message: [tf.data] Improve docstring for `tf.data.Dataset.padded_batch()`.
diff: @@ -774,11 +774,31 @@ class Dataset(object):
  def padded_batch(self, batch_size, padded_shapes, padding_values=None):
  """Combines consecutive elements of this dataset into padded batches.
  - Like `Dataset.dense_to_sparse_batch()`, this method combines
  - multiple consecutive elements of this dataset, which might have
  - different shapes, into a single element. The tensors in the
  - resulting element have an additional outer dimension, and are
  - padded to the respective shape in `padded_shapes`.
  + This transformation combines multiple consecutive elements of the input
  + dataset into a single element. Like @{tf.data.Dataset.batch}, the tensors
  + in the resulting element have an additional outer dimension, which will be
  + `batch_size` for all but the last element, and `N % batch_size` for the
  + last element (where `N` is the number of elements in this dataset). Unlike
  + @{tf.data.Dataset.batch}, the elements may have different shapes for some
  + of their components, and this transformation will pad each component to
  + the respective shape in `padding_shapes`. The `padding_shapes` argument
  + determines the resulting shape for each dimension of each component in an
  + output element:
  +
  + * If the dimension is a constant (e.g. `tf.Dimension(37)`), the component
  + will be padded out to that length in that dimension.
  + * If the dimension is unknown (e.g. `tf.Dimension(None)`), the component
  + will be padded out to the maximum length of all elements in that
  + dimension.
  +
  + NOTE: If the number of elements (`N`) in this dataset is not an exact
  + multiple of `batch_size`, the final batch contain smaller tensors with
  + shape `N % batch_size` in the batch dimension. If your program depends on
  + the batches having the same shape, consider using the
  + @{tf.contrib.data.padded_batch_and_drop_remainder} transformation instead.
  +
  + See also @{tf.contrib.data.dense_to_sparse_batch}, which combines elements
  + that may have different shapes into a @{tf.SparseTensor}.
  Args:
  batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
  