code: def _normalize_score_results(scores, scaler_score_key="score"):
      """Creates a scoring dictionary based on the type of `scores`"""
      if isinstance(scores[0], dict):
          # multimetric scoring
          return _aggregate_score_dicts(scores)
      # scaler
      return {scaler_score_key: scores}
  
imports: import functools
  import logging
  import numbers
  import time
  import warnings
  from collections import Counter
  from contextlib import suppress
  from itertools import compress
  from traceback import format_exc
  import numpy
  import scipy.sparse
  from catboost import CatBoostClassifier, CatBoostRegressor
  from joblib import Parallel, logger
  from scipy.sparse import issparse
  from sklearn import config_context, get_config
  from sklearn.base import clone, is_classifier
  from sklearn.exceptions import FitFailedWarning, NotFittedError
  from sklearn.metrics import check_scoring
  from sklearn.metrics._scorer import _MultimetricScorer
  from sklearn.model_selection import check_cv
  from sklearn.utils.fixes import np_version, parse_version
  from sklearn.utils.validation import indexable
label_desc: Use image node_id 6 for calling a global function with example usage: _normalize_score_results(scores, scaler_score_key) and returns: dict, _aggregate_score_dicts