code: def test_calculate_importance_force_permutation_fail_on_dataframe(
      iris_split_dataset_and_model,
  ):
      # Arrange
      train_ds, _, adaboost = iris_split_dataset_and_model
      df_only_features = train_ds.data.drop(train_ds.label_name, axis=1)
  
      # Assert
      assert_that(
          calling(calculate_feature_importance).with_args(
              adaboost, df_only_features
          ),
          raises(
              DeepchecksValueError,
              "Cannot calculate permutation feature importance on a pandas Dataframe",
          ),
      )
  
imports: from hamcrest import assert_that, calling, close_to, raises
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular.feature_importance import calculate_feature_importance
  from deepchecks.tabular.metric_utils import DeepcheckScorer
label_desc: Use image node_id 3 for calling a global function with example usage: test_calculate_importance_force_permutation_fail_on_dataframe(iris_split_dataset_and_model) without return types