code: def download_dataset(
      url="http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz",
  ):
      fname = url.split("/")[-1]
  
      if os.path.isfile(fname):
          return fname
  
      # Download the file to local storage first.
      try:
          kwargs = {
              "compression": smart_open.compression.NO_COMPRESSION
          }
          fin = smart_open.open(url, "rb", **kwargs)
      except (AttributeError, TypeError):
          kwargs = {"ignore_ext": True}
          fin = smart_open.open(url, "rb", **kwargs)
      if fin:
          with smart_open.open(fname, "wb", **kwargs) as fout:
              while True:
                  buf = fin.read(io.DEFAULT_BUFFER_SIZE)
                  if not buf:
                      break
                  fout.write(buf)
          fin.close()
  
      return fname
  
imports: import logging
  import collections
  import io
  import re
  import tarfile
  import os.path
  import smart_open
  import gensim.utils
  import multiprocessing
  from collections import OrderedDict
  import gensim.models.doc2vec
  from gensim.models.doc2vec import Doc2Vec
  from gensim.test.test_doc2vec import ConcatenatedDoc2Vec
  import numpy
  import statsmodels.api
  from random import sample
  from collections import defaultdict
  from random import shuffle
  import random
  import random
  from gensim.test.utils import datapath
label_desc: Use image node_id 1 for calling a global function with example usage: download_dataset(url) and returns: fname, fname