class_docstr: Test mol graphs.
class_docstr_tok: ["Test","mol","graphs","."]
docstr: Test that the atom features are computed properly.
docstr_tok: ["Test","that","the","atom","features","are","computed","properly","."]
code: def test_get_atom_features(self):
      """Test that the atom features are computed properly."""
      atom_features = np.array(
          [
              [40, 41, 42, 43],
              [44, 45, 46, 47],
              [48, 49, 50, 51],
              [52, 53, 54, 55],
              [56, 57, 58, 59],
          ]
      )
      canon_adj_list = [[1, 2], [0, 3], [0, 3], [1, 2, 4], [3]]
      mol = ConvMol(atom_features, canon_adj_list)
      # atom 4 has 0 neighbors
      # atom 0 has 2 neighbors
      # atom 1 has 2 neighbors
      # atom 2 has 2 neighbors
      # atom 3 has 3 neighbors.
      # Verify that atom features have been sorted by atom degree.
      assert np.array_equal(
          mol.get_atom_features(),
          np.array(
              [
                  [56, 57, 58, 59],
                  [40, 41, 42, 43],
                  [44, 45, 46, 47],
                  [48, 49, 50, 51],
                  [52, 53, 54, 55],
              ]
          ),
      )
  
file_path: deepchem/deepchem/feat/tests/test_mol_graphs.py
imports: import unittest
  import numpy
  from deepchem.feat.mol_graphs import ConvMol
label_desc: Use image node_id 3 for calling the TestMolGraphs obj's underlying member method code with example usage: obj.test_get_atom_features() without return types