code: def window_unpartition(windows, window_size, pad_hw, hw):
      """
      Args:
          windows: (num_windows*B, window_size, window_size, C)
          window_size (int): Window size
          H (int): Height of image
          W (int): Width of image
      Returns:
          x: (B, H, W, C)
      """
      Hp, Wp = pad_hw
      H, W = hw
      B = windows.shape[0] // (Hp * Wp // window_size // window_size)
      x = windows.view(
          B,
          Hp // window_size,
          Wp // window_size,
          window_size,
          window_size,
          -1,
      )
      x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
  
      if Hp > H or Wp > W:
          x = x[:, :H, :W, :].contiguous()
      return x
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import build_activation_layer, build_norm_layer
  from mmcv.cnn.bricks import DropPath
  from mmengine.logging import MMLogger
  from mmengine.model import BaseModule
  from mmengine.runner.checkpoint import CheckpointLoader
  from mmdet.registry import MODELS
label_desc: Use image node_id 5 for calling a global function with example usage: window_unpartition(windows, window_size, pad_hw, hw) and returns: x