code: def _check_serial_actions(self):
      # serial actions
      if len(self.serial_actions) > 0:
          least_rho = 10.0
          best_idx = -1
          best_act = None
          for i, (act, sub_id) in enumerate(self.serial_actions):
              assert (
                  self.observation.time_before_cooldown_sub[sub_id] == 0
              )
  
              legal_action_vec = deepcopy(act.to_vect())
  
              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].astype(
                      "int"
                  )  # reference
                  action_topo[
                      np.where(sub_topo == -1)[0]
                  ] = 0  # done't change bus=-1
  
                  legal_action_vec[start:end] = action_topo
  
              legal_action = self.action_space.from_vect(
                  legal_action_vec
              )
  
              (
                  obs_simulate,
                  reward_simulate,
                  done_simulate,
                  info_simulate,
              ) = self.observation.simulate(legal_action)
              self.observation._obs_env._reset_to_orig_state()
  
              assert (
                  not info_simulate["is_illegal"]
                  and not info_simulate["is_ambiguous"]
              )
  
              if done_simulate:
                  continue
  
              assert not any(np.isnan(obs_simulate.rho))
  
              max_rho = np.max(obs_simulate.rho)
              if max_rho < least_rho:
                  least_rho = max_rho
                  best_idx = i
                  best_act = legal_action
          if best_idx != -1:
              del self.serial_actions[best_idx]
              return best_act
          else:
              self.serial_actions = []
              return None
  
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 10 for calling the RLAgent obj's underlying member method code with example usage: obj._check_serial_actions() and returns: best_act, None