class_docstr: Here we also overwrite some of the tests of test_modeling_common.py, as YOLOS does not use input_ids, inputs_embeds,
  attention_mask and seq_length.
class_docstr_tok: ["Here","we","also","overwrite","some","of","the","tests","of","test_modeling_common.py",",","as","YOLOS","does","not","use","input_ids",",","inputs_embeds",",","attention_mask","and","seq_length","."]
code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ):
      inputs_dict = super()._prepare_for_class(
          inputs_dict, model_class, return_labels=return_labels
      )
  
      if return_labels:
          if model_class.__name__ == "YolosForObjectDetection":
              labels = []
              for i in range(self.model_tester.batch_size):
                  target = {}
                  target["class_labels"] = torch.ones(
                      size=(self.model_tester.n_targets,),
                      device=torch_device,
                      dtype=torch.long,
                  )
                  target["boxes"] = torch.ones(
                      self.model_tester.n_targets,
                      4,
                      device=torch_device,
                      dtype=torch.float,
                  )
                  labels.append(target)
              inputs_dict["labels"] = labels
  
      return inputs_dict
  
imports: import unittest
  from transformers import YolosConfig
  from transformers.testing_utils import require_torch, require_vision, 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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the YolosModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict