code: def forward(self, x):
      x = x + self.pos_embed(x)
      x = x.permute(0, 2, 3, 1)
      B, H, W, C = x.shape
      shortcut = x
      x = self.norm1(x)
  
      pad_l = pad_t = 0
      pad_r = (
          self.window_size - W % self.window_size
      ) % self.window_size
      pad_b = (
          self.window_size - H % self.window_size
      ) % self.window_size
      x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
      _, Hp, Wp, _ = x.shape
  
      x_windows = window_partition(
          x, self.window_size
      )  # nW*B, window_size, window_size, C
      x_windows = x_windows.view(
          -1, self.window_size * self.window_size, C
      )  # nW*B, window_size*window_size, C
  
      # W-MSA/SW-MSA
      attn_windows = self.attn(
          x_windows
      )  # nW*B, window_size*window_size, C
  
      # merge windows
      attn_windows = attn_windows.view(
          -1, self.window_size, self.window_size, C
      )
      x = window_reverse(
          attn_windows, self.window_size, Hp, Wp
      )  # B H' W' C
  
      # reverse cyclic shift
      if pad_r > 0 or pad_b > 0:
          x = x[:, :H, :W, :].contiguous()
  
      x = shortcut + self.drop_path(x)
      x = x + self.drop_path(self.mlp(self.norm2(x)))
      x = x.permute(0, 3, 1, 2).reshape(B, C, H, W)
      return x
  
imports: from collections import OrderedDict
  import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  import numpy
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
  from annotator.uniformer.mmcv_custom import load_checkpoint
  from annotator.uniformer.mmseg.utils import get_root_logger
  from ..builder import BACKBONES
label_desc: Use image node_id 2 for calling the SABlock_Windows obj's underlying member method code with example usage: obj.forward(x) and returns: x