code: def test_opencv_all_coeff(self, device, dtype):
      # Test using 14 distortion coefficients
      pts = torch.tensor(
          [
              [1028.0374, 788.7520],
              [1025.1218, 716.8726],
              [1022.1792, 645.1857],
          ],
          device=device,
          dtype=dtype,
      )
  
      K = torch.tensor(
          [
              [1.7315e03, 0.0000e00, 6.2289e02],
              [0.0000e00, 1.7320e03, 5.3537e02],
              [0.0000e00, 0.0000e00, 1.0000e00],
          ],
          device=device,
          dtype=dtype,
      )
  
      dist = torch.tensor(
          [
              -5.6388e-02,
              2.3881e-01,
              8.3374e-02,
              2.0710e-03,
              7.1349e00,
              5.6335e-02,
              -3.1738e-01,
              4.9981e00,
              -4.0287e-03,
              -2.8246e-02,
              -8.6064e-02,
              1.5543e-02,
              -1.7322e-01,
              2.3154e-03,
          ],
          device=device,
          dtype=dtype,
      )
  
      # Expected output generated with OpenCV:
      # import cv2
      # ptsu_expected = cv2.undistortPoints(pts.numpy().reshape(-1,1,2), K.numpy(),
      #                               dist2.numpy(), None, None, K.numpy()).reshape(-1,2)
      ptsu_expected = torch.tensor(
          [
              [1030.8245, 786.3807],
              [1027.5505, 715.0732],
              [1024.2753, 644.0319],
          ],
          device=device,
          dtype=dtype,
      )
      ptsu = undistort_points(pts, K, dist)
      assert_close(ptsu, ptsu_expected, rtol=1e-4, atol=1e-4)
  
      new_K = K * 2
      new_K[2, 2] = 1
      # Expected output generated with OpenCV:
      # import cv2
      # ptsu_expected = cv2.undistortPoints(pts.numpy().reshape(-1,1,2), K.numpy(),
      #                                    dist.numpy(), None, None, new_K.numpy()).reshape(-1,2)
      print(ptsu_expected)
      ptsu_expected = 2 * torch.tensor(
          [
              [1030.8245, 786.3807],
              [1027.5505, 715.0732],
              [1024.2753, 644.0319],
          ],
          device=device,
          dtype=dtype,
      )
      print(ptsu_expected)
      ptsu = undistort_points(pts, K, dist, new_K)
      assert_close(ptsu, ptsu_expected, rtol=1e-4, atol=1e-4)
  
imports: import pytest
  import torch
  from kornia.geometry.calibration.undistort import undistort_image, undistort_points
  from kornia.testing import assert_close, gradcheck
label_desc: Use image node_id 5 for calling the TestUndistortPoints obj's underlying member method code with example usage: obj.test_opencv_all_coeff(device, dtype) without return types