code: def fit_predict(
      self,
      X,
      treatment,
      y,
      p=None,
      return_ci=False,
      n_bootstraps=1000,
      bootstrap_size=10000,
      return_components=False,
      verbose=True,
  ):
      self.fit(X, treatment, y, p)
      return self.predict(
          X, treatment, y, p, return_components, verbose
      )
  
imports: from abc import ABCMeta, abstractclassmethod
  import logging
  import numpy
  import pandas
  from causalml.inference.meta.explainer import Explainer
  from causalml.inference.meta.utils import check_p_conditions, convert_pd_to_np
  from causalml.propensity import compute_propensity_score
label_desc: Use image node_id 3 for calling the BaseLearner obj's underlying member method code with example usage: obj.fit_predict(X, treatment, y, p, return_ci, n_bootstraps, bootstrap_size, return_components, verbose) and returns: self