code: def get_deep_features(audio_data, verbose=True):
      """
      Calculates the deep features used by the Sound Classifier.
  
      Internally the Sound Classifier calculates deep features for both model
      creation and predictions. If the same data will be used multiple times,
      calculating the deep features just once will result in a significant speed
      up.
  
      Parameters
      ----------
      audio_data : SArray
          Audio data is represented as dicts with key 'data' and 'sample_rate',
          see `turicreate.load_audio(...)`.
  
      Examples
      --------
      >>> my_audio_data['deep_features'] = get_deep_features(my_audio_data['audio'])
      >>> train, test = my_audio_data.random_split(.8)
      >>> model = tc.sound_classifier.create(train, 'label', 'deep_features')
      >>> predictions = model.predict(test)
      """
      from ._audio_feature_extractor import _get_feature_extractor
  
      if not _is_audio_data_sarray(audio_data):
          raise TypeError("Input must be audio data")
  
      feature_extractor_name = "VGGish"
      feature_extractor = _get_feature_extractor(feature_extractor_name)
  
      return feature_extractor.get_deep_features(
          audio_data, verbose=verbose
      )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import logging
  from math import ceil
  import numpy
  import six
  import turicreate
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits._main import ToolkitError
  from turicreate.toolkits._model import CustomModel
  from turicreate.toolkits._model import PythonProxy
  from turicreate.toolkits import _coreml_utils
  from turicreate._deps.minimal_package import _minimal_package_import_check
label_desc: Use image node_id 5 for calling a global function with example usage: get_deep_features(audio_data, verbose) and returns: feature_extractor