code: def __init__(
      self,
      n_atom_types=23,
      max_n_atoms=200,
      add_hydrogens=False,
      max_valence=4,
      num_atoms_feature=False,
  ):
      self.n_atom_types = n_atom_types
      self.max_n_atoms = max_n_atoms
      self.add_hydrogens = add_hydrogens
      self.max_valence = max_valence
      self.num_atoms_feature = num_atoms_feature
  
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 1 to create a new AdjacencyFingerprint object from inherited base classes: Featurizer with example: obj = AdjacencyFingerprint(n_atom_types, max_n_atoms, add_hydrogens, max_valence, num_atoms_feature)