class_docstr: *LibriMix* :cite:`cosentino2020librimix` dataset.
  
  Args:
      root (str or Path): The path where the directory ``Libri2Mix`` or
          ``Libri3Mix`` is stored. Not the path of those directories.
      subset (str, optional): The subset to use. Options: [``"train-360"``, ``"train-100"``,
          ``"dev"``, and ``"test"``] (Default: ``"train-360"``).
      num_speakers (int, optional): The number of speakers, which determines the directories
          to traverse. The Dataset will traverse ``s1`` to ``sN`` directories to collect
          N source audios. (Default: 2)
      sample_rate (int, optional): Sample rate of audio files. The ``sample_rate`` determines
          which subdirectory the audio are fetched. If any of the audio has a different sample
          rate, raises ``ValueError``. Options: [8000, 16000] (Default: 8000)
      task (str, optional): The task of LibriMix.
          Options: [``"enh_single"``, ``"enh_both"``, ``"sep_clean"``, ``"sep_noisy"``]
          (Default: ``"sep_clean"``)
      mode (str, optional): The mode when creating the mixture. If set to ``"min"``, the lengths of mixture
          and sources are the minimum length of all sources. If set to ``"max"``, the lengths of mixture and
          sources are zero padded to the maximum length of all sources.
          Options: [``"min"``, ``"max"``]
          (Default: ``"min"``)
  
  Note:
      The LibriMix dataset needs to be manually generated. Please check https://github.com/JorisCos/LibriMix
class_docstr_tok: ["*","LibriMix","*",":","cite",":","`","cosentino2020librimix","`","dataset",".","Args",":","root","(","str","or","Path",")",":","The","path","where","the","directory","``","Libri2Mix","``","or","``","Libri3Mix","``","is","stored",".","Not","the","path","of","those","directories",".","subset","(","str",",","optional",")",":","The","subset","to","use",".","Options",":","[","``","``","train-360","''","``",",","``","``","train-100","''","``",",","``","``","dev","''","``",",","and","``","``","test","''","``","]","(","Default",":","``","``","train-360","''","``",")",".","num_speakers","(","int",",","optional",")",":","The","number","of","speakers",",","which","determines","the","directories","to","traverse",".","The","Dataset","will","traverse","``","s1","``","to","``","sN","``","directories","to","collect","N","source","audios",".","(","Default",":","2",")","sample_rate","(","int",",","optional",")",":","Sample","rate","of","audio","files",".","The","``","sample_rate","``","determines","which","subdirectory","the","audio","are","fetched",".","If","any","of","the","audio","has","a","different","sample","rate",",","raises","``","ValueError","``",".","Options",":","[","8000",",","16000","]","(","Default",":","8000",")","task","(","str",",","optional",")",":","The","task","of","LibriMix",".","Options",":","[","``","``","enh_single","''","``",",","``","``","enh_both","''","``",",","``","``","sep_clean","''","``",",","``","``","sep_noisy","''","``","]","(","Default",":","``","``","sep_clean","''","``",")","mode","(","str",",","optional",")",":","The","mode","when","creating","the","mixture",".","If","set","to","``","``","min","''","``",",","the","lengths","of","mixture","and","sources","are","the","minimum","length","of","all","sources",".","If","set","to","``","``","max","''","``",",","the","lengths","of","mixture","and","sources","are","zero","padded","to","the","maximum","length","of","all","sources",".","Options",":","[","``","``","min","''","``",",","``","``","max","''","``","]","(","Default",":","``","``","min","''","``",")","Note",":","The","LibriMix","dataset","needs","to","be","manually","generated",".","Please","check","https",":","\/\/github.com\/JorisCos\/LibriMix"]
code: def _load_sample(
      self, key
  ) -> Tuple[int, torch.Tensor, List[torch.Tensor]]:
      metadata = self.get_metadata(key)
      mixed = _load_waveform(self.root, metadata[1], metadata[0])
      srcs = []
      for i, path_ in enumerate(metadata[2]):
          src = _load_waveform(self.root, path_, metadata[0])
          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/src/torchaudio/datasets/librimix.py
imports: import os
  from pathlib import Path
  from typing import List, Tuple, Union
  import torch
  from torch.utils.data import Dataset
  from torchaudio.datasets.utils import _load_waveform
label_desc: Use image node_id 2 for calling the LibriMix obj's underlying member method code with example usage: obj._load_sample(key) and returns: self,  mixed,  srcs