code: def test_smoke(self, device, dtype):
      input_tensor = torch.tensor(
          [
              [[0.1, 1.0], [0.2, 0.1]],
              [[0.1, 0.8], [0.2, 0.5]],
              [[0.1, 0.3], [0.2, 0.8]],
          ],
          device=device,
          dtype=dtype,
      )
  
      # With rescale
      expected_tensor = torch.tensor(
          [
              [[0.1269, 1.0], [0.2537, 0.5400]],
              [[0.1269, 1.0], [0.2537, 0.5403]],
              [[0.1269, 1.0], [0.2538, 0.5403]],
          ],
          device=device,
          dtype=dtype,
      )
      actual = kornia.color.sepia(input_tensor, rescale=True)
  
      assert actual.shape[:] == (3, 2, 2)
      self.assert_close(actual, expected_tensor, rtol=1e-2, atol=1e-2)
  
      # Without rescale
      expected_tensor = torch.tensor(
          [
              [[0.1351, 1.0649], [0.2702, 0.5750]],
              [[0.1203, 0.9482], [0.2406, 0.5123]],
              [[0.0937, 0.7385], [0.1874, 0.3990]],
          ],
          device=device,
          dtype=dtype,
      )
  
      actual = kornia.color.sepia(input_tensor, rescale=False)
      assert actual.shape[:] == (3, 2, 2)
      self.assert_close(actual, expected_tensor, rtol=1e-2, atol=1e-2)
  
file_path: kornia/test/color/test_sepia.py
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import BaseTester
label_desc: Use image node_id 1 for calling the TestSepia obj's underlying member method code with example usage: obj.test_smoke(device, dtype) without return types