code: def get_dbpedia(vocab=None, shrink=1, char_based=False):
      tf = download_dbpedia()
  
      print("read dbpedia")
      train = read_dbpedia(
          tf, "train", shrink=shrink, char_based=char_based
      )
      test = read_dbpedia(
          tf, "test", shrink=shrink, char_based=char_based
      )
  
      if vocab is None:
          print("constract vocabulary based on frequency")
          vocab = make_vocab(train)
  
      train = transform_to_array(train, vocab)
      test = transform_to_array(test, vocab)
  
      return train, test, vocab
  
imports: import csv
  import glob
  import io
  import os
  import shutil
  import sys
  import tarfile
  import tempfile
  import numpy
  import chainer
  from nlp_utils import make_vocab
  from nlp_utils import normalize_text
  from nlp_utils import split_text
  from nlp_utils import transform_to_array
label_desc: Use image node_id 3 for calling a global function with example usage: get_dbpedia(vocab, shrink, char_based) and returns: train,  test,  vocab