code: def test_multi_gpu_data_parallel_forward(self):
      from torch import nn
  
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
  
      # some params shouldn't be scattered by nn.DataParallel
      # so just remove them if they are present.
      blacklist_non_batched_params = [
          "head_mask",
          "decoder_head_mask",
          "cross_attn_head_mask",
      ]
      for k in blacklist_non_batched_params:
          inputs_dict.pop(k, None)
  
      # move input tensors to cuda:O
      for k, v in inputs_dict.items():
          if torch.is_tensor(v):
              inputs_dict[k] = v.to(0)
  
      for model_class in self.all_model_classes:
          # Skip this case since it will fail sometimes, as described above.
          if model_class == SplinterForPreTraining:
              continue
  
          model = model_class(config=config)
          model.to(0)
          model.eval()
  
          # Wrap model in nn.DataParallel
          model = nn.DataParallel(model)
          with torch.no_grad():
              _ = model(
                  **self._prepare_for_class(inputs_dict, model_class)
              )
  
imports: import copy
  import unittest
  from transformers import is_torch_available
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 11 for calling the SplinterModelTest obj's underlying member method code with example usage: obj.test_multi_gpu_data_parallel_forward() without return types