code: def get_deep_features(self, audio_data, verbose):
          '''
          Performs both audio preprocessing and VGGish deep feature extraction.
          '''
          preprocessed_data, row_ids = self._preprocess_data(audio_data, verbose)
          deep_features = self._extract_features(preprocessed_data, verbose)
  
          output = _tc.SFrame({'deep features': deep_features, 'row id': row_ids})
          output = output.unstack('deep features')
  
          max_row_id = len(audio_data)
          missing_ids = set(range(max_row_id)) - set(output['row id'].unique())
          if len(missing_ids) != 0:
              empty_rows = _tc.SFrame({'List of deep features': [ [] for _ in range(len(missing_ids)) ],
                                       'row id': missing_ids})
              output = output.append(empty_rows)
  
          output = output.sort('row id')
          return output['List of deep features'f get_deep_features(self, audio_data, verbose):
          '''
          Performs both audio preprocessing and VGGish deep feature extraction.
          '''
          preprocessed_data, row_ids = self._preprocess_data(audio_data, verbose)
          deep_features = self._extract_features(preprocessed_data, verbose)
  
          output = _tc.SFrame({'deep features': deep_features, 'row id': row_ids})
          output = output.unstack('deep features')
  
          max_row_id = len(audio_data)
          missing_ids = set(range(max_row_id)) - set(output['row id'].unique())
          if len(missing_ids) != 0:
              empty_rows = _tc.SFrame({'List of deep features': [ [] for _ in range(len(missing_ids)) ],
                                       'row id': missing_ids})
              output = output.append(empty_rows)
  
          output = output.sort('row id')
          return output['List of deep features']
docs: Performs both audio preprocessing and VGGish deep feature extraction.