old_contents: # Copyright 2018 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.
  # ==============================================================================
  """Keras estimator API."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.util.tf_export import tf_export
  
  # Keras has undeclared dependency on tensorflow/estimator:estimator_py.
  # As long as you depend //third_party/py/tensorflow:tensorflow target
  # everything will work as normal.
  
  
  # LINT.IfChange
  @tf_export('keras.estimator.model_to_estimator')
  def model_to_estimator(
      keras_model=None,
      keras_model_path=None,
      custom_objects=None,
      model_dir=None,
      config=None):
    """Constructs an `Estimator` instance from given keras model.
  
    For usage example, please see:
    [Creating estimators from Keras
    Models](https://tensorflow.org/guide/estimators#model_to_estimator).
  
    Args:
      keras_model: A compiled Keras model object. This argument is mutually
        exclusive with `keras_model_path`.
      keras_model_path: Path to a compiled Keras model saved on disk, in HDF5
        format, which can be generated with the `save()` method of a Keras model.
        This argument is mutually exclusive with `keras_model`.
      custom_objects: Dictionary for custom objects.
      model_dir: Directory to save `Estimator` model parameters, graph, summary
        files for TensorBoard, etc.
      config: `RunConfig` to config `Estimator`.
  
    Returns:
      An Estimator from given keras model.
  
    Raises:
      ValueError: if neither keras_model nor keras_model_path was given.
      ValueError: if both keras_model and keras_model_path was given.
      ValueError: if the keras_model_path is a GCS URI.
      ValueError: if keras_model has not been compiled.
    """
    try:
      from tensorflow_estimator.python.estimator import keras as keras_lib  # pylint: disable=g-import-not-at-top
    except ImportError:
      raise NotImplementedError(
          'tf.keras.estimator.model_to_estimator function not available in your '
          'installation.')
    keras_lib.model_to_estimator(
        keras_model=keras_model,
        keras_model_path=keras_model_path,
        custom_objects=custom_objects,
        model_dir=model_dir,
        config=config)
  
  # LINT.ThenChange(//third_party/tensorflow_estimator/python/estimator/keras.py)
  
  
  
new_contents: # Copyright 2018 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.
  # ==============================================================================
  """Keras estimator API."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.util.tf_export import tf_export
  
  # Keras has undeclared dependency on tensorflow/estimator:estimator_py.
  # As long as you depend //third_party/py/tensorflow:tensorflow target
  # everything will work as normal.
  
  
  # LINT.IfChange
  @tf_export('keras.estimator.model_to_estimator')
  def model_to_estimator(
      keras_model=None,
      keras_model_path=None,
      custom_objects=None,
      model_dir=None,
      config=None):
    """Constructs an `Estimator` instance from given keras model.
  
    For usage example, please see:
    [Creating estimators from Keras
    Models](https://tensorflow.org/guide/estimators#model_to_estimator).
  
    Args:
      keras_model: A compiled Keras model object. This argument is mutually
        exclusive with `keras_model_path`.
      keras_model_path: Path to a compiled Keras model saved on disk, in HDF5
        format, which can be generated with the `save()` method of a Keras model.
        This argument is mutually exclusive with `keras_model`.
      custom_objects: Dictionary for custom objects.
      model_dir: Directory to save `Estimator` model parameters, graph, summary
        files for TensorBoard, etc.
      config: `RunConfig` to config `Estimator`.
  
    Returns:
      An Estimator from given keras model.
  
    Raises:
      ValueError: if neither keras_model nor keras_model_path was given.
      ValueError: if both keras_model and keras_model_path was given.
      ValueError: if the keras_model_path is a GCS URI.
      ValueError: if keras_model has not been compiled.
    """
    try:
      from tensorflow_estimator.python.estimator import keras as keras_lib  # pylint: disable=g-import-not-at-top
    except ImportError:
      raise NotImplementedError(
          'tf.keras.estimator.model_to_estimator function not available in your '
          'installation.')
    return keras_lib.model_to_estimator(
        keras_model=keras_model,
        keras_model_path=keras_model_path,
        custom_objects=custom_objects,
        model_dir=model_dir,
        config=config)
  
  # LINT.ThenChange(//third_party/tensorflow_estimator/python/estimator/keras.py)
  
  
  
subject: Fix exported version of keras's model_to_estimator.