code: def assm(self, mol_batch, jtmpn_holder, x_mol_vecs, x_tree_mess):
      jtmpn_holder, batch_idx = jtmpn_holder
      fatoms, fbonds, agraph, bgraph, scope = jtmpn_holder
      batch_idx = create_var(batch_idx)
  
      cand_vecs = self.jtmpn(
          fatoms, fbonds, agraph, bgraph, scope, x_tree_mess
      )
  
      x_mol_vecs = x_mol_vecs.index_select(0, batch_idx)
      x_mol_vecs = self.A_assm(x_mol_vecs)  # bilinear
      scores = torch.bmm(
          x_mol_vecs.unsqueeze(1), cand_vecs.unsqueeze(-1)
      ).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.data[label] >= 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 = sum(all_loss) / len(mol_batch)
      return all_loss, acc * 1.0 / cnt
  
imports: import copy
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  import rdkit
  import rdkit.Chem
  from .mol_tree import MolTree
  from .nnutils import create_var
  from .jtnn_enc import JTNNEncoder
  from .jtnn_dec import JTNNDecoder
  from .mpn import MPN
  from .jtmpn import JTMPN
  from .datautils import tensorize
  from .chemutils import enum_assemble, set_atommap, copy_edit_mol, attach_mols
label_desc: Use image node_id 8 for calling the JTNNVAE obj's underlying member method code with example usage: obj.assm(mol_batch, jtmpn_holder, x_mol_vecs, x_tree_mess) and returns: all_loss,  unknown