code: def preprocess_mask(mask, scale_factor=8):
      mask = mask.convert("L")
      w, h = mask.size
      w, h = (
          x - x % 32 for x in (w, h)
      )  # resize to integer multiple of 32
      mask = mask.resize(
          (w // scale_factor, h // scale_factor),
          resample=PIL.Image.NEAREST,
      )
      mask = np.array(mask).astype(np.float32) / 255.0
      mask = np.tile(mask, (4, 1, 1))
      mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
      mask = 1 - mask  # repaint white, keep black
      return mask
  
file_path: llmga/llmga/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py
imports: import inspect
  from typing import Callable, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, CLIPTokenizer
  from ...configuration_utils import FrozenDict
  from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from ...utils import deprecate, logging
  from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
  from ..pipeline_utils import DiffusionPipeline
  from .None import StableDiffusionPipelineOutput
label_desc: Use image node_id 2 for calling a global function with example usage: preprocess_mask(mask, scale_factor) and returns: mask