code: def choose_action(self, curr_state):
      if type(self.spaces.action) != BoxActionSpace:
          raise ValueError(
              "NAF 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, "main"
      )
      naf_head = self.networks["main"].online_network.output_heads[0]
      action_values = self.networks["main"].online_network.predict(
          tf_input_state, outputs=naf_head.mu, squeeze_output=False
      )
  
      # get the actual action to use
      action = self.exploration_policy.get_action(action_values)
  
      # get the internal values for logging
      outputs = [
          naf_head.mu,
          naf_head.Q,
          naf_head.L,
          naf_head.A,
          naf_head.V,
      ]
      result = self.networks["main"].online_network.predict(
          {**tf_input_state, "output_0_0": action_values},
          outputs=outputs,
      )
      mu, Q, L, A, V = result
  
      # store the q values statistics for logging
      self.q_values.add_sample(Q)
      self.l_values.add_sample(L)
      self.a_values.add_sample(A)
      self.mu_values.add_sample(mu)
      self.v_values.add_sample(V)
  
      action_info = ActionInfo(action=action, action_value=Q)
  
      return action_info
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import NAFHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters
  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
label_desc: Use image node_id 4 for calling the NAFAgent obj's underlying member method code with example usage: obj.choose_action(curr_state) and returns: action_info