code: def test_gradcheck(self, device):
      points_src_st = torch.rand(
          1,
          10,
          2,
          device=device,
          dtype=torch.float64,
          requires_grad=True,
      )
      points_src_end = torch.rand(
          1,
          10,
          2,
          device=device,
          dtype=torch.float64,
          requires_grad=True,
      )
  
      points_dst_st = torch.rand_like(points_src_st)
      points_dst_end = torch.rand_like(points_src_end)
      weights = utils.tensor_to_gradcheck_var(
          torch.ones_like(points_src_st)[..., 0]
      )
      ls1 = utils.tensor_to_gradcheck_var(
          torch.stack([points_src_st, points_src_end], axis=2)
      )
      ls2 = utils.tensor_to_gradcheck_var(
          torch.stack([points_dst_st, points_dst_end], axis=2)
      )
  
      assert gradcheck(
          find_homography_lines_dlt,
          (ls1, ls2, weights),
          rtol=1e-6,
          atol=1e-6,
          raise_exception=True,
          fast_mode=True,
      )
  
imports: import sys
  import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.geometry.homography import find_homography_dlt, find_homography_dlt_iterated, find_homography_lines_dlt, find_homography_lines_dlt_iterated, line_segment_transfer_error_one_way, oneway_transfer_error, sample_is_valid_for_homography, symmetric_transfer_error
  from kornia.testing import assert_close
  from kornia.utils._compat import torch_version_le
label_desc: Use image node_id 9 for calling the TestFindHomographyFromLinesDLT obj's underlying member method code with example usage: obj.test_gradcheck(device) without return types