code: def translate(self):
      self.model.eval()
      with torch.no_grad():
          pred_collect = []
          gt_collect = []
          for i, data in enumerate(self.test_dataloader):
              data = all_to_cuda(data, self.config["device"])
              if self.use_copy:
                  oov_dict = prepare_ext_vocab(
                      data["graph_data"],
                      self.vocab,
                      device=self.config["device"],
                  )
                  ref_dict = oov_dict
              else:
                  oov_dict = None
                  ref_dict = self.vocab.out_word_vocab
  
              batch_gd = self.model.encode_init_node_feature(data)
              prob = self.model.g2s.encoder_decoder_beam_search(
                  batch_gd,
                  self.config["beam_size"],
                  topk=1,
                  oov_dict=oov_dict,
              )
  
              pred_ids = (
                  torch.zeros(
                      len(prob),
                      self.config["decoder_args"][
                          "rnn_decoder_private"
                      ]["max_decoder_step"],
                  )
                  .fill_(ref_dict.EOS)
                  .to(self.config["device"])
                  .int()
              )
              for i, item in enumerate(prob):
                  item = item[0]
                  seq = [j.view(1, 1) for j in item]
                  seq = torch.cat(seq, dim=1)
                  pred_ids[i, : seq.shape[1]] = seq
  
              pred_str = wordid2str(pred_ids.detach().cpu(), ref_dict)
  
              pred_collect.extend(pred_str)
              gt_collect.extend(data["tgt_text"])
  
          scores = self.evaluate_predictions(gt_collect, pred_collect)
  
          return scores
  
imports: import argparse
  import os
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.squad import SQuADDataset
  from graph4nlp.pytorch.modules.config import get_basic_args
  from graph4nlp.pytorch.modules.evaluation import BLEU, METEOR, ROUGE
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.config_utils import get_yaml_config, update_values
  from graph4nlp.pytorch.modules.utils.copy_utils import prepare_ext_vocab
  from graph4nlp.pytorch.modules.utils.generic_utils import all_to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
  from .main import QGModel
label_desc: Use image node_id 5 for calling the ModelHandler obj's underlying member method code with example usage: obj.translate() and returns: scores