code: def __init__(self, action_space):
      BaseAgent.__init__(self, action_space=action_space)
      self.simulate_times = 0
  
      self.combined_model_1 = CombinedActionModel()
      self.combined_model_2 = CombinedActionModel()
      self.unitary_model = UnitaryActionModel()
  
      unitary_actions_vec = np.load(
          os.path.join("saved_files", "v6_top500_unitary_actions.npz")
      )["actions"]
      self.unitary_actions = []
      for i in range(unitary_actions_vec.shape[0]):
          action = action_space.from_vect(unitary_actions_vec[i])
          self.unitary_actions.append(action)
  
      redispatch_actions_vec = np.load(
          os.path.join("saved_files", "redispatch_actions.npz")
      )["actions"]
      self.redispatch_actions = []
      for i in range(redispatch_actions_vec.shape[0]):
          action = action_space.from_vect(redispatch_actions_vec[i])
          self.redispatch_actions.append(action)
  
      with open(
          os.path.join("saved_files", "action_to_sub_id.pickle"), "rb"
      ) as f:
          self.action_to_sub_id = pickle.load(f)
  
      self.after_line56_or_line45_disconnect_actions = []
      self.three_sub_action_to_sub_ids = {}
  
      actions_vec = np.load(
          os.path.join("saved_files", "v10_merge_three_sub_actions.npz")
      )["actions"]
      for i in range(actions_vec.shape[0]):
          action = action_space.from_vect(actions_vec[i])
          self.after_line56_or_line45_disconnect_actions.append(action)
  
      with open(
          os.path.join(
              "saved_files", "three_sub_action_to_sub_ids.pickle"
          ),
          "rb",
      ) as f:
          self.three_sub_action_to_sub_ids = pickle.load(f)
  
      self.used_combine_actions = False
      self.redispatch_cnt = 0
      self.max_redispatch_cnt = 3
      self.serial_actions = []
  
      self.do_nothing_action = action_space({})
      self.action_space = action_space
  
      offset = 59
      self.action_to_sub_topo = {}
      for sub_id, sub_elem_num in enumerate(action_space.sub_info):
          self.action_to_sub_topo[sub_id] = (
              offset,
              offset + sub_elem_num,
          )
          offset += sub_elem_num
  
      self.observation = None
      self.feature_processor = FeatureProcessor(
          os.path.join("saved_files", "track1_scalar.npz")
      )
      self.unitary_feature_processor = UnitaryFeatureProcessor(
          os.path.join("saved_files", "track1_unitary_scalar.npz")
      )
  
      self.redispatch_months = set([3])
  
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 1 to create a new RLAgent object from inherited base classes: BaseAgent with example: obj = RLAgent(action_space)