class_docstr: DQN that allows maxQ also backpropagate gradients.
code: def _compute_y_and_t(self, exp_batch):
      batch_state = exp_batch["state"]
  
      # Compute Q-values for current states
      if self.recurrent:
          qout, _ = self.model.n_step_forward(
              batch_state,
              exp_batch["recurrent_state"],
              output_mode="concat",
          )
      else:
          qout = self.model(batch_state)
  
      batch_actions = exp_batch["action"]
      batch_q = qout.evaluate_actions(batch_actions)[..., None]
  
      # Target values must also backprop gradients
      batch_q_target = self._compute_target_values(exp_batch)[..., None]
  
      return batch_q, scale_grad.scale_grad(
          batch_q_target, self.grad_scale
      )
  
file_path: chainerrl/chainerrl/agents/residual_dqn.py
imports: from chainerrl.agents.dqn import DQN
  from chainerrl.functions import scale_grad
label_desc: Use image node_id 4 for calling the ResidualDQN obj's underlying member method code with example usage: obj._compute_y_and_t(exp_batch) and returns: batch_q,  scale_grad