code: def extract_logfbank_with_delta(waveforms, params):
      """
      params:
        waveforms: float32 tensor with shape [max_len]
      """
      p = params
      mel_fbanks = compute_mel_filterbank_features(
          waveforms,
          sample_rate=p.audio_sample_rate,
          preemphasis=p.audio_preemphasis,
          frame_length=p.audio_frame_length,
          frame_step=p.audio_frame_step,
          lower_edge_hertz=p.audio_lower_edge_hertz,
          upper_edge_hertz=p.audio_upper_edge_hertz,
          num_mel_bins=p.audio_num_mel_bins,
          apply_mask=False,
      )
  
      if p.audio_add_delta_deltas:
          mel_fbanks = delta_delta(mel_fbanks)
      else:
          mel_fbanks = tf.expand_dims(mel_fbanks, axis=-1)
      # shape: [nframes, nbins, nchannels]
      return mel_fbanks
  
file_path: delta/delta/data/feat/speech_ops.py
imports: import numpy
  from absl import logging
  import delta.compat
  from tensorflow.python.ops import gen_audio_ops
  from delta import utils
  from delta.utils.hparam import HParams
  from core.ops import py_x_ops
label_desc: Use image node_id 7 for calling a global function with example usage: extract_logfbank_with_delta(waveforms, params) and returns: mel_fbanks