code: def rsample(self, z_vecs, W_mean, W_var):
      batch_size = z_vecs.size(0)
      z_mean = W_mean(z_vecs)
      z_log_var = -torch.abs(W_var(z_vecs))  # Following Mueller et al.
      kl_loss = (
          -0.5
          * torch.sum(
              1.0 + z_log_var - z_mean * z_mean - torch.exp(z_log_var)
          )
          / batch_size
      )
      epsilon = create_var(torch.randn_like(z_mean))
      z_vecs = z_mean + torch.exp(z_log_var / 2) * epsilon
      return z_vecs, kl_loss
  
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 5 for calling the JTNNVAE obj's underlying member method code with example usage: obj.rsample(z_vecs, W_mean, W_var) and returns: z_vecs,  kl_loss