message: Doc string clean-ups for class DistributionStrategy.
diff: @@ -376,7 +376,9 @@ class DistributionStrategy(object):
  update. Allreduce is an algorithm for performing a reduction on
  values from multiple devices and making the result available on
  all of those devices.
  - * TODO(josh11b): Future: partitioned variables
  + * In the future we will have support for TensorFlows' partitioned
  + variables, where a single variable is split across multiple
  + devices.
  We have then a few approaches we want to support:
  * Code written (as if) with no knowledge of class `DistributionStrategy`.
  @@ -390,7 +392,6 @@ class DistributionStrategy(object):
  ```
  with my_distribution.scope():
  iterator = my_distribution.distribute_dataset(dataset)
  - # TODO(josh11b): iterator = dataset.make_one_shot_iterator()
  tower_train_ops = my_distribution.call_for_each_tower(
  tower_fn, iterator.get_next())
  train_op = tf.group(my_distribution.unwrap(tower_train_ops))
  @@ -402,6 +403,10 @@ class DistributionStrategy(object):
  using `my_distribution`'s policy, and library functions called by
  `tower_fn` can use the `get_tower_context()` API to get enhanced
  behavior in this case.
  +
  + Note that in the future we will add support for initializable
  + Dataset iterators, at which point this example code will change.
  +
  * If you want to write a distributed algorithm, you may use any of
  the `DistributionStrategy` APIs inside a
  `with my_distribution.scope():` block of code.
  @@ -514,7 +519,7 @@ class DistributionStrategy(object):
  Steps 3 and 4 are done automatically by class `Optimizer` if you call
  its `apply_gradients` method in a tower context. Otherwise you can
  - manually call its `distributed_apply` method in a cross-tower context.
  + manually call its `_distributed_apply` method in a cross-tower context.
  Another thing you might want to do in the middle of your tower function
  is an all-reduce of some intermediate value, using `d.reduce()` or
  