code: def test_simple_normalized(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,
      )
  
      normals_expected = torch.tensor(
          [
              [
                  [
                      [0.3432, 0.4861, 0.7628],
                      [0.2873, 0.4260, 0.6672],
                      [0.2284, 0.3683, 0.5596],
                      [0.1695, 0.2980, 0.4496],
                  ],
                  [
                      [0.3432, 0.2873, 0.2363],
                      [0.4861, 0.4260, 0.3785],
                      [0.8079, 0.7261, 0.6529],
                      [0.8948, 0.8237, 0.7543],
                  ],
                  [
                      [0.8743, 0.8253, 0.6019],
                      [0.8253, 0.7981, 0.6415],
                      [0.5432, 0.5807, 0.5105],
                      [0.4129, 0.4824, 0.4784],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      normals = kornia.geometry.depth.depth_to_normals(
          depth, camera_matrix, normalize_points=True
      )
      assert_close(normals, normals_expected, rtol=1e-3, atol=1e-3)
  
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 5 for calling the TestDepthToNormals obj's underlying member method code with example usage: obj.test_simple_normalized(device, dtype) without return types