code: def load_commonvoice_item(
      line: List[str],
      header: List[str],
      path: str,
      folder_audio: str,
      ext_audio: str,
  ) -> Tuple[Tensor, int, Dict[str, str]]:
      # Each line as the following data:
      # client_id, path, sentence, up_votes, down_votes, age, gender, accent
  
      if header[1] != "path":
          raise ValueError(
              f"expect `header[1]` to be 'path', but got {header[1]}"
          )
      fileid = line[1]
      filename = os.path.join(path, folder_audio, fileid)
      if not filename.endswith(ext_audio):
          filename += ext_audio
      waveform, sample_rate = torchaudio.load(filename)
  
      dic = dict(zip(header, line))
  
      return waveform, sample_rate, dic
  
file_path: audio/src/torchaudio/datasets/commonvoice.py
imports: import csv
  import os
  from pathlib import Path
  from typing import Dict, List, Tuple, Union
  import torchaudio
  from torch import Tensor
  from torch.utils.data import Dataset
label_desc: Use image node_id 1 for calling a global function with example usage: load_commonvoice_item(line, header, path, folder_audio, ext_audio) and returns: waveform,  sample_rate,  dic