code: def build_program(self):
      self.pred_program = fluid.Program()
      self.learn_program = fluid.Program()
  
      with fluid.program_guard(self.pred_program):
          obs = layers.data(
              name="obs",
              shape=[CONTEXT_LEN, IMAGE_SIZE[0], IMAGE_SIZE[1]],
              dtype="float32",
          )
          self.value = self.alg.predict(obs)
  
      with fluid.program_guard(self.learn_program):
          obs = layers.data(
              name="obs",
              shape=[CONTEXT_LEN, IMAGE_SIZE[0], IMAGE_SIZE[1]],
              dtype="float32",
          )
          action = layers.data(name="act", shape=[1], dtype="int32")
          reward = layers.data(name="reward", shape=[], dtype="float32")
          next_obs = layers.data(
              name="next_obs",
              shape=[CONTEXT_LEN, IMAGE_SIZE[0], IMAGE_SIZE[1]],
              dtype="float32",
          )
          terminal = layers.data(
              name="terminal", shape=[], dtype="bool"
          )
          sample_weight = layers.data(
              name="sample_weight", shape=[1], dtype="float32"
          )
          self.cost, self.delta = self.alg.learn(
              obs, action, reward, next_obs, terminal, sample_weight
          )
  
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 2 for calling the AtariAgent obj's underlying member method code with example usage: obj.build_program() without return types