code: def test_sample_weight(data, method, ensemble):
      n_samples = N_SAMPLES // 2
      X, y = data
  
      sample_weight = np.random.RandomState(seed=42).uniform(
          size=len(y)
      )
      X_train, y_train, sw_train = (
          X[:n_samples],
          y[:n_samples],
          sample_weight[:n_samples],
      )
      X_test = X[n_samples:]
  
      estimator = LinearSVC(dual="auto", random_state=42)
      calibrated_clf = CalibratedClassifierCV(
          estimator, method=method, ensemble=ensemble
      )
      calibrated_clf.fit(X_train, y_train, sample_weight=sw_train)
      probs_with_sw = calibrated_clf.predict_proba(X_test)
  
      # As the weights are used for the calibration, they should still yield
      # different predictions
      calibrated_clf.fit(X_train, y_train)
      probs_without_sw = calibrated_clf.predict_proba(X_test)
  
      diff = np.linalg.norm(probs_with_sw - probs_without_sw)
      assert diff > 0.1
  
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 5 for calling a global function with example usage: test_sample_weight(data, method, ensemble) without return types