code: def decode(self, x_tree_vecs, prob_decode):
      assert x_tree_vecs.size(0) == 1
  
      stack = []
      init_hiddens = create_var(torch.zeros(1, self.hidden_size))
      zero_pad = create_var(torch.zeros(1, 1, self.hidden_size))
      contexts = create_var(torch.LongTensor(1).zero_())
  
      # Root Prediction
      root_score = self.aggregate(
          init_hiddens, contexts, x_tree_vecs, "word"
      )
      _, root_wid = torch.max(root_score, dim=1)
      root_wid = root_wid.item()
  
      root = MolTreeNode(self.vocab.get_smiles(root_wid))
      root.wid = root_wid
      root.idx = 0
      stack.append((root, self.vocab.get_slots(root.wid)))
  
      all_nodes = [root]
      h = {}
      for step in range(MAX_DECODE_LEN):
          node_x, fa_slot = stack[-1]
          cur_h_nei = [
              h[(node_y.idx, node_x.idx)] for node_y in node_x.neighbors
          ]
          if len(cur_h_nei) > 0:
              cur_h_nei = torch.stack(cur_h_nei, dim=0).view(
                  1, -1, self.hidden_size
              )
          else:
              cur_h_nei = zero_pad
  
          cur_x = create_var(torch.LongTensor([node_x.wid]))
          cur_x = self.embedding(cur_x)
  
          # Predict stop
          cur_h = cur_h_nei.sum(dim=1)
          stop_hiddens = torch.cat([cur_x, cur_h], dim=1)
          stop_hiddens = F.relu(self.U_i(stop_hiddens))
          stop_score = self.aggregate(
              stop_hiddens, contexts, x_tree_vecs, "stop"
          )
  
          if prob_decode:
              backtrack = (
                  torch.bernoulli(torch.sigmoid(stop_score)).item() == 0
              )
          else:
              backtrack = stop_score.item() < 0
  
          if not backtrack:  # Forward: Predict next clique
              new_h = GRU(
                  cur_x,
                  cur_h_nei,
                  self.W_z,
                  self.W_r,
                  self.U_r,
                  self.W_h,
              )
              pred_score = self.aggregate(
                  new_h, contexts, x_tree_vecs, "word"
              )
  
              if prob_decode:
                  sort_wid = torch.multinomial(
                      F.softmax(pred_score, dim=1).squeeze(), 5
                  )
              else:
                  _, sort_wid = torch.sort(
                      pred_score, dim=1, descending=True
                  )
                  sort_wid = sort_wid.data.squeeze()
  
              next_wid = None
              for wid in sort_wid[:5]:
                  slots = self.vocab.get_slots(wid)
                  node_y = MolTreeNode(self.vocab.get_smiles(wid))
                  if have_slots(fa_slot, slots) and can_assemble(
                      node_x, node_y
                  ):
                      next_wid = wid
                      next_slots = slots
                      break
  
              if next_wid is None:
                  backtrack = True  # No more children can be added
              else:
                  node_y = MolTreeNode(self.vocab.get_smiles(next_wid))
                  node_y.wid = next_wid
                  node_y.idx = len(all_nodes)
                  node_y.neighbors.append(node_x)
                  h[(node_x.idx, node_y.idx)] = new_h[0]
                  stack.append((node_y, next_slots))
                  all_nodes.append(node_y)
  
          if backtrack:  # Backtrack, use if instead of else
              if len(stack) == 1:
                  break  # At root, terminate
  
              node_fa, _ = stack[-2]
              cur_h_nei = [
                  h[(node_y.idx, node_x.idx)]
                  for node_y in node_x.neighbors
                  if node_y.idx != node_fa.idx
              ]
              if len(cur_h_nei) > 0:
                  cur_h_nei = torch.stack(cur_h_nei, dim=0).view(
                      1, -1, self.hidden_size
                  )
              else:
                  cur_h_nei = zero_pad
  
              new_h = GRU(
                  cur_x,
                  cur_h_nei,
                  self.W_z,
                  self.W_r,
                  self.U_r,
                  self.W_h,
              )
              h[(node_x.idx, node_fa.idx)] = new_h[0]
              node_fa.neighbors.append(node_x)
              stack.pop()
  
      return root, all_nodes
  
file_path: DIG/dig/ggraph/method/JTVAE/fast_jtnn/jtnn_dec.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .mol_tree import MolTree, MolTreeNode
  from .chemutils import enum_assemble, set_atommap
  from .nnutils import create_var, GRU
label_desc: Use image node_id 4 for calling the JTNNDecoder obj's underlying member method code with example usage: obj.decode(x_tree_vecs, prob_decode) and returns: root,  all_nodes