class_docstr: Test ConvMolFeaturizer featurizes properly.
docstr: checks if instantiating featurizer with per_atom_fragmentation=True
  leads to  as many fragments' features, as many atoms mol has for any mol
docstr_tok: ["checks","if","instantiating","featurizer","with","per_atom_fragmentation=True","leads","to","as","many","fragments","'","features",",","as","many","atoms","mol","has","for","any","mol"]
code: def test_per_atom_fragmentation(self):
      """checks if instantiating featurizer with per_atom_fragmentation=True
      leads to  as many fragments' features, as many atoms mol has for any mol
      """
      import rdkit.Chem
  
      raw_smiles = ["CC(CO)Cc1ccccc1", "CC"]
      mols = [rdkit.Chem.MolFromSmiles(m) for m in raw_smiles]
      featurizer = ConvMolFeaturizer(per_atom_fragmentation=True)
      feat = featurizer.featurize(mols)
      for i, j in zip(feat, mols):
          assert len(i) == j.GetNumHeavyAtoms()
  
file_path: deepchem/deepchem/feat/tests/test_graph_features.py
imports: import unittest
  import numpy
  from deepchem.feat.graph_features import ConvMolFeaturizer
label_desc: Use image node_id 4 for calling the TestConvMolFeaturizer obj's underlying member method code with example usage: obj.test_per_atom_fragmentation() without return types