code: def find_distance(
      a1: RDKitAtom, num_atoms: int, bond_adj_list, max_distance=7
  ) -> np.ndarray:
      """Computes distances from provided atom.
  
      Parameters
      ----------
      a1: RDKit atom
          The source atom to compute distances from.
      num_atoms: int
          The total number of atoms.
      bond_adj_list: list of lists
          `bond_adj_list[i]` is a list of the atom indices that atom `i` shares a
          bond with. This list is symmetrical so if `j in bond_adj_list[i]` then `i in
          bond_adj_list[j]`.
      max_distance: int, optional (default 7)
          The max distance to search.
  
      Returns
      -------
      distances: np.ndarray
          Of shape `(num_atoms, max_distance)`. Provides a one-hot encoding of the
          distances. That is, `distances[i]` is a one-hot encoding of the distance
          from `a1` to atom `i`.
      """
      distance = np.zeros((num_atoms, max_distance))
      radial = 0
      # atoms `radial` bonds away from `a1`
      adj_list = set(bond_adj_list[a1])
      # atoms less than `radial` bonds away
      all_list = set([a1])
      while radial < max_distance:
          distance[list(adj_list), radial] = 1
          all_list.update(adj_list)
          # find atoms `radial`+1 bonds away
          next_adj = set()
          for adj in adj_list:
              next_adj.update(bond_adj_list[adj])
          adj_list = next_adj - all_list
          radial = radial + 1
      return distance
  
imports: import numpy
  import deepchem
  from deepchem.feat.base_classes import MolecularFeaturizer
  from deepchem.feat.mol_graphs import ConvMol, WeaveMol
  from deepchem.data import DiskDataset
  import logging
  from typing import Optional, List, Tuple, Union, Iterable
  from deepchem.utils.typing import RDKitMol, RDKitAtom
  from deepchem.utils.molecule_feature_utils import one_hot_encode
label_desc: Use image node_id 13 for calling a global function with example usage: find_distance(a1, num_atoms, bond_adj_list, max_distance) and returns: distance