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
          ),
          "task_inputs": torch.randint(
              high=self.model_tester.vocab_size,
              size=(2, 77),
              device=torch_device,
          ).long(),
          "text_inputs": torch.randint(
              high=self.model_tester.vocab_size,
              size=(2, 6, 77),
              device=torch_device,
          ).long(),
          "mask_labels": torch.randn(
              (2, 150, *size), device=torch_device
          ),
          "class_labels": torch.zeros(
              2, 150, device=torch_device
          ).long(),
      }
  
      config = self.model_tester.get_config()
      config.is_training = True
  
      model = OneFormerForUniversalSegmentation(config).to(torch_device)
      outputs = model(**inputs)
      self.assertTrue(outputs.loss is not None)
  
imports: import copy
  import inspect
  import unittest
  import numpy
  from tests.test_modeling_common import floats_tensor
  from transformers import OneFormerConfig, is_torch_available, is_vision_available
  from transformers.testing_utils import require_torch, require_torch_accelerator, require_torch_fp16, 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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 17 for calling the OneFormerModelTest obj's underlying member method code with example usage: obj.test_model_with_labels() without return types