class_docstr: A parent class for T-learner regressor classes.
  
  A T-learner estimates treatment effects with two machine learning models.
  
  Details of T-learner are available at Kunzel et al. (2018) (https://arxiv.org/abs/1706.03461).
class_docstr_tok: ["A","parent","class","for","T-learner","regressor","classes",".","A","T-learner","estimates","treatment","effects","with","two","machine","learning","models",".","Details","of","T-learner","are","available","at","Kunzel","et","al",".","(","2018",")","(","https",":","\/\/arxiv.org\/abs\/1706.03461",")","."]
code: def __repr__(self):
      return "{}(model_c={}, model_t={})".format(
          self.__class__.__name__,
          self.model_c.__repr__(),
          self.model_t.__repr__(),
      )
  
imports: from copy import deepcopy
  import logging
  import numpy
  from packaging import version
  from scipy.stats import norm
  import sklearn
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.neural_network import MLPRegressor
  from tqdm import tqdm
  from xgboost import XGBRegressor
  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 BaseTLearner obj's underlying member method code with example usage: obj.__repr__() and returns: str