code: def run(
      self,
      cand_graphs,
      cand_line_graph,
      tree_mess_src_edges,
      tree_mess_tgt_edges,
      tree_mess_tgt_nodes,
      mol_tree_batch,
  ):
      n_nodes = cand_graphs.num_nodes()
  
      cand_graphs.apply_edges(
          func=lambda edges: {"src_x": edges.src["x"]},
      )
      cand_line_graph.ndata.update(cand_graphs.edata)
  
      bond_features = cand_line_graph.ndata["x"]
      source_features = cand_line_graph.ndata["src_x"]
      features = torch.cat([source_features, bond_features], 1)
      msg_input = self.W_i(features)
      cand_line_graph.ndata.update(
          {
              "msg_input": msg_input,
              "msg": torch.relu(msg_input),
              "accum_msg": torch.zeros_like(msg_input),
          }
      )
      zero_node_state = bond_features.new(
          n_nodes, self.hidden_size
      ).zero_()
      cand_graphs.ndata.update(
          {
              "m": zero_node_state.clone(),
              "h": zero_node_state.clone(),
          }
      )
  
      cand_graphs.edata["alpha"] = cuda(
          torch.zeros(cand_graphs.num_edges(), self.hidden_size)
      )
      cand_graphs.ndata["alpha"] = zero_node_state
      if tree_mess_src_edges.shape[0] > 0:
          if PAPER:
              src_u, src_v = tree_mess_src_edges.unbind(1)
              tgt_u, tgt_v = tree_mess_tgt_edges.unbind(1)
              src_u = src_u.to(mol_tree_batch.device)
              src_v = src_v.to(mol_tree_batch.device)
              eid = mol_tree_batch.edge_ids(src_u, src_v)
              alpha = mol_tree_batch.edata["m"][eid]
              cand_graphs.edges[tgt_u, tgt_v].data["alpha"] = alpha
          else:
              src_u, src_v = tree_mess_src_edges.unbind(1)
              src_u = src_u.to(mol_tree_batch.device)
              src_v = src_v.to(mol_tree_batch.device)
              eid = mol_tree_batch.edge_ids(src_u, src_v)
              alpha = mol_tree_batch.edata["m"][eid]
              node_idx = tree_mess_tgt_nodes.to(
                  device=zero_node_state.device
              )[:, None].expand_as(alpha)
              node_alpha = zero_node_state.clone().scatter_add(
                  0, node_idx, alpha
              )
              cand_graphs.ndata["alpha"] = node_alpha
              cand_graphs.apply_edges(
                  func=lambda edges: {"alpha": edges.src["alpha"]},
              )
  
      cand_line_graph.ndata.update(cand_graphs.edata)
      for i in range(self.depth - 1):
          cand_line_graph.update_all(
              DGLF.copy_u("msg", "msg"), DGLF.sum("msg", "accum_msg")
          )
          cand_line_graph.apply_nodes(self.loopy_bp_updater)
  
      cand_graphs.edata.update(cand_line_graph.ndata)
  
      cand_graphs.update_all(
          DGLF.copy_e("msg", "msg"), DGLF.sum("msg", "m")
      )
      if PAPER:
          cand_graphs.update_all(
              DGLF.copy_e("alpha", "alpha"),
              DGLF.sum("alpha", "accum_alpha"),
          )
      cand_graphs.apply_nodes(self.gather_updater)
  
      return cand_graphs
  
imports: import os
  import dgl
  import dgl.function
  import rdkit.Chem
  import torch
  import torch.nn
  from dgl import line_graph, mean_nodes
  from .nnutils import cuda
label_desc: Use image node_id 3 for calling the DGLJTMPN obj's underlying member method code with example usage: obj.run(cand_graphs, cand_line_graph, tree_mess_src_edges, tree_mess_tgt_edges, tree_mess_tgt_nodes, mol_tree_batch) and returns: cand_graphs