class_docstr: Env in which the observation at timestep minus n must be repeated.
code: def step(self, action):
      obs = self.history[-(1 + self.delay)]
  
      reward = 0.0
      # Box: -abs(diff).
      if isinstance(self.action_space, Box):
          reward = -np.sum(np.abs(action - obs))
      # Discrete: +1.0 if exact match, -1.0 otherwise.
      if isinstance(self.action_space, Discrete):
          reward = 1.0 if action == obs else -1.0
  
      done = truncated = len(self.history) > self.episode_len
      return self._next_obs(), reward, done, truncated, {}
  
file_path: ray/python/ray/rllib/examples/env/repeat_after_me_env.py
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete
  import numpy
label_desc: Use image node_id 3 for calling the RepeatAfterMeEnv obj's underlying member method code with example usage: obj.step(action) and returns: self,  reward,  done,  truncated,  dict