code: def get_windows_shift_mask(
      N: int, window_size: int, window_shift: int, device: torch.device
  ) -> torch.Tensor:
      """
      Get the mask window required due to window shifting (needed for shifted
      window attention).
  
      This produces a tensor with mask values for each window. Most windows don't
      require masking, but windows that bleed across the beginning/end of the
      tensor (due to shifting) require it.
  
      Args:
          N: The sequence length.
          window_size: The window size.
          window_shift: The window shift.
          device: The device on which to create the tensor.
  
      Returns:
          A tensor of shape [N // window_size, window_size, window_size]
          containing mask values. The values are 0 (unmasked) or float("-inf")
          (masked).
      """
      ret = torch.zeros(
          N // window_size, window_size, window_size, device=device
      )
      ret[-1].fill_(float("-inf"))
      ret[
          -1, : window_size - window_shift, : window_size - window_shift
      ] = 0
      ret[-1, -window_shift:, -window_shift:] = 0
      return ret
  
file_path: ml-cvnets/cvnets/modules/windowed_transformer.py
imports: import argparse
  from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from cvnets.layers import token_merging
  from cvnets.modules import transformer
label_desc: Use image node_id 3 for calling a global function with example usage: get_windows_shift_mask(N, window_size, window_shift, device) and returns: ret