code: def window_x_and_key_padding_mask(
      x: torch.Tensor,
      key_padding_mask: torch.Tensor,
      window_size: int,
      window_shift: int,
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
      """
      Perform windowing on @x and @key_padding_mask in preparation for windowed
      attention.
  
      Args:
          x: The input tensor of shape [batch_size, sequence_length, num_channels].
          key_padding_mask: The mask, as a tensor of shape [batch_size, sequence_length].
          window_size: The window size to be used for windowed attention.
          window_shift: The window shift to be used for windowed attention.
  
      Returns:
          A tuple containing 3 tensors. The first is the windowed input. The second
          is the windowed mask. The third is the mask needed to perform shifted
          window attention (to avoid the first and last windows from bleeding
          into each other).
      """
      B, N = key_padding_mask.shape
      assert x.shape[:2] == (B, N)
  
      x, key_padding_mask = token_merging.pad_x_and_mask(
          x, key_padding_mask, window_size
      )
  
      # Now, perform the windowing.
      if window_shift > 0:
          x = torch.roll(x, shifts=(-window_shift), dims=1)
          key_padding_mask = torch.roll(
              key_padding_mask, shifts=(-window_shift), dims=1
          )
  
      x_windows = window_partition(x, window_size)
      token_mask_windows = key_padding_mask.reshape(
          B * x.shape[1] // window_size, window_size
      )
      window_mask = get_windows_shift_mask(
          x.shape[1], window_size, window_shift, x_windows.device
      ).expand(B, -1, -1, -1)
      window_mask = window_mask.reshape(
          window_mask.shape[0] * window_mask.shape[1],
          window_mask.shape[2],
          window_mask.shape[3],
      )
  
      return x_windows, token_mask_windows, window_mask
  
file_path: ml-cvnets/cvnets/modules/windowed_transformer.py
imports: import argparse
  from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from cvnets.layers import token_merging
  from cvnets.modules import transformer
label_desc: Use image node_id 4 for calling a global function with example usage: window_x_and_key_padding_mask(x, key_padding_mask, window_size, window_shift) and returns: x_windows,  token_mask_windows,  window_mask