code: def window_unpartition(
      windows: Tensor,
      window_size: int,
      pad_hw: tuple[int, int],
      hw: tuple[int, int],
  ) -> Tensor:
      """Window unpartition into original sequences and removing padding.
  
      Args:
          x: input tokens with [B * num_windows, window_size, window_size, C].
          window_size: window size.
          pad_hw: padded height and width (Hp, Wp).
          hw: 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
  
imports: from __future__ import annotations
  import torch
  import torch.nn.functional
  from torch import nn
  from kornia.core import Module, Tensor, pad
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