class_docstr: The QM9 dataset from the `"MoleculeNet: A Benchmark for Molecular
  Machine Learning" <https://arxiv.org/abs/1703.00564>`_ paper, consisting of
  about 130,000 molecules with 19 regression targets.
  Each molecule includes complete spatial information for the single low
  energy conformation of the atoms in the molecule.
  In addition, we provide the atom features from the `"Neural Message
  Passing for Quantum Chemistry" <https://arxiv.org/abs/1704.01212>`_ paper.
  
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | Target | Property                         | Description                                                                       | Unit                                        |
  +========+==================================+===================================================================================+=============================================+
  | 0      | :math:`\mu`                      | Dipole moment                                                                     | :math:`\textrm{D}`                          |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 1      | :math:`\alpha`                   | Isotropic polarizability                                                          | :math:`{a_0}^3`                             |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 2      | :math:`\epsilon_{\textrm{HOMO}}` | Highest occupied molecular orbital energy                                         | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 3      | :math:`\epsilon_{\textrm{LUMO}}` | Lowest unoccupied molecular orbital energy                                        | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 4      | :math:`\Delta \epsilon`          | Gap between :math:`\epsilon_{\textrm{HOMO}}` and :math:`\epsilon_{\textrm{LUMO}}` | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 5      | :math:`\langle R^2 \rangle`      | Electronic spatial extent                                                         | :math:`{a_0}^2`                             |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 6      | :math:`\textrm{ZPVE}`            | Zero point vibrational energy                                                     | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 7      | :math:`U_0`                      | Internal energy at 0K                                                             | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 8      | :math:`U`                        | Internal energy at 298.15K                                                        | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 9      | :math:`H`                        | Enthalpy at 298.15K                                                               | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 10     | :math:`G`                        | Free energy at 298.15K                                                            | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 11     | :math:`c_{\textrm{v}}`           | Heat capavity at 298.15K                                                          | :math:`\frac{\textrm{cal}}{\textrm{mol K}}` |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 12     | :math:`U_0^{\textrm{ATOM}}`      | Atomization energy at 0K                                                          | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 13     | :math:`U^{\textrm{ATOM}}`        | Atomization energy at 298.15K                                                     | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 14     | :math:`H^{\textrm{ATOM}}`        | Atomization enthalpy at 298.15K                                                   | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 15     | :math:`G^{\textrm{ATOM}}`        | Atomization free energy at 298.15K                                                | :math:`\textrm{eV}`                         |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 16     | :math:`A`                        | Rotational constant                                                               | :math:`\textrm{GHz}`                        |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 17     | :math:`B`                        | Rotational constant                                                               | :math:`\textrm{GHz}`                        |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  | 18     | :math:`C`                        | Rotational constant                                                               | :math:`\textrm{GHz}`                        |
  +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
  
  .. note::
  
      We also provide a pre-processed version of the dataset in case
      :class:`rdkit` is not installed. The pre-processed version matches with
      the manually processed version as outlined in :meth:`process`.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      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
        - #tasks
      * - 130,831
        - ~18.0
        - ~37.3
        - 11
        - 19
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code: def process(self) -> None:
      try:
          import rdkit
          from rdkit import Chem, RDLogger
          from rdkit.Chem.rdchem import BondType as BT
          from rdkit.Chem.rdchem import HybridizationType
  
          RDLogger.DisableLog("rdApp.*")
  
      except ImportError:
          rdkit = None
  
      if rdkit is None:
          print(
              (
                  "Using a pre-processed version of the dataset. Please "
                  "install 'rdkit' to alternatively process the raw data."
              ),
              file=sys.stderr,
          )
  
          data_list = torch.load(self.raw_paths[0])
          data_list = [Data(**data_dict) for data_dict in data_list]
  
          if self.pre_filter is not None:
              data_list = [d for d in data_list if self.pre_filter(d)]
  
          if self.pre_transform is not None:
              data_list = [self.pre_transform(d) for d in data_list]
  
          self.save(data_list, self.processed_paths[0])
          return
  
      types = {"H": 0, "C": 1, "N": 2, "O": 3, "F": 4}
      bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2, BT.AROMATIC: 3}
  
      with open(self.raw_paths[1], "r") as f:
          target = [
              [float(x) for x in line.split(",")[1:20]]
              for line in f.read().split("\n")[1:-1]
          ]
          y = torch.tensor(target, dtype=torch.float)
          y = torch.cat([y[:, 3:], y[:, :3]], dim=-1)
          y = y * conversion.view(1, -1)
  
      with open(self.raw_paths[2], "r") as f:
          skip = [
              int(x.split()[0]) - 1 for x in f.read().split("\n")[9:-2]
          ]
  
      suppl = Chem.SDMolSupplier(
          self.raw_paths[0], removeHs=False, sanitize=False
      )
  
      data_list = []
      for i, mol in enumerate(tqdm(suppl)):
          if i in skip:
              continue
  
          N = mol.GetNumAtoms()
  
          conf = mol.GetConformer()
          pos = conf.GetPositions()
          pos = torch.tensor(pos, dtype=torch.float)
  
          type_idx = []
          atomic_number = []
          aromatic = []
          sp = []
          sp2 = []
          sp3 = []
          num_hs = []
          for atom in mol.GetAtoms():
              type_idx.append(types[atom.GetSymbol()])
              atomic_number.append(atom.GetAtomicNum())
              aromatic.append(1 if atom.GetIsAromatic() else 0)
              hybridization = atom.GetHybridization()
              sp.append(
                  1 if hybridization == HybridizationType.SP else 0
              )
              sp2.append(
                  1 if hybridization == HybridizationType.SP2 else 0
              )
              sp3.append(
                  1 if hybridization == HybridizationType.SP3 else 0
              )
  
          z = torch.tensor(atomic_number, dtype=torch.long)
  
          rows, cols, edge_types = [], [], []
          for bond in mol.GetBonds():
              start, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
              rows += [start, end]
              cols += [end, start]
              edge_types += 2 * [bonds[bond.GetBondType()]]
  
          edge_index = torch.tensor([rows, cols], dtype=torch.long)
          edge_type = torch.tensor(edge_types, dtype=torch.long)
          edge_attr = one_hot(edge_type, num_classes=len(bonds))
  
          perm = (edge_index[0] * N + edge_index[1]).argsort()
          edge_index = edge_index[:, perm]
          edge_type = edge_type[perm]
          edge_attr = edge_attr[perm]
  
          row, col = edge_index
          hs = (z == 1).to(torch.float)
          num_hs = scatter(
              hs[row], col, dim_size=N, reduce="sum"
          ).tolist()
  
          x1 = one_hot(torch.tensor(type_idx), num_classes=len(types))
          x2 = (
              torch.tensor(
                  [atomic_number, aromatic, sp, sp2, sp3, num_hs],
                  dtype=torch.float,
              )
              .t()
              .contiguous()
          )
          x = torch.cat([x1, x2], dim=-1)
  
          name = mol.GetProp("_Name")
          smiles = Chem.MolToSmiles(mol, isomericSmiles=True)
  
          data = Data(
              x=x,
              z=z,
              pos=pos,
              edge_index=edge_index,
              smiles=smiles,
              edge_attr=edge_attr,
              y=y[i].unsqueeze(0),
              name=name,
              idx=i,
          )
  
          if self.pre_filter is not None and not self.pre_filter(data):
              continue
          if self.pre_transform is not None:
              data = self.pre_transform(data)
  
          data_list.append(data)
  
      self.save(data_list, self.processed_paths[0])
  
file_path: pytorch_geometric/torch_geometric/datasets/qm9.py
imports: import os
  import os.path
  import sys
  from typing import Callable, List, Optional
  import torch
  from torch import Tensor
  from tqdm import tqdm
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
  from torch_geometric.utils import one_hot, scatter
label_desc: Use image node_id 8 for calling the QM9 obj's underlying member method code with example usage: obj.process() without return types