code: def test_predict_using_proba(
      iris_binary_string_split_dataset_and_model,
  ):
      train, test, clf = iris_binary_string_split_dataset_and_model
      y_pred_train, _, y_proba_train, _ = _dummify_model(
          train, test, clf
      )
  
      check = SingleDatasetPerformance(scorers=["f1_per_class"])
  
      proba_result = check.run(train, y_proba=y_proba_train)
      pred_result = check.run(train, y_pred=y_pred_train)
  
      assert_that(
          proba_result.value.iloc[0, -1],
          close_to(pred_result.value.iloc[0, -1], 0.001),
      )
  
imports: from hamcrest import assert_that, calling, close_to, has_items, raises
  from deepchecks.core.check_result import CheckResult
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.core.errors import DeepchecksValueError, ValidationError
  from deepchecks.tabular.base_checks import TrainTestCheck
  from deepchecks.tabular.checks import SingleDatasetPerformance
  from deepchecks.tabular.checks.model_evaluation.regression_error_distribution import RegressionErrorDistribution
  from deepchecks.tabular.checks.model_evaluation.roc_report import RocReport
  from deepchecks.tabular.checks.model_evaluation.simple_model_comparison import SimpleModelComparison
  from deepchecks.tabular.context import Context
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.suites.default_suites import full_suite
  from deepchecks.tabular.utils.task_type import TaskType
  from tests.base.utils import equal_condition_result
  from tests.common import get_expected_results_length, validate_suite_result
  from tests.tabular.checks.model_evaluation.simple_model_comparison_test import assert_regression
label_desc: Use image node_id 12 for calling a global function with example usage: test_predict_using_proba(iris_binary_string_split_dataset_and_model) without return types