code: def test_synthetic_sampson(self, device, dtype):
      calibrated_x1 = torch.tensor(
          [
              [
                  [0.0640, 0.7799],
                  [-0.2011, 0.2836],
                  [-0.1355, 0.2907],
                  [0.0520, 1.0086],
                  [-0.0361, 0.6533],
              ]
          ],
          device=device,
          dtype=dtype,
      )
      calibrated_x2 = torch.tensor(
          [
              [
                  [0.3470, -0.4274],
                  [-0.1818, -0.1281],
                  [-0.1766, -0.1617],
                  [0.4066, -0.0706],
                  [0.1137, 0.0363],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      weights = torch.ones_like(calibrated_x2)[..., 0]
      E_est = epi.essential.find_essential(
          calibrated_x1, calibrated_x2, weights
      )
      error = epi.sampson_epipolar_distance(
          calibrated_x1, calibrated_x2, E_est
      )
      assert_close(
          error,
          torch.zeros(
              (calibrated_x1.shape[:2]), device=device, dtype=dtype
          ),
          atol=1e-4,
          rtol=1e-4,
      )
  
imports: import pytest
  import test_common
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.geometry.epipolar
  from kornia.testing import assert_close
label_desc: Use image node_id 5 for calling the TestFindEssential obj's underlying member method code with example usage: obj.test_synthetic_sampson(device, dtype) without return types