code: def test_caleq():
      ceo = CalibratedEqOddsPostprocessing(
          cost_constraint="fnr",
          unprivileged_groups=[{"sex": 0}],
          privileged_groups=[{"sex": 1}],
          seed=1234567,
      )
      pred_ceo = ceo.fit(val, val_pred).predict(pred)
  
      cm_ceo = ClassificationMetric(
          test,
          pred_ceo,
          unprivileged_groups=[{"sex": 0}],
          privileged_groups=[{"sex": 1}],
      )
      # accuracy drop should be less than 10% (arbitrary)
      assert (
          cm_lr.accuracy() - cm_ceo.accuracy()
      ) / cm_lr.accuracy() < 0.1
      # approximate GFNR parity
      assert (
          abs(cm_ceo.difference(cm_ceo.generalized_false_negative_rate))
          < 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 2 for calling a global function with example usage: test_caleq() without return types