code: def test_normalize_homography_general(
      self, batch_size, device, dtype
  ):
      # create input data
      dst_homo_src = torch.eye(4, device=device, dtype=dtype)
      dst_homo_src[..., 0, 0] = 0.5
      dst_homo_src[..., 1, 1] = 0.5
      dst_homo_src[..., 2, 2] = 2.0
      dst_homo_src[..., 0, 3] = 1.0
      dst_homo_src[..., 1, 3] = 2.0
      dst_homo_src[..., 2, 3] = 3.0
      dst_homo_src = dst_homo_src.expand(batch_size, -1, -1)
  
      norm_homo = kornia.geometry.conversions.normalize_homography3d(
          dst_homo_src, (2, 2, 5), (2, 2, 5)
      )
      res = torch.tensor(
          [
              [
                  [0.5, 0.0, 0.0, 0.0],
                  [0.0, 0.5, 0.0, 3.5],
                  [0.0, 0.0, 2.0, 7.0],
                  [0.0, 0.0, 0.0, 1.0],
              ]
          ],
          device=device,
          dtype=dtype,
      ).expand(batch_size, -1, -1)
      assert_close(norm_homo, res)
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import assert_close
  from kornia.utils.helpers import _torch_inverse_cast
label_desc: Use image node_id 2 for calling the TestHomographyWarper3D obj's underlying member method code with example usage: obj.test_normalize_homography_general(batch_size, device, dtype) without return types