class_docstr: The generic class for graph networks
  Can be generically added to any other kind of network
code: def add_analysis_to_output(self, output):
      # Add graphicx graph so we can see what nodes were activated / see subgraphs
      output["graph"] = self.graph
      output["graph_idx"] = self.graph_idx
  
      # Add structs so we can easily convert between vocabs
      output["name2node_idx"] = self.name2node_idx
      output["node_acts"] = self.qid2nodeact
      output["index_in_ans"] = self.index_in_ans
      output["index_in_node"] = self.index_in_node
      output["graph_answers"] = self.graph_answers
      output["graph_ans_node_idx"] = self.graph_ans_node_idx
  
      output["graph_hidden_act"] = self.graph_hidden_debug.cpu()
  
      # Return output with new keys
      return output
  
imports: import os
  import pickle
  import networkx
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from gensim.models import KeyedVectors
  from gensim.scripts.glove2word2vec import glove2word2vec
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.utils.text import VocabDict
  from networkx import convert_node_labels_to_integers
  from torch_geometric.nn import BatchNorm, GCNConv, RGCNConv, SAGEConv
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling the GraphNetworkModule obj's underlying member method code with example usage: obj.add_analysis_to_output(output) and returns: output