code: def _get_test_data():
      # fetch data from OpenML
      data = fetch_openml(data_id=42193, parser="auto")
      X = (
          pd.DataFrame(data["data"], columns=data["feature_names"])
          .drop(columns=["race_Caucasian", "c_charge_degree_F"])
          .astype(float)
      )
      y = data["target"].astype(int)
  
      # split the data in train-validation-test sets
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.20, random_state=0
      )
      race_train = X_train["race_African-American"]
      race_test = X_test["race_African-American"]
      return X_train, X_test, y_train, y_test, race_train, race_test
  
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 3 for calling a global function with example usage: _get_test_data() and returns: X_train,  X_test,  y_train,  y_test,  race_train,  race_test