code: def saveattnmaps(self, output, h, w, th, step, regions):
      masks = []
      for i, mask in enumerate(self.history[step].values()):
          img, _, mask = makepmask(
              self, mask, h, w, th[i % len(th)], step
          )
          if self.ex:
              masks = [x - mask for x in masks]
              masks.append(mask)
              if len(masks) == regions - 1:
                  output.images.extend(
                      [FF.to_pil_image(mask) for mask in masks]
                  )
                  masks = []
          else:
              output.images.append(img)
  
imports: import math
  from typing import Dict, Optional
  import torch
  import torchvision.transforms.functional
  from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
  from diffusers import StableDiffusionPipeline
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import USE_PEFT_BACKEND
label_desc: Use image node_id 7 for calling a global function with example usage: saveattnmaps(output, h, w, th, step, regions) without return types