code: def test_numeric_values(adult_split_dataset_and_model):
      # Arrange
      _, test, model = adult_split_dataset_and_model
      check = PerformanceBias("sex")
  
      expected_value = pd.DataFrame(
          {
              "sex": {0: " Male", 1: " Female"},
              "_scorer": {0: "Accuracy", 1: "Accuracy"},
              "_score": {0: 0.8111418047882136, 1: 0.9177273565762775},
              "_baseline": {
                  0: 0.8466310423192679,
                  1: 0.8466310423192679,
              },
              "_baseline_count": {0: 16281, 1: 16281},
              "_count": {0: 10860, 1: 5421},
              "_diff": {
                  0: -0.03548923753105426,
                  1: 0.07109631425700957,
              },
          }
      )
  
      # 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 5 for calling a global function with example usage: test_numeric_values(adult_split_dataset_and_model) without return types