code: def test_calculate_importance_with_kwargs(
      iris_split_dataset_and_model,
  ):
      # Arrange
      train_ds, _, adaboost = iris_split_dataset_and_model
  
      # Act
      scorer = DeepcheckScorer("accuracy", [0, 1, 2], [0, 1, 2])
      fi = calculate_feature_importance(
          adaboost,
          train_ds,
          n_repeats=30,
          mask_high_variance_features=False,
          n_samples=10_000,
          alternative_scorer=scorer,
      )
  
      # Assert
      assert_that(fi.sum(), close_to(1, 0.000001))
  
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 2 for calling a global function with example usage: test_calculate_importance_with_kwargs(iris_split_dataset_and_model) without return types