code: def test_rej_opt_clf_scoring(prot_attr, scoring, new_adult):
      """Test all scoring options work."""
      X, y, sample_weight = new_adult
      pp = PostProcessingMeta(
          LogisticRegression(solver="lbfgs", max_iter=500),
          RejectOptionClassifierCV(
              prot_attr,
              scoring=scoring,
              step=0.1,
              refit=scoring,
              n_jobs=-1,
          ),
      )
      pp.fit(X, y, sample_weight=sample_weight)
  
      if scoring == "disparate_impact":
          assert pp.score(X, y) >= 0.8
      else:
          assert pp.score(X, y) >= -0.1
  
file_path: AIF360/tests/sklearn/test_reject_option_classification.py
imports: from itertools import product
  import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import make_scorer
  from aif360.algorithms.postprocessing import RejectOptionClassification
  from aif360.sklearn.postprocessing import RejectOptionClassifier, RejectOptionClassifierCV, PostProcessingMeta
  from aif360.sklearn.metrics import generalized_entropy_error
label_desc: Use image node_id 9 for calling a global function with example usage: test_rej_opt_clf_scoring(prot_attr, scoring, new_adult) without return types