code: def check_batch_states(states, max_len, pad_id):
      """Check decoder hidden states and left pad or trim if necessary.
  
      Args:
          state: Decoder hidden states. [N x (B, ?, D_dec)]
          max_len: maximum sequence length.
          pad_id: Padding symbol ID.
  
      Returns:
          final: Decoder hidden states. [N x (B, max_len, dec_dim)]
  
      """
      final_dims = (len(states), max_len, states[0].size(1))
      final = states[0].data.new(*final_dims).fill_(pad_id)
  
      for i, s in enumerate(states):
          curr_len = s.size(0)
  
          if curr_len < max_len:
              final[i, (max_len - curr_len) : max_len, :] = s
          else:
              final[i, :, :] = s[(curr_len - max_len) :, :]
  
      return final
  
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 12 for calling a global function with example usage: check_batch_states(states, max_len, pad_id) and returns: final