class_docstr: A `LearningRateSchedule` that uses a polynomial decay schedule.
  
  It is commonly observed that a monotonically decreasing learning rate, whose
  degree of change is carefully chosen, results in a better performing model.
  This schedule applies a polynomial decay function to an optimizer step,
  given a provided `initial_learning_rate`, to reach an `end_learning_rate`
  in the given `decay_steps`.
  
  It requires a `step` value to compute the decayed learning rate. You
  can just pass a backend variable that you increment at each training
  step.
  
  The schedule is a 1-arg callable that produces a decayed learning rate
  when passed the current optimizer step. This can be useful for changing the
  learning rate value across different invocations of optimizer functions.
  It is computed as:
  
  ```python
  def decayed_learning_rate(step):
      step = min(step, decay_steps)
      return ((initial_learning_rate - end_learning_rate) *
              (1 - step / decay_steps) ^ (power)
             ) + end_learning_rate
  ```
  
  If `cycle` is True then a multiple of `decay_steps` is used, the first one
  that is bigger than `step`.
  
  ```python
  def decayed_learning_rate(step):
      decay_steps = decay_steps * ceil(step / decay_steps)
      return ((initial_learning_rate - end_learning_rate) *
              (1 - step / decay_steps) ^ (power)
             ) + end_learning_rate
  ```
  
  You can pass this schedule directly into a `keras.optimizers.Optimizer`
  as the learning rate.
  Example: Fit a model while decaying from 0.1 to 0.01 in 10000 steps using
  sqrt (i.e. power=0.5):
  
  ```python
  ...
  starter_learning_rate = 0.1
  end_learning_rate = 0.01
  decay_steps = 10000
  learning_rate_fn = keras.optimizers.schedules.PolynomialDecay(
      starter_learning_rate,
      decay_steps,
      end_learning_rate,
      power=0.5)
  
  model.compile(optimizer=keras.optimizers.SGD(
                    learning_rate=learning_rate_fn),
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy'])
  
  model.fit(data, labels, epochs=5)
  ```
  
  The learning rate schedule is also serializable and deserializable using
  `keras.optimizers.schedules.serialize` and
  `keras.optimizers.schedules.deserialize`.
  
  Args:
      initial_learning_rate: A Python float. The initial learning rate.
      decay_steps: A Python integer. Must be positive. See the decay
          computation above.
      end_learning_rate: A Python float. The minimal end learning rate.
      power: A Python float. The power of the polynomial. Defaults to
          `1.0`.
      cycle: A boolean, whether it should cycle beyond decay_steps.
      name: String.  Optional name of the operation. Defaults to
          `"PolynomialDecay"`.
  
  Returns:
      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`.
code: def __init__(
      self,
      initial_learning_rate,
      decay_steps,
      end_learning_rate=0.0001,
      power=1.0,
      cycle=False,
      name="PolynomialDecay",
  ):
      super().__init__()
  
      self.initial_learning_rate = initial_learning_rate
      self.decay_steps = decay_steps
      self.end_learning_rate = end_learning_rate
      self.power = power
      self.cycle = cycle
      self.name = name
  
      if self.decay_steps <= 0:
          raise ValueError(
              "Argument `decay_steps` must be > 0. "
              f"Received: decay_steps={self.decay_steps}"
          )
  
imports: import math
  from keras import ops
  from keras.api_export import keras_export
  from keras.saving import serialization_lib
label_desc: Use image node_id 1 to create a new PolynomialDecay object from inherited base classes: LearningRateSchedule with example: obj = PolynomialDecay(initial_learning_rate, decay_steps, end_learning_rate, power, cycle, name)