code: def calibrate(ps, treatment):
      """Calibrate propensity scores with logistic GAM.
  
      Ref: https://pygam.readthedocs.io/en/latest/api/logisticgam.html
  
      Args:
          ps (numpy.array): a propensity score vector
          treatment (numpy.array): a binary treatment vector (0: control, 1: treated)
  
      Returns:
          (numpy.array): a calibrated propensity score vector
      """
  
      gam = LogisticGAM(s(0)).fit(ps, treatment)
  
      return gam.predict_proba(ps)
  
imports: from abc import ABCMeta, abstractmethod
  import logging
  import numpy
  from pygam import LogisticGAM, s
  from sklearn.metrics import roc_auc_score
  from sklearn.linear_model import LogisticRegressionCV
  from sklearn.model_selection import StratifiedKFold, train_test_split
  import xgboost
label_desc: Use image node_id 1 for calling a global function with example usage: calibrate(ps, treatment) and returns: gam