code: def forward(self, graph, tgt=None, require_loss=True):
      batch_graph = self.graph_initializer(graph)
  
      if self.use_gnn is False:
          batch_graph.node_features[
              "node_emb"
          ] = batch_graph.node_features["node_feat"]
          batch_graph.node_features["node_emb"] = self.dropout_tag(
              F.elu(
                  self.linear0_(
                      self.dropout_rnn_out(
                          batch_graph.node_features["node_emb"]
                      )
                  )
              )
          )
  
      else:
          # run GNN
          if self.gnn_type == "ggnn":
              batch_graph.node_features[
                  "node_feat"
              ] = batch_graph.node_features["node_feat"]
          else:
              batch_graph.node_features["node_feat"] = self.dropout_tag(
                  F.elu(
                      self.linear0(
                          self.dropout_rnn_out(
                              batch_graph.node_features["node_feat"]
                          )
                      )
                  )
              )
  
          batch_graph = self.gnn(batch_graph)
  
      # down-task
      loss, logits = self.bilstmcrf(batch_graph, tgt)
      pred_index = logits2index(logits)
      self.loss = loss
  
      if require_loss is True:
          return pred_index, self.loss
      else:
          loss = None
          return pred_index, self.loss
  
imports: import os
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  from graph4nlp.pytorch.modules.graph_construction import NodeEmbeddingBasedRefinedGraphConstruction
  from graph4nlp.pytorch.modules.graph_construction.node_embedding_based_graph_construction import NodeEmbeddingBasedGraphConstruction
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  from graph4nlp.pytorch.modules.graph_embedding_initialization.graph_embedding_initialization import GraphEmbeddingInitialization
  from graph4nlp.pytorch.modules.graph_embedding_learning.gat import GAT
  from graph4nlp.pytorch.modules.graph_embedding_learning.gcn import GCN
  from graph4nlp.pytorch.modules.graph_embedding_learning.ggnn import GGNN
  from graph4nlp.pytorch.modules.graph_embedding_learning.graphsage import GraphSAGE
  from graph4nlp.pytorch.modules.prediction.classification.node_classification import BiLSTMFeedForwardNN
label_desc: Use image node_id 2 for calling the Word2tag obj's underlying member method code with example usage: obj.forward(graph, tgt, require_loss) and returns: pred_index,  self, pred_index,  self