message: Improve docstring for `data.experimental.bucket_by_sequence_length` API.
diff: @@ -142,6 +142,31 @@ def bucket_by_sequence_length(element_length_func,
  Grouping together elements that have similar lengths reduces the total
  fraction of padding in a batch which increases training step efficiency.
  + Below is an example to bucketize the input data to the 3 buckets
  + "[0, 3), [3, 5), [5, inf)" based on sequence length, with batch size 2.
  +
  + >>> elements = [
  + ... [0], [1, 2, 3, 4], [5, 6, 7],
  + ... [7, 8, 9, 10, 11], [13, 14, 15, 16, 19, 20], [21, 22]]
  +
  + >>> dataset = tf.data.Dataset.from_generator(
  + ... lambda: elements, tf.int64, output_shapes=[None])
  +
  + >>> dataset = dataset.apply(
  + ... tf.data.experimental.bucket_by_sequence_length(
  + ... element_length_func=lambda elem: tf.shape(elem)[0],
  + ... bucket_boundaries=[3, 5],
  + ... bucket_batch_sizes=[2, 2, 2]))
  +
  + >>> for elem in dataset.as_numpy_iterator():
  + ... print(elem)
  + [[1 2 3 4]
  + [5 6 7 0]]
  + [[ 7 8 9 10 11 0]
  + [13 14 15 16 19 20]]
  + [[ 0 0]
  + [21 22]]
  +
  Args:
  element_length_func: function from element in `Dataset` to `tf.int32`,
  determines the length of the element, which will determine the bucket it
  