code: def learn(self, obs, act, reward):
      act = np.expand_dims(act, axis=-1)
      feed = {
          "obs": obs.astype("float32"),
          "act": act.astype("int64"),
          "reward": reward.astype("float32"),
      }
      cost = self.fluid_executor.run(
          self.learn_program, feed=feed, fetch_list=[self.cost]
      )[0]
      return cost
  
file_path: PARL/benchmark/fluid/QuickStart/cartpole_agent.py
imports: import numpy
  import paddle.fluid
  import parl
  from parl import layers
label_desc: Use image node_id 5 for calling the CartpoleAgent obj's underlying member method code with example usage: obj.learn(obs, act, reward) and returns: cost