code: def __init__(self, quantized_data, speaker_mapping=None):
      self.quantized_data = quantized_data
      self.speaker_mapping = (
          speaker_mapping
          if speaker_mapping is not None
          else self.get_speaker_ids(quantized_data.dataset._walker)
      )
      self.collater = self.quantized_data.collater
      self.max_length = (
          10
          * 16_000
          // self.quantized_data.speech_encoder.code_hop_size
      )
  
file_path: textlesslib/examples/speaker_probing/train.py
imports: import torch
  import argparse
  from textless.data.quantized_datasets import QuantizedLibriSpeech
  from torch.utils.data import DataLoader
  import torch.nn.functional
  from probes import ContinuousClassifier, DiscreteClassifier, ConstantBaseline
  from textless.data.speech_encoder import SpeechEncoder
  from textless import dispatch_dense_model, dispatch_quantizer
label_desc: Use image node_id 1 to create a new SpeakerDatasetWrapper object  with example: obj = SpeakerDatasetWrapper(quantized_data, speaker_mapping)