code: def prepare_data(self, generate_regression_data) -> tuple:
      y, X, treatment, tau, b, e = generate_regression_data(mode=2)
      df = pd.DataFrame(X)
      feature_names = [f"feature_{i}" for i in range(X.shape[1])]
      df.columns = feature_names
      df["outcome"] = y
      df["treatment"] = treatment
      df["treatment_effect"] = tau
      self.df_train, self.df_test = train_test_split(
          df, test_size=self.test_size, random_state=RANDOM_SEED
      )
      X_train, X_test = (
          self.df_train[feature_names].values,
          self.df_test[feature_names].values,
      )
      y_train, y_test = (
          self.df_train["outcome"].values,
          self.df_test["outcome"].values,
      )
      treatment_train, treatment_test = (
          self.df_train["treatment"].values,
          self.df_test["treatment"].values,
      )
      return (
          X_train,
          X_test,
          y_train,
          y_test,
          treatment_train,
          treatment_test,
      )
  
imports: import multiprocessing
  from abc import abstractmethod
  import pandas
  import pytest
  from sklearn.model_selection import train_test_split
  from causalml.inference.tree import CausalTreeRegressor, CausalRandomForestRegressor
  from causalml.metrics import ape
  from causalml.metrics import qini_score
  from .const import RANDOM_SEED, ERROR_THRESHOLD
label_desc: Use image node_id 4 for calling the CausalTreeBase obj's underlying member method code with example usage: obj.prepare_data(generate_regression_data) and returns: X_train,  X_test,  y_train,  y_test,  treatment_train,  treatment_test