message: Add examples to top_k
diff: @@ -5203,13 +5203,37 @@ def top_k(input, k=1, sorted=True, name=None): # pylint: disable=redefined-buil
  and outputs their values and indices as vectors. Thus `values[j]` is the
  `j`-th largest entry in `input`, and its index is `indices[j]`.
  + >>> result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
  + ... k=3)
  + >>> result.values.numpy()
  + array([99, 98, 96], dtype=int32)
  + >>> result.indices.numpy()
  + array([5, 2, 9], dtype=int32)
  +
  For matrices (resp. higher rank input), computes the top `k` entries in each
  row (resp. vector along the last dimension). Thus,
  - values.shape = indices.shape = input.shape[:-1] + [k]
  + >>> input = tf.random.normal(shape=(3,4,5,6))
  + >>> k = 2
  + >>> values, indices = tf.math.top_k(input, k=k)
  + >>> values.shape.as_list()
  + [3, 4, 5, 2]
  + >>>
  + >>> values.shape == indices.shape == input.shape[:-1] + [k]
  + True
  +
  + The indices can be used to `gather` from a tensor who's shape matches `input`.
  +
  + >>> gathered_values = tf.gather(input, indices, batch_dims=-1)
  + >>> assert tf.reduce_all(gathered_values == values)
  If two elements are equal, the lower-index element appears first.
  + >>> result = tf.math.top_k([1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],
  + ... k=3)
  + >>> result.indices.numpy()
  + array([0, 1, 3], dtype=int32)
  +
  Args:
  input: 1-D or higher `Tensor` with last dimension at least `k`.
  k: 0-D `int32` `Tensor`. Number of top elements to look for along the last
  @@ -5219,6 +5243,7 @@ def top_k(input, k=1, sorted=True, name=None): # pylint: disable=redefined-buil
  name: Optional name for the operation.
  Returns:
  + A tuple with two named fields:
  values: The `k` largest elements along each last dimensional slice.
  indices: The indices of `values` within the last dimension of `input`.
  """
  