code: def sample(self, tree_vec, mol_vec, e1=None, e2=None):
      tree_mean = self.T_mean(tree_vec)
      tree_log_var = -torch.abs(self.T_var(tree_vec))
      mol_mean = self.G_mean(mol_vec)
      mol_log_var = -torch.abs(self.G_var(mol_vec))
  
      epsilon = (
          cuda(torch.randn(*tree_mean.shape)) if e1 is None else e1
      )
      tree_vec = tree_mean + torch.exp(tree_log_var / 2) * epsilon
      epsilon = cuda(torch.randn(*mol_mean.shape)) if e2 is None else e2
      mol_vec = mol_mean + torch.exp(mol_log_var / 2) * epsilon
  
      z_mean = torch.cat([tree_mean, mol_mean], 1)
      z_log_var = torch.cat([tree_log_var, mol_log_var], 1)
  
      return tree_vec, mol_vec, z_mean, z_log_var
  
imports: import copy
  import rdkit.Chem
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import batch, unbatch
  from .chemutils import attach_mols_nx, copy_edit_mol, decode_stereo, enum_assemble_nx, set_atommap
  from .jtmpn import DGLJTMPN, mol2dgl_single
  from .jtnn_dec import DGLJTNNDecoder
  from .jtnn_enc import DGLJTNNEncoder
  from .mpn import DGLMPN, mol2dgl_single
  from .nnutils import cuda
label_desc: Use image node_id 4 for calling the DGLJTNNVAE obj's underlying member method code with example usage: obj.sample(tree_vec, mol_vec, e1, e2) and returns: tree_vec,  mol_vec,  z_mean,  z_log_var