code: def forward_train(self, actions):
      self.prepare_for_train()
  
      stop = self.add_node_and_update(a=actions[self.action_step])
  
      while not stop:
          to_add_edge = self.add_edge_or_not(
              a=actions[self.action_step]
          )
          while to_add_edge:
              self.choose_dest_and_update(a=actions[self.action_step])
              to_add_edge = self.add_edge_or_not(
                  a=actions[self.action_step]
              )
          stop = self.add_node_and_update(a=actions[self.action_step])
  
      return self.get_log_prob()
  
imports: from functools import partial
  import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.distributions import Bernoulli, Categorical
label_desc: Use image node_id 9 for calling the DGMG obj's underlying member method code with example usage: obj.forward_train(actions) and returns: self