class_docstr: *LibriSpeech* :cite:`7178964` dataset with prefix-tree construction and biasing support.
  
  Args:
      root (str or Path): Path to the directory where the dataset is found or downloaded.
      url (str, optional): The URL to download the dataset from,
          or the type of the dataset to dowload.
          Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``,
          ``"test-other"``, ``"train-clean-100"``, ``"train-clean-360"`` and
          ``"train-other-500"``. (default: ``"train-clean-100"``)
      folder_in_archive (str, optional):
          The top-level directory of the dataset. (default: ``"LibriSpeech"``)
      download (bool, optional):
          Whether to download the dataset if it is not found at root path. (default: ``False``).
      blist (list, optional):
          The list of biasing words (default: ``[]``).
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docstr: Load the n-th sample from the dataset.
  
  Args:
      n (int): The index of the sample to be loaded
  
  Returns:
      Tuple of the following items;
  
      Tensor:
          Waveform
      int:
          Sample rate
      str:
          Transcript
      int:
          Speaker ID
      int:
          Chapter ID
      int:
          Utterance ID
      list:
          List of biasing words in the utterance
docstr_tok: ["Load","the","n-th","sample","from","the","dataset",".","Args",":","n","(","int",")",":","The","index","of","the","sample","to","be","loaded","Returns",":","Tuple","of","the","following","items",";","Tensor",":","Waveform","int",":","Sample","rate","str",":","Transcript","int",":","Speaker","ID","int",":","Chapter","ID","int",":","Utterance","ID","list",":","List","of","biasing","words","in","the","utterance"]
code: def __getitem__(
      self, n: int
  ) -> Tuple[Tensor, int, str, int, int, int]:
      """Load the n-th sample from the dataset.
  
      Args:
          n (int): The index of the sample to be loaded
  
      Returns:
          Tuple of the following items;
  
          Tensor:
              Waveform
          int:
              Sample rate
          str:
              Transcript
          int:
              Speaker ID
          int:
              Chapter ID
          int:
              Utterance ID
          list:
              List of biasing words in the utterance
      """
      metadata = self.get_metadata(n)
      waveform = _load_waveform(self._archive, metadata[0], metadata[1])
      return (waveform,) + metadata[1:]
  
file_path: audio/src/torchaudio/datasets/librispeech_biasing.py
imports: import os
  from pathlib import Path
  from typing import List, Tuple, Union
  from torch import Tensor
  from torch.utils.data import Dataset
  from torchaudio._internal import download_url_to_file
  from torchaudio.datasets.utils import _extract_tar, _load_waveform
label_desc: Use image node_id 3 for calling the LibriSpeechBiasing obj's underlying member method code with example usage: obj.__getitem__(n) and returns: unknown