code: def test_random_state_threshold_optimizer():
      """Test that the random_state argument works as expected.
  
      This test case reproduces the problem reported in issue 588 if the
      random_state does not work as intended within ThresholdOptimizer.
      https://github.com/fairlearn/fairlearn/issues/588
      """
      (
          X_train,
          X_test,
          y_train,
          y_test,
          race_train,
          race_test,
      ) = _get_test_data()
  
      # Train a simple logistic regression model
      lr = LogisticRegression(max_iter=1000, random_state=0)
      lr.fit(X_train, y_train)
  
      # Train threshold optimizer
      to = ThresholdOptimizer(
          estimator=lr,
          constraints="equalized_odds",
          grid_size=1000,
          predict_method="predict",
      )
      to.fit(X_train, y_train, sensitive_features=race_train)
  
      # score groups
      y_pred_test = to.predict(
          X_test, sensitive_features=race_test, random_state=0
      )
      for _ in range(100):
          assert (
              y_pred_test
              == to.predict(
                  X_test, sensitive_features=race_test, random_state=0
              )
          ).all()
      assert (
          y_pred_test
          != to.predict(
              X_test, sensitive_features=race_test, random_state=1
          )
      ).any()
  
file_path: fairlearn/test/unit/test_random_state.py
imports: import pandas
  from sklearn.datasets import fetch_openml
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  from fairlearn.postprocessing import ThresholdOptimizer
  from fairlearn.reductions import EqualizedOdds, ExponentiatedGradient
label_desc: Use image node_id 1 for calling a global function with example usage: test_random_state_threshold_optimizer() without return types