code: def test_condition_all_score_not_passed(iris_split_dataset_and_model):
      # Arrange
      _, test, model = iris_split_dataset_and_model
      check = SingleDatasetPerformance().add_condition_greater_than(
          0.99
      )
      # Act
      result: List[ConditionResult] = check.conditions_decision(
          check.run(test, model)
      )
      # Assert
      assert_that(
          result,
          has_items(
              equal_condition_result(
                  is_pass=False,
                  details="Failed for metrics: ['Accuracy', 'Precision - Macro Average', 'Recall - Macro Average']",
                  name="Selected metrics scores are greater than 0.99",
              )
          ),
      )
  
imports: from typing import List
  from hamcrest import assert_that, calling, close_to, equal_to, has_entries, has_items, has_length, instance_of, none, raises
  from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from deepchecks.core import ConditionResult
  from deepchecks.core.errors import DeepchecksNotSupportedError, DeepchecksValueError, ModelValidationError
  from deepchecks.tabular.checks import SingleDatasetPerformance
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.metric_utils.scorers import DEFAULT_MULTICLASS_SCORERS, DEFAULT_REGRESSION_SCORERS
  from tests.base.utils import equal_condition_result
  from tests.common import is_nan
label_desc: Use image node_id 20 for calling a global function with example usage: test_condition_all_score_not_passed(iris_split_dataset_and_model) without return types