code: def __init__(self):
      super().__init__(
          algorithm=SoftActorCriticAlgorithmParameters(),
          exploration=AdditiveNoiseParameters(),
          memory=ExperienceReplayParameters(),  # SAC doesnt use episodic related data
          # network wrappers:
          networks=OrderedDict(
              [
                  ("policy", SACPolicyNetworkParameters()),
                  ("q", SACCriticNetworkParameters()),
                  ("v", SACValueNetworkParameters()),
              ]
          ),
      )
  
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 SoftActorCriticAgentParameters object from inherited base classes: AgentParameters with example: obj = SoftActorCriticAgentParameters()