class_docstr: 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.data.experimental.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.data.experimental.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.
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code: def begin(self):
      # Build a Saver that saves all iterators in the `GLOBAL_ITERATORS`
      # collection if no `Saver` or `Scaffold` is provided.
      # pylint: disable=protected-access
      if (
          self._checkpoint_saver_hook._saver is None
          and self._checkpoint_saver_hook._scaffold is None
      ):
          iterators = ops.get_collection(iterator_ops.GLOBAL_ITERATORS)
          saveables = [
              iterator_ops._IteratorSaveable(
                  i,
                  i.name,
                  external_state_policy=self._external_state_policy,
              )
              for i in iterators
          ]
          self._checkpoint_saver_hook._saver = _CustomSaver(
              saveables, self._latest_filename, sharded=True
          )
      # pylint: enable=protected-access
      self._checkpoint_saver_hook.begin()
  
imports: from tensorflow.python.checkpoint import checkpoint_management
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.data.ops import options
  from tensorflow.python.framework import ops
  from tensorflow.python.training import basic_session_run_hooks
  from tensorflow.python.training import saver
  from tensorflow.python.training import session_run_hook
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling the CheckpointInputPipelineHook obj's underlying member method code with example usage: obj.begin() without return types