code: def test_wrong_class_weight_label():
      # ValueError due to wrong class_weight label.
      X2 = np.array(
          [
              [-1.0, -1.0],
              [-1.0, 0],
              [-0.8, -1.0],
              [1.0, 1.0],
              [1.0, 0.0],
          ]
      )
      y2 = [1, 1, 1, -1, -1]
  
      clf = PassiveAggressiveClassifier(
          class_weight={0: 0.5}, max_iter=100
      )
      with pytest.raises(ValueError):
          clf.fit(X2, y2)
  
imports: import numpy
  import pytest
  from sklearn.base import ClassifierMixin
  from sklearn.datasets import load_iris
  from sklearn.linear_model import PassiveAggressiveClassifier, PassiveAggressiveRegressor
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 9 for calling a global function with example usage: test_wrong_class_weight_label() without return types