code: def __getitem__(self, k):
      item = self.quantized_data[k]
      speaker = item["rest"][2]
      item["rest"][2] = self.speaker_mapping[speaker]
  
      if self.max_length < item["dense"].size(0):
          item["dense"] = item["dense"][: self.max_length, :]
          item["units"] = item["units"][: self.max_length]
          item["durations"] = item["durations"][: self.max_length]
  
      return item
  
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 3 for calling the SpeakerDatasetWrapper obj's underlying member method code with example usage: obj.__getitem__(k) and returns: item