code: def example_smiles_n_b_features():
      """
      Sample data for testing
  
      Returns
      -------
      dictionary
      format {'smiles':required feature vector}
      """
      feature_vector_C1OC1 = [
          [
              0,
              True,
              False,
              False,
              False,
              False,
              True,
              1,
              0,
              0,
              0,
              0,
              0,
              0,
          ],
          [
              0,
              True,
              False,
              False,
              False,
              False,
              True,
              1,
              0,
              0,
              0,
              0,
              0,
              0,
          ],
          [
              0,
              True,
              False,
              False,
              False,
              False,
              True,
              1,
              0,
              0,
              0,
              0,
              0,
              0,
          ],
      ]
      feature_vector_NN = [
          [
              0,
              False,
              False,
              True,
              False,
              False,
              False,
              1,
              0,
              0,
              0,
              0,
              0,
              0,
          ]
      ]
      return {"C1OC1": feature_vector_C1OC1, "N#N": feature_vector_NN}
  
file_path: deepchem/deepchem/feat/tests/test_features_generator_dmpnn.py
imports: from deepchem.feat.molecule_featurizers.dmpnn_featurizer import bond_features, map_reac_to_prod
  from rdkit import Chem
  import pytest
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: example_smiles_n_b_features() and returns: dict