code: def test_sync_weights_in_one_program(self):
      pred_program = fluid.Program()
      with fluid.program_guard(pred_program):
          last_hidden_states = fluid.data(
              name="last_hidden_states",
              shape=[batch_size, n_agents, rnn_hidden_dim],
          )
          target_last_hidden_states = fluid.data(
              name="target_last_hidden_states",
              shape=[batch_size, n_agents, rnn_hidden_dim],
          )
          obs_batch = fluid.data(
              name="obs_batch",
              shape=[batch_size, episode_len, n_agents, obs_shape],
          )
  
          hidden_states = last_hidden_states
          target_hidden_states = target_last_hidden_states
  
          # origin model
          for t in range(episode_len):
              obs = obs_batch[:, t, :, :]
              obs = fluid.layers.reshape(
                  obs, shape=(batch_size * n_agents, obs_shape)
              )
              hidden_states = layers.reshape(
                  hidden_states,
                  shape=(batch_size * n_agents, rnn_hidden_dim),
              )
              local_q, hidden_states = self.gru_model(
                  obs, hidden_states
              )
  
          # target model
          for t in range(episode_len):
              obs = obs_batch[:, t, :, :]
              obs = fluid.layers.reshape(
                  obs, shape=(batch_size * n_agents, obs_shape)
              )
              target_hidden_states = layers.reshape(
                  target_hidden_states,
                  shape=(batch_size * n_agents, rnn_hidden_dim),
              )
              target_local_q, target_hidden_states = self.target_model(
                  obs, target_hidden_states
              )
  
      self.executor.run(fluid.default_startup_program())
  
      N = 10
      random_last_hidden_states = np.zeros(
          (N, batch_size, n_agents, rnn_hidden_dim)
      ).astype("float32")
      random_obs_batch = np.random.random(
          size=(N, batch_size, episode_len, n_agents, obs_shape)
      ).astype("float32")
      for i in range(N):
          last_hidden_states_test = random_last_hidden_states[i]
          obs_batch_test = random_obs_batch[i]
          feed = {
              "last_hidden_states": last_hidden_states_test,
              "target_last_hidden_states": last_hidden_states_test,
              "obs_batch": obs_batch_test,
          }
          (
              fetch_local_q,
              fetch_hidden_states,
              fetch_target_local_q,
              fetch_target_hidden_states,
          ) = self.executor.run(
              pred_program,
              feed=feed,
              fetch_list=[
                  local_q,
                  hidden_states,
                  target_local_q,
                  target_hidden_states,
              ],
          )
          self.assertFalse(
              (
                  fetch_local_q.flatten()
                  == fetch_target_local_q.flatten()
              ).any()
          )
          self.assertFalse(
              (
                  fetch_hidden_states.flatten()
                  == fetch_target_hidden_states.flatten()
              ).any()
          )
  
      self.gru_model.sync_weights_to(self.target_model)
  
      random_last_hidden_states = np.zeros(
          (N, batch_size, n_agents, rnn_hidden_dim)
      ).astype("float32")
      random_obs_batch = np.random.random(
          size=(N, batch_size, episode_len, n_agents, obs_shape)
      ).astype("float32")
      for i in range(N):
          last_hidden_states_test = random_last_hidden_states[i]
          obs_batch_test = random_obs_batch[i]
          feed = {
              "last_hidden_states": last_hidden_states_test,
              "target_last_hidden_states": last_hidden_states_test,
              "obs_batch": obs_batch_test,
          }
          (
              fetch_local_q,
              fetch_hidden_states,
              fetch_target_local_q,
              fetch_target_hidden_states,
          ) = self.executor.run(
              pred_program,
              feed=feed,
              fetch_list=[
                  local_q,
                  hidden_states,
                  target_local_q,
                  target_hidden_states,
              ],
          )
  
          self.assertTrue(
              (
                  fetch_local_q.flatten()
                  == fetch_target_local_q.flatten()
              ).all()
          )
          self.assertTrue(
              (
                  fetch_hidden_states.flatten()
                  == fetch_target_hidden_states.flatten()
              ).all()
          )
  
file_path: PARL/parl/core/fluid/tests/gru_cell_test_fluid.py
imports: from parl import layers
  from copy import deepcopy
  import unittest
  import paddle.fluid
  import parl
  from parl.utils import get_gpu_count
  import numpy
label_desc: Use image node_id 4 for calling the GRUModelTest obj's underlying member method code with example usage: obj.test_sync_weights_in_one_program() without return types