code: def forward(self, actions=None):
      # The graph you will work on
      self.g = dgl.DGLGraph()
  
      # If there are some features for nodes and edges,
      # zero tensors will be set for those of new nodes and edges.
      self.g.set_n_initializer(dgl.frame.zero_initializer)
      self.g.set_e_initializer(dgl.frame.zero_initializer)
  
      if self.training:
          return self.forward_train(actions=actions)
      else:
          return self.forward_inference()
  
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 7 for calling the DGMGSkeleton obj's underlying member method code with example usage: obj.forward(actions) and returns: self, self