code: def load_libritts_item(
      fileid: str,
      path: str,
      ext_audio: str,
      ext_original_txt: str,
      ext_normalized_txt: str,
  ) -> Tuple[Tensor, int, str, str, int, int, str]:
      speaker_id, chapter_id, segment_id, utterance_id = fileid.split(
          "_"
      )
      utterance_id = fileid
  
      normalized_text = utterance_id + ext_normalized_txt
      normalized_text = os.path.join(
          path, speaker_id, chapter_id, normalized_text
      )
  
      original_text = utterance_id + ext_original_txt
      original_text = os.path.join(
          path, speaker_id, chapter_id, original_text
      )
  
      file_audio = utterance_id + ext_audio
      file_audio = os.path.join(
          path, speaker_id, chapter_id, file_audio
      )
  
      # Load audio
      waveform, sample_rate = torchaudio.load(file_audio)
  
      # Load original text
      with open(original_text) as ft:
          original_text = ft.readline()
  
      # Load normalized text
      with open(normalized_text, "r") as ft:
          normalized_text = ft.readline()
  
      return (
          waveform,
          sample_rate,
          original_text,
          normalized_text,
          int(speaker_id),
          int(chapter_id),
          utterance_id,
      )
  
file_path: audio/src/torchaudio/datasets/libritts.py
imports: import os
  from pathlib import Path
  from typing import Tuple, Union
  import torchaudio
  from torch import Tensor
  from torch.utils.data import Dataset
  from torchaudio._internal import download_url_to_file
  from torchaudio.datasets.utils import _extract_tar
label_desc: Use image node_id 1 for calling a global function with example usage: load_libritts_item(fileid, path, ext_audio, ext_original_txt, ext_normalized_txt) and returns: waveform,  sample_rate,  original_text,  normalized_text,  int,  int,  utterance_id