code: def test_real_clean_8pt(self, device, dtype, data):
      torch.random.manual_seed(0)
      # generate input data
      data_dev = utils.dict_to(data, device, dtype)
      pts_src = data_dev["pts0"]
      pts_dst = data_dev["pts1"]
      # compute transform from source to target
      ransac = RANSAC(
          "fundamental", inl_th=0.5, max_iter=20, max_lo_iters=10
      ).to(device=device, dtype=dtype)
      fundamental_matrix, _ = ransac(pts_src, pts_dst)
      gross_errors = (
          sampson_epipolar_distance(
              pts_src[None],
              pts_dst[None],
              fundamental_matrix[None],
              squared=False,
          )
          > 1.0
      )
      assert gross_errors.sum().item() == 0
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia.testing
  from kornia.geometry import RANSAC, transform_points
  from kornia.geometry.epipolar import sampson_epipolar_distance
  from kornia.testing import assert_close
label_desc: Use image node_id 2 for calling the TestRANSACFundamental obj's underlying member method code with example usage: obj.test_real_clean_8pt(device, dtype, data) without return types