code: def create_housing_data(create_small_dataset=True):
      """Create California housing dataset for regression.
  
      param create_small_dataset: Whether to create a small dataset or not.
      type create_small_dataset: bool
      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 California housing dataset
      housing = fetch_california_housing()
      # Split data into train and test
      if create_small_dataset:
          x_train, x_test, y_train, y_test = train_test_split(
              housing.data,
              housing.target,
              train_size=500,
              test_size=50,
              random_state=7,
          )
      else:
          x_train, x_test, y_train, y_test = train_test_split(
              housing.data,
              housing.target,
              test_size=0.2,
              random_state=7,
          )
      return x_train, x_test, y_train, y_test, housing.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 2 for calling a global function with example usage: create_housing_data(create_small_dataset) and returns: x_train,  x_test,  y_train,  y_test,  housing