code: def download(self):
      import tarfile
      import shutil
      from tqdm import tqdm
  
      if self._check_integrity():
          return
  
      download_file_from_google_drive(
          self.gdrive_id, self.root, self.tar_filename, md5=self.tar_md5
      )
  
      filename = os.path.join(self.root, self.tar_filename)
      with tarfile.open(filename, "r") as f:
          f.extractall(self.root)
      tar_folder = os.path.join(self.root, self.tar_folder)
  
      for split in ["train", "val", "test"]:
          filename = os.path.join(
              self.root, self.filename.format(split)
          )
          if os.path.isfile(filename):
              continue
  
          images_filename = os.path.join(
              tar_folder, "{0}_images_png.pkl".format(split)
          )
          if not os.path.isfile(images_filename):
              raise IOError(images_filename)
          with open(images_filename, "rb") as f:
              images = pickle.load(f, encoding="bytes")
  
          labels_filename = os.path.join(
              tar_folder, "{0}_labels.pkl".format(split)
          )
          if not os.path.isfile(labels_filename):
              raise IOError()
          with open(labels_filename, "rb") as f:
              labels = pickle.load(f, encoding="latin1")
  
          labels_str = labels["label_specific_str"]
          general_labels_str = labels["label_general_str"]
          general_labels = labels["label_general"]
          with open(
              os.path.join(
                  self.root, self.filename_labels.format(split)
              ),
              "w",
          ) as f:
              json.dump(labels_str, f)
  
          with h5py.File(filename, "w") as f:
              group = f.create_group("datasets")
              dtype = h5py.special_dtype(vlen=np.uint8)
              for i, label in enumerate(
                  tqdm(labels_str, desc=filename)
              ):
                  (indices,) = np.where(labels["label_specific"] == i)
                  dataset = group.create_dataset(
                      label, (len(indices),), dtype=dtype
                  )
                  general_idx = general_labels[indices[0]]
                  dataset.attrs["label_general"] = (
                      general_labels_str[general_idx]
                      if general_idx < len(general_labels_str)
                      else ""
                  )
                  dataset.attrs["label_specific"] = label
                  for j, k in enumerate(indices):
                      dataset[j] = np.squeeze(images[k])
  
      if os.path.isdir(tar_folder):
          shutil.rmtree(tar_folder)
  
imports: import numpy
  from PIL import Image
  import h5py
  import json
  import os
  import io
  import pickle
  from torchmeta.utils.data import Dataset, ClassDataset, CombinationMetaDataset
  from torchmeta.datasets.utils import download_file_from_google_drive
label_desc: Use image node_id 8 for calling the TieredImagenetClassDataset obj's underlying member method code with example usage: obj.download() without return types