code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ):
      entity_inputs_dict = {
          k: v for k, v in inputs_dict.items() if k.startswith("entity")
      }
      inputs_dict = {
          k: v
          for k, v in inputs_dict.items()
          if not k.startswith("entity")
      }
  
      inputs_dict = super()._prepare_for_class(
          inputs_dict, model_class, return_labels=return_labels
      )
      if model_class == LukeForMultipleChoice:
          entity_inputs_dict = {
              k: v.unsqueeze(1)
              .expand(-1, self.model_tester.num_choices, -1)
              .contiguous()
              if v.ndim == 2
              else v.unsqueeze(1)
              .expand(-1, self.model_tester.num_choices, -1, -1)
              .contiguous()
              for k, v in entity_inputs_dict.items()
          }
      inputs_dict.update(entity_inputs_dict)
  
      if model_class == LukeForEntitySpanClassification:
          inputs_dict["entity_start_positions"] = torch.zeros(
              (
                  self.model_tester.batch_size,
                  self.model_tester.entity_length,
              ),
              dtype=torch.long,
              device=torch_device,
          )
          inputs_dict["entity_end_positions"] = torch.ones(
              (
                  self.model_tester.batch_size,
                  self.model_tester.entity_length,
              ),
              dtype=torch.long,
              device=torch_device,
          )
  
      if return_labels:
          if model_class in (
              LukeForEntityClassification,
              LukeForEntityPairClassification,
              LukeForSequenceClassification,
              LukeForMultipleChoice,
          ):
              inputs_dict["labels"] = torch.zeros(
                  self.model_tester.batch_size,
                  dtype=torch.long,
                  device=torch_device,
              )
          elif model_class == LukeForEntitySpanClassification:
              inputs_dict["labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.entity_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
          elif model_class == LukeForTokenClassification:
              inputs_dict["labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
          elif model_class == LukeForMaskedLM:
              inputs_dict["labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
              inputs_dict["entity_labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.entity_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
  
      return inputs_dict
  
imports: import unittest
  from transformers import LukeConfig, is_torch_available
  from transformers.testing_utils import require_torch, 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 2 for calling the LukeModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict