code: def compute_mask(
      H: int, W: int, window_size: int, shift_size: int, device
  ):
      # calculate attention mask for SW-MSA
      img_mask = torch.zeros((1, H, W, 1), device=device)  # 1 H W 1
      h_slices = (
          slice(0, -window_size),
          slice(-window_size, -shift_size),
          slice(-shift_size, None),
      )
      w_slices = (
          slice(0, -window_size),
          slice(-window_size, -shift_size),
          slice(-shift_size, None),
      )
      cnt = 0
      for h in h_slices:
          for w in w_slices:
              img_mask[:, h, w, :] = cnt
              cnt += 1
  
      mask_windows = window_partition(
          img_mask, window_size
      )  # nW, window_size, window_size, 1
      mask_windows = mask_windows.view(-1, window_size * window_size)
      attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
      attn_mask = attn_mask.masked_fill(
          attn_mask != 0, float(-100.0)
      ).masked_fill(attn_mask == 0, float(0.0))
      return attn_mask
  
imports: import math
  from functools import lru_cache
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from vissl.config import AttrDict
  from vissl.models.model_helpers import DropPath, rearrange, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
label_desc: Use image node_id 3 for calling a global function with example usage: compute_mask(H, W, window_size, shift_size, device) and returns: attn_mask