code: def test_condition_degradation_ratio_less_than_not_passed_regression(
      diabetes_split_dataset_and_model,
  ):
      # Arrange
      train, test, model = diabetes_split_dataset_and_model
      check = TrainTestPerformance().add_condition_train_test_relative_degradation_less_than(
          0
      )
      # Act
      result: List[ConditionResult] = check.conditions_decision(
          check.run(train, test, model)
      )
      # Assert
      assert_that(
          result,
          has_items(
              equal_condition_result(
                  is_pass=False,
                  details=r"3 scores failed. Found max degradation of 94.98% for metric Neg MAE",
                  name="Train-Test scores relative degradation is less than 0",
              )
          ),
      )
  
imports: import re
  from typing import List
  import numpy
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_items, has_length, instance_of, raises
  from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
  from sklearn.metrics import jaccard_score, make_scorer
  from sklearn.model_selection import train_test_split
  from deepchecks.core import ConditionResult
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError, ModelValidationError
  from deepchecks.tabular.checks.model_evaluation import TrainTestPerformance
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.metric_utils.scorers import DEFAULT_REGRESSION_SCORERS, MULTICLASS_SCORERS_NON_AVERAGE
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 20 for calling a global function with example usage: test_condition_degradation_ratio_less_than_not_passed_regression(diabetes_split_dataset_and_model) without return types