message: Update math_ops.py docs.
diff: @@ -2344,6 +2344,26 @@ def reduce_max(input_tensor, axis=None, keepdims=False, name=None):
  If `axis` is None, all dimensions are reduced, and a
  tensor with a single element is returned.
  + Usage example:
  +
  + >>> x = tf.constant([5, 1, 2, 4])
  + >>> print(tf.reduce_max(x))
  + tf.Tensor(5, shape=(), dtype=int32)
  + >>> x = tf.constant([-5, -1, -2, -4])
  + >>> print(tf.reduce_max(x))
  + tf.Tensor(-1, shape=(), dtype=int32)
  + >>> x = tf.constant([4, float('nan')])
  + >>> print(tf.reduce_max(x))
  + tf.Tensor(4.0, shape=(), dtype=float32)
  + >>> x = tf.constant([float('nan'), float('nan')])
  + >>> print(tf.reduce_max(x))
  + tf.Tensor(-inf, shape=(), dtype=float32)
  + >>> x = tf.constant([float('-inf'), float('inf')])
  + >>> print(tf.reduce_max(x))
  + tf.Tensor(inf, shape=(), dtype=float32)
  +
  + See the numpy docs for `np.amax` and `np.nanmax` behavior.
  +
  Args:
  input_tensor: The tensor to reduce. Should have real numeric type.
  axis: The dimensions to reduce. If `None` (the default), reduces all
  @@ -2354,10 +2374,6 @@ def reduce_max(input_tensor, axis=None, keepdims=False, name=None):
  Returns:
  The reduced tensor.
  -
  - @compatibility(numpy)
  - Equivalent to np.max
  - @end_compatibility
  """
  return reduce_max_with_dims(input_tensor, axis, keepdims, name,
  _ReductionDims(input_tensor, axis))
  