code: def _update_evaluated_individuals_(self, result_score_list, eval_individuals_str, operator_counts, stats_dicts):
          
          for result_score, individual_str in zip(result_score_list, eval_individuals_str):
              if type(result_score) in [float, np.float64, np.float32]:
                  self.evaluated_individuals_[individual_str] = self._combine_individual_stats(operator_counts[individual_str],
                                                                                               result_score,
                                                                                               stats_dicts[individual_str])
              else:
                  raise ValueError('Scoring function does not return a float.')
docs: Update self.evaluated_individuals_ and error message during pipeline evaluation.
  
          Parameters
          ----------
          result_score_list: list
              A list of CV scores for evaluated pipelines
          eval_individuals_str: list
              A list of strings for evaluated pipelines
          operator_counts: dict
              A dict where 'key' is the string representation of an individual and 'value' is the number of operators in the pipeline
          stats_dicts: dict
              A dict where 'key' is the string representation of an individual and 'value' is a dict containing statistics about the individual
  
  
          Returns
          -------
          None