code: def test_equalized_odds():
      # Have to do this one longhand, since it combines tpr and fpr
      X, y = loan_scenario_generator(n, f, sfs, ibs, seed=632753)
      X_dummy = pd.get_dummies(X)
  
      metrics = {"tpr": true_positive_rate, "fpr": false_positive_rate}
  
      unmitigated = LogisticRegression()
      unmitigated.fit(X_dummy, y)
      y_pred = unmitigated.predict(X_dummy)
      mf_unmitigated = MetricFrame(
          metrics=metrics,
          y_true=y,
          y_pred=y_pred,
          sensitive_features=X["sens"],
          control_features=X["ctrl"],
      )
  
      expgrad_basic = ExponentiatedGradient(
          LogisticRegression(),
          constraints=EqualizedOdds(difference_bound=0.01),
          eps=0.01,
      )
      expgrad_basic.fit(X_dummy, y, sensitive_features=X["sens"])
      y_pred_basic = expgrad_basic.predict(X_dummy, random_state=9235)
      mf_basic = MetricFrame(
          metrics=metrics,
          y_true=y,
          y_pred=y_pred_basic,
          sensitive_features=X["sens"],
          control_features=X["ctrl"],
      )
  
      expgrad_control = ExponentiatedGradient(
          LogisticRegression(),
          constraints=EqualizedOdds(difference_bound=0.01),
          eps=0.01,
      )
      expgrad_control.fit(
          X_dummy,
          y,
          sensitive_features=X["sens"],
          control_features=X["ctrl"],
      )
      y_pred_control = expgrad_control.predict(
          X_dummy, random_state=8152
      )
      mf_control = MetricFrame(
          metrics=metrics,
          y_true=y,
          y_pred=y_pred_control,
          sensitive_features=X["sens"],
          control_features=X["ctrl"],
      )
  
      compare_unmitigated = mf_control.difference(
          method="to_overall"
      ) <= mf_unmitigated.difference(method="to_overall")
      print(compare_unmitigated)
  
      compare_basic = mf_control.difference(
          method="to_overall"
      ) <= mf_basic.difference(method="to_overall")
      print(compare_basic)
  
      assert compare_basic.values.reshape(6).all()
      assert compare_unmitigated.values.reshape(6).all()
  
file_path: fairlearn/test/unit/reductions/exponentiated_gradient/test_control_features.py
imports: from test.unit.reductions.data_generators import loan_scenario_generator
  import pandas
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import accuracy_score
  from fairlearn.metrics import MetricFrame, false_positive_rate, selection_rate, true_positive_rate
  from fairlearn.reductions import DemographicParity, EqualizedOdds, ErrorRateParity, ExponentiatedGradient, FalsePositiveRateParity, TruePositiveRateParity
label_desc: Use image node_id 6 for calling a global function with example usage: test_equalized_odds() without return types