code: def test_eqodds():
      eqo = EqOddsPostprocessing(
          unprivileged_groups=[{"sex": 0}],
          privileged_groups=[{"sex": 1}],
          seed=1234567,
      )
      pred_eqo = eqo.fit(val, val_pred).predict(pred)
      cm_eqo = ClassificationMetric(
          test,
          pred_eqo,
          unprivileged_groups=[{"sex": 0}],
          privileged_groups=[{"sex": 1}],
      )
      # accuracy drop should be less than 10% (arbitrary)
      assert (
          cm_lr.accuracy() - cm_eqo.accuracy()
      ) / cm_lr.accuracy() < 0.1
      # approximately equal odds
      assert cm_eqo.average_abs_odds_difference() < 0.1
  
imports: from sklearn.linear_model import LogisticRegression
  from aif360.datasets import AdultDataset
  from aif360.algorithms.postprocessing import EqOddsPostprocessing
  from aif360.algorithms.postprocessing import CalibratedEqOddsPostprocessing
  from aif360.metrics import ClassificationMetric
label_desc: Use image node_id 1 for calling a global function with example usage: test_eqodds() without return types