code: def test_regression_positive_scorers(
      diabetes_split_dataset_and_model,
  ):
      # Arrange
      train, test, model = diabetes_split_dataset_and_model
      check = SingleDatasetPerformance(scorers=["mse", "rmse", "mae"])
      # Act
      result = check.run(test, model).value
      # Assert
      assert_that(result["Value"].iloc[0], close_to(3296, 1))
      assert_that(
          result["Value"].iloc[0],
          close_to(result["Value"].iloc[1] ** 2, 0.001),
      )
      assert_that(result["Value"].iloc[2], close_to(45, 1))
  
      assert_that(check.greater_is_better(), equal_to(False))
  
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 17 for calling a global function with example usage: test_regression_positive_scorers(diabetes_split_dataset_and_model) without return types