code: def test_default_device(self, data_type):
      device = "cuda" if torch.cuda.is_available() else "cpu:0"
      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):
          return data + 1
  
      inputs.requires_grad = True
      result = sliding_window_inference(
          inputs, roi_shape, sw_batch_size, compute
      )
      self.assertTrue(result.requires_grad)
      np.testing.assert_string_equal(
          inputs.device.type, result.device.type
      )
      expected_val = np.ones((1, 3, 16, 15, 7), dtype=np.float32) + 1
      np.testing.assert_allclose(
          result.detach().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 3 for calling the TestSlidingWindowInference obj's underlying member method code with example usage: obj.test_default_device(data_type) and returns: unknown