code: def build(self):
      extra_config = {}
      extra_config["feed_vb_to_graph"] = False
      extra_config["feed_q_to_graph"] = False
      extra_config["feed_mode"] = None
      extra_config["feed_graph_to_vb"] = False
      extra_config["feed_special_node"] = False
      extra_config["topk_ans_feed"] = None
      extra_config["compress_crossmodel"] = False
      extra_config["crossmodel_compress_dim"] = None
      extra_config["analysis_mode"] = False
      extra_config["noback_vb"] = False
      self.graph_module = GraphNetworkModule(self.config, extra_config)
  
      if self.config.output_type in [
          "graph_level",
          "graph_level_ansonly",
          "graph_level_inputonly",
      ]:
          # Make a linear last layer
          self.fc = nn.Linear(
              self.graph_module.gn.output_dim, self.config.num_labels
          )
      else:
          assert self.config.output_type in ["graph_prediction"]
  
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 3 for calling the GraphNetworkBare obj's underlying member method code with example usage: obj.build() without return types