code: def __init__(self):
      super().__init__()
      self.input_embedders_parameters = {
          "observation": InputEmbedderParameters()
      }
      self.middleware_parameters = FCMiddlewareParameters()
      self.heads_parameters = [DDPGActorHeadParameters(batchnorm=False)]
      self.optimizer_type = "Adam"
      self.adam_optimizer_beta2 = 0.999
      self.optimizer_epsilon = 1e-8
      self.batch_size = 100
      self.async_training = False
      self.learning_rate = 0.001
      self.create_target_network = True
      self.shared_optimizer = True
      self.scale_down_gradients_by_number_of_workers_for_sync_training = (
          False
      )
  
imports: import copy
  from typing import Union
  from collections import OrderedDict
  import numpy
  from rl_coach.agents.agent import Agent
  from rl_coach.agents.ddpg_agent import DDPGAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import DDPGActorHeadParameters, TD3VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, AgentParameters, EmbedderScheme
  from rl_coach.core_types import ActionInfo, TrainingSteps, Transition
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import BoxActionSpace, GoalsSpace
label_desc: Use image node_id 1 to create a new TD3ActorNetworkParameters object from inherited base classes: NetworkParameters with example: obj = TD3ActorNetworkParameters()