code: def adjust_threshold(gain_df, threshold):
      if gain_df.shape[0] == 0:
          raise BaseException("ran out of features to prune!")
      banned_df = gain_df.loc[gain_df >= threshold].index
      banned_features = list(banned_df)
  
      if len(banned_features) == 0:
          if threshold < -100000000:  # FIXME: Hacked for regression
              raise BaseException("threshold already max!")
          elif threshold > 0.000001:
              threshold_new = threshold / 2
          elif threshold > 0:
              threshold_new = 0
          elif threshold == 0:
              threshold_new = -0.00000001
          elif (threshold < 0) and (threshold > -0.0001):
              threshold_new = threshold * 2
          else:
              threshold_new = gain_df.max()
  
          logger.log(10, "Adjusting threshold to %s" % threshold_new)
          return FeaturePruner.adjust_threshold(
              gain_df=gain_df, threshold=threshold_new
          )
      else:
          return threshold
  
file_path: autogluon/tabular/src/autogluon/tabular/tuning/feature_pruner.py
imports: import copy
  import logging
  import pandas
label_desc: Use image node_id 4 for calling the FeaturePruner obj's underlying member method code with example usage: obj.adjust_threshold(gain_df, threshold) and returns: FeaturePruner, threshold