code: def recon_eval(self, smiles):
      mol_tree = MolTree(smiles)
      mol_tree.recover()
      _, tree_vec, mol_vec = self.encode([mol_tree])
  
      tree_mean = self.T_mean(tree_vec)
      # Following Mueller et al.
      tree_log_var = -torch.abs(self.T_var(tree_vec))
      mol_mean = self.G_mean(mol_vec)
      # Following Mueller et al.
      mol_log_var = -torch.abs(self.G_var(mol_vec))
  
      all_smiles = []
      for i in range(10):
          epsilon = create_var(
              torch.randn(1, self.latent_size / 2), False
          )
          tree_vec = tree_mean + torch.exp(tree_log_var / 2) * epsilon
          epsilon = create_var(
              torch.randn(1, self.latent_size / 2), False
          )
          mol_vec = mol_mean + torch.exp(mol_log_var / 2) * epsilon
          for j in range(10):
              new_smiles = self.decode(
                  tree_vec, mol_vec, prob_decode=True
              )
              all_smiles.append(new_smiles)
      return all_smiles
  
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 8 for calling the JTNNVAEBO obj's underlying member method code with example usage: obj.recon_eval(smiles) and returns: all_smiles