code: def forward(self, x_batch, beta):
      x_batch, x_jtenc_holder, x_mpn_holder, x_jtmpn_holder = x_batch
      x_tree_vecs, x_tree_mess, x_mol_vecs = self.encode(
          x_jtenc_holder, x_mpn_holder
      )
      z_tree_vecs, tree_kl = self.rsample(
          x_tree_vecs, self.T_mean, self.T_var
      )
      z_mol_vecs, mol_kl = self.rsample(
          x_mol_vecs, self.G_mean, self.G_var
      )
  
      kl_div = tree_kl + mol_kl
      word_loss, topo_loss, word_acc, topo_acc = self.decoder(
          x_batch, z_tree_vecs
      )
      assm_loss, assm_acc = self.assm(
          x_batch, x_jtmpn_holder, z_mol_vecs, x_tree_mess
      )
  
      return (
          word_loss + topo_loss + assm_loss + beta * kl_div,
          kl_div.item(),
          word_acc,
          topo_acc,
          assm_acc,
      )
  
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 7 for calling the JTNNVAE obj's underlying member method code with example usage: obj.forward(x_batch, beta) and returns: unknown,  kl_div,  word_acc,  topo_acc,  assm_acc