code: def check_table_one(
      self, tableone, matched, score_cols, pihat_threshold, caliper
  ):
      # check if better than past runs
      smd_values = np.abs(
          tableone[tableone.index != "n"]["SMD"].astype(float)
      )
      num_cols_over_smd = (smd_values >= self.max_smd).sum()
      self.cols_to_fix = (
          smd_values[smd_values >= self.max_smd]
          .sort_values(ascending=False)
          .index.values
      )
      if self.user_col is None:
          num_users_per_group = (
              matched.reset_index()
              .groupby(self.treatment_col)["index"]
              .count()
              .min()
          )
      else:
          num_users_per_group = (
              matched.groupby(self.treatment_col)[self.user_col]
              .count()
              .min()
          )
      deviations = [
          np.abs(
              self.original_stats[col]
              / matched[matched[self.treatment_col] == 1][col].mean()
              - 1
          )
          for col in self.dev_cols_transformations.keys()
      ]
  
      score = num_cols_over_smd
      score += len(
          [
              col
              for col in self.smd_cols
              if smd_values.loc[col] >= self.max_smd
          ]
      )
      score += np.sum(
          [dev * 10 * self.dev_factor for dev in deviations]
      )
  
      # check if can be considered as best score
      if (
          score < self.best_score
          and num_users_per_group > self.min_users_per_group
      ):
          self.best_score = score
          self.best_params = {
              "score_cols": score_cols.copy(),
              "pihat": pihat_threshold,
              "caliper": caliper,
          }
          self.best_matched = matched.copy()
      if self.verbose:
          logger.info(
              "\tScore: {:.03f} (Best Score: {:.03f})\n".format(
                  score, self.best_score
              )
          )
  
      # check if passes all criteria
      self.pass_all = (
          (num_users_per_group > self.min_users_per_group)
          and (num_cols_over_smd == 0)
          and all(dev < self.max_deviation for dev in deviations)
      )
  
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 3 for calling the MatchOptimizer obj's underlying member method code with example usage: obj.check_table_one(tableone, matched, score_cols, pihat_threshold, caliper) without return types