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__ in [
              "ConditionalDetrForObjectDetection",
              "ConditionalDetrForSegmentation",
          ]:
              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,
                  )
                  target["masks"] = torch.ones(
                      self.model_tester.n_targets,
                      self.model_tester.min_size,
                      self.model_tester.max_size,
                      device=torch_device,
                      dtype=torch.float,
                  )
                  labels.append(target)
              inputs_dict["labels"] = labels
  
      return inputs_dict
  
imports: import inspect
  import math
  import unittest
  from transformers import ConditionalDetrConfig, ResNetConfig, is_torch_available, is_vision_available
  from transformers.testing_utils import require_timm, require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the ConditionalDetrModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict