fixed docs

  >>> featurizer=dc.feat.ConvMolFeaturizer(per_atom_fragmentation=True)

  >>> f = featurizer.featurize(smiles)

  >>> len(f) # contains 2 lists with  featurized fragments from 2 mols

  2

  See Also

  --------

  >>> import tempfile

  >>> import deepchem as dc

  >>> fin = tempfile.NamedTemporaryFile(mode='w', delete=False)

  >>> fin.write("smiles,endpoint\\nc1ccccc1,1")

  >>> fin.close()

  >>> with tempfile.NamedTemporaryFile(mode='wt', delete=False) as fin:

  ...     tmp = fin.write("smiles,endpoint\\nc1ccccc1,1")

  >>> loader = dc.data.CSVLoader([], feature_field="smiles",

              featurizer = dc.feat.ConvMolFeaturizer(per_atom_fragmentation=False))

  >>> dataset = loader.create_dataset(fin.name) # dataset of molecules ready for prediction stage

  >>> # prepare dataset of molecules ready for prediction stage

  ... dataset = loader.create_dataset(fin.name)

  >>> loader = dc.data.CSVLoader([], feature_field="smiles",

  ...    featurizer=dc.feat.ConvMolFeaturizer(per_atom_fragmentation=True))

  >>> frag_dataset = loader.create_dataset(fin.name)

  >>> transformer = dc.trans.FlatteningTransformer(dataset=frag_dataset)

  >>> frag_dataset = transformer.transform(frag_dataset) # dataset of fragments ready for prediction stage

  >>> # prepare dataset of fragments ready for prediction stage,

  ... # thereafter difference with molecules' predictions can be calculated

  ... frag_dataset = transformer.transform(frag_dataset)

  See Also

  --------