code: def test_scorers_types_no_error(adult_split_dataset_and_model):
      # Arrange
      _, test, model = adult_split_dataset_and_model
      scorer_types = [
          None,
          "f1",
          ("f1_score", make_scorer(f1_score, average="micro")),
          {"f1_score": make_scorer(f1_score, average="micro")},
      ]
  
      # Act
      for scorer in scorer_types:
          check = PerformanceBias("sex", scorer=scorer)
          check.run(test, model)
  
      # Assert
      pass
  
file_path: deepchecks/tests/tabular/checks/model_evaluation/performance_bias_test.py
imports: import numpy
  import pandas
  from hamcrest import all_of, any_of, assert_that, calling, close_to, equal_to, has_entries, has_items, has_length, has_properties, has_property, raises
  from sklearn.metrics import f1_score, make_scorer
  from deepchecks import ConditionCategory
  from deepchecks.core.errors import DeepchecksNotSupportedError, DeepchecksValueError
  from deepchecks.tabular.checks.model_evaluation import PerformanceBias
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 9 for calling a global function with example usage: test_scorers_types_no_error(adult_split_dataset_and_model) without return types