code: def __init__(self, use_batchnorm=False):
      super().__init__(
          algorithm=DDPGAlgorithmParameters(),
          exploration=OUProcessParameters(),
          memory=EpisodicExperienceReplayParameters(),
          networks=OrderedDict(
              [
                  (
                      "actor",
                      DDPGActorNetworkParameters(
                          use_batchnorm=use_batchnorm
                      ),
                  ),
                  (
                      "critic",
                      DDPGCriticNetworkParameters(
                          use_batchnorm=use_batchnorm
                      ),
                  ),
              ]
          ),
      )
  
imports: import copy
  from typing import Union
  from collections import OrderedDict
  import numpy
  from rl_coach.agents.actor_critic_agent import ActorCriticAgent
  from rl_coach.agents.agent import Agent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import DDPGActorHeadParameters, DDPGVHeadParameters
  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, EnvironmentSteps
  from rl_coach.exploration_policies.ou_process import OUProcessParameters
  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 DDPGAgentParameters object from inherited base classes: AgentParameters with example: obj = DDPGAgentParameters(use_batchnorm)