code: def _save_sample(
      dataset_dir, folder, language, index, sample_rate, seed
  ):
      # create and save audio samples to corresponding files
      path = os.path.join(dataset_dir, folder)
      os.makedirs(path, exist_ok=True)
      filename = _get_filename(folder, index)
      file_path = os.path.join(path, filename)
  
      data = get_whitenoise(
          sample_rate=sample_rate,
          duration=0.01,
          n_channels=1,
          seed=seed,
      )
      save_wav(file_path, data, sample_rate)
  
      sample = (data, sample_rate, Path(file_path).with_suffix("").name)
  
      # add audio files and language data to language key files
      scoring_path = os.path.join(dataset_dir, "scoring")
      os.makedirs(scoring_path, exist_ok=True)
      wav_file = f"quesst14Database/{folder}/{filename}"
      line = f"{wav_file} {language}"
  
      key = _get_key(folder)
      language_key_file = f"language_key_{key}.lst"
      language_key_file = os.path.join(scoring_path, language_key_file)
      with open(language_key_file, "a") as f:
          f.write(line + "\n")
  
      return sample
  
imports: import os
  from collections import defaultdict
  from pathlib import Path
  from parameterized import parameterized
  from torchaudio.datasets import quesst14
  from torchaudio_unittest.common_utils import get_whitenoise, save_wav, TempDirMixin, TorchaudioTestCase
label_desc: Use image node_id 3 for calling a global function with example usage: _save_sample(dataset_dir, folder, language, index, sample_rate, seed) and returns: sample