code: def test_torchscript_device_change(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
          # BertForMultipleChoice behaves incorrectly in JIT environments.
          if model_class == DistilBertForMultipleChoice:
              return
  
          config.torchscript = True
          model = model_class(config=config)
  
          inputs_dict = self._prepare_for_class(
              inputs_dict, model_class
          )
          traced_model = torch.jit.trace(
              model,
              (
                  inputs_dict["input_ids"].to("cpu"),
                  inputs_dict["attention_mask"].to("cpu"),
              ),
          )
  
          with tempfile.TemporaryDirectory() as tmp:
              torch.jit.save(
                  traced_model, os.path.join(tmp, "traced_model.pt")
              )
              loaded = torch.jit.load(
                  os.path.join(tmp, "traced_model.pt"),
                  map_location=torch_device,
              )
              loaded(
                  inputs_dict["input_ids"].to(torch_device),
                  inputs_dict["attention_mask"].to(torch_device),
              )
  
imports: import os
  import tempfile
  import unittest
  from transformers import DistilBertConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 10 for calling the DistilBertModelTest obj's underlying member method code with example usage: obj.test_torchscript_device_change() without return types