code: def dfs_assemble(
      self,
      tree_mess,
      mol_vec,
      all_nodes,
      cur_mol,
      global_amap,
      fa_amap,
      cur_node,
      fa_node,
      prob_decode,
  ):
      fa_nid = fa_node.nid if fa_node is not None else -1
      prev_nodes = [fa_node] if fa_node is not None else []
  
      children = [
          nei for nei in cur_node.neighbors if nei.nid != fa_nid
      ]
      neighbors = [nei for nei in children if nei.mol.GetNumAtoms() > 1]
      neighbors = sorted(
          neighbors, key=lambda x: x.mol.GetNumAtoms(), reverse=True
      )
      singletons = [
          nei for nei in children if nei.mol.GetNumAtoms() == 1
      ]
      neighbors = singletons + neighbors
  
      cur_amap = [
          (fa_nid, a2, a1)
          for nid, a1, a2 in fa_amap
          if nid == cur_node.nid
      ]
      cands = enum_assemble(cur_node, neighbors, prev_nodes, cur_amap)
      if len(cands) == 0:
          return None
      cand_smiles, cand_mols, cand_amap = zip(*cands)
  
      cands = [(candmol, all_nodes, cur_node) for candmol in cand_mols]
  
      cand_vecs = self.jtmpn(cands, tree_mess)
      cand_vecs = self.G_mean(cand_vecs)
      mol_vec = mol_vec.squeeze()
      scores = torch.mv(cand_vecs, mol_vec) * 20
  
      if prob_decode:
          probs = (
              nn.Softmax()(scores.view(1, -1)).squeeze() + 1e-5
          )  # prevent prob = 0
          cand_idx = torch.multinomial(probs, probs.numel())
      else:
          _, cand_idx = torch.sort(scores, descending=True)
  
      backup_mol = Chem.RWMol(cur_mol)
      for i in range(cand_idx.numel()):
          cur_mol = Chem.RWMol(backup_mol)
          pred_amap = cand_amap[cand_idx[i].data]
          new_global_amap = copy.deepcopy(global_amap)
  
          for nei_id, ctr_atom, nei_atom in pred_amap:
              if nei_id == fa_nid:
                  continue
              new_global_amap[nei_id][nei_atom] = new_global_amap[
                  cur_node.nid
              ][ctr_atom]
  
          # father is already attached
          cur_mol = attach_mols(cur_mol, children, [], new_global_amap)
          new_mol = cur_mol.GetMol()
          new_mol = Chem.MolFromSmiles(Chem.MolToSmiles(new_mol))
  
          if new_mol is None:
              continue
  
          result = True
          for nei_node in children:
              if nei_node.is_leaf:
                  continue
              cur_mol = self.dfs_assemble(
                  tree_mess,
                  mol_vec,
                  all_nodes,
                  cur_mol,
                  new_global_amap,
                  pred_amap,
                  nei_node,
                  cur_node,
                  prob_decode,
              )
              if cur_mol is None:
                  result = False
                  break
          if result:
              return cur_mol
  
      return None
  
imports: import torch
  import torch.nn
  from .mol_tree import Vocab, MolTree
  from .nnutils import create_var
  from .jtnn_enc import JTNNEncoder
  from .jtnn_dec import JTNNDecoder
  from .mpn import MPN, mol2graph
  from .jtmpn import JTMPN
  from .chemutils import enum_assemble, set_atommap, copy_edit_mol, attach_mols, atom_equal, decode_stereo
  import rdkit
  import rdkit.Chem
  from rdkit import DataStructs
  from rdkit.Chem import AllChem
  import copy
  import math
label_desc: Use image node_id 11 for calling the JTPropVAE obj's underlying member method code with example usage: obj.dfs_assemble(tree_mess, mol_vec, all_nodes, cur_mol, global_amap, fa_amap, cur_node, fa_node, prob_decode) and returns: None, None, cur_mol