code: def test_bad_pred_proba(iris_labeled_dataset, iris_adaboost):
      # Arrange
      y_pred_train, _, y_proba_train, _ = _dummify_model(
          iris_labeled_dataset, None, iris_adaboost
      )
  
      y_proba_train = y_proba_train[:-1]
  
      # Act
      assert_that(
          calling(RocReport().run).with_args(
              dataset=iris_labeled_dataset,
              y_pred=y_pred_train,
              y_proba=y_proba_train,
          ),
          raises(
              ValidationError,
              r"Prediction probabilities expected to be of length 150 but was: 149",
          ),
      )
  
imports: from hamcrest import assert_that, calling, close_to, has_items, raises
  from deepchecks.core.check_result import CheckResult
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.core.errors import DeepchecksValueError, ValidationError
  from deepchecks.tabular.base_checks import TrainTestCheck
  from deepchecks.tabular.checks import SingleDatasetPerformance
  from deepchecks.tabular.checks.model_evaluation.regression_error_distribution import RegressionErrorDistribution
  from deepchecks.tabular.checks.model_evaluation.roc_report import RocReport
  from deepchecks.tabular.checks.model_evaluation.simple_model_comparison import SimpleModelComparison
  from deepchecks.tabular.context import Context
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.suites.default_suites import full_suite
  from deepchecks.tabular.utils.task_type import TaskType
  from tests.base.utils import equal_condition_result
  from tests.common import get_expected_results_length, validate_suite_result
  from tests.tabular.checks.model_evaluation.simple_model_comparison_test import assert_regression
label_desc: Use image node_id 10 for calling a global function with example usage: test_bad_pred_proba(iris_labeled_dataset, iris_adaboost) without return types