code: def test_gradcheck(self, device, dtype):
      batch_size, channels, height, width = 1, 2, 3, 4
      aff_ab = (
          torch.eye(2, 3, device=device, dtype=dtype)[None] + 1e-6
      )  # 1x2x3
      img_b = torch.rand(
          batch_size,
          channels,
          height,
          width,
          device=device,
          dtype=dtype,
      )
      aff_ab = utils.tensor_to_gradcheck_var(aff_ab)  # to var
      img_b = utils.tensor_to_gradcheck_var(img_b)  # to var
      assert gradcheck(
          kornia.geometry.warp_affine,
          (img_b, aff_ab, (height, width)),
          raise_exception=True,
          fast_mode=True,
      )
  
imports: import sys
  import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import assert_close
  from kornia.utils._compat import torch_version, torch_version_lt
  from kornia.utils.helpers import _torch_inverse_cast
label_desc: Use image node_id 7 for calling the TestWarpAffine obj's underlying member method code with example usage: obj.test_gradcheck(device, dtype) without return types