code: def test_load_to_gpu_from_cpu(self):
      # This test will load net1's parameters onto the GPU, then use net2's
      # on the CPU to initialize net1's parameters.
      self.net1.cuda(device=0)
  
      # Verify the parameters are on the GPU
      assert self.net1.linear_1.weight.is_cuda is True
      assert self.net1.linear_1.bias.is_cuda is True
  
      # net2's parameters are already saved to CPU from setup_method()
      applicator = self._get_applicator("linear_1.*", self.temp_file)
      applicator(self.net1)
  
      # Verify the parameters are on the GPU
      assert self.net1.linear_1.weight.is_cuda is True
      assert self.net1.linear_1.bias.is_cuda is True
  
      # Make sure the weights are identical
      assert self._are_equal(
          self.net1.linear_1.cpu(), self.net2.linear_1
      )
  
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 13 for calling the TestPretrainedModelInitializer obj's underlying member method code with example usage: obj.test_load_to_gpu_from_cpu() without return types