code: def test_inference_fp16(self):
      model = (
          OneFormerForUniversalSegmentation.from_pretrained(
              self.model_checkpoints
          )
          .to(torch_device, dtype=torch.float16)
          .eval()
      )
      processor = self.default_processor
      image = prepare_img()
      inputs = processor(image, ["semantic"], return_tensors="pt").to(
          torch_device, dtype=torch.float16
      )
  
      with torch.no_grad():
          _ = model(**inputs)
  
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 5 for calling the OneFormerModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_fp16() without return types