code: def learn(self, obs, act, reward, next_obs, terminal, sample_weight):
      if self.global_step % self.update_target_steps == 0:
          self.alg.sync_target()
      self.global_step += 1
  
      act = np.expand_dims(act, -1)
      reward = np.clip(reward, -1, 1)
      feed = {
          "obs": obs.astype("float32"),
          "act": act.astype("int32"),
          "reward": reward.astype("float32"),
          "next_obs": next_obs.astype("float32"),
          "terminal": terminal.astype("bool"),
          "sample_weight": sample_weight.astype("float32"),
      }
      cost, delta = self.fluid_executor.run(
          self.learn_program,
          feed=feed,
          fetch_list=[self.cost, self.delta],
      )
      return cost, delta
  
file_path: PARL/benchmark/fluid/Prioritized_DQN/atari_agent.py
imports: import numpy
  import paddle.fluid
  import parl
  from parl import layers
label_desc: Use image node_id 5 for calling the AtariAgent obj's underlying member method code with example usage: obj.learn(obs, act, reward, next_obs, terminal, sample_weight) and returns: cost,  delta