class_docstr: Environment in which an agent must learn to repeat the seen observations.
  
  Observations are float numbers indicating the to-be-repeated values,
  e.g. -1.0, 5.1, or 3.2.
  
  The action space is always the same as the observation space.
  
  Rewards are r=-abs(observation - action), for all steps.
docstr: Resets the episode and returns the initial observation of the new one.
docstr_tok: ["Resets","the","episode","and","returns","the","initial","observation","of","the","new","one","."]
code: def reset(self, *, seed=None, options=None):
      """Resets the episode and returns the initial observation of the new one."""
      # Reset the episode len.
      self.episode_len = 0
      # Sample a random number from our observation space.
      self.cur_obs = self.observation_space.sample()
      # Return initial observation.
      return self.cur_obs, {}
  
imports: import gymnasium
  from ray.rllib.algorithms.ppo import PPOConfig
label_desc: Use image node_id 2 for calling the ParrotEnv obj's underlying member method code with example usage: obj.reset() and returns: self,  dict