code: def test_load_to_cpu_from_gpu(self):
      # This test will load net2's parameters onto the GPU, then use them to
      # initialize net1 on the CPU
      self.net2.cuda(device=0)
  
      # Verify the parameters are on the GPU
      assert self.net2.linear_1.weight.is_cuda is True
      assert self.net2.linear_1.bias.is_cuda is True
  
      temp_file = self.TEST_DIR / "gpu_weights.th"
      torch.save(self.net2.state_dict(), temp_file)
  
      applicator = self._get_applicator("linear_1.*", temp_file)
      applicator(self.net1)
  
      # Verify the parameters are on the CPU
      assert self.net1.linear_1.weight.is_cuda is False
      assert self.net1.linear_1.bias.is_cuda is False
  
      # Make sure the weights are identical
      assert self._are_equal(
          self.net1.linear_1, self.net2.linear_1.cpu()
      )
  
imports: from typing import Dict, Optional
  import os
  import tempfile
  import tarfile
  import pytest
  import torch
  from allennlp.nn import InitializerApplicator, Initializer
  from allennlp.nn.initializers import PretrainedModelInitializer
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.common.params import Params
label_desc: Use image node_id 12 for calling the TestPretrainedModelInitializer obj's underlying member method code with example usage: obj.test_load_to_cpu_from_gpu() without return types