code: def learn(self, obs, actions, advantages, target_values):
      obs = torch.FloatTensor(obs).to(self.device)
      actions = torch.LongTensor(actions).to(self.device)
      advantages = torch.FloatTensor(advantages).to(self.device)
      target_values = torch.FloatTensor(target_values).to(self.device)
  
      lr = self.lr_scheduler.step(step_num=obs.shape[0])
      entropy_coeff = self.entropy_coeff_scheduler.step()
      total_loss, pi_loss, vf_losss, entropy = self.alg.learn(
          obs, actions, advantages, target_values, lr, entropy_coeff
      )
  
      return (
          total_loss.cpu().detach().numpy(),
          pi_loss.cpu().detach().numpy(),
          vf_losss.cpu().detach().numpy(),
          entropy.cpu().detach().numpy(),
          lr,
          entropy_coeff,
      )
  
file_path: PARL/benchmark/torch/a2c/atari_agent.py
imports: import torch
  import parl
  from parl.utils.scheduler import PiecewiseScheduler, LinearDecayScheduler
  import numpy
label_desc: Use image node_id 5 for calling the Agent obj's underlying member method code with example usage: obj.learn(obs, actions, advantages, target_values) and returns: total_loss,  pi_loss,  vf_losss,  entropy,  lr,  entropy_coeff