code: def old_ROC(old_adult, log_reg_probs):
      """Fit old ROC."""
      old_adult_pred = old_adult.copy()
      old_adult_pred.scores = log_reg_probs[:, [1]]
  
      ROC = RejectOptionClassification(
          unprivileged_groups=[{"sex": 0}],
          privileged_groups=[{"sex": 1}],
          low_class_thresh=0.1,
          high_class_thresh=0.9,
          num_class_thresh=9,
          num_ROC_margin=9,
          metric_name="Statistical parity difference",
          metric_ub=0.1,
          metric_lb=-0.1,
      )
      return ROC.fit(old_adult, old_adult_pred)
  
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 2 for calling a global function with example usage: old_ROC(old_adult, log_reg_probs) and returns: ROC