code: def test_jit(self, device, dtype):
      B, C, H, W = 2, 1, 13, 13
      patches = torch.rand(B, C, H, W, device=device, dtype=dtype)
      model = (
          VonMisesKernel(patch_size=13, coeffs=[0.38214156, 0.48090413])
          .to(patches.device, patches.dtype)
          .eval()
      )
      model_jit = torch.jit.script(
          VonMisesKernel(patch_size=13, coeffs=[0.38214156, 0.48090413])
          .to(patches.device, patches.dtype)
          .eval()
      )
      assert_close(model(patches), model_jit(patches))
  
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 7 for calling the TestVonMisesKernel obj's underlying member method code with example usage: obj.test_jit(device, dtype) without return types