code: def test_run_with_scorer_multilabel_w_none(
      multilabel_mock_dataset_and_probabilities,
  ):
      # Arrange
      data, probas = multilabel_mock_dataset_and_probabilities
      data = data.copy()
      assert_that(data.is_multi_label_classification(), equal_to(True))
      data._label = np.asarray(
          list(data._label[: round(len(data._label) / 2)])
          + [None] * round(len(data._label) / 2),
          dtype=object,
      )
      check = SingleDatasetPerformance(scorers=["f1_macro"])
  
      # Act
      result = check.run(data, probabilities=probas)
  
      # Assert
      assert_that(result.value.values[0][-1], close_to(0.831, 0.001))
  
imports: import numpy
  from hamcrest import assert_that, calling, close_to, equal_to, has_items, raises
  from deepchecks.core.errors import DeepchecksValueError, ValidationError
  from deepchecks.nlp.checks.model_evaluation.single_dataset_performance import SingleDatasetPerformance
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 9 for calling a global function with example usage: test_run_with_scorer_multilabel_w_none(multilabel_mock_dataset_and_probabilities) without return types