code: def _warn_about_fit_failures(results, error_score):
      fit_errors = [
          result["fit_error"]
          for result in results
          if result["fit_error"] is not None
      ]
      if fit_errors:
          num_failed_fits = len(fit_errors)
          num_fits = len(results)
          fit_errors_counter = Counter(fit_errors)
          delimiter = "-" * 80 + "\n"
          fit_errors_summary = "\n".join(
              f"{delimiter}{n} fits failed with the following error:\n{error}"
              for error, n in fit_errors_counter.items()
          )
  
          some_fits_failed_message = (
              f"\n{num_failed_fits} fits failed out of a total of {num_fits}.\n"
              "The score on these train-test partitions for these parameters"
              f" will be set to {error_score}.\n"
              "If these failures are not expected, you can try to debug them "
              "by setting error_score='raise'.\n\n"
              f"Below are more details about the failures:\n{fit_errors_summary}"
          )
          warnings.warn(
              some_fits_failed_message, FitFailedWarning, stacklevel=1
          )
  
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 5 for calling a global function with example usage: _warn_about_fit_failures(results, error_score) without return types