add skip transform

class _USPTOLoader(_MolnetLoader):

  def __init__(self, *args, subset: str, sep_reagent: bool, **kwargs):

  def __init__(self, *args, subset: str, sep_reagent: bool, skip_transform: bool, **kwargs):

    super(_USPTOLoader, self).__init__(*args, **kwargs)

    self.subset = subset

    self.sep_reagent = sep_reagent

    self.name = 'USPTO_' + subset

    self.skip_transform = skip_transform

  def create_dataset(self) -> Dataset:

    if self.subset not in ['MIT', 'STEREO', '50K', 'FULL']:

    data_dir: Optional[str] = None,

    save_dir: Optional[str] = None,

    subset: str = "MIT",

    sep_reagent: bool = False,

    sep_reagent: bool = True,

    skip_transform: bool = True,

    **kwargs

) -> Tuple[List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]]:

  """Load USPTO Datasets.

  subset: str (default 'MIT')

    Subset of dataset to download. 'FULL', 'MIT', 'STEREO', and '50K' are supported.

  sep_reagent: bool (default True)

    Toggle to load dataset with reactants and reado I call it

    Toggle to load dataset with reactants and reagents either separated or mixed.

  skip_transform: bool (default True)

    Toggle to skip the source/target transformation.

  Returns

  -------

  tasks, datasets, transformers : tuple

         arXiv preprint arXiv:2001.01408.

  """

  if skip_transform:

    if not sep_reagent:

      raise ValueError("To enable mixed training you must not skip the transformation.")

    transformers = []

  else:

    if sep_reagent:

      transformers = [TransformerGenerator(dc.trans.RxnSplitTransformer, sep_reagent=True)]

    else:

      save_dir,

      subset=subset,

      sep_reagent=sep_reagent,

      skip_transform=skip_transform,

      **kwargs)

  return loader.load_dataset(loader.name, reload)