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 TensorFlow 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 `tf.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 = tf.keras.optimizers.schedules.PolynomialDecay(
      starter_learning_rate,
      decay_steps,
      end_learning_rate,
      power=0.5)
  
  model.compile(optimizer=tf.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
  `tf.keras.optimizers.schedules.serialize` and
  `tf.keras.optimizers.schedules.deserialize`.
  
  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 get_config(self):
      return {
          "initial_learning_rate": self.initial_learning_rate,
          "decay_steps": self.decay_steps,
          "end_learning_rate": self.end_learning_rate,
          "power": self.power,
          "cycle": self.cycle,
          "name": self.name,
      }
  
imports: import abc
  import math
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import control_flow_case
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 3 for calling the PolynomialDecay obj's underlying member method code with example usage: obj.get_config() and returns: dict