code: def merge(graphs):
      r"""Merge a sequence of graphs together into a single graph.
  
      Nodes and edges that exist in ``graphs[i+1]`` but not in ``dgl.merge(graphs[0:i+1])``
      will be added to ``dgl.merge(graphs[0:i+1])`` along with their data.
      Nodes that exist in both ``dgl.merge(graphs[0:i+1])`` and ``graphs[i+1]``
      will be updated with ``graphs[i+1]``'s data if they do not match.
  
      Parameters
      ----------
      graphs : list[DGLGraph]
          Input graphs.
  
      Returns
      -------
      DGLGraph
          The merged graph.
  
      Notes
      ----------
      * Inplace updates are applied to a new, empty graph.
      * Features that exist in ``dgl.graphs[i+1]`` will be created in
        ``dgl.merge(dgl.graphs[i+1])`` if they do not already exist.
  
      Examples
      ----------
  
      The following example uses PyTorch backend.
  
      >>> import dgl
      >>> import torch
      >>> g = dgl.graph((torch.tensor([0,1]), torch.tensor([2,3])))
      >>> g.ndata["x"] = torch.zeros(4)
      >>> h = dgl.graph((torch.tensor([1,2]), torch.tensor([0,4])))
      >>> h.ndata["x"] = torch.ones(5)
      >>> m = dgl.merge([g, h])
  
      ``m`` now contains edges and nodes from ``h`` and ``g``.
  
      >>> m.edges()
      (tensor([0, 1, 1, 2]), tensor([2, 3, 0, 4]))
      >>> m.nodes()
      tensor([0, 1, 2, 3, 4])
  
      ``g``'s data has updated with ``h``'s in ``m``.
  
      >>> m.ndata["x"]
      tensor([1., 1., 1., 1., 1.])
  
      See Also
      ----------
      add_nodes
      add_edges
      """
  
      if len(graphs) == 0:
          raise DGLError("The input list of graphs cannot be empty.")
  
      ref = graphs[0]
      ntypes = ref.ntypes
      etypes = ref.canonical_etypes
      data_dict = {etype: ([], []) for etype in etypes}
      num_nodes_dict = {ntype: 0 for ntype in ntypes}
      merged = dgl.heterograph(
          data_dict, num_nodes_dict, ref.idtype, ref.device
      )
  
      # Merge edges and edge data.
      for etype in etypes:
          unmerged_us = []
          unmerged_vs = []
          edata_frames = []
          for graph in graphs:
              etype_id = graph.get_etype_id(etype)
              us, vs = graph.edges(etype=etype)
              unmerged_us.append(us)
              unmerged_vs.append(vs)
              edge_data = graph._edge_frames[etype_id]
              edata_frames.append(edge_data)
          keys = ref.edges[etype].data.keys()
          if len(keys) == 0:
              edges_data = None
          else:
              edges_data = {
                  k: F.cat([f[k] for f in edata_frames], dim=0)
                  for k in keys
              }
          merged_us = F.copy_to(
              F.astype(F.cat(unmerged_us, dim=0), ref.idtype),
              ref.device,
          )
          merged_vs = F.copy_to(
              F.astype(F.cat(unmerged_vs, dim=0), ref.idtype),
              ref.device,
          )
          merged.add_edges(merged_us, merged_vs, edges_data, etype)
  
      # Add node data and isolated nodes from next_graph to merged.
      for next_graph in graphs:
          for ntype in ntypes:
              merged_ntype_id = merged.get_ntype_id(ntype)
              next_ntype_id = next_graph.get_ntype_id(ntype)
              next_ndata = next_graph._node_frames[next_ntype_id]
              node_diff = next_graph.num_nodes(
                  ntype=ntype
              ) - merged.num_nodes(ntype=ntype)
              n_extra_nodes = max(0, node_diff)
              merged.add_nodes(n_extra_nodes, ntype=ntype)
              next_nodes = F.arange(
                  0,
                  next_graph.num_nodes(ntype=ntype),
                  merged.idtype,
                  merged.device,
              )
              merged._node_frames[merged_ntype_id].update_row(
                  next_nodes, next_ndata
              )
  
      return merged
  
label_desc: Use image node_id 1 for calling a global function with example usage: merge(graphs) and returns: merged