code: def translate(self):
      self.model.eval()
  
      pred_collect = []
      gt_collect = []
      dataloader = self.test_dataloader
      for data in dataloader:
          graph, gt_str = data["graph_data"], data["output_str"]
          graph = graph.to(self.device)
          if self.use_copy:
              oov_dict = prepare_ext_vocab(
                  batch_graph=graph,
                  vocab=self.vocab,
                  device=self.device,
              )
              ref_dict = oov_dict
          else:
              oov_dict = None
              ref_dict = self.vocab.out_word_vocab
  
          pred = self.model.translate(
              batch_graph=graph, oov_dict=oov_dict, beam_size=4, topk=1
          )
  
          pred_ids = pred[:, 0, :]  # we just use the top-1
  
          pred_str = wordid2str(pred_ids.detach().cpu(), ref_dict)
  
          pred_collect.extend(pred_str)
          gt_collect.extend(gt_str)
  
      score = self.metrics[0].calculate_scores(
          ground_truth=gt_collect, predict=pred_collect
      )
      self.logger.info(
          "Evaluation accuracy in `{}` split: {:.3f}".format(
              "test", score
          )
      )
      return score
  
imports: import numpy
  import torch
  import torch.optim
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.jobs import JobsDataset
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.utils.copy_utils import prepare_ext_vocab
  from args import get_args
  from build_model import get_model
  from evaluation import ExpressionAccuracy
  from utils import get_log, wordid2str
label_desc: Use image node_id 14 for calling the Jobs obj's underlying member method code with example usage: obj.translate() and returns: score