code: def test_gradcheck(self, batch_shape, device, dtype):
      # generate input data
      eye_size = 3  # identity 3x3
  
      # create checkerboard
      patch_src = torch.rand(batch_shape, device=device, dtype=dtype)
      patch_src = utils.tensor_to_gradcheck_var(patch_src)  # to var
  
      # create base homography
      batch_size, _, height, width = patch_src.shape
      dst_homo_src = utils.create_eye_batch(
          batch_size, eye_size, device=device, dtype=dtype
      )
      dst_homo_src = utils.tensor_to_gradcheck_var(
          dst_homo_src, requires_grad=False
      )  # to var
  
      # instantiate warper
      warper = kornia.geometry.transform.HomographyWarper(
          height, width, align_corners=True
      )
  
      # evaluate function gradient
      assert gradcheck(
          warper,
          (patch_src, dst_homo_src),
          nondet_tol=1e-8,
          raise_exception=True,
          fast_mode=True,
      )
  
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 12 for calling the TestHomographyWarper obj's underlying member method code with example usage: obj.test_gradcheck(batch_shape, device, dtype) without return types