code: def __init__(self, args, device=None, use_rnn=False):
      super(SentenceBiLSTMCRF, self).__init__()
      self.use_rnn = use_rnn
      # if self.use_rnn is True:
      self.prediction = BiLSTMFeedForwardNN(
          args.init_hidden_size * 1, args.init_hidden_size * 1
      ).to(device)
  
      # self.crf=CRFLayer(8).to(device)
      # self.use_crf=use_crf
      self.linear1 = nn.Linear(
          int(args.init_hidden_size * 1), args.hidden_size
      )
      self.linear1_ = nn.Linear(
          int(args.hidden_size * 1), args.num_class
      )
      self.dropout_tag = nn.Dropout(args.tag_dropout)
      self.dropout_rnn_out = nn.Dropout(p=args.rnn_dropout)
      self.logsoftmax = nn.LogSoftmax(dim=1)
      self.nll_loss = nn.NLLLoss()
  
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 1 to create a new SentenceBiLSTMCRF object from inherited base classes: nn with example: obj = SentenceBiLSTMCRF(args, device, use_rnn)