code: def __init__(self):
      super().__init__(
          algorithm=PPOAlgorithmParameters(),
          exploration={
              DiscreteActionSpace: CategoricalParameters(),
              BoxActionSpace: AdditiveNoiseParameters(),
          },
          memory=EpisodicExperienceReplayParameters(),
          networks={
              "critic": PPOCriticNetworkParameters(),
              "actor": PPOActorNetworkParameters(),
          },
      )
  
imports: import copy
  from collections import OrderedDict
  from typing import Union
  import numpy
  from rl_coach.agents.actor_critic_agent import ActorCriticAgent
  from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import EnvironmentSteps, Batch
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.logger import screen
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
  from rl_coach.utils import force_list
label_desc: Use image node_id 1 to create a new PPOAgentParameters object from inherited base classes: AgentParameters with example: obj = PPOAgentParameters()