code: def download(self):
      import zipfile
      import shutil
      import glob
      from tqdm import tqdm
  
      if self._check_integrity():
          return
  
      zip_filename = os.path.join(self.root, self.zip_filename)
      if not os.path.isfile(zip_filename):
          download_file_from_google_drive(
              self.gdrive_id,
              self.root,
              self.zip_filename,
              md5=self.zip_md5,
          )
  
      zip_foldername = os.path.join(self.root, self.image_folder)
      if not os.path.isdir(zip_foldername):
          with zipfile.ZipFile(zip_filename, "r") as f:
              for member in tqdm(f.infolist(), desc="Extracting "):
                  try:
                      f.extract(member, self.root)
                  except zipfile.BadZipFile:
                      print("Error: Zip file is corrupted")
  
      for split in ["train", "val", "test"]:
          filename = os.path.join(
              self.root, self.filename.format(split)
          )
          if os.path.isfile(filename):
              continue
  
          labels = get_asset(self.folder, "{0}.json".format(split))
          labels_filename = os.path.join(
              self.root, self.filename_labels.format(split)
          )
          with open(labels_filename, "w") as f:
              json.dump(labels, f)
  
          image_folder = os.path.join(zip_foldername, split)
  
          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, desc=filename)):
                  images = glob.glob(
                      os.path.join(image_folder, label, "*.png")
                  )
                  images.sort()
                  dataset = group.create_dataset(
                      label, (len(images),), dtype=dtype
                  )
                  for i, image in enumerate(images):
                      with open(image, "rb") as f:
                          array = bytearray(f.read())
                          dataset[i] = np.asarray(array, dtype=np.uint8)
  
      if os.path.isdir(zip_foldername):
          shutil.rmtree(zip_foldername)
  
imports: import numpy
  from PIL import Image
  import os
  import io
  import json
  import glob
  import h5py
  from torchmeta.utils.data import Dataset, ClassDataset, CombinationMetaDataset
  from torchmeta.datasets.utils import download_file_from_google_drive
  from torchmeta.datasets.utils import get_asset
label_desc: Use image node_id 8 for calling the TripleMNISTClassDataset obj's underlying member method code with example usage: obj.download() without return types