code: def forward(self, cand_batch, mol_tree_batch):
      (
          cand_graphs,
          tree_mess_src_edges,
          tree_mess_tgt_edges,
          tree_mess_tgt_nodes,
      ) = cand_batch
  
      n_samples = len(cand_graphs)
  
      cand_line_graph = line_graph(
          cand_graphs, backtracking=False, shared=True
      )
  
      n_nodes = cand_graphs.num_nodes()
      n_edges = cand_graphs.num_edges()
  
      cand_graphs = self.run(
          cand_graphs,
          cand_line_graph,
          tree_mess_src_edges,
          tree_mess_tgt_edges,
          tree_mess_tgt_nodes,
          mol_tree_batch,
      )
  
      g_repr = mean_nodes(cand_graphs, "h")
  
      self.n_samples_total += n_samples
      self.n_nodes_total += n_nodes
      self.n_edges_total += n_edges
      self.n_passes += 1
  
      return g_repr
  
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 2 for calling the DGLJTMPN obj's underlying member method code with example usage: obj.forward(cand_batch, mol_tree_batch) and returns: g_repr