message: Add documentation about output shape on `drop_remainder` for dataset `batch` function.
  This is especially useful when dealing with backend that requires static shape, e.g., XLA.
diff: @@ -1544,6 +1544,11 @@ class DatasetV2(collections_abc.Iterable, tracking_base.Trackable,
  batches having the same outer dimension, you should set the `drop_remainder`
  argument to `True` to prevent the smaller batch from being produced.
  + Note: If your program requires data to have a statically known shape (e.g.,
  + when using XLA), you should use `drop_remainder=True`. Without
  + `drop_remainder=True` the shape of the output dataset will have an unknown
  + leading dimension due to the possibility of a smaller final batch.
  +
  Args:
  batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
  consecutive elements of this dataset to combine in a single batch.
  