code: def get_mock_dataset(root_dir):
      """
      root_dir: directory to the mocked dataset
      """
      mocked_data = []
      base_dir = os.path.join(root_dir, "LibriTTS", "train-clean-100")
      for i, utterance_id in enumerate(_UTTERANCE_IDS):
          filename = f'{"_".join(str(u) for u in utterance_id)}.wav'
          file_dir = os.path.join(
              base_dir, str(utterance_id[0]), str(utterance_id[1])
          )
          os.makedirs(file_dir, exist_ok=True)
          path = os.path.join(file_dir, filename)
  
          data = get_whitenoise(
              sample_rate=24000,
              duration=2,
              n_channels=1,
              dtype="int16",
              seed=i,
          )
          save_wav(path, data, 24000)
          mocked_data.append(normalize_wav(data))
  
          original_text_filename = (
              f'{"_".join(str(u) for u in utterance_id)}.original.txt'
          )
          path_original = os.path.join(file_dir, original_text_filename)
          with open(path_original, "w") as file_:
              file_.write(_ORIGINAL_TEXT)
  
          normalized_text_filename = (
              f'{"_".join(str(u) for u in utterance_id)}.normalized.txt'
          )
          path_normalized = os.path.join(
              file_dir, normalized_text_filename
          )
          with open(path_normalized, "w") as file_:
              file_.write(_NORMALIZED_TEXT)
      return (
          mocked_data,
          _UTTERANCE_IDS,
          _ORIGINAL_TEXT,
          _NORMALIZED_TEXT,
      )
  
file_path: audio/test/torchaudio_unittest/datasets/libritts_test.py
imports: import os
  from pathlib import Path
  from torchaudio.datasets.libritts import LIBRITTS
  from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
label_desc: Use image node_id 1 for calling a global function with example usage: get_mock_dataset(root_dir) and returns: mocked_data,  _UTTERANCE_IDS,  _ORIGINAL_TEXT,  _NORMALIZED_TEXT