message: Add description on how `sparse_reduce_max` behaves with respect to the missing values in a SparseTensor.
  Also added an extra example.
diff: @@ -796,6 +796,11 @@ def sparse_reduce_max(sp_input, axis=None, keepdims=None,
  with a single element is returned. Additionally, the axes can be negative,
  similar to the indexing rules in Python.
  + The values not defined in `sp_input` don't participate in the reduce max,
  + as opposed to be implicitly assumed 0 -- hence it can return negative values
  + for sparse `reduction_axes`. But, in case there are no values in
  + `reduction_axes`, it will reduce to 0. See second example below.
  +
  For example:
  ```python
  @@ -807,6 +812,11 @@ def sparse_reduce_max(sp_input, axis=None, keepdims=None,
  tf.sparse_reduce_max(x, 1) ==> [2, 3] # Can also use -1 as the axis.
  tf.sparse_reduce_max(x, 1, keepdims=True) ==> [[2], [3]]
  tf.sparse_reduce_max(x, [0, 1]) ==> 3
  +
  + # 'y' represents [[-7, ?]
  + # [ 4, 3]
  + # [ ?, ?]
  + tf.sparse_reduce_max(x, 1) ==> [-7, 4, 0]
  ```
  Args:
  