code: def _create_and_check_torchscript(self, config, inputs_dict):
      if not self.test_torchscript:
          return
  
      configs_no_init = _config_zero_init(
          config
      )  # To be sure we have no Nan
      configs_no_init.torchscript = True
      for model_class in self.all_model_classes:
          model = model_class(config=configs_no_init)
          model.to(torch_device)
          model.eval()
          inputs = self._prepare_for_class(inputs_dict, model_class)
  
          try:
              model.config.use_cache = False  # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
              input_features = inputs["input_features"]
              decoder_input_ids = inputs["decoder_input_ids"]
              decoder_attention_mask = inputs["decoder_attention_mask"]
              traced_model = torch.jit.trace(
                  model,
                  (
                      input_features,
                      decoder_input_ids,
                      decoder_attention_mask,
                  ),
              )
          except RuntimeError:
              self.fail("Couldn't trace module.")
  
          with tempfile.TemporaryDirectory() as tmp_dir_name:
              pt_file_name = os.path.join(
                  tmp_dir_name, "traced_model.pt"
              )
  
              try:
                  torch.jit.save(traced_model, pt_file_name)
              except Exception:
                  self.fail("Couldn't save module.")
  
              try:
                  loaded_model = torch.jit.load(pt_file_name)
              except Exception:
                  self.fail("Couldn't load module.")
  
          model.to(torch_device)
          model.eval()
  
          loaded_model.to(torch_device)
          loaded_model.eval()
  
          model_state_dict = model.state_dict()
          loaded_model_state_dict = loaded_model.state_dict()
  
          self.assertEqual(
              set(model_state_dict.keys()),
              set(loaded_model_state_dict.keys()),
          )
  
          models_equal = True
          for layer_name, p1 in model_state_dict.items():
              p2 = loaded_model_state_dict[layer_name]
              if p1.data.ne(p2.data).sum() > 0:
                  models_equal = False
  
          self.assertTrue(models_equal)
  
imports: import copy
  import inspect
  import os
  import tempfile
  import unittest
  from transformers import WhisperConfig
  from transformers.testing_utils import is_torch_available, require_torch, require_torchaudio, slow, torch_device
  from transformers.utils import cached_property
  from transformers.utils.import_utils import is_datasets_available
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
label_desc: Use image node_id 23 for calling the WhisperModelTest obj's underlying member method code with example usage: obj._create_and_check_torchscript(config, inputs_dict) without return types