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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 _combine_individual_stats(self, operator_count, cv_score, individual_stats):
 """Combine the stats with operator count and cv score and preprare to be written to _evaluated_individuals Parameters operator_count: int number of components in the pipeline cv_score: float internal cross validation score individual_stats: dictionary dict containing statistics about the individual. currently: 'generation': generation in which the individual was evaluated 'mutation_count': number of mutation operations applied to the individual and its predecessor cumulatively 'crossover_count': number of crossover operations applied to the individual and its predecessor cumulatively 'predecessor': string representation of the individual Returns ------- stats: dictionary dict containing the combined statistics: 'operator_count': number of operators in the pipeline 'internal_cv_score': internal cross validation score and all the statistics contained in the 'individual_stats' parameter """

        stats = deepcopy(individual_stats)  # Deepcopy, since the string reference to predecessor should be cloned
        stats['operator_count'] = operator_count
        stats['internal_cv_score'] = cv_score
        return stats