code: def swin_attention_pattern(H, W, window_size, shift_size=0):
      assert H % window_size == 0
      assert W % window_size == 0
      assert (
          0 <= shift_size < window_size
      ), "shift_size must in 0-window_size"
  
      # input grid
      i, j = _generate_nd_grid(H, W)
      i, j = i + 0.5, j + 0.5
  
      # anchors grid
      # if shift is present, add extra element to the grid
      # to account for the uneven partitioning
      extra = int(shift_size % window_size != 0)
      grid_h = H // window_size + extra
      grid_w = W // window_size + extra
  
      ii, jj = _generate_nd_grid(grid_h, grid_w)
      # convert shift to be compatible with the paper representation
      s = (-shift_size) % window_size
      offset = window_size / 2 - s
      ii = ii * window_size + offset
      jj = jj * window_size + offset
  
      input_coords = torch.stack([i.flatten(), j.flatten()], 1).float()
      anchors_coords = torch.stack(
          [ii.flatten(), jj.flatten()], 1
      ).float()
  
      anchor_id = torch.cdist(input_coords, anchors_coords, p=2).argmin(
          1
      )
      mask = anchor_id[:, None] == anchor_id[None, :]
      return mask
  
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 17 for calling a global function with example usage: swin_attention_pattern(H, W, window_size, shift_size) and returns: mask