code: def max_pair_distance_pairs(
      mol: RDKitMol, max_pair_distance: Optional[int] = None
  ) -> np.ndarray:
      """Helper method which finds atom pairs within max_pair_distance graph distance.
  
      This helper method is used to find atoms which are within max_pair_distance
      graph_distance of one another. This is done by using the fact that the
      powers of an adjacency matrix encode path connectivity information. In
      particular, if `adj` is the adjacency matrix, then `adj**k` has a nonzero
      value at `(i, j)` if and only if there exists a path of graph distance `k`
      between `i` and `j`. To find all atoms within `max_pair_distance` of each
      other, we can compute the adjacency matrix powers `[adj, adj**2,
      ...,adj**max_pair_distance]` and find pairs which are nonzero in any of
      these matrices. Since adjacency matrices and their powers are positive
      numbers, this is simply the nonzero elements of `adj + adj**2 + ... +
      adj**max_pair_distance`.
  
      Parameters
      ----------
      mol: rdkit.Chem.rdchem.Mol
          RDKit molecules
      max_pair_distance: Optional[int], (default None)
          This value can be a positive integer or None. This
          parameter determines the maximum graph distance at which pair
          features are computed. For example, if `max_pair_distance==2`,
          then pair features are computed only for atoms at most graph
          distance 2 apart. If `max_pair_distance` is `None`, all pairs are
          considered (effectively infinite `max_pair_distance`)
  
      Examples
      --------
      >>> from rdkit import Chem
      >>> mol = Chem.MolFromSmiles('CCC')
      >>> features = dc.feat.graph_features.max_pair_distance_pairs(mol, 1)
      >>> type(features)
      <class 'numpy.ndarray'>
      >>> features.shape  # (2, num_pairs)
      (2, 7)
  
      Returns
      -------
      np.ndarray
          Of shape `(2, num_pairs)` where `num_pairs` is the total number of pairs
          within `max_pair_distance` of one another.
      """
      from rdkit import Chem
      from rdkit.Chem import rdmolops
  
      N = len(mol.GetAtoms())
      if max_pair_distance is None or max_pair_distance >= N:
          max_distance = N
      elif max_pair_distance is not None and max_pair_distance <= 0:
          raise ValueError(
              "max_pair_distance must either be a positive integer or None"
          )
      elif max_pair_distance is not None:
          max_distance = max_pair_distance
      adj = rdmolops.GetAdjacencyMatrix(mol)
      # Handle edge case of self-pairs (i, i)
      sum_adj = np.eye(N)
      for i in range(max_distance):
          # Increment by 1 since we don't want 0-indexing
          power = i + 1
          sum_adj += np.linalg.matrix_power(adj, power)
      nonzero_locs = np.where(sum_adj != 0)
      num_pairs = len(nonzero_locs[0])
      # This creates a matrix of shape (2, num_pairs)
      pair_edges = np.reshape(
          np.array(list(zip(nonzero_locs))), (2, num_pairs)
      )
      return pair_edges
  
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 11 for calling a global function with example usage: max_pair_distance_pairs(mol, max_pair_distance) and returns: pair_edges