code: def pad_collate_fn(self, batch):
      final_batch = {}
      for row in batch:
          for key in row:
              if key not in final_batch:
                  final_batch[key] = []
              final_batch[key].append(row[key])
  
      max_audio_len = max(
          [_audio_len.item() for _audio_len in final_batch["audio_len"]]
      )
  
      audios_padded = []
      for audio in final_batch["audio"]:
          audio_padded = torch.nn.functional.pad(
              audio, (0, max_audio_len - audio.size(0)), value=0
          )
          audios_padded.append(audio_padded)
  
      final_batch["audio"] = audios_padded
      for key in final_batch:
          if key not in ["rel_audio_path_as_text_id", "wav_path"]:
              final_batch[key] = torch.stack(final_batch[key])
  
      return final_batch
  
imports: import argparse
  import json
  import os
  import time
  from multiprocessing import Pool
  from pathlib import Path
  import hydra.utils
  import librosa
  import numpy
  import torch
  from omegaconf import open_dict
  from tqdm import tqdm
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
  from nemo.collections.tts.models import ssl_tts
  from nemo.collections.tts.parts.utils.tts_dataset_utils import get_base_dir
  from nemo.core.classes import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the AudioDataset obj's underlying member method code with example usage: obj.pad_collate_fn(batch) and returns: final_batch