message: Sync Premade and Custom estimator docs with example code.
diff: @@ -15,9 +15,8 @@ This document focuses on checkpoints. For details on SavedModel, see the
  ## Sample code
  -This document relies on the same Iris classification example detailed in
  -<!-- TODO (barryr): fill in link when module settles down. -->
  -@{$premade_estimators$Getting Started with TensorFlow}.
  +This document relies on the same
  +[https://github.com/tensorflow/models/blob/master/samples/core/get_started/premade_estimator.py](Iris classification example) detailed in @{$premade_estimators$Getting Started with TensorFlow}.
  To download and access the example, invoke the following two commands:
  ```shell
  @@ -228,10 +227,12 @@ This separation will keep your checkpoints recoverable.
  ## Summary
  -Checkpoints provide an easy automatic mechanism for storing and restoring
  -models created by Estimators. See the @{$saved_model$Saving and Restoring}
  +Checkpoints provide an easy automatic mechanism for saving and restoring
  +models created by Estimators.
  +
  +See the @{$saved_model$Saving and Restoring}
  chapter of the *TensorFlow Programmer's Guide* for details on:
  -* Saving and restoring models created by low-level TensorFlow APIs.
  -* Saving and restoring models in the SavedModel format, which is a
  +* Saving and restoring models using low-level TensorFlow APIs.
  +* Exporting and importing models in the SavedModel format, which is a
  language-neutral, recoverable, serialization format.
  