code: def translate(self, dataloader, write2file=True):
      self.model.eval()
      with torch.no_grad():
          pred_collect = []
          gt_collect = []
          for i, data in enumerate(dataloader):
              print(i)
              data = all_to_cuda(data, self.config["device"])
              data["graph_data"] = data["graph_data"].to(
                  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_graph = self.model.g2s.graph_initializer(
                  data["graph_data"]
              )
              prob = self.model.g2s.encoder_decoder_beam_search(
                  batch_graph,
                  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["output_str"])
  
          if write2file:
              with open(
                  "{}/{}_bs{}_pred.txt".format(
                      self.config["out_dir"],
                      self.config["out_dir"].split("/")[-1],
                      self.config["beam_size"],
                  ),
                  "w+",
              ) as f:
                  for line in pred_collect:
                      f.write(line + "\n")
  
              with open(
                  "{}/{}_bs{}_gt.txt".format(
                      self.config["out_dir"],
                      self.config["out_dir"].split("/")[-1],
                      self.config["beam_size"],
                  ),
                  "w+",
              ) as f:
                  for line in gt_collect:
                      f.write(line + "\n")
  
          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.optim
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.cnn import CNNDataset
  from graph4nlp.pytorch.modules.config import get_basic_args
  from graph4nlp.pytorch.modules.evaluation.rouge import 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 EarlyStopping, to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
  from graph4nlp.pytorch.modules.utils.summarization_utils import wordid2str
  from main import SumModel
label_desc: Use image node_id 6 for calling the ModelHandler obj's underlying member method code with example usage: obj.translate(dataloader, write2file) and returns: scores