code: def aggregate(self, hiddens, contexts, x_tree_vecs, mode):
      if mode == "word":
          V, V_o = self.W, self.W_o
      elif mode == "stop":
          V, V_o = self.U, self.U_o
      else:
          raise ValueError("aggregate mode is wrong")
  
      tree_contexts = x_tree_vecs.index_select(0, contexts)
      input_vec = torch.cat([hiddens, tree_contexts], dim=-1)
      output_vec = F.relu(V(input_vec))
      return V_o(output_vec)
  
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 2 for calling the JTNNDecoder obj's underlying member method code with example usage: obj.aggregate(hiddens, contexts, x_tree_vecs, mode) and returns: V_o