message: Update docstring about learning_rate_schedule.
diff: @@ -220,6 +220,36 @@ class OptimizerV2(trackable.Trackable):
  opt.minimize(loss, var_list=[var1, var2])
  ```
  + ### Callable learning rate.
  + Optimizer accepts a callable learning rate in two ways. The first way is
  + through built-in or customized
  + `tf.keras.optimizers.schedules.LearningRateSchedule`. The schedule will be
  + called on each iteration with `schedule(iteration)`, a `tf.Variable`
  + owned by the optimizer.
  +
  + Example:
  +
  + >>> var = tf.Variable(np.random.random(size=(1,)))
  + >>> learning_rate = tf.keras.optimizers.schedules.ExponentialDecay(
  + ... initial_learning_rate=.01, decay_steps=20, decay_rate=.1)
  + >>> opt = tf.keras.optimizers.SGD(learning_rate=learning_rate)
  + >>> loss = lambda: 3 * var
  + >>> opt.minimize(loss, var_list=[var])
  + <tf.Variable...
  +
  + The second way is through a callable function that
  + does not accept any arguments.
  +
  + Example:
  +
  + >>> var = tf.Variable(np.random.random(size=(1,)))
  + >>> def lr_callable():
  + ... return .1
  + >>> opt = tf.keras.optimizers.SGD(learning_rate=lr_callable)
  + >>> loss = lambda: 3 * var
  + >>> opt.minimize(loss, var_list=[var])
  + <tf.Variable...
  +
  ### Write a customized optimizer.
  If you intend to create your own optimization algorithm, simply inherit from
  this class and override the following methods:
  