, Tensor]]]:
      # List[LSTMState]: One state per layer
      output_states = jit.annotate(List[Tuple[Tensor, Tensor]], [])
      output = input
      # XXX: enumerate https://github.com/pytorch/pytorch/issues/14471
      i = 0
      for rnn_layer in self.layers:
          state = states[i]
          output, out_state = rnn_layer(output, state)
          output_states += [out_state]
          i += 1
      return output, output_states
  
imports: import numbers
  import warnings
  from collections import namedtuple
  from typing import List, Tuple
  import torch
  import torch.jit
  import torch.nn
  from torch import Tensor
  from torch.nn import Parameter
label_desc: Use image node_id 2 for calling the StackedLSTM obj's underlying member method code with example usage: obj.forward(input, states) and returns: output,  output_states