message: Fixes docstring for tf.math.add_n
diff: @@ -3093,8 +3093,6 @@ def _as_indexed_slices_list(inputs, optimize=True):
  def add_n(inputs, name=None):
  """Adds all input tensors element-wise.
  - Converts `IndexedSlices` objects into dense tensors prior to adding.
  -
  `tf.math.add_n` performs the same operation as `tf.math.accumulate_n`, but it
  waits for all of its inputs to be ready before beginning to sum.
  This buffering can result in higher memory consumption when inputs are ready
  @@ -3108,19 +3106,21 @@ def add_n(inputs, name=None):
  For example:
  - ```python
  - a = tf.constant([[3, 5], [4, 8]])
  - b = tf.constant([[1, 6], [2, 9]])
  - tf.math.add_n([a, b, a]) # [[7, 16], [10, 25]]
  - ```
  + >>> a = tf.constant([[3, 5], [4, 8]])
  + >>> b = tf.constant([[1, 6], [2, 9]])
  + >>> tf.math.add_n([a, b, a])
  + <tf.Tensor: shape=(2, 2), dtype=int32, numpy=
  + array([[ 7, 16],
  + [10, 25]], dtype=int32)>
  Args:
  - inputs: A list of `tf.Tensor` or `tf.IndexedSlices` objects, each with same
  - shape and type.
  + inputs: A list of `tf.Tensor` or `tf.IndexedSlices` objects, each with the
  + same shape and type. `tf.IndexedSlices` objects will be converted into
  + dense tensors prior to adding.
  name: A name for the operation (optional).
  Returns:
  - A `Tensor` of same shape and type as the elements of `inputs`.
  + A `tf.Tensor` of the same shape and type as the elements of `inputs`.
  Raises:
  ValueError: If `inputs` don't all have same shape and dtype or the shape
  