code: def _build_dataloader(self):
      dataset = IWSLT14Dataset(
          root_dir=self.opt["graph_construction_args"][
              "graph_construction_share"
          ]["root_dir"],
          val_split_ratio=self.opt["val_split_ratio"],
          merge_strategy=self.opt["graph_construction_args"][
              "graph_construction_private"
          ]["merge_strategy"],
          edge_strategy=self.opt["graph_construction_args"][
              "graph_construction_private"
          ]["edge_strategy"],
          seed=self.opt["seed"],
          word_emb_size=self.opt["word_emb_size"],
          share_vocab=self.opt["share_vocab"],
          graph_name=self.opt["graph_construction_args"][
              "graph_construction_share"
          ]["graph_name"],
          topology_subdir=self.opt["graph_construction_args"][
              "graph_construction_share"
          ]["topology_subdir"],
          for_inference=True,
          reused_vocab_model=self.model.vocab_model,
      )
  
      self.test_dataloader = DataLoader(
          dataset.test,
          batch_size=self.opt["batch_size"],
          shuffle=False,
          num_workers=8,
          collate_fn=dataset.collate_fn,
      )
      self.vocab = dataset.vocab_model
  
imports: import os
  import resource
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from torch.utils.tensorboard import SummaryWriter
  from graph4nlp.pytorch.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.modules.evaluation import BLEU
  from args import get_args
  from dataset import IWSLT14Dataset
  from utils import get_log, wordid2str
label_desc: Use image node_id 4 for calling the NMT obj's underlying member method code with example usage: obj._build_dataloader() without return types