code: def create_energy_data():
      """Create energy efficiency dataset for regression.
  
      return: Tuple of x_train, x_test, y_train, y_test, feature_names.
      rtype: Tuple of numpy.ndarray, numpy.ndarray, numpy.ndarray,
                numpy.ndarray, list
      """
      # Import energy data
      energy_data = retrieve_dataset(
          "energyefficiency2012_data.train.csv"
      )
      # Get the Y1 column
      target = energy_data.iloc[:, len(energy_data.columns) - 2]
      energy_data = energy_data.iloc[:, : len(energy_data.columns) - 3]
      feature_names = energy_data.columns.values
      # Split data into train and test
      x_train, x_test, y_train, y_validation = train_test_split(
          energy_data, target, test_size=0.2, random_state=0
      )
      return x_train, x_test, y_train, y_validation, feature_names
  
imports: from sklearn.datasets import fetch_california_housing, load_diabetes
  from sklearn.model_selection import train_test_split
  from rai_test_utils.utilities import retrieve_dataset
label_desc: Use image node_id 3 for calling a global function with example usage: create_energy_data() and returns: x_train,  x_test,  y_train,  y_validation,  feature_names