code: def _combine_individual_stats(self, operator_count, cv_score, individual_stats):
          
          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
docs: 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