<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:

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']