code: def choose_action(self, curr_state):
      if not (
          isinstance(self.spaces.action, BoxActionSpace)
          or isinstance(self.spaces.action, GoalsSpace)
      ):
          raise ValueError(
              "DDPG works only for continuous control problems"
          )
      # convert to batch so we can run it through the network
      tf_input_state = self.prepare_batch_for_inference(
          curr_state, "actor"
      )
      if self.ap.algorithm.use_target_network_for_evaluation:
          actor_network = self.networks["actor"].target_network
      else:
          actor_network = self.networks["actor"].online_network
  
      action_values = actor_network.predict(tf_input_state).squeeze()
  
      action = self.exploration_policy.get_action(action_values)
  
      self.action_signal.add_sample(action)
  
      # get q value
      tf_input_state = self.prepare_batch_for_inference(
          curr_state, "critic"
      )
      action_batch = np.expand_dims(action, 0)
      if type(action) != np.ndarray:
          action_batch = np.array([[action]])
      tf_input_state["action"] = action_batch
      q_value = self.networks["critic"].online_network.predict(
          tf_input_state
      )[0]
      self.q_values.add_sample(q_value)
  
      action_info = ActionInfo(action=action, action_value=q_value)
  
      return action_info
  
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 5 for calling the DDPGAgent obj's underlying member method code with example usage: obj.choose_action(curr_state) and returns: action_info