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.__name__ in [
              "BrosForTokenClassification",
              "BrosSpadeELForTokenClassification",
          ]:
              inputs_dict["labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
              inputs_dict["bbox_first_token_mask"] = torch.ones(
                  [
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ],
                  dtype=torch.bool,
                  device=torch_device,
              )
          elif model_class.__name__ in [
              "BrosSpadeEEForTokenClassification"
          ]:
              inputs_dict["initial_token_labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
              inputs_dict["subsequent_token_labels"] = torch.zeros(
                  (
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ),
                  dtype=torch.long,
                  device=torch_device,
              )
              inputs_dict["bbox_first_token_mask"] = torch.ones(
                  [
                      self.model_tester.batch_size,
                      self.model_tester.seq_length,
                  ],
                  dtype=torch.bool,
                  device=torch_device,
              )
  
      return inputs_dict
  
imports: import copy
  import unittest
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import is_torch_available
  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 3 for calling the BrosModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict