code: def window_unpartition(windows, window_size, pad_hw, hw):
      """
      Window unpartition into original sequences and removing padding.
      Args:
          x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
          window_size (int): window size.
          pad_hw (Tuple): padded height and width (Hp, Wp).
          hw (Tuple): original height and width (H, W) before padding.
  
      Returns:
          x: unpartitioned sequences with [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
  
file_path: detectron2/detectron2/modeling/backbone/utils.py
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling a global function with example usage: window_unpartition(windows, window_size, pad_hw, hw) and returns: x