code: def to_homogeneous(
      G, ndata=None, edata=None, store_type=True, return_count=False
  ):
      """Convert a heterogeneous graph to a homogeneous graph and return.
  
      By default, the function stores the node and edge types of the input graph as
      the ``dgl.NTYPE`` and ``dgl.ETYPE`` features in the returned graph.
      Each feature is an integer representing the type id, determined by the
      :meth:`DGLGraph.get_ntype_id` and :meth:`DGLGraph.get_etype_id` methods.
      One can omit it by specifying ``store_type=False``.
  
      The result graph assigns nodes and edges of the same type with IDs in continuous range
      (i.e., nodes of the first type have IDs 0 ~ ``G.num_nodes(G.ntypes[0])``; nodes
      of the second type come after; so on and so forth). Therefore, a more memory-efficient
      format for type information is an integer list; the i^th corresponds to
      the number of nodes/edges of the i^th type. One can choose this format by
      specifying ``return_count=True``.
  
      Parameters
      ----------
      G : DGLGraph
          The heterogeneous graph.
      ndata : list[str], optional
          The node features to combine across all node types. For each feature ``feat`` in
          :attr:`ndata`, it concatenates ``G.nodes[T].data[feat]`` across all node types ``T``.
          As a result, the feature ``feat`` of all node types should have the same shape and
          data type. By default, the returned graph will not have any node features.
      edata : list[str], optional
          The edge features to combine across all edge types. For each feature ``feat`` in
          :attr:`edata`, it concatenates ``G.edges[T].data[feat]`` across all edge types ``T``.
          As a result, the feature ``feat`` of all edge types should have the same shape and
          data type. By default, the returned graph will not have any edge features.
      store_type : bool, optional
          If True, store type information as the ``dgl.NTYPE`` and ``dgl.ETYPE`` features
          in the returned graph.
      return_count : bool, optional
          If True, return type information as an integer list; the i^th element corresponds to
          the number of nodes/edges of the i^th type.
  
      Returns
      -------
      DGLGraph
          A homogeneous graph.
      ntype_count : list[int], optional
          Number of nodes of each type. Return when ``return_count`` is True.
      etype_count : list[int], optional
          Number of edges of each type. Return when ``return_count`` is True.
  
      Notes
      -----
  
      * Calculating type information may introduce noticeable cost. Setting both ``store_type``
        and ``return_count`` to False can avoid such cost if type information is not needed.
        Otherwise, DGL recommends to use ``store_type=False`` and ``return_count=True`` due
        to its memory efficiency.
      * The ``ntype_count`` and ``etype_count`` lists can help speed up some operations.
        See :class:`~dgl.nn.pytorch.conv.RelGraphConv` for such an example.
      * Calling :func:`~dgl.to_homogeneous` then calling :func:`~dgl.to_heterogeneous` again
        yields the same result.
  
      Examples
      --------
  
      The following example uses PyTorch backend.
  
      >>> import dgl
      >>> import torch
  
      >>> hg = dgl.heterograph({
      ...     ('user', 'follows', 'user'): ([0, 1], [1, 2]),
      ...     ('developer', 'develops', 'game'): ([0, 1], [0, 1])
      ...     })
      >>> hg.nodes['user'].data['h'] = torch.ones(3, 1)
      >>> hg.nodes['developer'].data['h'] = torch.zeros(2, 1)
      >>> hg.nodes['game'].data['h'] = torch.ones(2, 1)
      >>> g = dgl.to_homogeneous(hg)
      >>> # The first three nodes are for 'user', the next two are for 'developer',
      >>> # and the last two are for 'game'
      >>> g.ndata
      {'_TYPE': tensor([0, 0, 0, 1, 1, 2, 2]), '_ID': tensor([0, 1, 2, 0, 1, 0, 1])}
      >>> # The first two edges are for 'follows', and the next two are for 'develops' edges.
      >>> g.edata
      {'_TYPE': tensor([0, 0, 1, 1]), '_ID': tensor([0, 1, 0, 1])}
  
      Combine feature 'h' across all node types in the conversion.
  
      >>> g = dgl.to_homogeneous(hg, ndata=['h'])
      >>> g.ndata['h']
      tensor([[1.], [1.], [1.], [0.], [0.], [1.], [1.]])
  
      See Also
      --------
      to_heterogeneous
      """
      num_nodes_per_ntype = [G.num_nodes(ntype) for ntype in G.ntypes]
      offset_per_ntype = np.insert(np.cumsum(num_nodes_per_ntype), 0, 0)
      srcs = []
      dsts = []
      nids = []
      eids = []
      if store_type:
          ntype_ids = []
          etype_ids = []
      if return_count:
          ntype_count = []
          etype_count = []
      total_num_nodes = 0
  
      for ntype_id, ntype in enumerate(G.ntypes):
          num_nodes = G.num_nodes(ntype)
          total_num_nodes += num_nodes
          if store_type:
              # Type ID is always in int64
              ntype_ids.append(
                  F.full_1d(num_nodes, ntype_id, F.int64, G.device)
              )
          if return_count:
              ntype_count.append(num_nodes)
          nids.append(F.arange(0, num_nodes, G.idtype, G.device))
  
      for etype_id, etype in enumerate(G.canonical_etypes):
          srctype, _, dsttype = etype
          src, dst = G.all_edges(etype=etype, order="eid")
          num_edges = len(src)
          srcs.append(
              src + int(offset_per_ntype[G.get_ntype_id(srctype)])
          )
          dsts.append(
              dst + int(offset_per_ntype[G.get_ntype_id(dsttype)])
          )
          if store_type:
              # Type ID is always in int64
              etype_ids.append(
                  F.full_1d(num_edges, etype_id, F.int64, G.device)
              )
          if return_count:
              etype_count.append(num_edges)
          eids.append(F.arange(0, num_edges, G.idtype, G.device))
  
      retg = graph(
          (F.cat(srcs, 0), F.cat(dsts, 0)),
          num_nodes=total_num_nodes,
          idtype=G.idtype,
          device=G.device,
      )
  
      # copy features
      if ndata is None:
          ndata = []
      if edata is None:
          edata = []
      comb_nf = combine_frames(
          G._node_frames, range(len(G.ntypes)), col_names=ndata
      )
      comb_ef = combine_frames(
          G._edge_frames, range(len(G.etypes)), col_names=edata
      )
      if comb_nf is not None:
          retg.ndata.update(comb_nf)
      if comb_ef is not None:
          retg.edata.update(comb_ef)
  
      retg.ndata[NID] = F.cat(nids, 0)
      retg.edata[EID] = F.cat(eids, 0)
      if store_type:
          retg.ndata[NTYPE] = F.cat(ntype_ids, 0)
          retg.edata[ETYPE] = F.cat(etype_ids, 0)
  
      if return_count:
          return retg, ntype_count, etype_count
      else:
          return retg
  
imports: from collections import defaultdict
  from collections.abc import Mapping
  import networkx
  import numpy
  from scipy.sparse import spmatrix
  from .None import backend, graph_index, heterograph_index, utils
  from .base import DGLError, EID, ETYPE, NID, NTYPE
  from .heterograph import combine_frames, DGLBlock, DGLGraph
label_desc: Use image node_id 7 for calling a global function with example usage: to_homogeneous(G, ndata, edata, store_type, return_count) and returns: retg,  ntype_count,  etype_count, retg