code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ):
      inputs_dict = super()._prepare_for_class(
          inputs_dict, model_class, return_labels=return_labels
      )
  
      if return_labels:
          if model_class.__name__ == "ViltForQuestionAnswering":
              inputs_dict["labels"] = torch.zeros(
                  self.model_tester.batch_size,
                  self.model_tester.num_labels,
                  device=torch_device,
              )
          elif model_class.__name__ in [
              "ViltForMaskedLM",
              "ViltForTokenClassification",
          ]:
              inputs_dict["labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
          elif (
              model_class.__name__
              == "ViltForImagesAndTextClassification"
          ):
              inputs_dict["labels"] = torch.zeros(
                  self.model_tester.batch_size,
                  dtype=torch.long,
                  device=torch_device,
              )
  
      return inputs_dict
  
imports: import unittest
  from datasets import load_dataset
  from packaging import version
  from transformers import ViltConfig, is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 1 for calling the ViltModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict