code: def test_calibration_votingclassifier():
      # Check that `CalibratedClassifier` works with `VotingClassifier`.
      # The method `predict_proba` from `VotingClassifier` is dynamically
      # defined via a property that only works when voting="soft".
      X, y = make_classification(
          n_samples=10, n_features=5, n_classes=2, random_state=7
      )
      vote = VotingClassifier(
          estimators=[
              ("lr" + str(i), LogisticRegression()) for i in range(3)
          ],
          voting="soft",
      )
      vote.fit(X, y)
  
      calib_clf = CalibratedClassifierCV(estimator=vote, cv="prefit")
      # smoke test: should not raise an error
      calib_clf.fit(X, y)
  
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 22 for calling a global function with example usage: test_calibration_votingclassifier() without return types