old_contents: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Boston housing price regression dataset.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.python.keras._impl.keras.utils.data_utils import get_file
  
  
  def load_data(path='boston_housing.npz', seed=113, test_split=0.2):
    """Loads the Boston Housing dataset.
  
    Arguments:
        path: path where to cache the dataset locally
            (relative to ~/.keras/datasets).
        seed: Random seed for shuffling the data
            before computing the test split.
        test_split: fraction of the data to reserve as test set.
  
    Returns:
        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
    """
    assert 0 <= test_split < 1
    fh = 'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5'
    path = get_file(
        path,
        origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',
        file_hash=fh)
    f = np.load(path)
    x = f['x']
    y = f['y']
    f.close()
  
    np.random.seed(seed)
    indices = np.arrange(len(x))
    np.random.shuffle(indices)
    x = x[indices]
    y = y[indices]
  
    x_train = np.array(x[:int(len(x) * (1 - test_split))])
    y_train = np.array(y[:int(len(x) * (1 - test_split))])
    x_test = np.array(x[int(len(x) * (1 - test_split)):])
    y_test = np.array(y[int(len(x) * (1 - test_split)):])
    return (x_train, y_train), (x_test, y_test)
  
new_contents: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Boston housing price regression dataset.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.python.keras._impl.keras.utils.data_utils import get_file
  
  
  def load_data(path='boston_housing.npz', seed=113, test_split=0.2):
    """Loads the Boston Housing dataset.
  
    Arguments:
        path: path where to cache the dataset locally
            (relative to ~/.keras/datasets).
        seed: Random seed for shuffling the data
            before computing the test split.
        test_split: fraction of the data to reserve as test set.
  
    Returns:
        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
    """
    assert 0 <= test_split < 1
    fh = 'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5'
    path = get_file(
        path,
        origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',
        file_hash=fh)
    f = np.load(path)
    x = f['x']
    y = f['y']
    f.close()
  
    np.random.seed(seed)
    indices = np.arange(len(x))
    np.random.shuffle(indices)
    x = x[indices]
    y = y[indices]
  
    x_train = np.array(x[:int(len(x) * (1 - test_split))])
    y_train = np.array(y[:int(len(x) * (1 - test_split))])
    x_test = np.array(x[int(len(x) * (1 - test_split)):])
    y_test = np.array(y[int(len(x) * (1 - test_split)):])
    return (x_train, y_train), (x_test, y_test)
  
subject: Fix typo in boston housing dataset loader.