message: Small additions to DistributedStrategy's API docs
diff: @@ -520,7 +520,10 @@ class StrategyBase(object):
  """A state & compute distribution policy on a list of devices.
  See [the guide](https://www.tensorflow.org/guide/distributed_training)
  - for overview and examples.
  + for overview and examples. See `tf.distribute.StrategyExtended` and
  + [`tf.distribute`](https://www.tensorflow.org/api_docs/python/tf/distribute)
  + for a glossory of concepts mentioned on this page such as "per-replica",
  + _replica_, and _reduce_.
  In short:
  @@ -736,12 +739,16 @@ class StrategyBase(object):
  # Iterate over the distributed dataset
  for x in dist_dataset:
  # process dataset elements
  - strategy.run(train_step, args=(x,))
  + strategy.run(replica_fn, args=(x,))
  ```
  - We will assume that the input dataset is batched by the
  - global batch size. With this assumption, we will make a best effort to
  - divide each batch across all the replicas (one or more workers).
  + In the code snippet above, the dataset `dist_dataset` is batched by
  + GLOBAL_BATCH_SIZE, and we iterate through it using `for x in dist_dataset`,
  + where x is one batch of data of GLOBAL_BATCH_SIZE containing N batches of
  + data of per-replica batch size, corresponding to N replicas.
  + `tf.distribute.Strategy.run` will take care of feeding
  + the right per-replica batch to the right `replica_fn` execution on each
  + replica.
  In a multi-worker setting, we will first attempt to distribute the dataset
  by attempting to detect whether the dataset is being created out of
  @@ -892,8 +899,13 @@ class StrategyBase(object):
  `tf.distribute.DistributedValues` containing tensors or composite tensors.
  IMPORTANT: Depending on the implementation of `tf.distribute.Strategy` and
  - whether eager execution is enabled, `fn` may be called one or more times (
  - once for each replica).
  + whether eager execution is enabled, `fn` may be called one or more times. If
  + `fn` is annotated with `tf.function` or `tf.distribute.Strategy.run` is
  + called inside a `tf.function`, eager execution is disabled and `fn` is
  + called once (or once per replica, if you are using MirroredStrategy) to
  + generate a Tensorflow graph, which will then be reused for execution with
  + new inputs. Otherwise, if eager execution is enabled, `fn` will be called
  + every step just like regular python code.
  Example usage:
  