class_docstr: *Snips* :cite:`coucke2018snips` dataset.
  
  Args:
      root (str or Path): Root directory where the dataset's top level directory is found.
      subset (str): Subset of the dataset to use. Options: [``"train"``, ``"valid"``, ``"test"``].
      speakers (List[str] or None, optional): The speaker list to include in the dataset. If ``None``,
          include all speakers in the subset. (Default: ``None``)
      audio_format (str, optional): The extension of the audios. Options: [``"mp3"``, ``"wav"``].
          (Default: ``"mp3"``)
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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:
          File name
      str:
          Transcription of audio
      str:
          Inside–outside–beginning (IOB) label of transcription
      str:
          Intention label of the audio.
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",":","File","name","str",":","Transcription","of","audio","str",":","Inside\u2013outside\u2013beginning","(","IOB",")","label","of","transcription","str",":","Intention","label","of","the","audio","."]
code: def __getitem__(
      self, n: int
  ) -> Tuple[torch.Tensor, int, str, str, str]:
      """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:
              File name
          str:
              Transcription of audio
          str:
              Inside–outside–beginning (IOB) label of transcription
          str:
              Intention label of the audio.
      """
      metadata = self.get_metadata(n)
      waveform = _load_waveform(self._path, metadata[0], metadata[1])
      return (waveform,) + metadata[1:]
  
file_path: audio/src/torchaudio/datasets/snips.py
imports: import os
  from pathlib import Path
  from typing import List, Optional, Tuple, Union
  import torch
  from torch.utils.data import Dataset
  from torchaudio.datasets.utils import _load_waveform
label_desc: Use image node_id 3 for calling the Snips obj's underlying member method code with example usage: obj.__getitem__(n) and returns: unknown