code: def test_adaboost_negative_weight_error(model, X, y):
      sample_weight = np.ones_like(y)
      sample_weight[-1] = -10
  
      err_msg = "Negative values in data passed to `sample_weight`"
      with pytest.raises(ValueError, match=err_msg):
          model.fit(X, y, sample_weight=sample_weight)
  
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 21 for calling a global function with example usage: test_adaboost_negative_weight_error(model, X, y) without return types