code: def _compute_target_values(self, exp_batch):
      batch_next_state = exp_batch["next_state"]
  
      with chainer.using_config("train", False):
          if self.recurrent:
              next_qout, _ = self.model.n_step_forward(
                  batch_next_state,
                  exp_batch["next_recurrent_state"],
                  output_mode="concat",
              )
          else:
              next_qout = self.model(batch_next_state)
  
      if self.recurrent:
          target_next_qout, _ = self.target_model.n_step_forward(
              batch_next_state,
              exp_batch["next_recurrent_state"],
              output_mode="concat",
          )
      else:
          target_next_qout = self.target_model(batch_next_state)
  
      next_q_max = target_next_qout.evaluate_actions(
          next_qout.greedy_actions
      )
  
      batch_rewards = exp_batch["reward"]
      batch_terminal = exp_batch["is_state_terminal"]
      discount = exp_batch["discount"]
  
      return (
          batch_rewards + discount * (1.0 - batch_terminal) * next_q_max
      )
  
imports: import chainer
  from chainerrl.agents import dqn
label_desc: Use image node_id 1 for calling the DoubleDQN obj's underlying member method code with example usage: obj._compute_target_values(exp_batch) and returns: unknown