code: def _take_action(self, action):
      action = self.action_space.clip_action_to_space(action)
  
      # We mimic the "action_repeat" mechanism of RobosuiteWrapper in Surreal.
      # Same concept as frame_skip, only returning the average reward across repeated actions instead
      # of the total reward.
      rewards = []
      for _ in range(self.frame_skip):
          obs, reward, done, info = self.env.step(action)
          rewards.append(reward)
          if done:
              break
      reward = np.mean(rewards)
      self.last_result = RobosuiteStepResult(obs, reward, done, info)
  
imports: from typing import Union, Dict, Any
  from enum import Enum, Flag, auto
  from copy import deepcopy
  import numpy
  import random
  from collections import namedtuple
  from rl_coach.base_parameters import Parameters, VisualizationParameters
  from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
  from rl_coach.spaces import BoxActionSpace, VectorObservationSpace, StateSpace, PlanarMapsObservationSpace
  import rl_coach.environments.robosuite.cube_exp
label_desc: Use image node_id 4 for calling the RobosuiteEnvironment obj's underlying member method code with example usage: obj._take_action(action) without return types