code: def test_sync_weights_in_one_program(self):
      obs = torch.randn(1, 4)
  
      N = 10
      random_obs = torch.randn(N, 4)
      for i in range(N):
          x = random_obs[i].view(1, -1)
          model_output = self.model.predict(x).item()
          target_model_output = self.target_model.predict(x).item()
          self.assertNotEqual(model_output, target_model_output)
  
      self.model.sync_weights_to(self.target_model)
  
      random_obs = torch.randn(N, 4)
      for i in range(N):
          x = random_obs[i].view(1, -1)
          model_output = self.model.predict(x).item()
          target_model_output = self.target_model.predict(x).item()
          self.assertEqual(model_output, target_model_output)
  
imports: import numpy
  import unittest
  import os
  from copy import deepcopy
  from collections import OrderedDict
  import torch
  import torch.nn
  import torch.optim
  from parl.utils import get_gpu_count
  import parl
label_desc: Use image node_id 2 for calling the ModelBaseTest obj's underlying member method code with example usage: obj.test_sync_weights_in_one_program() without return types