code: def test(self):
      # restored best saved model
      self.model = TextClassifier.load_checkpoint(
          self.stopper.save_model_path
      )
  
      t0 = time.time()
      acc = self.evaluate(self.test_dataloader)
      dur = time.time() - t0
      print(
          "Test examples: {} | Time: {:.2f}s |  Test Acc: {:.4f}".format(
              self.num_test, dur, acc
          )
      )
      self.logger.write(
          "Test examples: {} | Time: {:.2f}s |  Test Acc: {:.4f}".format(
              self.num_test, dur, acc
          )
      )
  
      return acc
  
imports: import argparse
  import os
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import yaml
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.trec import TrecDataset
  from graph4nlp.pytorch.modules.evaluation.accuracy import Accuracy
  from graph4nlp.pytorch.modules.graph_construction import NodeEmbeddingBasedGraphConstruction, NodeEmbeddingBasedRefinedGraphConstruction
  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 import GAT, GGNN, GraphSAGE
  from graph4nlp.pytorch.modules.loss.general_loss import GeneralLoss
  from graph4nlp.pytorch.modules.prediction.classification.graph_classification import FeedForwardNN
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.generic_utils import EarlyStopping, grid, to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
label_desc: Use image node_id 8 for calling the ModelHandler obj's underlying member method code with example usage: obj.test() and returns: acc