code: def prepare_config_and_inputs(self):
      pixel_values = floats_tensor(
          [
              self.batch_size,
              self.num_channels,
              self.image_size[0],
              self.image_size[1],
          ]
      )
  
      labels = None
      if self.use_labels:
          # labels is a list of Dict (each Dict being the labels for a given example in the batch)
          labels = []
          for i in range(self.batch_size):
              target = {}
              target["class_labels"] = torch.randint(
                  high=self.num_labels,
                  size=(self.n_targets,),
                  device=torch_device,
              )
              target["boxes"] = torch.rand(
                  self.n_targets, 4, device=torch_device
              )
              labels.append(target)
  
      config = self.get_config()
  
      return config, pixel_values, labels
  
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 2 for calling the YolosModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  pixel_values,  labels