message: Update Dataset.shuffle reshuffle_each_iteration doc to explain interaction with iterators.
  Fixes:
diff: @@ -915,6 +915,39 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  its space in the buffer is replaced by the next (i.e. 1,001-st) element,
  maintaining the 1,000 element buffer.
  + `reshuffle_each_iteration` controls whether the shuffle order should be
  + different for each epoch. In TF 1.X, the idiomatic way to create epochs
  + was through the `repeat` transformation:
  +
  + ```python
  + d = tf.data.Dataset.range(3)
  + d = d.shuffle(3, reshuffle_each_iteration=True)
  + d = d.repeat(2) # ==> [ 1, 0, 2, 1, 2, 0 ]
  +
  + d = tf.data.Dataset.range(3)
  + d = d.shuffle(3, reshuffle_each_iteration=False)
  + d = d.repeat(2) # ==> [ 1, 0, 2, 1, 0, 2 ]
  + ```
  +
  + In TF 2.0, tf.data.Dataset objects are Python iterables which makes it
  + possible to also create epochs through Python iteration:
  +
  + ```python
  + d = tf.data.Dataset.range(3)
  + d = d.shuffle(3, reshuffle_each_iteration=True)
  + for elem in d:
  + # ==> [ 1, 0, 2 ]
  + for elem in d:
  + # ==> [ 1, 2, 0 ]
  +
  + d = tf.data.Dataset.range(3)
  + d = d.shuffle(3, reshuffle_each_iteration=False)
  + for elem in d:
  + # ==> [ 1, 0, 2 ]
  + for elem in d:
  + # ==> [ 1, 0, 2 ]
  + ```
  +
  Args:
  buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
  elements from this dataset from which the new dataset will sample.
  