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_audio(self, path) -> torch.Tensor:
      waveform, sample_rate = torchaudio.load(path)
      if sample_rate != self.sample_rate:
          raise ValueError(
              f"The dataset contains audio file of sample rate {sample_rate}, "
              f"but the requested sample rate is {self.sample_rate}."
          )
      return waveform
  
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 2 for calling the WSJ0Mix obj's underlying member method code with example usage: obj._load_audio(path) and returns: waveform