code: def test_boosting_cat_classifier(iris_split_dataset_and_model_cat):
      # Arrange
      train, test, clf = iris_split_dataset_and_model_cat
  
      # Act
      result = BoostingOverfit().run(train, test, clf)
  
      # Assert
      train_scores = result.value["train"]
      test_scores = result.value["test"]
      assert_that(train_scores, has_length(20))
      assert_that(test_scores, has_length(20))
      assert_that(mean(train_scores), close_to(0.991, 0.001))
      assert_that(mean(test_scores), close_to(0.979, 0.001))
  
imports: from statistics import mean
  from hamcrest import assert_that, close_to, greater_than, has_length
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from deepchecks.tabular.checks.model_evaluation.boosting_overfit import BoostingOverfit
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 5 for calling a global function with example usage: test_boosting_cat_classifier(iris_split_dataset_and_model_cat) without return types