code: def _fixed_seq_collate_fn(self, batch):
      """collate batch of audio sig, audio len, tokens, tokens len
      Args:
          batch (Optional[FloatTensor], Optional[LongTensor], LongTensor,
              LongTensor):  A tuple of tuples of signal, signal lengths,
              encoded tokens, and encoded tokens length.  This collate func
              assumes the signals are 1d torch tensors (i.e. mono audio).
      """
      _, audio_lengths, _, tokens_lengths = zip(*batch)
  
      has_audio = audio_lengths[0] is not None
      fixed_length = int(max(audio_lengths))
  
      audio_signal, tokens, new_audio_lengths = [], [], []
      for sig, sig_len, tokens_i, _ in batch:
          if has_audio:
              sig_len = sig_len.item()
              chunck_len = sig_len - fixed_length
  
              if chunck_len < 0:
                  repeat = fixed_length // sig_len
                  rem = fixed_length % sig_len
                  sub = sig[-rem:] if rem > 0 else torch.tensor([])
                  rep_sig = torch.cat(repeat * [sig])
                  sig = torch.cat((rep_sig, sub))
              new_audio_lengths.append(torch.tensor(fixed_length))
  
              audio_signal.append(sig)
  
          tokens.append(tokens_i)
  
      if has_audio:
          audio_signal = torch.stack(audio_signal)
          audio_lengths = torch.stack(new_audio_lengths)
      else:
          audio_signal, audio_lengths = None, None
      tokens = torch.stack(tokens)
      tokens_lengths = torch.stack(tokens_lengths)
  
      return audio_signal, audio_lengths, tokens, tokens_lengths
  
imports: import io
  import os
  from typing import Dict, List, Optional, Union
  import torch
  import webdataset
  from nemo.collections.asr.data.audio_to_text import cache_datastore_manifests, expand_sharded_filepaths
  from nemo.collections.asr.parts.preprocessing.features import WaveformFeaturizer
  from nemo.collections.asr.parts.preprocessing.segment import available_formats
  from nemo.collections.common.parts.preprocessing import collections
  from nemo.core.classes import Dataset, IterableDataset
  from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, NeuralType, RegressionValuesType
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling a global function with example usage: _fixed_seq_collate_fn(batch) and returns: audio_signal,  audio_lengths,  tokens,  tokens_lengths