code: def test_numeric_values_classwise(adult_split_dataset_and_model):
      # Arrange
      _, test, model = adult_split_dataset_and_model
      check = PerformanceBias("sex", scorer="f1_per_class")
  
      expected_value = pd.DataFrame(
          {
              "sex": {1: " Female", 0: " Male"},
              "_scorer": {1: "f1_per_class", 0: "f1_per_class"},
              "_score": {1: 0.9557802895102122, 0: 0.6198331788693234},
              "_class": {1: " <=50K", 0: " >50K"},
              "_baseline": {
                  1: 0.9054560599750104,
                  0: 0.5940497480084539,
              },
              "_baseline_count": {1: 16281, 0: 16281},
              "_count": {1: 5421, 0: 10860},
              "_diff": {1: 0.05032422953520177, 0: 0.02578343086086954},
          }
      )
  
      # Act
      result = check.run(test, model)
  
      # Assert
      assert_that(result.display, has_length(2))
      assert_that(
          result.value["scores_df"].round(3).to_dict(),
          has_entries(expected_value.round(3).to_dict()),
      )
  
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 6 for calling a global function with example usage: test_numeric_values_classwise(adult_split_dataset_and_model) without return types