code: def test_sw_device(self, data_type, device, sw_device):
      inputs = data_type(torch.ones((3, 16, 15, 7))).to(device=device)
      inputs = list_data_collate([inputs])  # make a proper batch
      roi_shape = (4, 10, 7)
      sw_batch_size = 10
  
      def compute(data):
          self.assertEqual(data.device.type, sw_device or device)
          return data + torch.tensor(1, device=sw_device or device)
  
      result = sliding_window_inference(
          inputs,
          roi_shape,
          sw_batch_size,
          compute,
          sw_device=sw_device,
          device="cpu",
      )
      np.testing.assert_string_equal("cpu", result.device.type)
      expected_val = np.ones((1, 3, 16, 15, 7), dtype=np.float32) + 1
      np.testing.assert_allclose(result.cpu().numpy(), expected_val)
  
file_path: MONAI/tests/test_sliding_window_inference.py
imports: from __future__ import annotations
  import itertools
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.data.utils import list_data_collate
  from monai.inferers import SlidingWindowInferer, SlidingWindowInfererAdapt, sliding_window_inference
  from monai.utils import optional_import
  from tests.utils import TEST_TORCH_AND_META_TENSORS, skip_if_no_cuda, test_is_quick
label_desc: Use image node_id 4 for calling the TestSlidingWindowInference obj's underlying member method code with example usage: obj.test_sw_device(data_type, device, sw_device) and returns: unknown