code: def _build_dataloader(self):
      dataset = JobsDataset(
          root_dir=self.opt["graph_construction_args"][
              "graph_construction_share"
          ]["root_dir"],
          #   pretrained_word_emb_file=self.opt["pretrained_word_emb_file"],
          pretrained_word_emb_name=self.opt["pretrained_word_emb_name"],
          pretrained_word_emb_url=self.opt["pretrained_word_emb_url"],
          pretrained_word_emb_cache_dir=self.opt[
              "pretrained_word_emb_cache_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["graph_construction_args"][
              "graph_construction_share"
          ]["share_vocab"],
          graph_name=self.opt["graph_construction_args"][
              "graph_construction_share"
          ]["graph_name"],
          dynamic_init_graph_name=self.opt["graph_construction_args"][
              "graph_construction_private"
          ].get("dynamic_init_graph_type", None),
          topology_subdir=self.opt["graph_construction_args"][
              "graph_construction_share"
          ]["topology_subdir"],
          thread_number=self.opt["graph_construction_args"][
              "graph_construction_share"
          ]["thread_number"],
          port=self.opt["graph_construction_args"][
              "graph_construction_share"
          ]["port"],
      )
  
      self.train_dataloader = DataLoader(
          dataset.train,
          batch_size=self.opt["batch_size"],
          shuffle=True,
          num_workers=self.opt["num_works"],
          collate_fn=dataset.collate_fn,
      )
      self.test_dataloader = DataLoader(
          dataset.test,
          batch_size=self.opt["batch_size"],
          shuffle=False,
          num_workers=self.opt["num_works"],
          collate_fn=dataset.collate_fn,
      )
      self.vocab = dataset.vocab_model
  
imports: import numpy
  import torch
  import torch.optim
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.jobs import JobsDataset
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.utils.copy_utils import prepare_ext_vocab
  from args import get_args
  from build_model import get_model
  from evaluation import ExpressionAccuracy
  from utils import get_log, wordid2str
label_desc: Use image node_id 4 for calling the Jobs obj's underlying member method code with example usage: obj._build_dataloader() without return types