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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(preprocessing_queue, feature_list, model, output_semantics, recording, recording_id=None):
 """ Evaluate a model for a single recording, after everything has been loaded. 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 List that defines what an output means. recording : string in JSON format The handwritten recording in JSON format. recording_id : int or None For debugging purposes. """

    handwriting = handwritten_data.HandwrittenData(recording,
                                                   raw_data_id=recording_id)
    handwriting.preprocessing(preprocessing_queue)
    x = handwriting.feature_extraction(feature_list)
    import nntoolkit.evaluate
    model_output = nntoolkit.evaluate.get_model_output(model, [x])
    return nntoolkit.evaluate.get_results(model_output, output_semantics)