class_docstr: *Tedlium* :cite:`rousseau2012tedlium` dataset (releases 1,2 and 3).
  
  Args:
      root (str or Path): Path to the directory where the dataset is found or downloaded.
      release (str, optional): Release version.
          Allowed values are ``"release1"``, ``"release2"`` or ``"release3"``.
          (default: ``"release1"``).
      subset (str, optional): The subset of dataset to use. Valid options are ``"train"``, ``"dev"``,
          and ``"test"``. Defaults to ``"train"``.
      download (bool, optional):
          Whether to download the dataset if it is not found at root path. (default: ``False``).
      audio_ext (str, optional): extension for audio file (default: ``".sph"``)
docstr: Default load function used in TEDLIUM dataset, you can overwrite this function to customize functionality
  and load individual sentences from a full ted audio talk file.
  
  Args:
      path (str): Path to audio file
      start_time (int): Time in seconds where the sample sentence stars
      end_time (int): Time in seconds where the sample sentence finishes
      sample_rate (float, optional): Sampling rate
  
  Returns:
      [Tensor, int]: Audio tensor representation and sample rate
docstr_tok: ["Default","load","function","used","in","TEDLIUM","dataset",",","you","can","overwrite","this","function","to","customize","functionality","and","load","individual","sentences","from","a","full","ted","audio","talk","file",".","Args",":","path","(","str",")",":","Path","to","audio","file","start_time","(","int",")",":","Time","in","seconds","where","the","sample","sentence","stars","end_time","(","int",")",":","Time","in","seconds","where","the","sample","sentence","finishes","sample_rate","(","float",",","optional",")",":","Sampling","rate","Returns",":","[","Tensor",",","int","]",":","Audio","tensor","representation","and","sample","rate"]
code: def _load_audio(
      self,
      path: str,
      start_time: float,
      end_time: float,
      sample_rate: int = 16000,
  ) -> [Tensor, int]:
      """Default load function used in TEDLIUM dataset, you can overwrite this function to customize functionality
      and load individual sentences from a full ted audio talk file.
  
      Args:
          path (str): Path to audio file
          start_time (int): Time in seconds where the sample sentence stars
          end_time (int): Time in seconds where the sample sentence finishes
          sample_rate (float, optional): Sampling rate
  
      Returns:
          [Tensor, int]: Audio tensor representation and sample rate
      """
      start_time = int(float(start_time) * sample_rate)
      end_time = int(float(end_time) * sample_rate)
  
      kwargs = {
          "frame_offset": start_time,
          "num_frames": end_time - start_time,
      }
  
      return torchaudio.load(path, **kwargs)
  
file_path: audio/src/torchaudio/datasets/tedlium.py
imports: import os
  from pathlib import Path
  from typing import Tuple, Union
  import torchaudio
  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
label_desc: Use image node_id 3 for calling the TEDLIUM obj's underlying member method code with example usage: obj._load_audio(path, start_time, end_time, sample_rate) and returns: torchaudio