code: def _agg_cv_result(raw_results, eval_train_metric=False):
      
      cvmap = collections.defaultdict(list)
      metric_type = {}
      for one_result in raw_results:
          for one_line in one_result:
              if eval_train_metric:
                  key = "{} {}".format(one_line[0], one_line[1])
              else:
                  key = one_line[1]
              metric_type[key] = one_line[3]
              cvmap[key].append(one_line[2])
      return [('cv_agg', k, np.mean(v), metric_type[k], np.std(v)) for k, v in cvmap.items()]
docs: Aggregate cross-validation results.