code: def sample_prior(self, prob_decode=False):
      z_tree = torch.randn(1, self.latent_size).cuda()
      z_mol = torch.randn(1, self.latent_size).cuda()
      return self.decode(z_tree, z_mol, prob_decode)
  
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 6 for calling the JTNNVAE obj's underlying member method code with example usage: obj.sample_prior(prob_decode) and returns: self