class_docstr: Create a Dataset for wsj0-mix.
  
  Args:
      root (str or Path): Path to the directory where the dataset is found.
      num_speakers (int): The number of speakers, which determines the directories
          to traverse. The Dataset will traverse ``s1`` to ``sN`` directories to collect
          N source audios.
      sample_rate (int): Expected sample rate of audio files. If any of the audio has a
          different sample rate, raises ``ValueError``.
      audio_ext (str, optional): The extension of audio files to find. (default: ".wav")
code: def _load_sample(self, filename) -> SampleType:
      mixed = self._load_audio(str(self.mix_dir / filename))
      srcs = []
      for i, dir_ in enumerate(self.src_dirs):
          src = self._load_audio(str(dir_ / filename))
          if mixed.shape != src.shape:
              raise ValueError(
                  f"Different waveform shapes. mixed: {mixed.shape}, src[{i}]: {src.shape}"
              )
          srcs.append(src)
      return self.sample_rate, mixed, srcs
  
file_path: audio/examples/source_separation/utils/dataset/wsj0mix.py
imports: from pathlib import Path
  from typing import List, Tuple, Union
  import torch
  import torchaudio
  from torch.utils.data import Dataset
label_desc: Use image node_id 3 for calling the WSJ0Mix obj's underlying member method code with example usage: obj._load_sample(filename) and returns: self,  mixed,  srcs