class_docstr: A `Pytorch Geometric <https://pytorch-geometric.readthedocs.io/en/latest/index.html>`_ data interface for :obj:`ZINC800` dataset which contains 800 selected molecules with lowest penalized logP scores. While method :obj:`jt` selects from the test set and :obj:`graphaf` selects from the train set.
  
  Args:
      root (string, optional): Root directory where the dataset should be saved. 
      method (string, optional): Method name for :obj:`ZINC800` dataset, can be either :obj:`jt` or :obj:`graphaf`. (default: :obj:`jt`)
      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="./",
      method="jt",
      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 = "zinc800"
  
      if method in ["jt", "graphaf"]:
          name = "zinc_800" + "_" + method
      else:
          error_mssg = "Invalid method type {}.\n".format(method)
          error_mssg += "Available datasets are as follows:\n"
          error_mssg += "\n".join(["jt", "graphaf"])
          raise ValueError(error_mssg)
      super(ZINC800, 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 ZINC800 object from inherited base classes: PygDataset with example: obj = ZINC800(root, method, prop_name, conf_dict, transform, pre_transform, pre_filter, processed_filename, use_aug, one_shot)