code: def test_boosting_regressor(diabetes, diabetes_model):
      # Arrange
      train, test = diabetes
  
      # Act
      result = BoostingOverfit().run(train, test, diabetes_model)
  
      # 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(-44.52, 0.01))
      assert_that(mean(test_scores), close_to(-59.35, 0.01))
  
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 7 for calling a global function with example usage: test_boosting_regressor(diabetes, diabetes_model) without return types