code: def evaluate(self):
      untuned_score = self.score_in_iter[0]
      logger.debug("untuned_score: %s" % untuned_score)
      logger.debug("best_score: %s" % self.best_score)
      logger.debug("best_iteration: %s" % self.best_iteration)
  
      data_dict = {
          "score": self.score_in_iter,
          "feature_count": self.valid_feature_counts,
          "threshold": self.thresholds,
      }
      logger.debug(self.gain_dfs[self.best_iteration])
      z = pd.DataFrame(data=data_dict)
      # logger.debug(z)
      z_sorted = z.sort_values(by=["score"], ascending=False)
  
file_path: autogluon/tabular/src/autogluon/tabular/tuning/feature_pruner.py
imports: import copy
  import logging
  import pandas
label_desc: Use image node_id 2 for calling the FeaturePruner obj's underlying member method code with example usage: obj.evaluate() without return types