code: def get_mock_dataset(root_dir):
      """
      root_dir: root directory of the mocked data
      """
      mocked_samples = {}
  
      dataset_dir = Path(root_dir) / "DR-VCTK" / "DR-VCTK"
      dataset_dir.mkdir(parents=True, exist_ok=True)
  
      config_dir = dataset_dir / "configurations"
      config_dir.mkdir(parents=True, exist_ok=True)
  
      sample_rate = 16000
      seed = 0
  
      for subset in _SUBSETS:
          mocked_samples[subset] = []
  
          for condition in _CONDITIONS:
              audio_dir = (
                  dataset_dir / f"{condition}_{subset}set_wav_16k"
              )
              audio_dir.mkdir(parents=True, exist_ok=True)
  
          config_filepath = config_dir / f"{subset}_ch_log.txt"
          with open(config_filepath, "w") as f:
              if subset == "train":
                  f.write("\n")
              f.write("File Name\tMain Source\tChannel Idx\n")
  
              for speaker_id in _SPEAKER_IDS:
                  utterance_id = 1
                  for source in _SOURCES:
                      for channel_id in _CHANNEL_IDS:
                          filename = (
                              f"p{speaker_id}_{utterance_id:03d}.wav"
                          )
                          f.write(
                              f"{filename}\t{source}\t{channel_id}\n"
                          )
  
                          data = {}
                          for condition in _CONDITIONS:
                              data[condition] = get_whitenoise(
                                  sample_rate=sample_rate,
                                  duration=0.01,
                                  n_channels=1,
                                  dtype="float32",
                                  seed=seed,
                              )
                              audio_dir = (
                                  dataset_dir
                                  / f"{condition}_{subset}set_wav_16k"
                              )
                              audio_file_path = audio_dir / filename
                              save_wav(
                                  audio_file_path,
                                  data[condition],
                                  sample_rate,
                              )
                              seed += 1
  
                          sample = (
                              data[_CONDITIONS[0]],
                              sample_rate,
                              data[_CONDITIONS[1]],
                              sample_rate,
                              "p" + str(speaker_id),
                              f"{utterance_id:03d}",
                              source,
                              channel_id,
                          )
                          mocked_samples[subset].append(sample)
                          utterance_id += 1
  
      return mocked_samples
  
file_path: audio/test/torchaudio_unittest/datasets/dr_vctk_test.py
imports: from pathlib import Path
  import pytest
  from torchaudio.datasets import dr_vctk
  from torchaudio_unittest.common_utils import get_whitenoise, 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_samples