code: def _prepare_human_to_integer_label(self):
      URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
      self.human2integer = os.path.join(
          self.root, "imagenet1000_clsidx_to_labels.txt"
      )
      if not os.path.exists(self.human2integer):
          download(URL, self.human2integer)
      with open(self.human2integer, "r") as f:
          lines = f.read().splitlines()
          assert len(lines) == 1000
          self.human2integer_dict = dict()
          for line in lines:
              value, key = line.split(":")
              self.human2integer_dict[key] = int(value)
  
imports: import glob
  import os
  import pickle
  import shutil
  import tarfile
  from functools import partial
  import albumentations
  import cv2
  import numpy
  import PIL
  import taming.data.utils
  import torchvision.transforms.functional
  import yaml
  from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
  from omegaconf import OmegaConf
  from PIL import Image
  from taming.data.imagenet import ImagePaths, download, give_synsets_from_indices, retrieve, str_to_indices
  from torch.utils.data import Dataset, Subset
  from tqdm import tqdm
label_desc: Use image node_id 8 for calling the ImageNetBase obj's underlying member method code with example usage: obj._prepare_human_to_integer_label() without return types