docstr: This test is mainly for particular use cases and can be useful for future debugging
docstr_tok: ["This","test","is","mainly","for","particular","use","cases","and","can","be","useful","for","future","debugging"]
code: def test_asymetric_dilation_and_kernel_size(self) -> None:
      """
      This test is mainly for particular use cases and can be useful for future debugging
      """
      x = torch.randn(1, 1, 8, 1)
      layer = nn.Conv2d(
          1, 1, kernel_size=(3, 1), groups=1, dilation=(3, 1), bias=None
      )
  
      m = GradSampleModule(layer)
  
      y = m(x)
      backprops = torch.randn(*y.shape)
      y.backward(backprops)
  
      self.assertLess(
          torch.norm(
              m._module.weight.grad_sample[0] - m._module.weight.grad
          ),
          1e-7,
      )
  
file_path: opacus/opacus/tests/grad_samples/conv2d_test.py
imports: from typing import Callable
  import hypothesis.strategies
  import torch
  import torch.nn
  from hypothesis import given, settings
  from opacus.grad_sample.conv import convolution2d_backward_as_a_convolution
  from opacus.grad_sample.grad_sample_module import GradSampleModule
  from opacus.utils.tensor_utils import unfold2d
  from torch.testing import assert_close
  from .common import GradSampleHooks_test, expander, shrinker
label_desc: Use image node_id 3 for calling the Conv2d_test obj's underlying member method code with example usage: obj.test_asymetric_dilation_and_kernel_size() without return types