code: def test_toy(self, device):
      grads = torch.ones(1, 2, 6, 6).float().to(device)
      grads[0, 0, :, 3:] = 0
      emb_grads = EmbedGradients(patch_size=6, relative=True).to(device)
      out = emb_grads(grads)
      expected = torch.ones_like(out[0, 0, :, :3]).to(device)
      assert_close(
          out[0, 0, :, :3], expected * 0.3787, atol=1e-3, rtol=1e-3
      )
      assert_close(out[0, 0, :, 3:], expected * 0, 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 4 for calling the TestEmbedGradients obj's underlying member method code with example usage: obj.test_toy(device) without return types