code: def get_feats(self, path, ref_len=None):
      x = self.read_audio(path, ref_len=ref_len)
      with torch.no_grad():
          x = torch.from_numpy(x).float()
          x = x.view(1, -1)
  
          mfccs = torchaudio.compliance.kaldi.mfcc(
              waveform=x,
              sample_frequency=self.sample_rate,
              use_energy=False,
          )  # (time, freq)
          mfccs = mfccs.transpose(0, 1)  # (freq, time)
          deltas = torchaudio.functional.compute_deltas(mfccs)
          ddeltas = torchaudio.functional.compute_deltas(deltas)
          concat = torch.cat([mfccs, deltas, ddeltas], dim=0)
          concat = concat.transpose(0, 1).contiguous()  # (freq, time)
          return concat
  
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 3 for calling the MfccFeatureReader obj's underlying member method code with example usage: obj.get_feats(path, ref_len) and returns: concat