code: def test_jit(self, device, dtype):
      batch_size, channels, ps = 1, 1, 19
      patches = torch.rand(batch_size, channels, ps, ps).to(device)
      kt = "concat"
      wt = "lw"
      model = (
          MKDDescriptor(patch_size=ps, kernel_type=kt, whitening=wt)
          .to(patches.device, patches.dtype)
          .eval()
      )
      model_jit = torch.jit.script(
          MKDDescriptor(patch_size=ps, kernel_type=kt, whitening=wt)
          .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 TestMKDDescriptor obj's underlying member method code with example usage: obj.test_jit(device, dtype) without return types