code: def load_wmt_en_fr_dataset(path="data"):
      """Load WMT'15 English-to-French translation dataset.
  
      It will download the data from the WMT'15 Website (10^9-French-English corpus), and the 2013 news test from the same site as development set.
      Returns the directories of training data and test data.
  
      Parameters
      ----------
      path : str
          The path that the data is downloaded to, defaults is ``data/wmt_en_fr/``.
  
      References
      ----------
      - Code modified from /tensorflow/models/rnn/translation/data_utils.py
  
      Notes
      -----
      Usually, it will take a long time to download this dataset.
  
      """
      path = os.path.join(path, "wmt_en_fr")
      # URLs for WMT data.
      _WMT_ENFR_TRAIN_URL = "http://www.statmt.org/wmt10/"
      _WMT_ENFR_DEV_URL = "http://www.statmt.org/wmt15/"
  
      def gunzip_file(gz_path, new_path):
          """Unzips from gz_path into new_path."""
          logging.info("Unpacking %s to %s" % (gz_path, new_path))
          with gzip.open(gz_path, "rb") as gz_file:
              with open(new_path, "wb") as new_file:
                  for line in gz_file:
                      new_file.write(line)
  
      def get_wmt_enfr_train_set(path):
          """Download the WMT en-fr training corpus to directory unless it's there."""
          filename = "training-giga-fren.tar"
          maybe_download_and_extract(
              filename, path, _WMT_ENFR_TRAIN_URL, extract=True
          )
          train_path = os.path.join(path, "giga-fren.release2.fixed")
          gunzip_file(train_path + ".fr.gz", train_path + ".fr")
          gunzip_file(train_path + ".en.gz", train_path + ".en")
          return train_path
  
      def get_wmt_enfr_dev_set(path):
          """Download the WMT en-fr training corpus to directory unless it's there."""
          filename = "dev-v2.tgz"
          dev_file = maybe_download_and_extract(
              filename, path, _WMT_ENFR_DEV_URL, extract=False
          )
          dev_name = "newstest2013"
          dev_path = os.path.join(path, "newstest2013")
          if not (
              gfile.Exists(dev_path + ".fr")
              and gfile.Exists(dev_path + ".en")
          ):
              logging.info("Extracting tgz file %s" % dev_file)
              with tarfile.open(dev_file, "r:gz") as dev_tar:
                  fr_dev_file = dev_tar.getmember(
                      "dev/" + dev_name + ".fr"
                  )
                  en_dev_file = dev_tar.getmember(
                      "dev/" + dev_name + ".en"
                  )
                  fr_dev_file.name = (
                      dev_name + ".fr"
                  )  # Extract without "dev/" prefix.
                  en_dev_file.name = dev_name + ".en"
                  dev_tar.extract(fr_dev_file, path)
                  dev_tar.extract(en_dev_file, path)
          return dev_path
  
      logging.info(
          "Load or Download WMT English-to-French translation > {}".format(
              path
          )
      )
  
      train_path = get_wmt_enfr_train_set(path)
      dev_path = get_wmt_enfr_dev_set(path)
  
      return train_path, dev_path
  
imports: import gzip
  import os
  import tarfile
  from tensorflow.python.platform import gfile
  from tensorlayer import logging
  from tensorlayer.files.utils import maybe_download_and_extract
label_desc: Use image node_id 1 for calling a global function with example usage: load_wmt_en_fr_dataset(path) and returns: train_path,  dev_path, train_path, dev_path