code: def test_unproject_normalized(self, device, dtype):
      # this is for normalize_points=True
      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(
          [
              [
                  [
                      [0.0000, 1.4142, 1.7889],
                      [0.0000, 1.1547, 1.6330],
                      [0.0000, 0.8165, 1.3333],
                      [0.0000, 0.6030, 1.0690],
                  ],
                  [
                      [0.0000, 0.0000, 0.0000],
                      [1.4142, 1.1547, 0.8165],
                      [1.7889, 1.6330, 1.3333],
                      [1.8974, 1.8091, 1.6036],
                  ],
                  [
                      [2.0000, 1.4142, 0.8944],
                      [1.4142, 1.1547, 0.8165],
                      [0.8944, 0.8165, 0.6667],
                      [0.6325, 0.6030, 0.5345],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      points3d = kornia.geometry.depth.depth_to_3d(
          depth, camera_matrix, normalize_points=True
      )
      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 7 for calling the TestDepthTo3d obj's underlying member method code with example usage: obj.test_unproject_normalized(device, dtype) without return types