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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 evaluate_model_single_recording_preloaded_multisymbol(preprocessing_queue, feature_list, model, output_semantics, recording):
 """ Evaluate a model for a single recording, after everything has been loaded. Multiple symbols are recognized. Parameters preprocessing_queue : list List of all preprocessing objects. feature_list : list List of all feature objects. model : dict Neural network model. output_semantics : List that defines what an output means. recording : The handwritten recording in JSON format. """

    import json
    import nntoolkit.evaluate
    recording = json.loads(recording)
    logging.info(("## start (%i strokes)" % len(recording)) + "#" * 80)
    hypotheses = []  # [[{'score': 0.123, symbols: [123, 123]}]  # split0
                     #  []] # Split i...
    for split in get_possible_splits(len(recording)):
        recording_segmented = segment_by_split(split, recording)
        cur_split_results = []
        for i, symbol in enumerate(recording_segmented):
            handwriting = handwritten_data.HandwrittenData(json.dumps(symbol))
            handwriting.preprocessing(preprocessing_queue)
            x = handwriting.feature_extraction(feature_list)

            model_output = nntoolkit.evaluate.get_model_output(model, [x])
            results = nntoolkit.evaluate.get_results(model_output,
                                                     output_semantics)
            results = results[:10]
            cur_split_results.append([el for el in results if el['probability'] >= 0.01])
            # serve.show_results(results, n=10)

        # Now that I have all symbols of this split, I have to get all
        # combinations of the hypothesis
        import itertools
        for hyp in itertools.product(*cur_split_results):
            hypotheses.append({'score': reduce(lambda x, y: x*y,
                                               [s['probability'] for s in hyp])*len(hyp)/len(recording),
                               'symbols': [s['semantics'] for s in hyp],
                               'min_part': min([s['probability'] for s in hyp]),
                               'segmentation': split})

    hypotheses = sorted(hypotheses, key=lambda n: n['min_part'], reverse=True)[:10]
    for i, hyp in enumerate(hypotheses):
        if hyp['score'] > 0.001:
            logging.info("%0.4f: %s (seg: %s)", hyp['score'], hyp['symbols'], hyp['segmentation'])
    return nntoolkit.evaluate.get_results(model_output, output_semantics)