code: def run_thresholdoptimizer_classification(estimator):
      """Run classification test with ThresholdOptimizer."""
      X_train, Y_train, A_train, X_test, Y_test, A_test = fetch_adult()
  
      unmitigated = copy.deepcopy(estimator)
      unmitigated.fit(X_train, Y_train)
      unmitigated_predictions = unmitigated.predict(X_test)
  
      to = ThresholdOptimizer(
          estimator=estimator, prefit=False, predict_method="predict"
      )
      to.fit(X_train, Y_train, sensitive_features=A_train)
  
      mitigated_predictions = to.predict(
          X_test, sensitive_features=A_test
      )
  
      dp_diff_unmitigated = demographic_parity_difference(
          Y_test, unmitigated_predictions, sensitive_features=A_test
      )
  
      dp_diff_mitigated = demographic_parity_difference(
          Y_test, mitigated_predictions, sensitive_features=A_test
      )
      assert dp_diff_mitigated <= dp_diff_unmitigated
  
file_path: fairlearn/test_othermlpackages/package_test_common.py
imports: import copy
  import pandas
  from numpy import mean, random, number
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder
  from sklearn.compose import make_column_selector, make_column_transformer
  import fairlearn.utils._compatibility
  import fairlearn.datasets
  from fairlearn.metrics import demographic_parity_difference
  from fairlearn.postprocessing import ThresholdOptimizer
  from fairlearn.reductions import ExponentiatedGradient, GridSearch
  import logging
label_desc: Use image node_id 4 for calling a global function with example usage: run_thresholdoptimizer_classification(estimator) without return types