code: def forward(self, sample_list):
      # Forward with sample list
      output = self.graph_module(sample_list)
  
      # If graph_level, we need to now predict logits
      if self.config.output_type in [
          "graph_level",
          "graph_level_ansonly",
          "graph_level_inputonly",
      ]:
          # Do last layer
          logits = self.fc(output)
      else:
          assert self.config.output_type in ["graph_prediction"]
          logits = output
  
      # Do zerobias
      logits -= 6.58
  
      # For loss calculations (automatically done by MMF
      # as per the loss defined in the config),
      # we need to return a dict with "scores" key as logits
      output = {"scores": logits}
  
      # If we're in eval / analysis mode, add more to output
      if self.config.analysis_mode:
          output = self.graph_module.add_analysis_to_output(output)
  
      # MMF will automatically calculate loss
      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 4 for calling the GraphNetworkBare obj's underlying member method code with example usage: obj.forward(sample_list) and returns: output