code: def __init__(self):
      super(Conll, self).__init__()
      self.tag_types = [
          "I-PER",
          "O",
          "B-ORG",
          "B-LOC",
          "I-ORG",
          "I-MISC",
          "I-LOC",
          "B-MISC",
      ]
      if args.gpu > -1:
          self.device = torch.device("cuda")
      else:
          self.device = torch.device("cpu")
      self.checkpoint_save_path = "./checkpoints/"
  
      print("finish building model")
      self._build_model()
  
      print("finish dataloading")
      self._build_dataloader()
  
      self._build_optimizer()
      self._build_evaluation()
  
imports: import argparse
  import os
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import torch.optim
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.data.data import from_batch
  from graph4nlp.pytorch.modules.evaluation.accuracy import Accuracy
  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.utils.generic_utils import to_cuda
  from conll import ConllDataset
  from conlleval import evaluate
  from dependency_graph_construction_without_tokenize import DependencyBasedGraphConstruction_without_tokenizer
  from line_graph_construction import LineBasedGraphConstruction
  from model import Word2tag
label_desc: Use image node_id 1 to create a new Conll object  with example: obj = Conll()