code: def test_boosting_model_is_pipeline(iris):
      # Arrange
      train_df, test = train_test_split(
          iris, test_size=0.33, random_state=0
      )
      train = Dataset(train_df, label="target")
      test = Dataset(test, label="target")
  
      pipe = Pipeline(
          [
              ("scaler", StandardScaler()),
              ("lr", GradientBoostingClassifier(random_state=0)),
          ]
      )
      pipe.fit(train.data[train.features], train.data[train.label_name])
  
      # Act
      result = BoostingOverfit().run(train, test, pipe)
  
      # 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(0.999, 0.001))
      assert_that(mean(test_scores), close_to(0.976, 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 6 for calling a global function with example usage: test_boosting_model_is_pipeline(iris) without return types