code: def select_worker_gpu(self):
      worker_info = torch.utils.data.get_worker_info()
      if worker_info is None:  # not a worker process
          self.device = torch.cuda.current_device()
          self.speech_encoder.to(self.device)
      else:
          # we are in a worker process, we'll assign ourselves
          # to a random GPU among available
          worker_id = worker_info.id
          device_id = worker_id % torch.cuda.device_count()
          torch.cuda.set_device(device_id)
          self.device = torch.cuda.current_device()
          self.speech_encoder.cuda()
  
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 3 for calling the QuantizeDataset obj's underlying member method code with example usage: obj.select_worker_gpu() without return types