code: def build_program(self):
      self.policy_predict_program = fluid.Program()
      self.policy_sample_program = fluid.Program()
      self.policy_learn_program = fluid.Program()
      self.value_predict_program = fluid.Program()
      self.value_learn_program = fluid.Program()
  
      with fluid.program_guard(self.policy_sample_program):
          obs = layers.data(
              name="obs", shape=[self.obs_dim], dtype="float32"
          )
          sampled_act = self.alg.sample(obs)
          self.policy_sample_output = [sampled_act]
  
      with fluid.program_guard(self.policy_predict_program):
          obs = layers.data(
              name="obs", shape=[self.obs_dim], dtype="float32"
          )
          means = self.alg.predict(obs)
          self.policy_predict_output = [means]
  
      with fluid.program_guard(self.policy_learn_program):
          obs = layers.data(
              name="obs", shape=[self.obs_dim], dtype="float32"
          )
          actions = layers.data(
              name="actions", shape=[self.act_dim], dtype="float32"
          )
          advantages = layers.data(
              name="advantages", shape=[1], dtype="float32"
          )
          if self.loss_type == "KLPEN":
              beta = layers.data(name="beta", shape=[], dtype="float32")
              loss, kl = self.alg.policy_learn(
                  obs, actions, advantages, beta
              )
          else:
              loss, kl = self.alg.policy_learn(obs, actions, advantages)
  
          self.policy_learn_output = [loss, kl]
  
      with fluid.program_guard(self.value_predict_program):
          obs = layers.data(
              name="obs", shape=[self.obs_dim], dtype="float32"
          )
          value = self.alg.value_predict(obs)
          self.value_predict_output = [value]
  
      with fluid.program_guard(self.value_learn_program):
          obs = layers.data(
              name="obs", shape=[self.obs_dim], dtype="float32"
          )
          val = layers.data(name="val", shape=[], dtype="float32")
          value_loss = self.alg.value_learn(obs, val)
          self.value_learn_output = [value_loss]
  
file_path: PARL/benchmark/fluid/PPO/mujoco_agent.py
imports: import numpy
  import parl
  from parl import layers
  from paddle import fluid
  from parl.utils import logger
label_desc: Use image node_id 2 for calling the MujocoAgent obj's underlying member method code with example usage: obj.build_program() without return types