class_docstr: Self-attention module in Swin Transformer
      
docstr: generating the mask of SW-MSA
  Args:
      shift: shift parameters in CyclicShift.
  Returns:
      attn_mask: should be (1 1 w p p),
docstr_tok: ["generating","the","mask","of","SW-MSA","Args",":","shift",":","shift","parameters","in","CyclicShift",".","Returns",":","attn_mask",":","should","be","(","1","1","w","p","p",")",","]
code: def generate_mask(self, h, w, p, shift):
      """generating the mask of SW-MSA
      Args:
          shift: shift parameters in CyclicShift.
      Returns:
          attn_mask: should be (1 1 w p p),
      """
      # supporting square.
      attn_mask = torch.zeros(
          h,
          w,
          p,
          p,
          p,
          p,
          dtype=torch.bool,
          device=self.relative_position_params.device,
      )
      if self.type == "W":
          return attn_mask
  
      s = p - shift
      attn_mask[-1, :, :s, :, s:, :] = True
      attn_mask[-1, :, s:, :, :s, :] = True
      attn_mask[:, -1, :, :s, :, s:] = True
      attn_mask[:, -1, :, s:, :, :s] = True
      attn_mask = rearrange(
          attn_mask, "w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)"
      )
      return attn_mask
  
imports: import numpy
  import torch
  import torch.nn
  from einops import rearrange
  from einops.layers.torch import Rearrange
  from timm.models.layers import trunc_normal_, DropPath
label_desc: Use image node_id 2 for calling the WMSA obj's underlying member method code with example usage: obj.generate_mask(h, w, p, shift) and returns: attn_mask, attn_mask