code: def test_set_weights_between_different_models(self):
      model1 = TestModel()
      model2 = TestModel()
  
      N = 10
      random_obs = torch.randn(N, 4)
      for i in range(N):
          x = random_obs[i].view(1, -1)
          model1_output = model1.predict(x).item()
          model2_output = model2.predict(x).item()
          self.assertNotEqual(model1_output, model2_output)
  
      params = model1.get_weights()
      model2.set_weights(params)
  
      random_obs = torch.randn(N, 4)
      for i in range(N):
          x = random_obs[i].view(1, -1)
          model1_output = model1.predict(x).item()
          model2_output = model2.predict(x).item()
          self.assertEqual(model1_output, model2_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 10 for calling the ModelBaseTest obj's underlying member method code with example usage: obj.test_set_weights_between_different_models() without return types