code: def test_args_kwargs(self):
      device = "cuda" if torch.cuda.is_available() else "cpu:0"
      inputs = torch.ones((1, 1, 3, 3)).to(device=device)
      t1 = torch.ones(1).to(device=device)
      t2 = torch.ones(1).to(device=device)
      roi_shape = (5, 5)
      sw_batch_size = 10
  
      def compute(data, test1, test2):
          return data + test1 + test2
  
      result = sliding_window_inference(
          inputs,
          roi_shape,
          sw_batch_size,
          compute,
          0.5,
          "constant",
          1.0,
          "constant",
          0.0,
          device,
          device,
          has_tqdm,
          None,
          None,
          None,
          0,
          False,
          t1,
          test2=t2,
      )
      expected = np.ones((1, 1, 3, 3)) + 2.0
      np.testing.assert_allclose(
          result.cpu().numpy(), expected, rtol=1e-4
      )
  
      result = SlidingWindowHoVerNetInferer(
          roi_shape,
          sw_batch_size,
          overlap=0.5,
          mode="constant",
          cval=-1,
          progress=has_tqdm,
      )(inputs, compute, t1, test2=t2)
      np.testing.assert_allclose(
          result.cpu().numpy(), expected, rtol=1e-4
      )
  
file_path: MONAI/tests/test_sliding_window_hovernet_inference.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.apps.pathology.inferers import SlidingWindowHoVerNetInferer
  from monai.data import MetaTensor
  from monai.inferers import sliding_window_inference
  from monai.utils import optional_import
  from tests.test_sliding_window_inference import TEST_CASES
label_desc: Use image node_id 5 for calling the TestSlidingWindowHoVerNetInference obj's underlying member method code with example usage: obj.test_args_kwargs() and returns: unknown