code: def download(self, normalize):
      if self._check_integrity():
          return
  
      from sklearn.datasets import fetch_openml
  
      data = fetch_openml(data_id=self.open_ml_id)
      features = data.data
      targets = data.target
  
      os.makedirs(self.root, exist_ok=True)
  
      # for each meta-data-split, get the labels, then check which data-point belongs to the set (via a mask).
      # then, retrieve the features and targets belonging to the set. Then create hdf5 file for these features.
      for s, split in enumerate(["train", "val", "test"]):
          targets_assets_split = get_asset(
              self.folder, "{0}.json".format(split)
          )
  
          is_in_split = [t in targets_assets_split for t in targets]
          features_split = features.loc[is_in_split]
          targets_split = targets.loc[is_in_split]
          assert targets_split.shape[0] == features_split.shape[0]
  
          unique_targets_split = np.sort(np.unique(targets_split))
          if len(targets_assets_split) > unique_targets_split.shape[0]:
              print(
                  f"unique set of labels ({(unique_targets_split.shape[0])}) is smaller than set of labels "
                  f"given by assets ({len(targets_assets_split)}). Proceeding with unique set of labels."
              )
  
          # write unique targets to json file.
          labels_filename = os.path.join(
              self.root, self.filename_labels.format(split)
          )
          with open(labels_filename, "w") as f:
              json.dump(unique_targets_split.tolist(), f)
  
          # normalize to zero mean and standard deviation 1 with stats from 'train' split only
          if split == "train":
              mean, std = np.zeros(features.shape[1]), np.ones(
                  features.shape[1]
              )
              if normalize:
                  mean = np.mean(features_split, axis=0)
                  std = np.std(features_split, axis=0)
  
              self._mean_std = {
                  "mean": mean.tolist(),
                  "std": std.tolist(),
              }
              mean_std_filename = os.path.join(
                  self.root, self.filename_mean_std
              )
              with open(mean_std_filename, "w") as f:
                  json.dump(self._mean_std, f)
  
          mean_std = self.mean_std
          mean = np.array(mean_std["mean"])
          std = np.array(mean_std["std"])
          features_split = (features_split - mean) / (std + 1e-10)
  
          # write data (features and class labels)
          filename = os.path.join(
              self.root, self.filename.format(split)
          )
          with h5py.File(filename, "w") as f:
              group = f.create_group("datasets")
  
              for i, label in enumerate(
                  tqdm(unique_targets_split, desc=filename)
              ):
                  data_class = features_split.loc[
                      targets_split == label
                  ]
                  group.create_dataset(label, data=data_class)
  
imports: import numpy
  import os
  import json
  import h5py
  from tqdm import tqdm
  from torchmeta.utils.data import Dataset, ClassDataset, CombinationMetaDataset
  from torchmeta.datasets.utils import get_asset
label_desc: Use image node_id 9 for calling the PlantsShapeClassDataset obj's underlying member method code with example usage: obj.download(normalize) without return types