code: def get_feats(
      self,
      data: torch.Tensor,
      data_lens: torch.Tensor,
      ref_len: Optional[int] = None,
  ) -> Tuple[List[torch.Tensor], List[int]]:
      feats, feats_lens = [], []
      with torch.no_grad():
          x, x_lens = self.preprocess_data(data, data_lens)
          batch_size = x.shape[0]
          for i in range(batch_size):
              mfcc = torchaudio.compliance.kaldi.mfcc(
                  waveform=x[i : i + 1],
                  sample_frequency=self.sample_rate,
                  use_energy=False,
              ).transpose(
                  0, 1
              )  # (freq, time)
              delta = torchaudio.functional.compute_deltas(mfcc)
              ddelta = torchaudio.functional.compute_deltas(delta)
              concat = (
                  torch.cat([mfcc, delta, ddelta], dim=0)
                  .transpose(0, 1)
                  .contiguous()
              )
              feats.append(concat)
              feats_lens.append(
                  int(
                      (x_lens[i] - self.frame_length)
                      // self.frame_shift
                      + 1
                  )
              )
      return feats, feats_lens
  
imports: import json
  import logging
  import os
  import re
  import sys
  from typing import List, Optional, Tuple, Union
  import numpy
  import soundfile
  import torch
  import torchaudio
  from espnet2.asr.frontend.s3prl import S3prlFrontend
  from espnet2.iterators.sequence_iter_factory import SequenceIterFactory
  from espnet2.samplers.num_elements_batch_sampler import NumElementsBatchSampler
  from espnet2.train.collate_fn import CommonCollateFn
  from espnet2.train.dataset import ESPnetDataset
  from espnet.utils.cli_writers import file_writer_helper
label_desc: Use image node_id 2 for calling the MfccFeatureReader obj's underlying member method code with example usage: obj.get_feats(data, data_lens, ref_len) and returns: feats,  feats_lens