old_contents: # coding=utf-8
  # Copyright 2019 The TensorFlow Datasets Authors.
  #
  # 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.
  
  r"""Create fake data for Camelyon Patch dataset.
  
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import google_type_annotations
  from __future__ import print_function
  
  import os
  
  from absl import app
  from absl import flags
  
  import h5py
  import numpy as np
  from tensorflow_datasets.core.utils import py_utils
  
  
  flags.DEFINE_string('tfds_dir', py_utils.tfds_dir(),
                      'Path to tensorflow_datasets directory')
  FLAGS = flags.FLAGS
  
  
  def get_output_file_prefix(split):
    return os.path.join(FLAGS.tfds_dir, 'testing', 'test_data', 'fake_examples',
                        'patch_camelyon',
                        'camelyonpatch_level_2_split_%s' % split)
  
  
  def write_to_h5_file(filepath, dataset_name, content):
    with h5py.File(filepath, 'w') as h5_f:
      h5_f.create_dataset(dataset_name, data=content)
  
  
  def main(_):
    np.random.seed(0x12345)
    for split, num_examples in [('train', 5), ('test', 4), ('valid', 3)]:
      x = np.random.randint(
          low=0, high=256, size=(num_examples, 96, 96, 3), dtype=np.uint8)
      y = np.random.randint(
          low=0, high=2, size=(num_examples, 1, 1, 1), dtype=np.uint32)
      images_filepath = get_output_file_prefix(split) + '_x.h5'
      labels_filepath = get_output_file_prefix(split) + '_y.h5'
      write_to_h5_file(images_filepath, dataset_name='x', content=x)
      write_to_h5_file(labels_filepath, dataset_name='y', content=y)
  
  
  if __name__ == '__main__':
    app.run(main)
  
new_contents: # coding=utf-8
  # Copyright 2019 The TensorFlow Datasets Authors.
  #
  # 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.
  
  r"""Create fake data for Camelyon Patch dataset.
  
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  
  from absl import app
  from absl import flags
  
  import h5py
  import numpy as np
  from tensorflow_datasets.core.utils import py_utils
  
  
  flags.DEFINE_string('tfds_dir', py_utils.tfds_dir(),
                      'Path to tensorflow_datasets directory')
  FLAGS = flags.FLAGS
  
  
  def get_output_file_prefix(split):
    return os.path.join(FLAGS.tfds_dir, 'testing', 'test_data', 'fake_examples',
                        'patch_camelyon',
                        'camelyonpatch_level_2_split_%s' % split)
  
  
  def write_to_h5_file(filepath, dataset_name, content):
    with h5py.File(filepath, 'w') as h5_f:
      h5_f.create_dataset(dataset_name, data=content)
  
  
  def main(_):
    np.random.seed(0x12345)
    for split, num_examples in [('train', 5), ('test', 4), ('valid', 3)]:
      x = np.random.randint(
          low=0, high=256, size=(num_examples, 96, 96, 3), dtype=np.uint8)
      y = np.random.randint(
          low=0, high=2, size=(num_examples, 1, 1, 1), dtype=np.uint32)
      images_filepath = get_output_file_prefix(split) + '_x.h5'
      labels_filepath = get_output_file_prefix(split) + '_y.h5'
      write_to_h5_file(images_filepath, dataset_name='x', content=x)
      write_to_h5_file(labels_filepath, dataset_name='y', content=y)
  
  
  if __name__ == '__main__':
    app.run(main)
  
subject: Delete unfound reference `from __future__ import google_type_annotations`