code: def test_opencv_stereo(self, device, dtype):
      # Udistort stereo points with data given in two batches using 14 distortion coefficients
      pts = torch.tensor(
          [
              [
                  [1028.0374, 788.7520],
                  [1025.1218, 716.8726],
                  [1022.1792, 645.1857],
              ],
              [
                  [345.9135, 847.9113],
                  [344.0880, 773.9890],
                  [342.2381, 700.3029],
              ],
          ],
          device=device,
          dtype=dtype,
      )
  
      K = torch.tensor(
          [
              [
                  [3.3197e03, 0.0000e00, 6.1813e02],
                  [0.0000e00, 3.3309e03, 5.2281e02],
                  [0.0000e00, 0.0000e00, 1.0000e00],
              ],
              [
                  [1.9206e03, 0.0000e00, 6.1395e02],
                  [0.0000e00, 1.9265e03, 7.7164e02],
                  [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,
              ],
              [
                  1.4050e-03,
                  -3.0691e00,
                  -1.0209e-01,
                  -2.3687e-02,
                  -1.7082e02,
                  4.3593e-03,
                  -3.1904e00,
                  -1.7050e02,
                  1.7854e-02,
                  1.8999e-02,
                  9.9122e-02,
                  3.6675e-02,
                  3.0816e-03,
                  -5.7133e-02,
              ],
          ],
          device=device,
          dtype=dtype,
      )
  
      # Expected output generated with OpenCV:
      # import cv2
      # ptsu_expected1 = cv2.undistortPoints(pts[0].numpy().reshape(-1,1,2), K[0].numpy(),
      #                               dist[0].numpy(), None, None, K[0].numpy()).reshape(-1,2)
      # ptsu_expected2 = cv2.undistortPoints(pts[1].numpy().reshape(-1,1,2), K[1].numpy(),
      #                               dist[1].numpy(), None, None, K[1].numpy()).reshape(-1,2)
      ptsu_expected1 = torch.tensor(
          [
              [1029.3234, 785.4813],
              [1026.1599, 714.3689],
              [1023.02045, 643.5359],
          ],
          device=device,
          dtype=dtype,
      )
  
      ptsu_expected2 = torch.tensor(
          [
              [344.04456, 848.7696],
              [344.27606, 774.1254],
              [344.47018, 700.8522],
          ],
          device=device,
          dtype=dtype,
      )
  
      ptsu = undistort_points(pts, K, dist)
      assert_close(ptsu[0], ptsu_expected1, rtol=1e-4, atol=1e-4)
      assert_close(ptsu[1], ptsu_expected2, 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 6 for calling the TestUndistortPoints obj's underlying member method code with example usage: obj.test_opencv_stereo(device, dtype) without return types