code: def dilated_2d_pattern(H, W, k=2):
      """
      Returns a 2d pattern that samples 1 every k elements in the attention mask.
      Can be seen as a form of downsampling, where every pixel attends to a downsampled
      version of the input.
      """
      d_h = local_nd_distance(H, W, p=1, weights=(1, 0))
      d_w = local_nd_distance(H, W, p=1, weights=(0, 1))
      d = (d_h.floor() % k == 0) & (d_w.floor() % k == 0)
      return d
  
imports: import math
  from typing import List
  import numpy
  import torch
  from xformers.components.attention.sparsity_config import BigBirdSparsityConfig, BSLongformerSparsityConfig, FixedSparsityConfig, VariableSparsityConfig
label_desc: Use image node_id 18 for calling a global function with example usage: dilated_2d_pattern(H, W, k) and returns: d