code: def test_adaboost(iris_split_dataset_and_model):
      train_ds, _, adaboost = iris_split_dataset_and_model
      feature_importances, fi_type = run_fi_calculation(
          adaboost, train_ds
      )
      assert_that(feature_importances.sum(), equal_to(1))
      assert_that(fi_type, is_("feature_importances_"))
  
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 2 for calling a global function with example usage: test_adaboost(iris_split_dataset_and_model) without return types