code: def forward(
      self,
      input: torch.Tensor,
      states: Optional[List[Tuple[torch.Tensor, torch.Tensor]]],
  ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
      if states is None:
          temp_states: List[Tuple[torch.Tensor, torch.Tensor]] = []
          batch = input.size(1)
          for layer in self.layers:
              temp_states.append(
                  (
                      torch.zeros(
                          batch,
                          layer.cell.hidden_size,
                          dtype=input.dtype,
                          device=input.device,
                      ),
                      torch.zeros(
                          batch,
                          layer.cell.hidden_size,
                          dtype=input.dtype,
                          device=input.device,
                      ),
                  )
              )
  
          states = temp_states
  
      output_states: List[Tuple[torch.Tensor, torch.Tensor]] = []
      output = input
      for i, rnn_layer in enumerate(self.layers):
          state = states[i]
          output, out_state = rnn_layer(output, state)
          output_states.append(out_state)
          i += 1
      return output, output_states
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from nemo.utils import logging
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