code: def test_averaged_model_skip_deepcopy(self) -> None:
      device = torch.device("cpu")
      dnn = torch.nn.Sequential(
          torch.nn.Conv2d(1, 5, kernel_size=3),
          torch.nn.ReLU(),
          torch.nn.MaxPool2d(kernel_size=2),
          torch.nn.BatchNorm2d(5, momentum=0.3),
          torch.nn.Conv2d(5, 2, kernel_size=3),
          torch.nn.ReLU(),
          torch.nn.Linear(5, 5),
          torch.nn.ReLU(),
          torch.nn.Linear(5, 10),
      ).to(device)
      averaged_dnn = AveragedModel(dnn, device, skip_deepcopy=True)
      # check both modules are pointing to same reference (since no deepcopy)
      self.assertEqual(id(dnn), id(averaged_dnn.module))
  
      averaged_dnn2 = AveragedModel(dnn, device)
      self.assertNotEqual(id(dnn), id(averaged_dnn2.module))
  
file_path: tnt/tests/utils/test_swa.py
imports: import itertools
  import unittest
  from copy import deepcopy
  from typing import List, Tuple
  import torch
  from torchtnt.utils.swa import AveragedModel
label_desc: Use image node_id 7 for calling the TestSWA obj's underlying member method code with example usage: obj.test_averaged_model_skip_deepcopy() without return types