code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ):
      inputs_dict = copy.deepcopy(inputs_dict)
  
      if return_labels:
          if model_class in get_values(
              MODEL_FOR_QUESTION_ANSWERING_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_PRETRAINING_MAPPING):
              # special case for models like BERT that use multi-loss training for PreTraining
              inputs_dict["labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
      return inputs_dict
  
imports: import copy
  import unittest
  import numpy
  from transformers import LxmertConfig, is_tf_available, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
label_desc: Use image node_id 1 for calling the LxmertModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict