code: def _load(self):
      with open(self.txt_filelist, "r") as f:
          self.relpaths = f.read().splitlines()
          l1 = len(self.relpaths)
          self.relpaths = self._filter_relpaths(self.relpaths)
          print(
              "Removed {} files from filelist during filtering.".format(
                  l1 - len(self.relpaths)
              )
          )
  
      self.synsets = [p.split("/")[0] for p in self.relpaths]
      self.abspaths = [
          os.path.join(self.datadir, p) for p in self.relpaths
      ]
  
      unique_synsets = np.unique(self.synsets)
      class_dict = dict(
          (synset, i) for i, synset in enumerate(unique_synsets)
      )
      if not self.keep_orig_class_label:
          self.class_labels = [class_dict[s] for s in self.synsets]
      else:
          self.class_labels = [self.synset2idx[s] for s in self.synsets]
  
      with open(self.human_dict, "r") as f:
          human_dict = f.read().splitlines()
          human_dict = dict(
              line.split(maxsplit=1) for line in human_dict
          )
  
      self.human_labels = [human_dict[s] for s in self.synsets]
  
      labels = {
          "relpath": np.array(self.relpaths),
          "synsets": np.array(self.synsets),
          "class_label": np.array(self.class_labels),
          "human_label": np.array(self.human_labels),
      }
  
      if self.process_images:
          self.size = retrieve(self.config, "size", default=256)
          self.data = ImagePaths(
              self.abspaths,
              labels=labels,
              size=self.size,
              random_crop=self.random_crop,
          )
      else:
          self.data = self.abspaths
  
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 9 for calling the ImageNetBase obj's underlying member method code with example usage: obj._load() without return types