code: def test_classification_binary(iris_dataset_single_class_labeled):
      # Arrange
      train, test = train_test_split(
          iris_dataset_single_class_labeled.data,
          test_size=0.33,
          random_state=42,
      )
      train_ds = iris_dataset_single_class_labeled.copy(train)
      test_ds = iris_dataset_single_class_labeled.copy(test)
      clf = RandomForestClassifier(random_state=0)
      clf.fit(
          train_ds.data[train_ds.features],
          train_ds.data[train_ds.label_name],
      )
      check = SingleDatasetPerformance()
      # Act
      result = check.run(test_ds, clf).value
      # Assert
      assert_that(max(result["Value"]), close_to(1, 0.01))
      assert_that(
          list(result["Metric"]), has_items("Accuracy", "Recall")
      )
  
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 13 for calling a global function with example usage: test_classification_binary(iris_dataset_single_class_labeled) without return types