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.
code: def __init__(self, config):
      # Make the space (for actions and observations) configurable.
      self.action_space = config.get(
          "parrot_shriek_range", gym.spaces.Box(-1.0, 1.0, shape=(1,))
      )
      # Since actions should repeat observations, their spaces must be the
      # same.
      self.observation_space = self.action_space
      self.cur_obs = None
      self.episode_len = 0
  
imports: import gymnasium
  from ray.rllib.algorithms.ppo import PPOConfig
label_desc: Use image node_id 1 to create a new ParrotEnv object from inherited base classes: gym with example: obj = ParrotEnv(config)