code: def load_dict_per_class(self):
      new_exist_class_list = {}
  
      if self.meta_split == "train":
          fold_list = [0, 1, 2, 3]
          fold_list.remove(self.fold)
      else:
          fold_list = [self.fold]
  
      for fold in fold_list:
          f = open(
              os.path.join(
                  self.root,
                  "Binary_map_aug",
                  self.meta_split,
                  "split%1d_%s.txt" % (fold, self.meta_split),
              )
          )
          while True:
              item = f.readline()
              if item == "":
                  break
              img_name = item[:11]
              cat = int(item[13:15])
              if cat not in new_exist_class_list:
                  new_exist_class_list[cat] = []
              new_exist_class_list[cat].append(img_name)
  
      images = {}
      masks = {}
      classes_names = self.read_labels()
  
      for k, v in new_exist_class_list.items():
          cname = classes_names[k]
          for path in v:
              fname = os.path.join(
                  self.root,
                  "VOCdevkit/VOC2012/JPEGImages",
                  path + ".jpg",
              )
              if cname not in images:
                  images[cname] = []
              images[cname].append(fname)
              fname = os.path.join(
                  self.root,
                  "Binary_map_aug",
                  self.meta_split,
                  str(k),
                  path + ".png",
              )
              if cname not in masks:
                  masks[cname] = []
              masks[cname].append(fname)
      return images, masks
  
imports: import os
  import json
  import glob
  import h5py
  from PIL import Image, ImageOps
  from torchmeta.utils.data import Dataset, ClassDataset, CombinationMetaDataset
  from torchvision.datasets.utils import list_dir, download_url
  from torchmeta.datasets.utils import get_asset
  import numpy
label_desc: Use image node_id 4 for calling the Pascal5iClassDataset obj's underlying member method code with example usage: obj.load_dict_per_class() and returns: images,  masks