code: def test_gradcheck(self, kernel_type, device):
      batch_size, channels, ps = 1, 2, 13
      patches = torch.rand(batch_size, channels, ps, ps).to(device)
      patches = utils.tensor_to_gradcheck_var(patches)  # to var
  
      def explicit_spatial_describe(patches, ps=13):
          ese = ExplicitSpacialEncoding(
              kernel_type=kernel_type, fmap_size=ps, in_dims=2
          )
          ese.to(device)
          return ese(patches)
  
      assert gradcheck(
          explicit_spatial_describe,
          (patches, ps),
          raise_exception=True,
          nondet_tol=1e-4,
          fast_mode=True,
      )
  
imports: from math import pi
  import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia.testing
  from kornia.feature.mkd import COEFFS, EmbedGradients, ExplicitSpacialEncoding, MKDDescriptor, MKDGradients, SimpleKD, VonMisesKernel, Whitening, get_grid_dict, get_kron_order, spatial_kernel_embedding
  from kornia.testing import assert_close
label_desc: Use image node_id 5 for calling the TestExplicitSpacialEncoding obj's underlying member method code with example usage: obj.test_gradcheck(kernel_type, device) and returns: ese