code: def test_sync_weights_with_different_target_model(self):
      decay = 0.9
      updated_parameters = self._numpy_update(self.target_model, decay)
      (
          target_model_fc1_w,
          target_model_fc1_b,
          target_model_fc2_w,
          target_model_fc2_b,
          target_model_fc3_w,
          target_model_fc3_b,
      ) = (
          updated_parameters["actor.fc1.weight"],
          updated_parameters["actor.fc1.bias"],
          updated_parameters["actor.fc2.weight"],
          updated_parameters["actor.fc2.bias"],
          updated_parameters["actor.fc3.weight"],
          updated_parameters["actor.fc3.bias"],
      )
  
      self.model.sync_weights_to(self.target_model, decay)
  
      N = 10
      random_obs = np.random.randn(N, 4)
      for i in range(N):
          obs = np.expand_dims(random_obs[i], -1)  # 4, 1
          real_target_outputs = self.target_model.predict(
              torch.Tensor(obs).view(1, -1)
          )
  
          out_np = np.dot(target_model_fc1_w, obs) + np.expand_dims(
              target_model_fc1_b, -1
          )  # (256, 256)
          out_np = np.dot(target_model_fc2_w, out_np) + np.expand_dims(
              target_model_fc2_b, -1
          )
          out_np = np.dot(target_model_fc3_w, out_np) + np.expand_dims(
              target_model_fc3_b, -1
          )
          diff = (
              real_target_outputs.detach().numpy().sum() - out_np.sum()
          )
  
          self.assertLess(diff, 1e-5)
  
      decay = 0.8
  
      updated_parameters = self._numpy_update(self.target_model2, decay)
      (
          target_model_fc1_w,
          target_model_fc1_b,
          target_model_fc2_w,
          target_model_fc2_b,
          target_model_fc3_w,
          target_model_fc3_b,
      ) = (
          updated_parameters["actor.fc1.weight"],
          updated_parameters["actor.fc1.bias"],
          updated_parameters["actor.fc2.weight"],
          updated_parameters["actor.fc2.bias"],
          updated_parameters["actor.fc3.weight"],
          updated_parameters["actor.fc3.bias"],
      )
  
      self.model.sync_weights_to(self.target_model2, decay)
      random_obs = np.random.randn(N, 4)
      for i in range(N):
          obs = np.expand_dims(random_obs[i], -1)  # 4, 1
          real_target_outputs = (
              self.target_model2.predict(torch.Tensor(obs).view(1, -1))
              .detach()
              .numpy()
              .sum()
          )
  
          out_np = np.dot(target_model_fc1_w, obs) + np.expand_dims(
              target_model_fc1_b, -1
          )  # (256, 256)
          out_np = np.dot(target_model_fc2_w, out_np) + np.expand_dims(
              target_model_fc2_b, -1
          )
          out_np = np.dot(target_model_fc3_w, out_np) + np.expand_dims(
              target_model_fc3_b, -1
          )
  
          self.assertLess(
              np.abs(real_target_outputs - out_np.sum()), 1e-5
          )
  
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 7 for calling the ModelBaseTest obj's underlying member method code with example usage: obj.test_sync_weights_with_different_target_model() without return types