code: def translate(self):
      self.model.eval()
  
      pred_collect = []
      gt_collect = []
      dataloader = self.test_dataloader
      for data in dataloader:
          batch_graph, gt_str = data["graph_data"], data["output_str"]
  
          oov_dict = None
          ref_dict = self.vocab.out_word_vocab
          batch_graph = batch_graph.to(self.device)
  
          pred = self.model.translate(
              batch_graph=batch_graph,
              oov_dict=oov_dict,
              beam_size=3,
              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["BLEU"].calculate_scores(
          ground_truth=gt_collect, predict=pred_collect
      )
      self.logger.info(
          "Evaluation results in `{}` split: BLEU-1:{:.4f}\tBLEU-2:{:.4f}\tBLEU-3:{:.4f}\t"
          "BLEU-4:{:.4f}".format(
              "test", score[0][0], score[0][1], score[0][2], score[0][3]
          )
      )
      self.writer.add_scalar(
          "test" + "/BLEU@1", score[0][0] * 100, global_step=0
      )
      self.writer.add_scalar(
          "test" + "/BLEU@2", score[0][1] * 100, global_step=0
      )
      self.writer.add_scalar(
          "test" + "/BLEU@3", score[0][2] * 100, global_step=0
      )
      self.writer.add_scalar(
          "test" + "/BLEU@4", score[0][3] * 100, global_step=0
      )
      return score
  
imports: import os
  import resource
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from torch.utils.tensorboard import SummaryWriter
  from graph4nlp.pytorch.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.modules.evaluation import BLEU
  from args import get_args
  from dataset import IWSLT14Dataset
  from utils import get_log, wordid2str
label_desc: Use image node_id 8 for calling the NMT obj's underlying member method code with example usage: obj.translate() and returns: score