code: def test_noncontiguous(self, batch_size, device, dtype):
      inp = torch.rand(3, 5, 5, device=device, dtype=dtype).expand(
          batch_size, -1, -1, -1
      )
  
      actual = blur_pool2d(inp, 3)
      assert actual.is_contiguous()
  
imports: import pytest
  import torch
  from kornia.filters import BlurPool2D, EdgeAwareBlurPool2D, MaxBlurPool2D, blur_pool2d, edge_aware_blur_pool2d, max_blur_pool2d
  from kornia.testing import BaseTester, tensor_to_gradcheck_var
label_desc: Use image node_id 4 for calling the TestBlurPool obj's underlying member method code with example usage: obj.test_noncontiguous(batch_size, device, dtype) without return types