code: def cross_validate(
      estimator,
      X,
      y=None,
      *,
      groups=None,
      scoring=None,
      cv=None,
      n_jobs=None,
      verbose=0,
      fit_params=None,
      pre_dispatch="2*n_jobs",
      return_train_score=False,
      return_estimator=False,
      error_score=np.nan,
  ):
      """Evaluate metric(s) by cross-validation and also record fit/score times.
  
      Read more in the :ref:`User Guide <multimetric_cross_validation>`.
  
      Parameters
      ----------
      estimator : estimator object implementing 'fit'
          The object to use to fit the data.
  
      X : array-like of shape (n_samples, n_features)
          The data to fit. Can be for example a list, or an array.
  
      y : array-like of shape (n_samples,) or (n_samples, n_outputs), \
              default=None
          The target variable to try to predict in the case of
          supervised learning.
  
      groups : array-like of shape (n_samples,), default=None
          Group labels for the samples used while splitting the dataset into
          train/test set. Only used in conjunction with a "Group" :term:`cv`
          instance (e.g., :class:`GroupKFold`).
  
      scoring : str, callable, list, tuple, or dict, default=None
          Strategy to evaluate the performance of the cross-validated model on
          the test set.
  
          If `scoring` represents a single score, one can use:
  
          - a single string (see :ref:`scoring_parameter`);
          - a callable (see :ref:`scoring`) that returns a single value.
  
          If `scoring` represents multiple scores, one can use:
  
          - a list or tuple of unique strings;
          - a callable returning a dictionary where the keys are the metric
            names and the values are the metric scores;
          - a dictionary with metric names as keys and callables a values.
  
          See :ref:`multimetric_grid_search` for an example.
  
      cv : int, cross-validation generator or an iterable, default=None
          Determines the cross-validation splitting strategy.
          Possible inputs for cv are:
  
          - None, to use the default 5-fold cross validation,
          - int, to specify the number of folds in a `(Stratified)KFold`,
          - :term:`CV splitter`,
          - An iterable yielding (train, test) splits as arrays of indices.
  
          For int/None inputs, if the estimator is a classifier and ``y`` is
          either binary or multiclass, :class:`StratifiedKFold` is used. In all
          other cases, :class:`.Fold` is used. These splitters are instantiated
          with `shuffle=False` so the splits will be the same across calls.
  
          Refer :ref:`User Guide <cross_validation>` for the various
          cross-validation strategies that can be used here.
  
          .. versionchanged:: 0.22
              ``cv`` default value if None changed from 3-fold to 5-fold.
  
      n_jobs : int, default=None
          Number of jobs to run in parallel. Training the estimator and computing
          the score are parallelized over the cross-validation splits.
          ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
          ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
          for more details.
  
      verbose : int, default=0
          The verbosity level.
  
      fit_params : dict, default=None
          Parameters to pass to the fit method of the estimator.
  
      pre_dispatch : int or str, default='2*n_jobs'
          Controls the number of jobs that get dispatched during parallel
          execution. Reducing this number can be useful to avoid an
          explosion of memory consumption when more jobs get dispatched
          than CPUs can process. This parameter can be:
  
              - None, in which case all the jobs are immediately
                created and spawned. Use this for lightweight and
                fast-running jobs, to avoid delays due to on-demand
                spawning of the jobs
  
              - An int, giving the exact number of total jobs that are
                spawned
  
              - A str, giving an expression as a function of n_jobs,
                as in '2*n_jobs'
  
      return_train_score : bool, default=False
          Whether to include train scores.
          Computing training scores is used to get insights on how different
          parameter settings impact the overfitting/underfitting trade-off.
          However computing the scores on the training set can be computationally
          expensive and is not strictly required to select the parameters that
          yield the best generalization performance.
  
          .. versionadded:: 0.19
  
          .. versionchanged:: 0.21
              Default value was changed from ``True`` to ``False``
  
      return_estimator : bool, default=False
          Whether to return the estimators fitted on each split.
  
          .. versionadded:: 0.20
  
      error_score : 'raise' or numeric, default=np.nan
          Value to assign to the score if an error occurs in estimator fitting.
          If set to 'raise', the error is raised.
          If a numeric value is given, FitFailedWarning is raised.
  
          .. versionadded:: 0.20
  
      Returns
      -------
      scores : dict of float arrays of shape (n_splits,)
          Array of scores of the estimator for each run of the cross validation.
  
          A dict of arrays containing the score/time arrays for each scorer is
          returned. The possible keys for this ``dict`` are:
  
              ``test_score``
                  The score array for test scores on each cv split.
                  Suffix ``_score`` in ``test_score`` changes to a specific
                  metric like ``test_r2`` or ``test_auc`` if there are
                  multiple scoring metrics in the scoring parameter.
              ``train_score``
                  The score array for train scores on each cv split.
                  Suffix ``_score`` in ``train_score`` changes to a specific
                  metric like ``train_r2`` or ``train_auc`` if there are
                  multiple scoring metrics in the scoring parameter.
                  This is available only if ``return_train_score`` parameter
                  is ``True``.
              ``fit_time``
                  The time for fitting the estimator on the train
                  set for each cv split.
              ``score_time``
                  The time for scoring the estimator on the test set for each
                  cv split. (Note time for scoring on the train set is not
                  included even if ``return_train_score`` is set to ``True``
              ``estimator``
                  The estimator objects for each cv split.
                  This is available only if ``return_estimator`` parameter
                  is set to ``True``.
  
      Examples
      --------
      >>> from sklearn import datasets, linear_model
      >>> from sklearn.model_selection import cross_validate
      >>> from sklearn.metrics import make_scorer
      >>> from sklearn.metrics import confusion_matrix
      >>> from sklearn.svm import LinearSVC
      >>> diabetes = datasets.load_diabetes()
      >>> X = diabetes.data[:150]
      >>> y = diabetes.target[:150]
      >>> lasso = linear_model.Lasso()
  
      Single metric evaluation using ``cross_validate``
  
      >>> cv_results = cross_validate(lasso, X, y, cv=3)
      >>> sorted(cv_results.keys())
      ['fit_time', 'score_time', 'test_score']
      >>> cv_results['test_score']
      array([0.33150734, 0.08022311, 0.03531764])
  
      Multiple metric evaluation using ``cross_validate``
      (please refer the ``scoring`` parameter doc for more information)
  
      >>> scores = cross_validate(lasso, X, y, cv=3,
      ...                         scoring=('r2', 'neg_mean_squared_error'),
      ...                         return_train_score=True)
      >>> print(scores['test_neg_mean_squared_error'])
      [-3635.5... -3573.3... -6114.7...]
      >>> print(scores['train_r2'])
      [0.28010158 0.39088426 0.22784852]
  
      See Also
      ---------
      cross_val_score : Run cross-validation for single metric evaluation.
  
      cross_val_predict : Get predictions from each split of cross-validation for
          diagnostic purposes.
  
      sklearn.metrics.make_scorer : Make a scorer from a performance metric or
          loss function.
  
      """
      try:
          X, y, groups = indexable(X, y, groups)
  
          cv = check_cv(cv, y, classifier=is_classifier(estimator))
  
          if callable(scoring):
              scorers = scoring
          elif scoring is None or isinstance(scoring, str):
              scorers = check_scoring(estimator, scoring)
          else:
              scorers = _check_multimetric_scoring(estimator, scoring)
  
          # We clone the estimator to make sure that all the folds are
          # independent, and that it is pickle-able.
          parallel = Parallel(
              n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch
          )
          results = parallel(
              delayed(_fit_and_score)(
                  clone(estimator),
                  X,
                  y,
                  scorers,
                  train,
                  test,
                  verbose,
                  None,
                  fit_params,
                  return_train_score=return_train_score,
                  return_times=True,
                  return_estimator=return_estimator,
                  error_score=error_score,
              )
              for train, test in cv.split(X, y, groups)
          )
  
          _warn_about_fit_failures(results, error_score)
  
          # For callabe scoring, the return type is only know after calling. If the
          # return type is a dictionary, the error scores can now be inserted with
          # the correct key.
          if callable(scoring):
              _insert_error_scores(results, error_score)
  
          results = _aggregate_score_dicts(results)
  
          ret = {}
          ret["fit_time"] = results["fit_time"]
          ret["score_time"] = results["score_time"]
  
          if return_estimator:
              ret["estimator"] = results["estimator"]
  
          test_scores_dict = _normalize_score_results(
              results["test_scores"]
          )
          if return_train_score:
              train_scores_dict = _normalize_score_results(
                  results["train_scores"]
              )
  
          for name in test_scores_dict:
              ret["test_%s" % name] = test_scores_dict[name]
              if return_train_score:
                  key = "train_%s" % name
                  ret[key] = train_scores_dict[name]
  
          return ret
      except Exception:
          logging.exception(
              "Failed to execute overriden cross_validate. Fallback to original"
          )
          return cross_validate(
              estimator,
              X,
              y,
              groups=groups,
              scoring=scoring,
              cv=cv,
              n_jobs=n_jobs,
              verbose=verbose,
              fit_params=fit_params,
              pre_dispatch=pre_dispatch,
              return_train_score=return_train_score,
              return_estimator=return_estimator,
              error_score=error_score,
          )
  
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 1 for calling a global function with example usage: cross_validate(estimator, X, y) and returns: ret, cross_validate