code: def test_bad_pred_shape(diabetes_split_dataset_and_model):
      # Arrange
      train, test, clf = diabetes_split_dataset_and_model
      y_pred_train, _, _, _ = _dummify_model(train, test, clf)
  
      # Act
      assert_that(
          calling(RegressionErrorDistribution().run).with_args(
              dataset=test, y_pred=y_pred_train
          ),
          raises(
              ValidationError,
              r"Prediction array expected to be of same length as data 146, but was: 296",
          ),
      )
  
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 9 for calling a global function with example usage: test_bad_pred_shape(diabetes_split_dataset_and_model) without return types