code: def search_best_match(self, df):
      self.df = df
  
      self.original_stats = {}
      for col, trans in self.dev_cols_transformations.items():
          self.original_stats[col] = trans(
              self.df[self.df[self.treatment_col] == 1][col]
          )
  
      # search best max pihat
      if self.verbose:
          logger.info("SEARCHING FOR BEST PIHAT")
      score_cols = [self.ps_col]
      caliper = self.caliper_range[-1]
      for pihat_threshold in self.max_pihat_range:
          self.match_and_check(score_cols, pihat_threshold, caliper)
  
      # search best score_cols
      if self.verbose:
          logger.info("SEARCHING FOR BEST SCORE_COLS")
      pihat_threshold = self.best_params["pihat"]
      caliper = self.caliper_range[int(self.caliper_range.shape[0] / 2)]
      score_cols = [self.ps_col]
      while not self.pass_all:
          if len(self.cols_to_fix) == 0:
              break
          elif (
              np.intersect1d(self.cols_to_fix, score_cols).shape[0] > 0
          ):
              break
          else:
              score_cols.append(self.cols_to_fix[0])
              self.match_and_check(score_cols, pihat_threshold, caliper)
  
      # search best caliper
      if self.verbose:
          logger.info("SEARCHING FOR BEST CALIPER")
      score_cols = self.best_params["score_cols"]
      pihat_threshold = self.best_params["pihat"]
      for caliper in self.caliper_range:
          self.match_and_check(score_cols, pihat_threshold, caliper)
  
      # summarize
      if self.verbose:
          logger.info(
              "\n-----\nBest params are:\n{}".format(self.best_params)
          )
  
      return self.best_matched
  
imports: import argparse
  import logging
  import sys
  import numpy
  import pandas
  from sklearn.neighbors import NearestNeighbors
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils import check_random_state
label_desc: Use image node_id 5 for calling the MatchOptimizer obj's underlying member method code with example usage: obj.search_best_match(df) and returns: self