code: def get_babi_dataloaders(
      batch_size, train_size=50, task_id=4, q_type=0
  ):
      _download_babi_data()
  
      node_dict = dict(
          zip(
              list(string.ascii_uppercase),
              range(len(string.ascii_uppercase)),
          )
      )
  
      if task_id == 4:
          edge_dict = {"n": 0, "s": 1, "w": 2, "e": 3}
          reverse_edge = {}
          return _ns_dataloader(
              train_size,
              q_type,
              batch_size,
              node_dict,
              edge_dict,
              reverse_edge,
              "04",
          )
      elif task_id == 15:
          edge_dict = {"is": 0, "has_fear": 1}
          reverse_edge = {}
          return _ns_dataloader(
              train_size,
              q_type,
              batch_size,
              node_dict,
              edge_dict,
              reverse_edge,
              "15",
          )
      elif task_id == 16:
          edge_dict = {"is": 0, "has_color": 1}
          reverse_edge = {0: 0}
          return _ns_dataloader(
              train_size,
              q_type,
              batch_size,
              node_dict,
              edge_dict,
              reverse_edge,
              "16",
          )
      elif task_id == 18:
          edge_dict = {">": 0, "<": 1}
          label_dict = {"false": 0, "true": 1}
          reverse_edge = {0: 1, 1: 0}
          return _gc_dataloader(
              train_size,
              q_type,
              batch_size,
              node_dict,
              edge_dict,
              label_dict,
              reverse_edge,
              "18",
          )
      elif task_id == 19:
          edge_dict = {"n": 0, "s": 1, "w": 2, "e": 3, "<end>": 4}
          reverse_edge = {0: 1, 1: 0, 2: 3, 3: 2}
          max_seq_length = 2
          return _path_finding_dataloader(
              train_size,
              batch_size,
              node_dict,
              edge_dict,
              reverse_edge,
              "19",
              max_seq_length,
          )
  
file_path: dgl/examples/pytorch/ggnn/data_utils.py
imports: import os
  import string
  import dgl
  import torch
  from dgl.data.utils import _get_dgl_url, download, extract_archive, get_download_dir
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 for calling a global function with example usage: get_babi_dataloaders(batch_size, train_size, task_id, q_type) and returns: _ns_dataloader, _ns_dataloader, _ns_dataloader, _gc_dataloader, _path_finding_dataloader