code: def download(self):
      import tarfile
  
      if self._check_integrity():
          return
  
      download_file_from_google_drive(
          self.gdrive_id, self.root, self.gz_filename, md5=self.gz_md5
      )
  
      filename = os.path.join(self.root, self.gz_filename)
      with tarfile.open(filename, "r") as f:
          f.extractall(self.root)
  
      for split in ["train", "val", "test"]:
          filename = os.path.join(
              self.root, self.filename.format(split)
          )
          if os.path.isfile(filename):
              continue
  
          pkl_filename = os.path.join(
              self.root, self.pkl_filename.format(split)
          )
          if not os.path.isfile(pkl_filename):
              raise IOError()
          with open(pkl_filename, "rb") as f:
              data = pickle.load(f)
              images, classes = data["image_data"], data["class_dict"]
  
          with h5py.File(filename, "w") as f:
              group = f.create_group("datasets")
              for name, indices in classes.items():
                  group.create_dataset(name, data=images[indices])
  
          labels_filename = os.path.join(
              self.root, self.filename_labels.format(split)
          )
          with open(labels_filename, "w") as f:
              labels = sorted(list(classes.keys()))
              json.dump(labels, f)
  
          if os.path.isfile(pkl_filename):
              os.remove(pkl_filename)
  
imports: import os
  import pickle
  from PIL import Image
  import h5py
  import json
  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 MiniImagenetClassDataset obj's underlying member method code with example usage: obj.download() without return types