code: def assm(self, mol_batch, mol_vec, tree_mess):
      cands = []
      batch_idx = []
      for i, mol_tree in enumerate(mol_batch):
          for node in mol_tree.nodes:
              # Leaf node's attachment is determined by neighboring node's attachment
              if node.is_leaf or len(node.cands) == 1:
                  continue
              cands.extend(
                  [
                      (cand, mol_tree.nodes, node)
                      for cand in node.cand_mols
                  ]
              )
              batch_idx.extend([i] * len(node.cands))
  
      cand_vec = self.jtmpn(cands, tree_mess)
      cand_vec = self.G_mean(cand_vec)
  
      batch_idx = create_var(torch.LongTensor(batch_idx))
      mol_vec = mol_vec.index_select(0, batch_idx)
  
      mol_vec = mol_vec.view(-1, 1, self.latent_size / 2)
      cand_vec = cand_vec.view(-1, self.latent_size / 2, 1)
      scores = torch.bmm(mol_vec, cand_vec).squeeze()
  
      cnt, tot, acc = 0, 0, 0
      all_loss = []
      for i, mol_tree in enumerate(mol_batch):
          comp_nodes = [
              node
              for node in mol_tree.nodes
              if len(node.cands) > 1 and not node.is_leaf
          ]
          cnt += len(comp_nodes)
          for node in comp_nodes:
              label = node.cands.index(node.label)
              ncand = len(node.cands)
              cur_score = scores.narrow(0, tot, ncand)
              tot += ncand
  
              if cur_score[label].item() >= cur_score.max().item():
                  acc += 1
  
              label = create_var(torch.LongTensor([label]))
              all_loss.append(
                  self.assm_loss(cur_score.view(1, -1), label)
              )
  
      # all_loss = torch.stack(all_loss).sum() / len(mol_batch)
      all_loss = sum(all_loss) / len(mol_batch)
      return all_loss, acc * 1.0 / cnt
  
imports: import torch
  import torch.nn
  from .mol_tree import Vocab, MolTree
  from .nnutils import create_var
  from .jtnn_enc_bo import JTNNEncoderBO
  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 5 for calling the JTNNVAEBO obj's underlying member method code with example usage: obj.assm(mol_batch, mol_vec, tree_mess) and returns: all_loss,  unknown