code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ):
      inputs_dict = copy.deepcopy(inputs_dict)
      if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
          inputs_dict = {
              k: v.unsqueeze(1)
              .expand(-1, self.model_tester.num_choices, -1)
              .contiguous()
              if isinstance(v, torch.Tensor) and v.ndim > 1
              else v
              for k, v in inputs_dict.items()
          }
      if return_labels:
          if model_class in get_values(
              MODEL_FOR_MULTIPLE_CHOICE_MAPPING
          ):
              inputs_dict["labels"] = torch.ones(
                  self.model_tester.batch_size,
                  dtype=torch.long,
                  device=torch_device,
              )
          elif model_class in get_values(
              MODEL_FOR_QUESTION_ANSWERING_MAPPING
          ):
              inputs_dict["start_positions"] = torch.zeros(
                  self.model_tester.batch_size,
                  dtype=torch.long,
                  device=torch_device,
              )
              inputs_dict["end_positions"] = torch.zeros(
                  self.model_tester.batch_size,
                  dtype=torch.long,
                  device=torch_device,
              )
          elif model_class in [
              *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
          ]:
              inputs_dict["labels"] = torch.zeros(
                  self.model_tester.batch_size,
                  dtype=torch.long,
                  device=torch_device,
              )
          elif model_class in [
              *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
          ]:
              inputs_dict["labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.text_seq_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
  
      return inputs_dict
  
imports: import copy
  import unittest
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the LayoutLMv3ModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict