code: def test_get_perspective_transform3d(self, batch_size, device, dtype):
      # generate input data
      # d_max, h_max, w_max = 16, 64, 32  # height, width
      # d = torch.ceil(d_max * torch.rand(batch_size, device=device, dtype=dtype))
      # h = torch.ceil(h_max * torch.rand(batch_size, device=device, dtype=dtype))
      # w = torch.ceil(w_max * torch.rand(batch_size, device=device, dtype=dtype))
  
      norm = torch.rand(batch_size, 8, 3, device=device, dtype=dtype)
      points_src = torch.rand_like(norm, device=device, dtype=dtype)
      points_dst = points_src + norm
  
      # compute transform from source to target
      dst_homo_src = (
          kornia.geometry.transform.get_perspective_transform3d(
              points_src, points_dst
          )
      )
  
      # TODO: get_perspective_transform3d seems to be correct since it would result in the
      # expected output for cropping volumes. Not sure what is going on here.
      assert_close(
          kornia.geometry.linalg.transform_points(
              dst_homo_src, points_src
          ),
          points_dst,
          rtol=1e-4,
          atol=1e-4,
      )
  
      # compute gradient check
      points_src = utils.tensor_to_gradcheck_var(points_src)  # to var
      points_dst = utils.tensor_to_gradcheck_var(points_dst)  # to var
      assert gradcheck(
          kornia.geometry.transform.get_perspective_transform3d,
          (points_src, points_dst),
          raise_exception=True,
      )
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.geometry.transform
  import kornia.testing
  from kornia.testing import assert_close
  from kornia.utils.helpers import _torch_inverse_cast
label_desc: Use image node_id 1 for calling the TestPerspectiveTransform3D obj's underlying member method code with example usage: obj.test_get_perspective_transform3d(batch_size, device, dtype) without return types