code: def single_match(self, score_cols, pihat_threshold, caliper):
      matcher = NearestNeighborMatch(caliper=caliper, replace=True)
      df_matched = matcher.match(
          data=self.df[self.df[self.ps_col] < pihat_threshold],
          treatment_col=self.treatment_col,
          score_cols=score_cols,
      )
      return df_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 2 for calling the MatchOptimizer obj's underlying member method code with example usage: obj.single_match(score_cols, pihat_threshold, caliper) and returns: df_matched