code: def _build_dataloader(self):
      para_dic = {
          "root_dir": self.config["graph_construction_args"][
              "graph_construction_share"
          ]["root_dir"],
          "word_emb_size": self.config["word_emb_size"],
          "topology_subdir": self.config["graph_construction_args"][
              "graph_construction_share"
          ]["topology_subdir"],
          "edge_strategy": self.config["graph_construction_args"][
              "graph_construction_private"
          ]["edge_strategy"],
          "graph_name": self.config["graph_construction_args"][
              "graph_construction_share"
          ]["graph_name"],
          "share_vocab": self.config["share_vocab"],
          "min_word_vocab_freq": self.config["min_word_freq"],
          "thread_number": self.config["graph_construction_args"][
              "graph_construction_share"
          ]["thread_number"],
          "port": self.config["graph_construction_args"][
              "graph_construction_share"
          ]["port"],
          "timeout": self.config["graph_construction_args"][
              "graph_construction_share"
          ]["timeout"],
          "tokenizer": None,
          "for_inference": 1,
          "reused_vocab_model": self.model.vocab,
      }
  
      inference_dataset = CNNDataset(**para_dic)
  
      self.inference_data_loader = DataLoader(
          inference_dataset.test,
          batch_size=self.config["batch_size"],
          shuffle=False,
          num_workers=0,
          collate_fn=inference_dataset.collate_fn,
      )
  
      self.vocab = self.model.vocab
      self.src_vocab = self.vocab.in_word_vocab
      self.tgt_vocab = self.vocab.out_word_vocab
  
      self.num_test = len(inference_dataset.test)
  
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 2 for calling the ModelHandler obj's underlying member method code with example usage: obj._build_dataloader() without return types