code: def forward(self, batch_graph, tgt_tags):
      batch_graph = self.prediction(batch_graph)
      batch_emb = batch_graph.node_features["logits"]
  
      batch_graph.node_features["logits"] = self.linear1_(
          self.dropout_tag(
              F.elu(self.linear1(self.dropout_rnn_out(batch_emb)))
          )
      )
  
      logits = batch_graph.node_features["logits"][
          :, :
      ]  # [batch*sentence*num_nodes,num_lable]
      if tgt_tags is None:
          loss = 0
      else:
          tgt = torch.cat(tgt_tags)
          loss = self.nll_loss(self.logsoftmax(logits), tgt)
  
      return loss, logits
  
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 SentenceBiLSTMCRF obj's underlying member method code with example usage: obj.forward(batch_graph, tgt_tags) and returns: loss,  logits