code: def check_state(
      state: List[Optional[torch.Tensor]], max_len: int, pad_id: int
  ) -> List[Optional[torch.Tensor]]:
      """Check decoder hidden states and left pad or trim if necessary.
  
      Args:
          state: Decoder hidden states. [N x (?, D_dec)]
          max_len: maximum sequence length.
          pad_id: Padding symbol ID.
  
      Returns:
          final: Decoder hidden states. [N x (1, max_len, D_dec)]
  
      """
      if state is None or max_len < 1 or state[0].size(1) == max_len:
          return state
  
      curr_len = state[0].size(1)
  
      if curr_len > max_len:
          trim_val = int(state[0].size(1) - max_len)
  
          for i, s in enumerate(state):
              state[i] = s[:, trim_val:, :]
      else:
          layers = len(state)
          ddim = state[0].size(2)
  
          final_dims = (1, max_len, ddim)
          final = [
              state[0].data.new(*final_dims).fill_(pad_id)
              for _ in range(layers)
          ]
  
          for i, s in enumerate(state):
              final[i][:, (max_len - s.size(1)) : max_len, :] = s
  
          return final
  
      return state
  
file_path: espnet/espnet/nets/pytorch_backend/transducer/utils.py
imports: import os
  from typing import Any, Dict, List, Optional, Union
  import numpy
  import torch
  from espnet.nets.pytorch_backend.nets_utils import pad_list
  from espnet.nets.transducer_decoder_interface import ExtendedHypothesis, Hypothesis
label_desc: Use image node_id 11 for calling a global function with example usage: check_state(state, max_len, pad_id) and returns: state, state, final