code: def test(self):
      print("sucessfully loaded the existing saved model!")
      self.model.eval()
      pred_collect = []
      tokens_collect = []
      tgt_collect = []
      with torch.no_grad():
          for data in self.test_dataloader:
              graph, tgt = data["graph_data"], data["tgt_tag"]
              graph = graph.to(self.device)
              tgt_l = [tgt_.to(self.device) for tgt_ in tgt]
              pred, loss = self.model(graph, tgt_l, require_loss=True)
              # pred = logits2tag(g)
              pred_collect.extend(pred)
              tgt_collect.extend(tgt)
              tokens_collect.extend(get_tokens(from_batch(graph)))
      prec, rec, f1 = conll_score(
          pred_collect, tgt_collect, self.tag_types
      )
      print(
          "Testing results: precision is %5.2f, rec is %5.2f, f1 is %5.2f"
          % (prec, rec, f1)
      )
      return f1
  
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 6 for calling the Conll obj's underlying member method code with example usage: obj.test() and returns: f1