code: def test_classes_do_not_match_proba(kiss_dataset_and_model):
      # Arrange
      _, val, model = kiss_dataset_and_model
      check = WeakSegmentsPerformance()
  
      # Act & Assert
      assert_that(
          calling(check.run).with_args(
              val, model, model_classes=[1, 2, 3, 4, 5, 6, 7]
          ),
          raises(
              DeepchecksValueError,
              r"Predicted probabilities shape \(2, 3\) does not match the number of classes found in the "
              r"labels: \[1, 2, 3, 4, 5, 6, 7\]\.",
          ),
      )
  
file_path: deepchecks/tests/tabular/checks/model_evaluation/weak_segments_performance_test.py
imports: import numpy
  import pandas
  from hamcrest import any_of, assert_that, calling, close_to, equal_to, has_items, has_length, raises
  from sklearn.metrics import f1_score, make_scorer
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular.checks import WeakSegmentsPerformance
  from deepchecks.tabular.datasets.classification.phishing import load_data, load_fitted_model
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 6 for calling a global function with example usage: test_classes_do_not_match_proba(kiss_dataset_and_model) without return types