code: def test_sync_weights_with_decay(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["fc1.weight"],
          updated_parameters["fc1.bias"],
          updated_parameters["fc2.weight"],
          updated_parameters["fc2.bias"],
          updated_parameters["fc3.weight"],
          updated_parameters["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)
          ).item()
  
          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(
              float(np.abs(real_target_outputs - out_np)), 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 4 for calling the ModelBaseTest obj's underlying member method code with example usage: obj.test_sync_weights_with_decay() without return types