code: def step(self, action):
      total_reward = 0.0
      done = None
      info = None
      self.observations_stack = []
      for i in range(self.frameskip):
          observation, reward, done, info = self.env.step(action)
          if i >= self.first_frame_to_max_over:
              self.observations_stack.append(observation)
          total_reward += reward
          if done:
              # deal with last state in episode
              if not self.observations_stack:
                  self.observations_stack.append(observation)
              break
  
      max_over_frames_observation = np.max(
          self.observations_stack, axis=0
      )
  
      return max_over_frames_observation, total_reward, done, info
  
imports: import gym
  import numpy
  from enum import IntEnum
  import scipy.ndimage
  from rl_coach.graph_managers.graph_manager import ScheduleParameters
  from rl_coach.utils import lower_under_to_upper, short_dynamic_import
  from typing import Dict, Any, Union
  from rl_coach.core_types import RunPhase, EnvironmentSteps
  from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace, ImageObservationSpace, VectorObservationSpace, PlanarMapsObservationSpace, TensorObservationSpace, StateSpace, RewardSpace
  from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
  from rl_coach.filters.reward.reward_clipping_filter import RewardClippingFilter
  from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
  from rl_coach.filters.observation.observation_stacking_filter import ObservationStackingFilter
  from rl_coach.filters.observation.observation_rgb_to_y_filter import ObservationRGBToYFilter
  from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
  from rl_coach.filters.filter import InputFilter
  import random
  from rl_coach.base_parameters import VisualizationParameters
  from rl_coach.logger import screen
label_desc: Use image node_id 3 for calling the MaxOverFramesAndFrameskipEnvWrapper obj's underlying member method code with example usage: obj.step(action) and returns: max_over_frames_observation,  total_reward,  done,  info