class_docstr: A parent class for S-learner classes.
  An S-learner estimates treatment effects with one machine learning model.
  Details of S-learner are available at Kunzel et al. (2018) (https://arxiv.org/abs/1706.03461).
class_docstr_tok: ["A","parent","class","for","S-learner","classes",".","An","S-learner","estimates","treatment","effects","with","one","machine","learning","model",".","Details","of","S-learner","are","available","at","Kunzel","et","al",".","(","2018",")","(","https",":","\/\/arxiv.org\/abs\/1706.03461",")","."]
code: def __repr__(self):
      return "{}(model={})".format(
          self.__class__.__name__, self.model.__repr__()
      )
  
imports: import logging
  import numpy
  from tqdm import tqdm
  from scipy.stats import norm
  from sklearn.dummy import DummyRegressor
  import statsmodels.api
  from copy import deepcopy
  from causalml.inference.meta.base import BaseLearner
  from causalml.inference.meta.utils import check_treatment_vector, convert_pd_to_np
  from causalml.metrics import regression_metrics, classification_metrics
label_desc: Use image node_id 2 for calling the BaseSLearner obj's underlying member method code with example usage: obj.__repr__() and returns: str