code: def main(tsv_dir, split, nshard, rank, feat_dir, sample_rate):
      reader = MfccFeatureReader(sample_rate)
      generator, num = get_path_iterator(
          f"{tsv_dir}/{split}.tsv", nshard, rank
      )
      dump_feature(
          reader, generator, num, split, nshard, rank, feat_dir
      )
  
file_path: fairseq/examples/hubert/simple_kmeans/dump_mfcc_feature.py
imports: import logging
  import os
  import sys
  import soundfile
  import torch
  import torchaudio
  from feature_utils import get_path_iterator, dump_feature
  from fairseq.data.audio.audio_utils import get_features_or_waveform
label_desc: Use image node_id 1 for calling a global function with example usage: main(tsv_dir, split, nshard, rank, feat_dir, sample_rate) without return types