code: def featurize(self, rdkit_mols):
      featurized_mols = np.empty((len(rdkit_mols)), dtype=object)
  
      from rdkit import Chem
  
      for idx, mol in enumerate(rdkit_mols):
          if self.add_hydrogens:
              mol = Chem.AddHs(mol)
          featurized_mol = featurize_mol(
              mol,
              self.n_atom_types,
              self.max_n_atoms,
              self.max_valence,
              self.num_atoms_feature,
          )
          featurized_mols[idx] = featurized_mol
      return featurized_mols
  
file_path: deepchem/contrib/torch/adjacency_fingerprint.py
imports: from collections import deque
  import sys
  import tensorflow
  import pickle
  import os
  import fnmatch
  import numpy
  from scipy.spatial.distance import pdist, squareform
  import pandas
  from deepchem.feat.base_classes import Featurizer
  from deepchem.feat.graph_features import atom_features
  from scipy.sparse import csr_matrix
label_desc: Use image node_id 2 for calling the AdjacencyFingerprint obj's underlying member method code with example usage: obj.featurize(rdkit_mols) and returns: featurized_mols