code: def __init__(self):
      super().__init__()
      self.input_embedders_parameters = {
          "observation": InputEmbedderParameters(
              activation_function="tanh"
          )
      }
      self.middleware_parameters = FCMiddlewareParameters(
          activation_function="tanh"
      )
      self.heads_parameters = [VHeadParameters()]
      self.async_training = True
      self.l2_regularization = 0
      self.create_target_network = True
      self.batch_size = 128
  
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 PPOCriticNetworkParameters object from inherited base classes: NetworkParameters with example: obj = PPOCriticNetworkParameters()