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(), PPOHeadParameters()]
      self.batch_size = 64
      self.optimizer_type = "Adam"
      self.clip_gradients = None
      self.use_separate_networks_per_head = True
      self.async_training = False
      self.l2_regularization = 0
  
      # The target network is used in order to freeze the old policy, while making updates to the new one
      # in train_network()
      self.create_target_network = True
      self.shared_optimizer = True
      self.scale_down_gradients_by_number_of_workers_for_sync_training = (
          True
      )
  
imports: import copy
  import math
  from collections import OrderedDict
  from random import shuffle
  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
  from rl_coach.core_types import EnvironmentSteps, Batch, EnvResponse, StateType
  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.schedules import ConstantSchedule
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
label_desc: Use image node_id 1 to create a new ClippedPPONetworkParameters object from inherited base classes: NetworkParameters with example: obj = ClippedPPONetworkParameters()