code: def preprocess_mask_image(self, mask_image) -> torch.Tensor:
      if not isinstance(mask_image, list):
          mask_image = [mask_image]
  
      if isinstance(mask_image[0], torch.Tensor):
          mask_image = (
              torch.cat(mask_image, axis=0)
              if mask_image[0].ndim == 4
              else torch.stack(mask_image, axis=0)
          )
  
          if mask_image.ndim == 2:
              # Batch and add channel dim for single mask
              mask_image = mask_image.unsqueeze(0).unsqueeze(0)
          elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
              # Single mask, the 0'th dimension is considered to be
              # the existing batch size of 1
              mask_image = mask_image.unsqueeze(0)
          elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
              # Batch of mask, the 0'th dimension is considered to be
              # the batching dimension
              mask_image = mask_image.unsqueeze(1)
  
          mask_image[mask_image < 0.5] = 0
          mask_image[mask_image >= 0.5] = 1
  
      elif isinstance(mask_image[0], PIL.Image.Image):
          new_mask_image = []
  
          for mask_image_ in mask_image:
              mask_image_ = mask_image_.convert("L")
              mask_image_ = resize(mask_image_, self.unet.sample_size)
              mask_image_ = np.array(mask_image_)
              mask_image_ = mask_image_[None, None, :]
              new_mask_image.append(mask_image_)
  
          mask_image = new_mask_image
  
          mask_image = np.concatenate(mask_image, axis=0)
          mask_image = mask_image.astype(np.float32) / 255.0
          mask_image[mask_image < 0.5] = 0
          mask_image[mask_image >= 0.5] = 1
          mask_image = torch.from_numpy(mask_image)
  
      elif isinstance(mask_image[0], np.ndarray):
          mask_image = np.concatenate(
              [m[None, None, :] for m in mask_image], axis=0
          )
  
          mask_image[mask_image < 0.5] = 0
          mask_image[mask_image >= 0.5] = 1
          mask_image = torch.from_numpy(mask_image)
  
      return mask_image
  
imports: import html
  import inspect
  import re
  import urllib.parse
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
  from ...loaders import LoraLoaderMixin
  from ...models import UNet2DConditionModel
  from ...schedulers import DDPMScheduler
  from ...utils import BACKENDS_MAPPING, PIL_INTERPOLATION, is_accelerate_available, is_bs4_available, is_ftfy_available, logging, replace_example_docstring
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from .pipeline_output import IFPipelineOutput
  from .safety_checker import IFSafetyChecker
  from .watermark import IFWatermarker
label_desc: Use image node_id 10 for calling the IFInpaintingPipeline obj's underlying member method code with example usage: obj.preprocess_mask_image(mask_image) and returns: mask_image