message: Improving the documentation concerning the usage examples of various learning rate schedulers
diff: @@ -452,7 +452,7 @@ class InverseTimeDecay(LearningRateSchedule):
  decay_steps = 1.0
  decay_rate = 0.5
  learning_rate_fn = keras.optimizers.schedules.InverseTimeDecay(
  - initial_learning_rate, global_step, decay_steps, decay_rate)
  + initial_learning_rate, decay_steps, decay_rate)
  model.compile(optimizer=tf.keras.optimizers.SGD(
  learning_rate=learning_rate_fn),
  @@ -549,7 +549,7 @@ class CosineDecay(LearningRateSchedule):
  ```python
  decay_steps = 1000
  lr_decayed_fn = tf.keras.experimental.CosineDecay(
  - initial_learning_rate, global_step, decay_steps)
  + initial_learning_rate, decay_steps)
  ```
  You can pass this schedule directly into a `tf.keras.optimizers.Optimizer`
  @@ -640,7 +640,6 @@ class CosineDecayRestarts(LearningRateSchedule):
  lr_decayed_fn = (
  tf.keras.experimental.CosineDecayRestarts(
  initial_learning_rate,
  - global_step,
  first_decay_steps))
  ```
  @@ -665,8 +664,6 @@ class CosineDecayRestarts(LearningRateSchedule):
  A 1-arg callable learning rate schedule that takes the current optimizer
  step and outputs the decayed learning rate, a scalar `Tensor` of the same
  type as `initial_learning_rate`.
  - Raises:
  - ValueError: if `global_step` is not supplied.
  """
  super(CosineDecayRestarts, self).__init__()
  @@ -779,7 +776,7 @@ class LinearCosineDecay(LearningRateSchedule):
  decay_steps = 1000
  lr_decayed_fn = (
  tf.keras.experimental.LinearCosineDecay(
  - initial_learning_rate, global_step, decay_steps))
  + initial_learning_rate, decay_steps))
  ```
  You can pass this schedule directly into a `tf.keras.optimizers.Optimizer`
  @@ -899,7 +896,7 @@ class NoisyLinearCosineDecay(LearningRateSchedule):
  decay_steps = 1000
  lr_decayed_fn = (
  tf.keras.experimental.NoisyLinearCosineDecay(
  - initial_learning_rate, global_step, decay_steps))
  + initial_learning_rate, decay_steps))
  ```
  You can pass this schedule directly into a `tf.keras.optimizers.Optimizer`
  