code: def test_condition_score_percentage_decline_not_greater_than_not_pass(
      diabetes, diabetes_model
  ):
      # Arrange
      train, validation = diabetes
      check = BoostingOverfit().add_condition_test_score_percent_decline_less_than(
          0.01
      )
  
      # Act
      condition_result, *_ = check.conditions_decision(
          check.run(train, validation, diabetes_model)
      )
  
      # Assert
      assert_that(
          condition_result,
          equal_condition_result(
              is_pass=False,
              name="Test score over iterations is less than 1% from the best score",
              details="Found score decline of -3.64%",
          ),
      )
  
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 13 for calling a global function with example usage: test_condition_score_percentage_decline_not_greater_than_not_pass(diabetes, diabetes_model) without return types