code: def decode(self, tree_vec, mol_vec):
      mol_tree, nodes_dict, effective_nodes = self.decoder.decode(
          tree_vec
      )
      effective_nodes_list = effective_nodes.tolist()
      nodes_dict = [nodes_dict[v] for v in effective_nodes_list]
  
      for i, (node_id, node) in enumerate(
          zip(effective_nodes_list, nodes_dict)
      ):
          node["idx"] = i
          node["nid"] = i + 1
          node["is_leaf"] = True
          if mol_tree.graph.in_degrees(node_id) > 1:
              node["is_leaf"] = False
              set_atommap(node["mol"], node["nid"])
  
      mol_tree_sg = mol_tree.graph.subgraph(
          effective_nodes.to(tree_vec.device)
      )
      mol_tree_msg, _ = self.jtnn([mol_tree_sg])
      mol_tree_msg = unbatch(mol_tree_msg)[0]
      mol_tree_msg.nodes_dict = nodes_dict
  
      cur_mol = copy_edit_mol(nodes_dict[0]["mol"])
      global_amap = [{}] + [{} for node in nodes_dict]
      global_amap[1] = {
          atom.GetIdx(): atom.GetIdx() for atom in cur_mol.GetAtoms()
      }
  
      cur_mol = self.dfs_assemble(
          mol_tree_msg, mol_vec, cur_mol, global_amap, [], 0, None
      )
      if cur_mol is None:
          return None
  
      cur_mol = cur_mol.GetMol()
      set_atommap(cur_mol)
      cur_mol = Chem.MolFromSmiles(Chem.MolToSmiles(cur_mol))
      if cur_mol is None:
          return None
  
      smiles2D = Chem.MolToSmiles(cur_mol)
      stereo_cands = decode_stereo(smiles2D)
      if len(stereo_cands) == 1:
          return stereo_cands[0]
      stereo_graphs = [mol2dgl_enc(c) for c in stereo_cands]
      stereo_cand_graphs, atom_x, bond_x = zip(*stereo_graphs)
      stereo_cand_graphs = cuda(batch(stereo_cand_graphs))
      atom_x = cuda(torch.cat(atom_x))
      bond_x = cuda(torch.cat(bond_x))
      stereo_cand_graphs.ndata["x"] = atom_x
      stereo_cand_graphs.edata["x"] = bond_x
      stereo_cand_graphs.edata["src_x"] = atom_x.new(
          bond_x.shape[0], atom_x.shape[1]
      ).zero_()
      stereo_vecs = self.mpn(stereo_cand_graphs)
      stereo_vecs = self.G_mean(stereo_vecs)
      scores = F.cosine_similarity(stereo_vecs, mol_vec)
      _, max_id = scores.max(0)
      return stereo_cands[max_id.item()]
  
imports: import copy
  import rdkit.Chem
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import batch, unbatch
  from .chemutils import attach_mols_nx, copy_edit_mol, decode_stereo, enum_assemble_nx, set_atommap
  from .jtmpn import DGLJTMPN, mol2dgl_single
  from .jtnn_dec import DGLJTNNDecoder
  from .jtnn_enc import DGLJTNNEncoder
  from .mpn import DGLMPN, mol2dgl_single
  from .nnutils import cuda
label_desc: Use image node_id 8 for calling the DGLJTNNVAE obj's underlying member method code with example usage: obj.decode(tree_vec, mol_vec) and returns: stereo_cands, None, None, stereo_cands