code: def _agg_cv_result(
      raw_results: List[List[_LGBM_BoosterEvalMethodResultType]],
  ) -> List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType]:
      """Aggregate cross-validation results."""
      cvmap: Dict[str, List[float]] = OrderedDict()
      metric_type: Dict[str, bool] = {}
      for one_result in raw_results:
          for one_line in one_result:
              key = f"{one_line[0]} {one_line[1]}"
              metric_type[key] = one_line[3]
              cvmap.setdefault(key, [])
              cvmap[key].append(one_line[2])
      return [
          (
              "cv_agg",
              k,
              float(np.mean(v)),
              metric_type[k],
              float(np.std(v)),
          )
          for k, v in cvmap.items()
      ]
  
imports: import copy
  import json
  from collections import OrderedDict, defaultdict
  from operator import attrgetter
  from pathlib import Path
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
  import numpy
  from .None import callback
  from .basic import Booster, Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _InnerPredictor, _LGBM_BoosterEvalMethodResultType, _LGBM_BoosterEvalMethodResultWithStandardDeviationType, _LGBM_CategoricalFeatureConfiguration, _LGBM_CustomObjectiveFunction, _LGBM_EvalFunctionResultType, _LGBM_FeatureNameConfiguration, _log_warning
  from .compat import SKLEARN_INSTALLED, _LGBMBaseCrossValidator, _LGBMGroupKFold, _LGBMStratifiedKFold
label_desc: Use image node_id 3 for calling a global function with example usage: _agg_cv_result(raw_results) and returns: unknown