code: def test_bad_dataset_model(iris_random_forest, diabetes):
      ds, _ = diabetes
      assert_that(
          calling(_calculate_feature_importance).with_args(
              iris_random_forest, ds, None, None, None
          ),
          any_of(
              # NOTE:
              # depending on the installed version of the scikit-learn
              # will be raised DeepchecksValueError or ModelValidationError
              raises(
                  DeepchecksValueError,
                  r"(In order to evaluate model correctness we need not empty dataset with the "
                  r"same set of features that was used to fit the model. But function received "
                  r"dataset with a different set of features.)",
              ),
              raises(
                  ModelValidationError,
                  r"Got error when trying to predict with model on dataset:(.*)",
              ),
          ),
      )
  
imports: import pandas
  import pytest
  from hamcrest import any_of, assert_that, calling, close_to, contains_exactly, contains_string, equal_to, has_length, is_, none, not_none, raises
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from sklearn.neural_network import MLPClassifier, MLPRegressor
  from sklearn.pipeline import Pipeline
  from deepchecks.core.errors import DeepchecksTimeoutError, DeepchecksValueError, ModelValidationError
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.utils.feature_importance import _calculate_feature_importance, calculate_feature_importance_or_none, column_importance_sorter_df, column_importance_sorter_dict, validate_feature_importance
  from deepchecks.tabular.utils.task_inference import get_all_labels, infer_classes_from_model, infer_task_type_by_class_number, infer_task_type_by_labels
  from deepchecks.tabular.utils.task_type import TaskType
label_desc: Use image node_id 13 for calling a global function with example usage: test_bad_dataset_model(iris_random_forest, diabetes) without return types