code: def _insert_error_scores(results, error_score):
      """Insert error in `results` by replacing them inplace with `error_score`.
  
      This only applies to multimetric scores because `_fit_and_score` will
      handle the single metric case.
      """
  
      successful_score = None
      failed_indices = []
      for i, result in enumerate(results):
          if result["fit_error"] is not None:
              failed_indices.append(i)
          elif successful_score is None:
              successful_score = result["test_scores"]
  
      if successful_score is None:
          raise NotFittedError("All estimators failed to fit")
  
      if isinstance(successful_score, dict):
          formatted_error = {
              name: error_score for name in successful_score
          }
          for i in failed_indices:
              results[i]["test_scores"] = formatted_error.copy()
              if "train_scores" in results[i]:
                  results[i]["train_scores"] = formatted_error.copy()
  
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 4 for calling a global function with example usage: _insert_error_scores(results, error_score) without return types