code: def test_gradcheck(self, device):
      batch_size, channels, height, width = 1, 1, 40, 40
      patches = torch.rand(
          batch_size, channels, height, width, device=device
      )
      patches = utils.tensor_to_gradcheck_var(patches)  # to var
      laf = torch.tensor(
          [[[[5.0, 0.0, 26.0], [0.0, 5.0, 26.0]]]], device=device
      )
      laf = utils.tensor_to_gradcheck_var(laf)  # to var
      assert gradcheck(
          LAFAffineShapeEstimator(11).to(device),
          (laf, patches),
          raise_exception=True,
          rtol=1e-3,
          atol=1e-3,
          nondet_tol=1e-4,
          fast_mode=True,
      )
  
imports: import torch
  from torch.autograd import gradcheck
  import kornia.testing
  from kornia.feature.affine_shape import LAFAffineShapeEstimator, LAFAffNetShapeEstimator, PatchAffineShapeEstimator
  from kornia.testing import assert_close
label_desc: Use image node_id 7 for calling the TestLAFAffineShapeEstimator obj's underlying member method code with example usage: obj.test_gradcheck(device) without return types