code: def test_toy(self, device):
      patch = torch.ones(1, 1, 6, 6).float().to(device)
      patch[0, 0, :, 3:] = 0
      vm = VonMisesKernel(
          patch_size=6, coeffs=[0.38214156, 0.48090413]
      ).to(device)
      out = vm(patch)
      expected = torch.ones_like(out[0, 0, :, :]).to(device)
      assert_close(
          out[0, 0, :, :], expected * 0.6182, atol=1e-3, rtol=1e-3
      )
  
      expected = torch.Tensor(
          [0.3747, 0.3747, 0.3747, 0.6935, 0.6935, 0.6935]
      ).to(device)
      expected = expected.unsqueeze(0).repeat(6, 1)
      assert_close(out[0, 1, :, :], expected, atol=1e-3, rtol=1e-3)
  
      expected = torch.Tensor(
          [0.5835, 0.5835, 0.5835, 0.0000, 0.0000, 0.0000]
      ).to(device)
      expected = expected.unsqueeze(0).repeat(6, 1)
      assert_close(out[0, 2, :, :], expected, atol=1e-3, rtol=1e-3)
  
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 TestVonMisesKernel obj's underlying member method code with example usage: obj.test_toy(device) without return types