code: def load_data(
      path="boston_housing.npz",
      dest_dir="/tmp/.zoo/dataset",
      test_split=0.2,
  ):
      """Loads the Boston Housing dataset, the source url of download
         is copied from keras.datasets
      # Arguments
          dest_dir: where to cache the data (relative to `~/.zoo/dataset`).
          nb_words: number of words to keep, the words are already indexed by frequency
                    so that the less frequent words would be abandoned
          oov_char: index to pad the abandoned words, if None, one abandoned word
                    would be taken place with its next word and total length -= 1
          test_split: the ratio to split part of dataset to test data,
                      the remained data would be train data
  
      # Returns
          Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
      """
  
      path = base.maybe_download(
          path,
          dest_dir,
          "https://s3.amazonaws.com/keras-datasets/boston_housing.npz",
      )
  
      with np.load(path) as f:
          x = f["x"]
          y = f["y"]
  
      shuffle_by_seed([x, y])
      split_index = int(len(x) * (1 - test_split))
  
      x_train, y_train = x[:split_index], y[:split_index]
  
      x_test, y_test = x[split_index:], y[split_index:]
  
      return (x_train, y_train), (x_test, y_test)
  
imports: import numpy
  from bigdl.dataset import base
label_desc: Use image node_id 1 for calling a global function with example usage: load_data(path, dest_dir, test_split) and returns: x_train,  y_train,  x_test,  y_test