code: def _three_sub_action(self):
      predicted_rho = self._predict_combined_action_rho(
          self.observation
      )
      sorted_idx = np.argsort(predicted_rho[0])
  
      sub_ids = []
      for best_idx in sorted_idx:
          best_act = self.after_line56_or_line45_disconnect_actions[
              best_idx
          ]
          sub_ids = self.three_sub_action_to_sub_ids[best_idx]
  
          if not np.all(
              self.observation.time_before_cooldown_sub[sub_ids] == 0
          ):
              sub_ids = []
              continue
  
          legal_action_vec = deepcopy(best_act.to_vect())
          for sub_id in sub_ids:
              sub_topo = self.sub_toop_dict[sub_id]
              if np.any(sub_topo == -1):  # line disconnected
                  start, end = self.action_to_sub_topo[sub_id]
  
                  action_topo = legal_action_vec[start:end]
                  action_topo[
                      np.where(sub_topo == -1)[0]
                  ] = 0  # done't change bus=-1
  
                  legal_action_vec[start:end] = action_topo
  
          best_act = self.action_space.from_vect(legal_action_vec)
          break
  
      self.used_combine_actions = True
  
      self.serial_actions = []
      for sub_id in sub_ids:
          start, end = self.action_to_sub_topo[sub_id]
  
          action_topo = best_act.to_vect()[start:end]
  
          act = self.action_space({}).to_vect()
          act[start:end] = action_topo
  
          self.serial_actions.append(
              (self.action_space.from_vect(act), sub_id)
          )
  
      action = self._check_serial_actions()
  
      return action
  
imports: import torch
  import numpy
  import pickle
  import os
  from grid2op.Agent import BaseAgent
  from copy import deepcopy
  from powernet_model import CombinedActionModel, UnitaryActionModel
  from utils import FeatureProcessor, UnitaryFeatureProcessor
label_desc: Use image node_id 9 for calling the RLAgent obj's underlying member method code with example usage: obj._three_sub_action() and returns: action