code: def forward_inference(self):
      stop = self.add_node_and_update()
      while (not stop) and (self.g.num_nodes() < self.v_max + 1):
          num_trials = 0
          to_add_edge = self.add_edge_or_not()
          while to_add_edge and (num_trials < self.g.num_nodes() - 1):
              self.choose_dest_and_update()
              num_trials += 1
              to_add_edge = self.add_edge_or_not()
          stop = self.add_node_and_update()
      return self.g
  
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 1 for calling a global function with example usage: forward_inference() and returns: self