code: def test_unfold2d(
      self,
      B: int,
      C: int,
      H: int,
      W: int,
      k_h: int,
      k_w: int,
      pad_h: int,
      pad_w: int,
      stride_h: int,
      stride_w: int,
      dilation_h: int,
      dilation_w: int,
  ):
      X = torch.randn(B, C, H, W)
      X_unfold_torch = torch.nn.functional.unfold(
          X,
          kernel_size=(k_h, k_w),
          padding=(pad_h, pad_w),
          stride=(stride_w, stride_h),
          dilation=(dilation_w, dilation_h),
      )
  
      X_unfold_opacus = unfold2d(
          X,
          kernel_size=(k_h, k_w),
          padding=(pad_h, pad_w),
          stride=(stride_w, stride_h),
          dilation=(dilation_w, dilation_h),
      )
  
      assert_close(X_unfold_torch, X_unfold_opacus, atol=0, rtol=0)
  
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 2 for calling the Conv2d_test obj's underlying member method code with example usage: obj.test_unfold2d(B, C, H, W, k_h, k_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w) without return types