code: def test_calibration_cv_splitter(data, ensemble):
      # Check when `cv` is a CV splitter
      X, y = data
  
      splits = 5
      kfold = KFold(n_splits=splits)
      calib_clf = CalibratedClassifierCV(cv=kfold, ensemble=ensemble)
      assert isinstance(calib_clf.cv, KFold)
      assert calib_clf.cv.n_splits == splits
  
      calib_clf.fit(X, y)
      expected_n_clf = splits if ensemble else 1
      assert len(calib_clf.calibrated_classifiers_) == expected_n_clf
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose
  from sklearn.base import BaseEstimator, clone
  from sklearn.calibration import CalibratedClassifierCV, CalibrationDisplay, _CalibratedClassifier, _sigmoid_calibration, _SigmoidCalibration, calibration_curve
  from sklearn.datasets import load_iris, make_blobs, make_classification
  from sklearn.dummy import DummyClassifier
  from sklearn.ensemble import RandomForestClassifier, VotingClassifier
  from sklearn.exceptions import NotFittedError
  from sklearn.feature_extraction import DictVectorizer
  from sklearn.impute import SimpleImputer
  from sklearn.isotonic import IsotonicRegression
  from sklearn.linear_model import LogisticRegression, SGDClassifier
  from sklearn.metrics import brier_score_loss
  from sklearn.model_selection import KFold, LeaveOneOut, check_cv, cross_val_predict, cross_val_score, train_test_split
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.pipeline import Pipeline, make_pipeline
  from sklearn.preprocessing import LabelEncoder, StandardScaler
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils._mocking import CheckingClassifier
  from sklearn.utils._testing import _convert_container, assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.extmath import softmax
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 4 for calling a global function with example usage: test_calibration_cv_splitter(data, ensemble) without return types