code: def evaluate(self, dataloader, write2file=False, part="dev"):
      self.model.eval()
      with torch.no_grad():
          pred_collect = []
          gt_collect = []
          for data in dataloader:
              data = all_to_cuda(data, self.config["device"])
              to_cuda(data["graph_data"], self.config["device"])
  
              if self.use_copy:
                  oov_dict, tgt = prepare_ext_vocab(
                      data["graph_data"],
                      self.vocab,
                      gt_str=data["output_str"],
                      device=self.config["device"],
                  )
                  data["tgt_text"] = tgt
                  ref_dict = oov_dict
              else:
                  oov_dict = None
                  ref_dict = self.vocab.out_word_vocab
  
              prob = self.model(
                  data, oov_dict=oov_dict, require_loss=False
              )
              pred = prob.argmax(dim=-1)
  
              pred_str = wordid2str(pred.detach().cpu(), ref_dict)
              pred_collect.extend(pred_str)
              gt_collect.extend(data["output_str"])
  
          if write2file:
              with open(
                  "{}/{}_pred.txt".format(
                      self.config["out_dir"],
                      self.config["out_dir"].split("/")[-1],
                  ),
                  "w+",
              ) as f:
                  for line in pred_collect:
                      f.write(line + "\n")
  
              with open(
                  "{}/{}_gt.txt".format(
                      self.config["out_dir"],
                      self.config["out_dir"].split("/")[-1],
                  ),
                  "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.nn
  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.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.config import get_basic_args
  from graph4nlp.pytorch.modules.evaluation.rouge import ROUGE
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  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
label_desc: Use image node_id 7 for calling the ModelHandler obj's underlying member method code with example usage: obj.evaluate(dataloader, write2file, part) and returns: scores