code: def test_adaboost_classifier_sample_weight_error():
      # Test that it gives proper exception on incorrect sample weight.
      clf = AdaBoostClassifier()
      msg = re.escape("sample_weight.shape == (1,), expected (6,)")
      with pytest.raises(ValueError, match=msg):
          clf.fit(X, y_class, sample_weight=np.asarray([-1]))
  
imports: import re
  import numpy
  import pytest
  from sklearn import datasets
  from sklearn.base import BaseEstimator, clone
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.ensemble import AdaBoostClassifier, AdaBoostRegressor
  from sklearn.ensemble._weight_boosting import _samme_proba
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import GridSearchCV, train_test_split
  from sklearn.svm import SVC, SVR
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.utils import shuffle
  from sklearn.utils._mocking import NoSampleWeightWrapper
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal, assert_array_less
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 11 for calling a global function with example usage: test_adaboost_classifier_sample_weight_error() without return types