code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ):
      inputs_dict = copy.deepcopy(inputs_dict)
      if return_labels:
          if issubclass(model_class, SplinterForPreTraining):
              inputs_dict["start_positions"] = torch.zeros(
                  self.model_tester.batch_size,
                  self.model_tester.num_questions,
                  dtype=torch.long,
                  device=torch_device,
              )
              inputs_dict["end_positions"] = torch.zeros(
                  self.model_tester.batch_size,
                  self.model_tester.num_questions,
                  dtype=torch.long,
                  device=torch_device,
              )
              inputs_dict["question_positions"] = torch.zeros(
                  self.model_tester.batch_size,
                  self.model_tester.num_questions,
                  dtype=torch.long,
                  device=torch_device,
              )
          elif issubclass(model_class, SplinterForQuestionAnswering):
              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,
              )
  
      return inputs_dict
  
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 2 for calling the SplinterModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict