code: def __init__(self, config):
      super(ModelHandler, self).__init__()
      self.config = config
      self.use_copy = self.config["decoder_args"]["rnn_decoder_share"][
          "use_copy"
      ]
      self.use_coverage = self.config["decoder_args"][
          "rnn_decoder_share"
      ]["use_coverage"]
      self.logger = Logger(
          config["out_dir"],
          config={k: v for k, v in config.items() if k != "device"},
          overwrite=True,
      )
      self.logger.write(config["out_dir"])
      self._build_dataloader()
      self._build_model()
      self._build_optimizer()
      self._build_evaluation()
  
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 1 to create a new ModelHandler object  with example: obj = ModelHandler(config)