code: def forward_train(self, actions):
      """
      - actions: list
          - Contains a_1, ..., a_T described above
      - self.prepare_for_train()
          - Initializes self.action_step to be 0, which will get
            incremented by 1 every time it is called.
          - Initializes objects recording log p(a_t|a_1,...a_{t-1})
  
      Returns
      -------
      - self.get_log_prob(): log p(a_1, ..., a_T)
      """
      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: import os
  import dgl
  import torch.nn
  import torch
  from functools import partial
  import torch.nn.functional
  from torch.distributions import Bernoulli
  from torch.distributions import Categorical
  import torch.utils.model_zoo
label_desc: Use image node_id 2 for calling a global function with example usage: forward_train(actions) and returns: self