code: def forward(self, data, oov_dict=None, require_loss=True):
      if require_loss:
          prob, enc_attn_weights, coverage_vectors = self.g2s(
              data["graph_data"],
              oov_dict=oov_dict,
              tgt_seq=data["tgt_seq"],
          )
  
          tgt = data["tgt_seq"]
          min_length = min(prob.shape[1], tgt.shape[1])
          prob = prob[:, :min_length, :]
          tgt = tgt[:, :min_length]
          loss = self.loss_calc(
              prob,
              label=tgt,
              enc_attn_weights=enc_attn_weights,
              coverage_vectors=coverage_vectors,
          )
          return prob, loss * min_length / 2
      else:
          prob, enc_attn_weights, coverage_vectors = self.g2s(
              data["graph_data"], oov_dict=oov_dict
          )
          return prob
  
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 2 for calling the SumModel obj's underlying member method code with example usage: obj.forward(data, oov_dict, require_loss) and returns: prob,  unknown, prob