code: def _reset_topology(self):
      if np.max(self.observation.rho) < 0.95:
          offset = 0
          for sub_id, sub_elem_num in enumerate(
              self.observation.sub_info
          ):
              sub_topo = self.sub_toop_dict[sub_id]
  
              if sub_id == 28:
                  sub28_topo = np.array([2.0, 1.0, 2.0, 1.0, 1.0])
                  if (
                      not np.all(
                          sub_topo.astype(int) == sub28_topo.astype(int)
                      )
                      and self.observation.time_before_cooldown_sub[
                          sub_id
                      ]
                      == 0
                  ):
                      sub_id = 28
                      act = self.action_space(
                          {
                              "set_bus": {
                                  "substations_id": [
                                      (sub_id, sub28_topo)
                                  ]
                              }
                          }
                      )
  
                      (
                          obs_simulate,
                          reward_simulate,
                          done_simulate,
                          info_simulate,
                      ) = self.observation.simulate(act)
                      self.observation._obs_env._reset_to_orig_state()
                      assert (
                          not info_simulate["is_illegal"]
                          and not info_simulate["is_ambiguous"]
                      )
                      if (
                          not done_simulate
                          and obs_simulate is not None
                          and not any(np.isnan(obs_simulate.rho))
                      ):
                          if np.max(obs_simulate.rho) < 0.95:
                              return act
                  continue
  
              if (
                  np.any(sub_topo == 2)
                  and self.observation.time_before_cooldown_sub[sub_id]
                  == 0
              ):
                  sub_topo = np.where(
                      sub_topo == 2, 1, sub_topo
                  )  # bus 2 to bus 1
                  sub_topo = np.where(
                      sub_topo == -1, 0, sub_topo
                  )  # don't do action in bus=-1
                  reconfig_sub = self.action_space(
                      {
                          "set_bus": {
                              "substations_id": [(sub_id, sub_topo)]
                          }
                      }
                  )
  
                  (
                      obs_simulate,
                      reward_simulate,
                      done_simulate,
                      info_simulate,
                  ) = self.observation.simulate(reconfig_sub)
                  self.observation._obs_env._reset_to_orig_state()
  
                  assert (
                      not info_simulate["is_illegal"]
                      and not info_simulate["is_ambiguous"]
                  )
  
                  if not done_simulate:
                      assert np.any(
                          obs_simulate.topo_vect
                          != self.observation.topo_vect
                      )  # have some impact
  
                  if (
                      not done_simulate
                      and obs_simulate is not None
                      and not any(np.isnan(obs_simulate.rho))
                  ):
                      if np.max(obs_simulate.rho) < 0.95:
                          return reconfig_sub
  
      if np.max(self.observation.rho) >= 1.0:
          sub_id = 28
          sub_topo = self.sub_toop_dict[sub_id]
          if (
              np.any(sub_topo == 2)
              and self.observation.time_before_cooldown_sub[sub_id] == 0
          ):
              sub28_topo = np.array([1.0, 1.0, 1.0, 1.0, 1.0])
              act = self.action_space(
                  {
                      "set_bus": {
                          "substations_id": [(sub_id, sub28_topo)]
                      }
                  }
              )
  
              (
                  obs_simulate,
                  reward_simulate,
                  done_simulate,
                  info_simulate,
              ) = self.observation.simulate(act)
              self.observation._obs_env._reset_to_orig_state()
              assert (
                  not info_simulate["is_illegal"]
                  and not info_simulate["is_ambiguous"]
              )
              if (
                  not done_simulate
                  and obs_simulate is not None
                  and not any(np.isnan(obs_simulate.rho))
              ):
                  if np.max(obs_simulate.rho) < 0.99:
                      return act
  
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 13 for calling the RLAgent obj's underlying member method code with example usage: obj._reset_topology() and returns: act, reconfig_sub, act