code: def window_reverse(
      windows: Tensor, window_size: Tuple[int, int], H: int, W: int
  ):
      """
      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)
      """
      C = windows.shape[-1]
      x = windows.view(
          -1,
          H // window_size[0],
          W // window_size[1],
          window_size[0],
          window_size[1],
          C,
      )
      x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C)
      return x
  
imports: from functools import partial
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, trunc_normal_, Mlp, LayerNorm2d, get_norm_layer, use_fused_attn
  from timm.layers import NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model
label_desc: Use image node_id 2 for calling a global function with example usage: window_reverse(windows, window_size, H, W) and returns: x