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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def _preprocess_data(audio_data, verbose=True):
        '''
        Preprocess each example, breaking it up into frames.

        Returns two numpy arrays: preprocessed frame and their indexes
        '''
        from .vggish_input import waveform_to_examples

        last_progress_update = _time.time()
        progress_header_printed = False

        # Can't run as a ".apply(...)" due to numba.jit decorator issue:
        # https://github.com/apple/turicreate/issues/1216
        preprocessed_data, audio_data_index = [], []
        for i, audio_dict in enumerate(audio_data):
            scaled_data = audio_dict['data'] / 32768.0
            data = waveform_to_examples(scaled_data, audio_dict['sample_rate'])

            for j in data:
                preprocessed_data.append([j])
                audio_data_index.append(i)

            # If `verbose` is set, print an progress update about every 20s
            if verbose and _time.time() - last_progress_update >= 20:
                if not progress_header_printed:
                    print("Preprocessing audio data -")
                    progress_header_printed = True
                print("Preprocessed {} of {} examples".format(i, len(audio_data)))
                last_progress_update = _time.time()

        if progress_header_printed:
            print("Preprocessed {} of {} examples\n".format(len(audio_data), len(audio_data)))
        return _np.asarray(preprocessed_data), audio_data_index