code: def prepare_mask(masks):
      prepared_masks = []
      for mask in masks:
          old_mask = deepcopy(mask)
          for i in range(mask.shape[1]):
              for j in range(mask.shape[2]):
                  if old_mask[0][i][j] == 1:
                      continue
                  if i != 0:
                      mask[:, i - 1, j] = 0
                  if j != 0:
                      mask[:, i, j - 1] = 0
                  if i != 0 and j != 0:
                      mask[:, i - 1, j - 1] = 0
                  if i != mask.shape[1] - 1:
                      mask[:, i + 1, j] = 0
                  if j != mask.shape[2] - 1:
                      mask[:, i, j + 1] = 0
                  if i != mask.shape[1] - 1 and j != mask.shape[2] - 1:
                      mask[:, i + 1, j + 1] = 0
          prepared_masks.append(mask)
      return torch.stack(prepared_masks, dim=0)
  
imports: from copy import deepcopy
  from typing import Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from packaging import version
  from PIL import Image
  from ...None import __version__
  from ...models import UNet2DConditionModel, VQModel
  from ...schedulers import DDPMScheduler
  from ...utils import deprecate, logging
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_mask(masks) and returns: torch