class_docstr: The extension of MoleculeNet with `MUTAG <https://pubs.acs.org/doi/10.1021/jm00106a046>`_.
  
  The `MoleculeNet benchmark collection <http://moleculenet.ai/datasets-1>`_ from the
  `MoleculeNet: A Benchmark for Molecular Machine Learning <https://arxiv.org/abs/1703.00564>`_
  paper, containing datasets from physical chemistry, biophysics and physiology.
  
  The MoleculeNet datasets come with the additional node and edge features introduced by
  the `Open Graph Benchmark <https://ogb.stanford.edu/docs/graphprop/>`_, and the node features
  in MUTAG dataset are one hot features denoting the atom types.
  
  Args:
      root (string): Root directory where the dataset should be saved.
      name (string): The name of the dataset (:obj:`"MUTAG"`, :obj:`"ESOL"`,
          :obj:`"FreeSolv"`, :obj:`"Lipo"`, :obj:`"PCBA"`, :obj:`"MUV"`,
          :obj:`"HIV"`, :obj:`"BACE"`, :obj:`"BBPB"`, :obj:`"Tox21"`,
          :obj:`"ToxCast"`, :obj:`"SIDER"`, :obj:`"ClinTox"`).
      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`)
code: def __init__(
      self,
      root,
      name,
      transform=None,
      pre_transform=None,
      pre_filter=None,
  ):
      if Chem is None:
          raise ImportError("`MoleculeNet` requires `rdkit`.")
  
      self.name = name.lower()
      assert self.name in self.names.keys()
      super(MoleculeDataset, self).__init__(
          root, transform, pre_transform, pre_filter
      )
      self.data, self.slices = torch.load(self.processed_paths[0])
  
imports: import os
  import re
  import torch
  import numpy
  from torch_geometric.utils import dense_to_sparse
  from torch_geometric.data import Data, InMemoryDataset, download_url
  import os.path
  import zipfile
  import gzip
label_desc: Use image node_id 1 to create a new MoleculeDataset object from inherited base classes: InMemoryDataset with example: obj = MoleculeDataset(root, name, transform, pre_transform, pre_filter)