code: def evaluate_predictions(self, ground_truth, predict):
      output = {}
      for name, scorer in self.metrics.items():
          score = scorer.calculate_scores(
              ground_truth=ground_truth, predict=predict
          )
          if name.upper() == "BLEU":
              for i in range(len(score[0])):
                  output["BLEU_{}".format(i + 1)] = score[0][i]
          else:
              output[name] = score[0]
  
      return output
  
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 8 for calling the ModelHandler obj's underlying member method code with example usage: obj.evaluate_predictions(ground_truth, predict) and returns: output