code: def extract_feature(*wavefiles, **kwargs):
      """tensorflow fbank feat"""
      dry_run = kwargs.get("dry_run")
      feat_name = "fbank"
      feat_name = kwargs.get("feature_name")
      assert feat_name
  
      graph, (input_tensor, output_tensor) = _freq_feat_graph(
          feat_name, **kwargs
      )
      sess = _get_session(_get_out_tensor_name(feat_name, 0), graph)
  
      for wavpath in wavefiles:
          savepath = os.path.splitext(wavpath)[0] + ".npy"
          logging.debug(
              "extract_feat: input: {}, output: {}".format(
                  wavpath, savepath
              )
          )
  
          feat = sess.run(
              output_tensor, feed_dict={input_tensor: wavpath}
          )
  
          # save feat
          if dry_run:
              logging.info(
                  "save feat: path {} shape:{} dtype:{}".format(
                      savepath, feat.shape, feat.dtype
                  )
              )
          else:
              np.save(savepath, feat)
  
file_path: delta/delta/data/feat/speech_feature.py
imports: import os
  import numpy
  import delta.compat
  from absl import logging
  from delta.data.feat import speech_ops
  from core.ops import py_x_ops
  from delta.data.feat import python_speech_features
label_desc: Use image node_id 4 for calling a global function with example usage: extract_feature() without return types