code: def read_labels(self, fold=None):
      labels = []
      if fold is not None:
          if self.meta_train:
              in_classes = set(range(21)) - set(
                  range(fold * 5 + 1, (fold + 1) * 5 + 1)
              )
          else:
              in_classes = set(range(fold * 5 + 1, (fold + 1) * 5 + 1))
      else:
          in_classes = set(range(21))
  
      with open(
          os.path.join(self.root, self.split_filename_labels), "r"
      ) as f:
          for it, line in enumerate(f):
              if line.strip() == "":
                  break
              if it in in_classes:
                  labels.append(line.strip())
      return labels
  
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 6 for calling the Pascal5iClassDataset obj's underlying member method code with example usage: obj.read_labels(fold) and returns: labels