def generate_tile(patches: list, masks: list, tile_size: int, scale: list) -> Tuple["torch.Tensor", "torch.Tensor"]:
    """
    Generate different size of perturbed tiles from randomly selected patches.

    :param patch: Candidate patches.
    :param mask: Masks for each patch.
    :param tile_size: The size of each tile.
    :param scale: Scale factor for various tiling size.
    :return: Perturbed tiles and corresponding masks.
    """
    import torch

    if len(patches) == 0:
        raise ValueError("candidates should not be empty.")
    device = patches[0].device

    tile = torch.zeros((0, 3, tile_size, tile_size), device=device)
    mask = torch.zeros((0, 3, tile_size, tile_size), device=device)
    for cur_s in scale:
        cur_strides = tile_size // cur_s
        cur_tile = []
        cur_mask = []

        for _ in range(cur_s):
            t1_list = []
            m1_list = []
            for _ in range(cur_s):
                g_tile, f_mask = _generate_tile_kernel(patches, masks, tile_size=cur_strides)
                t1_list.append(g_tile[None, :])
                m1_list.append(f_mask[None, :])
            cur_t = torch.cat(t1_list, dim=-2)
            cur_m = torch.cat(m1_list, dim=-2)
            cur_tile.append(cur_t)
            cur_mask.append(cur_m)
        cur_tile = torch.cat(cur_tile, dim=-1)  # type: ignore
        cur_mask = torch.cat(cur_mask, dim=-1)  # type: ignore

        tile = torch.cat([tile, cur_tile], dim=0)  # type: ignore
        mask = torch.cat([mask, cur_mask], dim=0)  # type: ignore

    return tile, mask

    Generate different size of perturbed tiles from randomly selected patches.

    :param patch: Candidate patches.
    :param mask: Masks for each patch.
    :param tile_size: The size of each tile.
    :param scale: Scale factor for various tiling size.
    :return: Perturbed tiles and corresponding masks.
    
art/attacks/evasion/steal_now_attack_later/steal_now_attack_later.py