code: def test_dilated_2d_pattern(H, W, k):
      d = AP.dilated_2d_pattern(H, W, k)
      d = d.reshape(H, W, H, W)
  
      product_HW = itertools.product(range(H), range(W))
      product_kk = itertools.product(range(k), range(k))
      for h, w in product_HW:
          i = h % k
          j = w % k
          # every kth element is taken
          assert torch.all(d[h, w][i::k, j::k])
          for ii, jj in product_kk:
              if ii == i and jj == j:
                  continue
              # and the other elements are discarded
              assert torch.all(~d[h, w][ii::k, jj::k])
  
imports: import itertools
  import pytest
  import torch
  import xformers.components.attention.attention_patterns
  from xformers.components.attention.sparsity_config import BigBirdSparsityConfig, BSLongformerSparsityConfig, DenseSparsityConfig, FixedSparsityConfig, VariableSparsityConfig
label_desc: Use image node_id 13 for calling a global function with example usage: test_dilated_2d_pattern(H, W, k) without return types