code: def supervised_eval(self, obs, act, reward, next_obs, terminal):
      act = np.expand_dims(act, -1)
      reward = np.clip(reward, -1, 1)
      feed = {
          "obs": obs.astype("float32"),
          "act": act.astype("int32"),
          "reward": reward,
          "next_obs": next_obs.astype("float32"),
          "terminal": terminal,
      }
      cost = self.fluid_executor.run(
          self.supervised_eval_program,
          feed=feed,
          fetch_list=[self.supervised_cost],
      )[0]
      return cost
  
file_path: PARL/benchmark/fluid/offline-Q-learning/atari_agent.py
imports: import numpy
  import paddle.fluid
  import parl
  from parl import layers
label_desc: Use image node_id 6 for calling the AtariAgent obj's underlying member method code with example usage: obj.supervised_eval(obs, act, reward, next_obs, terminal) and returns: cost