code: def run_AdversarialFairness_classification(estimator):
      """Run classification test with AdversarialFairness."""
      random.seed(123)
  
      X, y = fld.fetch_adult(return_X_y=True)
  
      non_NaN_rows = ~X.isna().any(axis=1)
  
      X = X[non_NaN_rows]
      y = y[non_NaN_rows]
  
      sensitive_feature = X["sex"]
  
      def preprocess(X):
          if isinstance(X, pd.Series):
              X = X.to_frame()
          """Make the Transformer for a single dataframe."""
          ct = make_column_transformer(
              (
                  StandardScaler(),
                  make_column_selector(dtype_include=number),
              ),
              (
                  OneHotEncoder(
                      drop="if_binary", **compat._SPARSE_OUTPUT_FALSE
                  ),
                  make_column_selector(dtype_include="category"),
              ),
          )
          return ct.fit_transform(X)
  
      X = preprocess(X)
      y = preprocess(y)
      sensitive_feature = preprocess(sensitive_feature)
  
      (
          X_train,
          X_test,
          Y_train,
          Y_test,
          A_train,
          A_test,
      ) = train_test_split(
          X,
          y,
          sensitive_feature,
          test_size=0.2,
          random_state=12345,
          stratify=y,
      )
  
      estimator.epochs = 100
      estimator.batch_size = 2**10
      estimator.shuffle = True
      estimator.progress_updates = None
  
      estimator.fit(X_train, Y_train, sensitive_features=A_train)
  
      predictions = estimator.predict(X_test)
  
      dp_diff = demographic_parity_difference(
          Y_test, predictions, sensitive_features=A_test
      )
  
      accuracy = mean(predictions == Y_test)
  
      # This might give problems as it is a bit random (though we set a seed,
      # it may depend on other factors such as version).
      if not (accuracy > 0.8 and dp_diff < 0.2):
          logger.warning(
              "Training of AdversarialFairness is worse than usual."  # noqa
              + f"Accuracy {str(accuracy)} with a disparity difference of"  # noqa
              + f"{str(dp_diff)}."  # noqa
          )
      assert estimator is not None
  
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 5 for calling a global function with example usage: run_AdversarialFairness_classification(estimator) and returns: ct