class_docstr: The AQSOL dataset from the `Benchmarking Graph Neural Networks
  <http://arxiv.org/abs/2003.00982>`_ paper based on
  `AqSolDB <https://www.nature.com/articles/s41597-019-0151-1>`_, a
  standardized database of 9,982 molecular graphs with their aqueous
  solubility values, collected from 9 different data sources.
  
  The aqueous solubility targets are collected from experimental measurements
  and standardized to LogS units in AqSolDB. These final values denote the
  property to regress in the :class:`AQSOL` dataset. After filtering out few
  graphs with no bonds/edges, the total number of molecular graphs is 9,833.
  For each molecular graph, the node features are the types of heavy atoms
  and the edge features are the types of bonds between them, similar as in
  the :class:`~torch_geometric.datasets.ZINC` dataset.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      split (str, optional): If :obj:`"train"`, loads the training dataset.
          If :obj:`"val"`, loads the validation dataset.
          If :obj:`"test"`, loads the test dataset.
          (default: :obj:`"train"`)
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      pre_filter (callable, optional): A function that takes in an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in
          the final dataset. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 10 10 10 10 10
      :header-rows: 1
  
      * - #graphs
        - #nodes
        - #edges
        - #features
        - #classes
      * - 9,833
        - ~17.6
        - ~35.8
        - 1
        - 1
code: def raw_file_names(self) -> List[str]:
      return [
          "train.pickle",
          "val.pickle",
          "test.pickle",
          "atom_dict.pickle",
          "bond_dict.pickle",
      ]
  
file_path: pytorch_geometric/torch_geometric/datasets/aqsol.py
imports: import os
  import os.path
  import pickle
  from typing import Callable, List, Optional
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
  from torch_geometric.io import fs
label_desc: Use image node_id 2 for calling the AQSOL obj's underlying member method code with example usage: obj.raw_file_names() and returns: list