text: # Copyright 2017 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.
  # ==============================================================================
  """Iterator ops."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.data.experimental.ops import iterator_ops
  from tensorflow.python.util import deprecation
  
  
  @deprecation.deprecated(
      None, "Use `tf.data.experimental.make_saveable_from_iterator(...)`.")
  def make_saveable_from_iterator(iterator):
    """Returns a SaveableObject for saving/restore iterator state using Saver.
  
    Args:
      iterator: Iterator.
  
    For example:
  
    ```python
    with tf.Graph().as_default():
      ds = tf.data.Dataset.range(10)
      iterator = ds.make_initializable_iterator()
      # Build the iterator SaveableObject.
      saveable_obj = tf.contrib.data.make_saveable_from_iterator(iterator)
      # Add the SaveableObject to the SAVEABLE_OBJECTS collection so
      # it can be automatically saved using Saver.
      tf.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, saveable_obj)
      saver = tf.train.Saver()
  
      while continue_training:
        ... Perform training ...
        if should_save_checkpoint:
          saver.save()
    ```
  
    Note: When restoring the iterator, the existing iterator state is completely
    discarded. This means that any changes you may have made to the Dataset
    graph will be discarded as well! This includes the new Dataset graph
    that you may have built during validation. So, while running validation,
    make sure to run the initializer for the validation input pipeline after
    restoring the checkpoint.
  
    Note: Not all iterators support checkpointing yet. Attempting to save the
    state of an unsupported iterator will throw an error.
    """
    return iterator_ops.make_saveable_from_iterator(iterator)
  
  
  class CheckpointInputPipelineHook(iterator_ops.CheckpointInputPipelineHook):
    """Checkpoints input pipeline state every N steps or seconds.
  
    This hook saves the state of the iterators in the `Graph` so that when
    training is resumed the input pipeline continues from where it left off.
    This could potentially avoid overfitting in certain pipelines where the
    number of training steps per eval are small compared to the dataset
    size or if the training pipeline is pre-empted.
  
    Differences from `CheckpointSaverHook`:
    1. Saves only the input pipelines in the "iterators" collection and not the
       global variables or other saveable objects.
    2. Does not write the `GraphDef` and `MetaGraphDef` to the summary.
  
    Example of checkpointing the training pipeline:
  
    ```python
    est = tf.estimator.Estimator(model_fn)
    while True:
      est.train(
          train_input_fn,
          hooks=[tf.contrib.data.CheckpointInputPipelineHook(est)],
          steps=train_steps_per_eval)
      # Note: We do not pass the hook here.
      metrics = est.evaluate(eval_input_fn)
      if should_stop_the_training(metrics):
        break
    ```
  
    This hook should be used if the input pipeline state needs to be saved
    separate from the model checkpoint. Doing so may be useful for a few reasons:
    1. The input pipeline checkpoint may be large, if there are large shuffle
       or prefetch buffers for instance, and may bloat the checkpoint size.
    2. If the input pipeline is shared between training and validation, restoring
       the checkpoint during validation may override the validation input
       pipeline.
  
    For saving the input pipeline checkpoint alongside the model weights use
    `tf.contrib.data.make_saveable_from_iterator` directly to create a
    `SaveableObject` and add to the `SAVEABLE_OBJECTS` collection. Note, however,
    that you will need to be careful not to restore the training iterator during
    eval. You can do that by not adding the iterator to the SAVEABLE_OBJECTS
    collector when building the eval graph.
    """
  
    @deprecation.deprecated(
        None, "Use `tf.data.experimental.CheckpointInputPipelineHook(...)`.")
    def __init__(self, estimator):
      super(CheckpointInputPipelineHook, self).__init__(estimator)
  