code: def line_graph(g, backtracking=True, shared=False):
      """Return the line graph of this graph.
  
      The line graph ``L(G)`` of a given graph ``G`` is defined as another graph where
      the nodes in ``L(G)`` correspond to the edges in ``G``.  For any pair of edges ``(u, v)``
      and ``(v, w)`` in ``G``, the corresponding node of edge ``(u, v)`` in ``L(G)`` will
      have an edge connecting to the corresponding node of edge ``(v, w)``.
  
      Parameters
      ----------
      g : DGLGraph
          Input graph.  Must be homogeneous.
      backtracking : bool, optional
          If False, the line graph node corresponding to edge ``(u, v)`` will not have
          an edge connecting to the line graph node corresponding to edge ``(v, u)``.
  
          Default: True.
      shared : bool, optional
          Whether to copy the edge features of the original graph as the node features
          of the result line graph.
  
      Returns
      -------
      G : DGLGraph
          The line graph of this graph.
  
      Notes
      -----
      * If :attr:`shared` is True, the node features of the resulting graph share the same
        storage with the edge features of the input graph. Hence, users should try to
        avoid in-place operations which will be visible to both graphs.
      * The function supports input graph on GPU but copies it to CPU during computation.
      * This function discards the batch information. Please use
        :func:`dgl.DGLGraph.set_batch_num_nodes`
        and :func:`dgl.DGLGraph.set_batch_num_edges` on the transformed graph
        to maintain the information.
  
      Examples
      --------
      Assume that the graph has the following adjacency matrix: ::
  
         A = [[0, 0, 1],
              [1, 0, 1],
              [1, 1, 0]]
  
      >>> g = dgl.graph(([0, 1, 1, 2, 2],[2, 0, 2, 0, 1]), 'user', 'follows')
      >>> lg = g.line_graph()
      >>> lg
      Graph(num_nodes=5, num_edges=8,
      ndata_schemes={}
      edata_schemes={})
      >>> lg.edges()
      (tensor([0, 0, 1, 2, 2, 3, 4, 4]), tensor([3, 4, 0, 3, 4, 0, 1, 2]))
      >>> lg = g.line_graph(backtracking=False)
      >>> lg
      Graph(num_nodes=5, num_edges=4,
      ndata_schemes={}
      edata_schemes={})
      >>> lg.edges()
      (tensor([0, 1, 2, 4]), tensor([4, 0, 3, 1]))
      """
      assert g.is_homogeneous, "only homogeneous graph is supported"
  
      dev = g.device
      lg = DGLGraph(
          _CAPI_DGLHeteroLineGraph(
              g._graph.copy_to(nd.cpu()), backtracking
          )
      )
      lg = lg.to(dev)
      if shared:
          new_frames = utils.extract_edge_subframes(g, None)
          utils.set_new_frames(lg, node_frames=new_frames)
  
      return lg
  
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 10 for calling a global function with example usage: line_graph(g, backtracking, shared) and returns: lg