code: def _reset_redispatch(self):
      if np.max(self.observation.rho) < 1.0:
          # reset redispatch
          if not np.all(self.observation.target_dispatch == 0.0):
              gen_ids = np.where(self.observation.gen_redispatchable)[0]
              gen_ramp = self.observation.gen_max_ramp_up[gen_ids]
              changed_idx = np.where(
                  self.observation.target_dispatch[gen_ids] != 0.0
              )[0]
              redispatchs = []
              for idx in changed_idx:
                  target_value = self.observation.target_dispatch[
                      gen_ids
                  ][idx]
                  value = min(abs(target_value), gen_ramp[idx])
                  value = -1 * target_value / abs(target_value) * value
                  redispatchs.append((gen_ids[idx], value))
              act = self.action_space({"redispatch": redispatchs})
  
              (
                  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) < 1.0:
                      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 12 for calling the RLAgent obj's underlying member method code with example usage: obj._reset_redispatch() and returns: act