class_docstr: A parent class for R-learner classes.
  
  An R-learner estimates treatment effects with two machine learning models and the propensity score.
  
  Details of R-learner are available at Nie and Wager (2019) (https://arxiv.org/abs/1712.04912).
code: def __repr__(self):
      return (
          f"{self.__class__.__name__}\n"
          f"\toutcome_learner={self.model_mu.__repr__()}\n"
          f"\teffect_learner={self.model_tau.__repr__()}\n"
          f"\tpropensity_learner={self.model_p.__repr__()}"
      )
  
imports: from copy import deepcopy
  import logging
  import numpy
  import pandas
  from tqdm import tqdm
  from scipy.stats import norm
  from sklearn.model_selection import cross_val_predict, KFold, train_test_split
  from xgboost import XGBRegressor
  from causalml.inference.meta.base import BaseLearner
  from causalml.inference.meta.utils import check_treatment_vector, get_xgboost_objective_metric, convert_pd_to_np, get_weighted_variance
  from causalml.propensity import compute_propensity_score, ElasticNetPropensityModel
label_desc: Use image node_id 2 for calling the BaseRLearner obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str, self