docstr: Returns "texless" representation of the k-th element of the wrapped dataset in
  a form of aligned unit, durarion, and (optionally) F0 streams.
  Args:
      k (int): example index.
  Returns:
      Dict[str, Union[torch.Tensor, Any]]: A dict that has contains
      speech_encoder(waveform) outputs and the remainder of what
      the underlying dataset returned under the "rest" key.
      All tensors are placed on CPU.
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code: def __getitem__(self, k: int) -> Dict[str, Union[torch.Tensor, Any]]:
      """Returns "texless" representation of the k-th element of the wrapped dataset in
      a form of aligned unit, durarion, and (optionally) F0 streams.
      Args:
          k (int): example index.
      Returns:
          Dict[str, Union[torch.Tensor, Any]]: A dict that has contains
          speech_encoder(waveform) outputs and the remainder of what
          the underlying dataset returned under the "rest" key.
          All tensors are placed on CPU.
      """
      waveform, *rest = self.dataset[k]
  
      if self.randomize_device_on_next_call and self.device is None:
          self.select_worker_gpu()
          self.randomize_device_on_next_call = False
  
      speaker = self.speaker_extractor(rest)
      encoded = self.speech_encoder(waveform.to(self.device), speaker)
      # dataset
      for k, v in encoded.items():
          if isinstance(v, torch.Tensor):
              encoded[k] = v.cpu()
      encoded["rest"] = rest
  
      return encoded
  
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 the QuantizeDataset obj's underlying member method code with example usage: obj.__getitem__(k) and returns: encoded