code: def test_model_with_labels(self):
      size = (self.model_tester.min_size,) * 2
      inputs = {
          "pixel_values": torch.randn(
              (2, 3, *size), device=torch_device
          ),
          "mask_labels": torch.randn(
              (2, 10, *size), device=torch_device
          ),
          "class_labels": torch.zeros(
              2, 10, device=torch_device
          ).long(),
      }
  
      model = MaskFormerForInstanceSegmentation(MaskFormerConfig()).to(
          torch_device
      )
      outputs = model(**inputs)
      self.assertTrue(outputs.loss is not None)
  
imports: import inspect
  import unittest
  import numpy
  from tests.test_modeling_common import floats_tensor
  from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin
label_desc: Use image node_id 12 for calling the MaskFormerModelTest obj's underlying member method code with example usage: obj.test_model_with_labels() without return types