message: Update docstring and added a doc example for "broadcasting" single padding_values to dataset
diff: @@ -1461,6 +1461,16 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  >>> list(dataset.as_numpy_iterator())
  [(array([[ 1, 2, 3, -1], [ 4, 5, -1, -1]], dtype=int32),
  array([[ 10, 100], [ 11, 12]], dtype=int32))]
  + >>> # Pad with a single value and multiple components.
  + >>> E = tf.data.Dataset.zip((A, A)).padded_batch(2, padding_values=-1)
  + >>> for element in E.as_numpy_iterator():
  + ... print(element)
  + (array([[ 1, -1],
  + [ 2, 2]], dtype=int32), array([[ 1, -1],
  + [ 2, 2]], dtype=int32))
  + (array([[ 3, 3, 3, -1],
  + [ 4, 4, 4, 4]], dtype=int32), array([[ 3, 3, 3, -1],
  + [ 4, 4, 4, 4]], dtype=int32))
  See also `tf.data.experimental.dense_to_sparse_batch`, which combines
  elements that may have different shapes into a `tf.sparse.SparseTensor`.
  @@ -1479,7 +1489,12 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  `tf.Tensor`, representing the padding values to use for the respective
  components. None represents that the nested structure should be padded
  with default values. Defaults are `0` for numeric types and the empty
  - string for string types.
  + string for string types. The `padding_values` should have the
  + same structure as the input dataset. If `padding_values` is a single
  + element and the input dataset has multiple components, then the same
  + `padding_values` will be used to pad every component of the dataset.
  + If `padding_values` is a scalar, then its value will be broadcasted
  + to match the shape of each component.
  drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
  whether the last batch should be dropped in the case it has fewer than
  `batch_size` elements; the default behavior is not to drop the smaller
  