code: def test_smoke(
      self,
      batch_size,
      guide_dim,
      input_dim,
      kernel_size,
      eps,
      device,
      dtype,
  ):
      H, W = 8, 16
      guide = torch.randn(
          batch_size, guide_dim, H, W, device=device, dtype=dtype
      )
      inp = torch.randn(
          batch_size, input_dim, H, W, device=device, dtype=dtype
      )
  
      # tensor eps -> with batch dim
      eps = torch.rand(batch_size, device=device, dtype=dtype)
      actual_A = guided_blur(guide, inp, kernel_size, eps)
      assert isinstance(actual_A, torch.Tensor)
      assert actual_A.shape == (batch_size, input_dim, H, W)
  
      # float and tuple sigmas -> same sigmas across batch
      eps_ = eps[0].item()
      actual_B = guided_blur(guide, inp, kernel_size, eps_)
      assert isinstance(actual_B, torch.Tensor)
      assert actual_B.shape == (batch_size, input_dim, H, W)
  
      self.assert_close(actual_A[0], actual_B[0])
  
      # fast guided filter
      actual_C = guided_blur(guide, inp, kernel_size, eps_, subsample=4)
      assert isinstance(actual_C, torch.Tensor)
      assert actual_C.shape == (batch_size, input_dim, H, W)
  
      # self-guidance
      actual_D = guided_blur(inp, inp, kernel_size, eps_)
      assert isinstance(actual_D, torch.Tensor)
      assert actual_D.shape == (batch_size, input_dim, H, W)
  
file_path: kornia/test/filters/test_guided.py
imports: import pytest
  import torch
  from kornia.filters import GuidedBlur, guided_blur
  from kornia.testing import BaseTester, tensor_to_gradcheck_var
  from kornia.utils._compat import torch_version
label_desc: Use image node_id 1 for calling the TestGuidedBlur obj's underlying member method code with example usage: obj.test_smoke(batch_size, guide_dim, input_dim, kernel_size, eps, device, dtype) without return types