code: def encode_latent(self, jtenc_holder, mpn_holder):
      tree_vecs, _ = self.jtnn(*jtenc_holder)
      mol_vecs = self.mpn(*mpn_holder)
      tree_mean = self.T_mean(tree_vecs)
      mol_mean = self.G_mean(mol_vecs)
      tree_var = -torch.abs(self.T_var(tree_vecs))
      mol_var = -torch.abs(self.G_var(mol_vecs))
      return torch.cat([tree_mean, mol_mean], dim=1), torch.cat(
          [tree_var, mol_var], dim=1
      )
  
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 4 for calling the JTNNVAE obj's underlying member method code with example usage: obj.encode_latent(jtenc_holder, mpn_holder) and returns: torch,  torch