code: def fetch_adult():
      """Grab dataset for testing."""
      data = fld.fetch_adult()
      X = data.data.drop(labels=["sex"], axis=1)
      X = pd.get_dummies(X)
      Y = (data.target == ">50K") * 1
      A = data.data["sex"]
  
      le = LabelEncoder()
      Y = le.fit_transform(Y)
  
      le = LabelEncoder()
  
      sc = StandardScaler()
      X_scaled = sc.fit_transform(X)
      X_scaled = pd.DataFrame(X_scaled, columns=X.columns)
  
      (
          X_train,
          X_test,
          Y_train,
          Y_test,
          A_train,
          A_test,
      ) = train_test_split(
          X_scaled, Y, A, test_size=0.3, random_state=12345, stratify=Y
      )
  
      # Ensure indices are aligned
      X_train = X_train.reset_index(drop=True)
      A_train = A_train.reset_index(drop=True)
      X_test = X_test.reset_index(drop=True)
      A_test = A_test.reset_index(drop=True)
  
      return X_train, Y_train, A_train, X_test, Y_test, A_test
  
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 1 for calling a global function with example usage: fetch_adult() and returns: X_train,  Y_train,  A_train,  X_test,  Y_test,  A_test