code: def pad_x_and_mask(
      x: torch.Tensor, key_padding_mask: torch.Tensor, window_size: int
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """
      Apply padding to @x and @key_padding_mask to make their lengths divisible
      by @window_size.
  
      Args:
          x: The input tensor of shape [B, N, C].
          key_padding_mask: The mask of shape [B, N].
          window_size: the N dimension of @x and @key_padding_mask will be padded
              to make them divisble by this number.
  
      Returns:
          A tuple containing @x and @key_padding_mask, with padding applied.
      """
      B, N, _ = x.shape
      padding = (window_size - (N % window_size)) % window_size
  
      if key_padding_mask is not None:
          key_padding_mask = F.pad(
              key_padding_mask, (0, padding), value=float("-inf")
          )
  
      # Apply similar padding to x which is [B, N, C] before padding.
      x = F.pad(x, (0, 0, 0, padding), value=0)
  
      return x, key_padding_mask
  
file_path: ml-cvnets/cvnets/layers/token_merging.py
imports: from typing import Tuple
  import torch
  from torch import nn
  from torch.nn import functional
  from cvnets.layers import linear_layer
  from cvnets.layers.normalization import layer_norm
label_desc: Use image node_id 1 for calling a global function with example usage: pad_x_and_mask(x, key_padding_mask, window_size) and returns: x,  key_padding_mask