code: def shortest_dist(g, root=None, return_paths=False):
      r"""Compute shortest distance and paths on the given graph.
  
      Only unweighted cases are supported. Only directed paths (in which the
      edges are all oriented in the same direction) are considered effective.
  
      Parameters
      ----------
      g : DGLGraph
          The input graph. Must be homogeneous.
      root : int, optional
          Given a root node ID, it returns the shortest distance and paths
          (optional) between the root node and all the nodes. If None, it returns
          the results for all node pairs. Default: None.
      return_paths : bool, optional
          If True, it returns the shortest paths corresponding to the shortest
          distances. Default: False.
  
      Returns
      -------
      dist : Tensor
          The shortest distance tensor.
  
          * If :attr:`root` is a node ID, it is a tensor of shape :math:`(N,)`,
            where :math:`N` is the number of nodes. :attr:`dist[j]` gives the
            shortest distance from :attr:`root` to node :attr:`j`.
          * Otherwise, it is a tensor of shape :math:`(N, N)`. :attr:`dist[i][j]`
            gives the shortest distance from node :attr:`i` to node :attr:`j`.
          * The distance values of unreachable node pairs are filled with -1.
      paths : Tensor, optional
          The shortest path tensor. It is only returned when :attr:`return_paths`
          is True.
  
          * If :attr:`root` is a node ID, it is a tensor of shape :math:`(N, L)`,
            where :math:`L` is the length of the longest path. :attr:`path[j]` is
            the shortest path from node :attr:`root` to node :attr:`j`.
          * Otherwise, it is a tensor of shape :math:`(N, N, L)`.
            :attr:`path[i][j]` is the shortest path from node :attr:`i` to node
            :attr:`j`.
          * Each path is a vector that consists of edge IDs with paddings of -1
            at the end.
          * Shortest path between a node and itself is a vector filled with -1's.
  
      Example
      -------
      >>> import dgl
  
      >>> g = dgl.graph(([0, 1, 1, 2], [2, 0, 3, 3]))
      >>> dgl.shortest_dist(g, root=0)
      tensor([ 0,  -1,  1, 2])
      >>> dist, paths = dgl.shortest_dist(g, root=None, return_paths=True)
      >>> print(dist)
      tensor([[ 0, -1,  1,  2],
              [ 1,  0,  2,  1],
              [-1, -1,  0,  1],
              [-1, -1, -1,  0]])
      >>> print(paths)
      tensor([[[-1, -1],
               [-1, -1],
               [ 0, -1],
               [ 0,  3]],
      <BLANKLINE>
              [[ 1, -1],
               [-1, -1],
               [ 1,  0],
               [ 2, -1]],
      <BLANKLINE>
              [[-1, -1],
               [-1, -1],
               [-1, -1],
               [ 3, -1]],
      <BLANKLINE>
              [[-1, -1],
               [-1, -1],
               [-1, -1],
               [-1, -1]]])
      """
      if root is None:
          dist, pred = sparse.csgraph.shortest_path(
              g.adj_external(scipy_fmt="csr"),
              return_predecessors=True,
              unweighted=True,
              directed=True,
          )
      else:
          dist, pred = sparse.csgraph.dijkstra(
              g.adj_external(scipy_fmt="csr"),
              directed=True,
              indices=root,
              return_predecessors=True,
              unweighted=True,
          )
      dist[np.isinf(dist)] = -1
  
      if not return_paths:
          return F.copy_to(F.tensor(dist, dtype=F.int64), g.device)
  
      def _get_nodes(pred, i, j):
          r"""return node IDs of a path from i to j given predecessors"""
          if i == j:
              return []
          prev = pred[j]
          nodes = [j, prev]
          while prev != i:
              prev = pred[prev]
              nodes.append(prev)
          nodes.reverse()
  
          return nodes
  
      # construct paths with given predecessors
      max_len = int(dist[~np.isinf(dist)].max())
      N = g.num_nodes()
      roots = list(range(N)) if root is None else [root]
      paths = np.ones([len(roots), N, max_len], dtype=np.int64) * -1
      masks, u, v = [], [], []
      for i in roots:
          pred_ = pred[i] if root is None else pred
          masks_i = np.zeros([N, max_len], dtype=bool)
          for j in range(N):
              if pred_[j] < 0:
                  continue
              nodes = _get_nodes(pred_, i, j)
              u.extend(nodes[:-1])
              v.extend(nodes[1:])
              if nodes:
                  masks_i[j, : len(nodes) - 1] = True
          masks.append(masks_i)
      masks = np.stack(masks, axis=0)
  
      u, v = np.array(u), np.array(v)
      edge_ids = g.edge_ids(u, v)
      paths[masks] = F.asnumpy(edge_ids)
      if root is not None:
          paths = paths[0]
  
      return F.copy_to(
          F.tensor(dist, dtype=F.int64), g.device
      ), F.copy_to(F.tensor(paths, dtype=F.int64), g.device)
  
imports: import copy
  from collections.abc import Iterable, Mapping
  import numpy
  import scipy.sparse
  import scipy.sparse.linalg
  from ..utils import version
  from ..None import backend, batch, convert, function, ndarray, subgraph, utils
  from .._ffi.function import _init_api
  from ..base import dgl_warning, DGLError, EID, NID
  from ..frame import Frame
  from ..heterograph import DGLGraph
  from ..heterograph_index import create_heterograph_from_relations, create_metagraph_index
  from ..partition import metis_partition, metis_partition_assignment, partition_graph_with_halo
  from ..sampling.neighbor import sample_neighbors
label_desc: Use image node_id 44 for calling a global function with example usage: shortest_dist(g, root, return_paths) and returns: F,  F, F, nodes, list