code: def _load_labels(file: Path, subset: str):
      """Load transcirpt, iob, and intent labels for all utterances.
  
      Args:
          file (Path): The path to the label file.
          subset (str): Subset of the dataset to use. Options: [``"train"``, ``"valid"``, ``"test"``].
  
      Returns:
          Dictionary of labels, where the key is the filename of the audio,
              and the label is a Tuple of transcript, Inside–outside–beginning (IOB) label, and intention label.
      """
      labels = {}
      with open(file, "r") as f:
          for line in f:
              line = line.strip().split(" ")
              index = line[0]
              trans, iob_intent = " ".join(line[1:]).split("\t")
              trans = " ".join(trans.split(" ")[1:-1])
              iob = " ".join(iob_intent.split(" ")[1:-1])
              intent = iob_intent.split(" ")[-1]
              if subset in index:
                  labels[index] = (trans, iob, intent)
      return labels
  
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 1 for calling a global function with example usage: _load_labels(file, subset) and returns: labels