code: def test_unproject_denormalized(self, device, dtype):
      # this is for default normalize_points=False
      depth = 2 * torch.tensor(
          [
              [
                  [
                      [1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      camera_matrix = torch.tensor(
          [[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]],
          device=device,
          dtype=dtype,
      )
  
      points3d_expected = torch.tensor(
          [
              [
                  [
                      [0.0, 2.0, 4.0],
                      [0.0, 2.0, 4.0],
                      [0.0, 2.0, 4.0],
                      [0.0, 2.0, 4.0],
                  ],
                  [
                      [0.0, 0.0, 0.0],
                      [2.0, 2.0, 2.0],
                      [4.0, 4.0, 4.0],
                      [6.0, 6.0, 6.0],
                  ],
                  [
                      [2.0, 2.0, 2.0],
                      [2.0, 2.0, 2.0],
                      [2.0, 2.0, 2.0],
                      [2.0, 2.0, 2.0],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      points3d = kornia.geometry.depth.depth_to_3d(
          depth, camera_matrix
      )  # default is normalize_points=False
      assert_close(points3d, points3d_expected, atol=1e-4, rtol=1e-4)
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import assert_close
label_desc: Use image node_id 6 for calling the TestDepthTo3d obj's underlying member method code with example usage: obj.test_unproject_denormalized(device, dtype) without return types