class_docstr: A `Pytorch Geometric <https://pytorch-geometric.readthedocs.io/en/latest/index.html>`_ data interface for :obj:`QM9` dataset which is from `"MoleculeNet: A Benchmark for Molecular Machine Learning" <https://arxiv.org/abs/1703.00564>`_ paper and connsists of about 130,000 molecules with 2 property optimization targets: :obj:`penalized_logp` and :obj:`qed`.
  
  Args:
      root (string, optional): Root directory where the dataset should be saved.
      prop_name (string, optional): The molecular property desired and used as the optimization target. (default: :obj:`penalized_logp`)
      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`)
      use_aug (bool, optional): If :obj:`True`, data augmentation will be used. (default: :obj:`False`)
      one_shot (bool, optional): If :obj:`True`, the returned data will use one-shot format with an extra dimension of virtual node and edge feature. (default: :obj:`False`)
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code: def __init__(
      self,
      root="./",
      prop_name="penalized_logp",
      conf_dict=None,
      transform=None,
      pre_transform=None,
      pre_filter=None,
      processed_filename="data.pt",
      use_aug=False,
      one_shot=False,
  ):
      name = "qm9_property"
      super(QM9, self).__init__(
          root,
          name,
          prop_name,
          conf_dict,
          transform,
          pre_transform,
          pre_filter,
          processed_filename,
          use_aug,
          one_shot,
      )
  
file_path: DIG/dig/ggraph/dataset/ggraph_dataset.py
imports: import numpy
  from rdkit import Chem
  from dig.ggraph.dataset import PygDataset
label_desc: Use image node_id 1 to create a new QM9 object from inherited base classes: PygDataset with example: obj = QM9(root, prop_name, conf_dict, transform, pre_transform, pre_filter, processed_filename, use_aug, one_shot)