code: def test_BaseXRegressor(generate_regression_data):
      y, X, treatment, tau, b, e = generate_regression_data()
  
      learner = BaseXRegressor(learner=XGBRegressor())
  
      # check the accuracy of the ATE estimation
      ate_p, lb, ub = learner.estimate_ate(
          X=X, treatment=treatment, y=y, p=e
      )
      assert (ate_p >= lb) and (ate_p <= ub)
      assert ape(tau.mean(), ate_p) < ERROR_THRESHOLD
  
      # check pre-train model
      ate_p_pt, lb_pt, ub_pt = learner.estimate_ate(
          X=X, treatment=treatment, y=y, p=e, pretrain=True
      )
      assert (ate_p_pt == ate_p) and (lb_pt == lb) and (ub_pt == ub)
  
      # check the accuracy of the CATE estimation with the bootstrap CI
      cate_p, _, _ = learner.fit_predict(
          X=X,
          treatment=treatment,
          y=y,
          p=e,
          return_ci=True,
          n_bootstraps=10,
      )
  
      auuc_metrics = pd.DataFrame(
          {
              "cate_p": cate_p.flatten(),
              "W": treatment,
              "y": y,
              "treatment_effect_col": tau,
          }
      )
  
      cumgain = get_cumgain(
          auuc_metrics,
          outcome_col="y",
          treatment_col="W",
          treatment_effect_col="tau",
      )
  
      # Check if the cumulative gain when using the model's prediction is
      # higher than it would be under random targeting
      assert cumgain["cate_p"].sum() > cumgain["Random"].sum()
  
imports: import numpy
  import pandas
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  from xgboost import XGBRegressor
  from xgboost import XGBClassifier
  from sklearn.ensemble import RandomForestRegressor
  from causalml.dataset import synthetic_data
  from causalml.inference.meta import BaseSLearner, BaseSRegressor, BaseSClassifier, LRSRegressor
  from causalml.inference.meta import BaseTLearner, BaseTRegressor, BaseTClassifier, XGBTRegressor, MLPTRegressor
  from causalml.inference.meta import BaseXLearner, BaseXClassifier, BaseXRegressor
  from causalml.inference.meta import BaseRLearner, BaseRClassifier, BaseRRegressor, XGBRRegressor
  from causalml.inference.meta import TMLELearner
  from causalml.inference.meta import BaseDRLearner
  from causalml.metrics import ape, get_cumgain
  from .const import RANDOM_SEED, N_SAMPLE, ERROR_THRESHOLD, CONTROL_NAME, CONVERSION
label_desc: Use image node_id 10 for calling a global function with example usage: test_BaseXRegressor(generate_regression_data) without return types