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.stopper = EarlyStopping(
          os.path.join(
              "/raid/user8/graph4nlp",
              self.config["out_dir"],
              Constants._SAVED_WEIGHTS_FILE,
          ),
          patience=self.config["patience"],
      )
      self._build_model()
  
imports: import argparse
  import os
  import numpy
  import torch
  import torch.backends.cudnn
  from nltk.tokenize import word_tokenize
  from graph4nlp.pytorch.inference_wrapper.generator_inference_wrapper import GeneratorInferenceWrapper
  from graph4nlp.pytorch.modules.config import get_basic_args
  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.generic_utils import EarlyStopping, to_cuda
  from main import SumModel
label_desc: Use image node_id 1 to create a new ModelHandler object  with example: obj = ModelHandler(config)