code: def load_dataset_from_disk(
      save_dir: str,
  ) -> Tuple[
      bool,
      Optional[
          Tuple[
              "dc.data.DiskDataset",
              "dc.data.DiskDataset",
              "dc.data.DiskDataset",
          ]
      ],
      List["dc.trans.Transformer"],
  ]:
      """Loads MoleculeNet train/valid/test/transformers from disk.
  
      Expects that data was saved using `save_dataset_to_disk` below. Expects the
      following directory structure for `save_dir`:
      save_dir/
        |
        ---> train_dir/
        |
        ---> valid_dir/
        |
        ---> test_dir/
        |
        ---> transformers.pkl
  
      Parameters
      ----------
      save_dir: str
          Directory name to load datasets.
  
      Returns
      -------
      loaded: bool
          Whether the load succeeded
      all_dataset: Tuple[DiskDataset, DiskDataset, DiskDataset]
          The train, valid, test datasets
      transformers: Transformer
          The transformers used for this dataset
  
      See Also
      --------
      save_dataset_to_disk
      """
  
      train_dir = os.path.join(save_dir, "train_dir")
      valid_dir = os.path.join(save_dir, "valid_dir")
      test_dir = os.path.join(save_dir, "test_dir")
      if (
          not os.path.exists(train_dir)
          or not os.path.exists(valid_dir)
          or not os.path.exists(test_dir)
      ):
          return False, None, list()
      loaded = True
      train = dc.data.DiskDataset(train_dir)
      valid = dc.data.DiskDataset(valid_dir)
      test = dc.data.DiskDataset(test_dir)
      train.memory_cache_size = 40 * (1 << 20)  # 40 MB
      all_dataset = (train, valid, test)
      transformers = load_transformers(save_dir)
      return loaded, all_dataset, transformers
  
imports: import joblib
  import gzip
  import pickle
  import os
  import tempfile
  import tarfile
  import zipfile
  import logging
  from urllib.request import urlretrieve
  from typing import Any, Iterator, List, Optional, Tuple, Union, cast, IO
  import pandas
  import numpy
  import deepchem
label_desc: Use image node_id 16 for calling a global function with example usage: load_dataset_from_disk(save_dir) and returns: loaded,  all_dataset,  transformers, False,  None,  list