code: def test_parallel_execution(data, method, ensemble):
      """Test parallel calibration"""
      X, y = data
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=42
      )
  
      estimator = make_pipeline(
          StandardScaler(), LinearSVC(dual="auto", random_state=42)
      )
  
      cal_clf_parallel = CalibratedClassifierCV(
          estimator, method=method, n_jobs=2, ensemble=ensemble
      )
      cal_clf_parallel.fit(X_train, y_train)
      probs_parallel = cal_clf_parallel.predict_proba(X_test)
  
      cal_clf_sequential = CalibratedClassifierCV(
          estimator, method=method, n_jobs=1, ensemble=ensemble
      )
      cal_clf_sequential.fit(X_train, y_train)
      probs_sequential = cal_clf_sequential.predict_proba(X_test)
  
      assert_allclose(probs_parallel, probs_sequential)
  
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 6 for calling a global function with example usage: test_parallel_execution(data, method, ensemble) without return types