code: def __init__(self):
      super().__init__()
      self.input_embedders_parameters = {
          "observation": InputEmbedderParameters(
              activation_function="relu"
          )
      }
      self.middleware_parameters = FCMiddlewareParameters(
          activation_function="relu"
      )
      self.heads_parameters = [SACPolicyHeadParameters()]
      self.rescale_gradient_from_head_by_factor = [1]
      self.optimizer_type = "Adam"
      self.batch_size = 256
      self.async_training = False
      self.learning_rate = 0.0003
      self.create_target_network = False
      self.l2_regularization = 0
  
imports: from typing import Union
  import copy
  import numpy
  from collections import OrderedDict
  from rl_coach.agents.agent import Agent
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.architectures.head_parameters import SACQHeadParameters, SACPolicyHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, EmbedderScheme, MiddlewareScheme
  from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.spaces import BoxActionSpace
label_desc: Use image node_id 1 to create a new SACPolicyNetworkParameters object from inherited base classes: NetworkParameters with example: obj = SACPolicyNetworkParameters()