code: def QuantizedCommonVoice(
      speech_encoder: Callable,
      root: Union[str, pathlib.Path],
      tsv: Optional[str] = "train.tsv",
      device: Optional[str] = None,
  ):
      dataset = COMMONVOICE(root, tsv)
      return QuantizeDataset(
          dataset,
          speech_encoder,
          device,
          speaker_extractor=default_speaker_commonvoice,
      )
  
imports: from typing import Callable, Union, Optional, Any, Dict, List
  import pathlib
  import torch
  import logging
  from torchaudio.datasets import LIBRISPEECH, LJSPEECH, COMMONVOICE, VCTK_092, YESNO
  from torch.utils.data import Dataset
  from textless.data.speech_encoder import SpeechEncoder
  from textless.data.collater_utils import collate_tensors
label_desc: Use image node_id 4 for calling a global function with example usage: QuantizedCommonVoice(speech_encoder, root, tsv, device) and returns: QuantizeDataset