code: def test_condition_class_performance_imbalance_ratio_less_than_passed(
      iris_split_dataset_and_model,
  ):
      # Arrange
      train, test, model = iris_split_dataset_and_model
      check = TrainTestPerformance().add_condition_class_performance_imbalance_ratio_less_than(
          threshold=1, score="F1"
      )
      # Act
      result: List[ConditionResult] = check.conditions_decision(
          check.run(train, test, model)
      )
      # Assert
      assert_that(
          result,
          has_items(
              equal_condition_result(
                  is_pass=True,
                  details=re.compile(
                      "Relative ratio difference between highest and lowest in Test dataset classes is 14.29%"
                  ),
                  name="Relative ratio difference between labels 'F1' score is less than 100%",
              )
          ),
      )
  
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 22 for calling a global function with example usage: test_condition_class_performance_imbalance_ratio_less_than_passed(iris_split_dataset_and_model) without return types