class_docstr: A LearningRateSchedule that uses a linear cosine decay schedule.
  
  See [Bello et al., ICML2017] Neural Optimizer Search with RL.
  https://arxiv.org/abs/1709.07417
  
  For the idea of warm starts here controlled by `num_periods`,
  see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent
  with Warm Restarts. https://arxiv.org/abs/1608.03983
  
  Note that linear cosine decay is more aggressive than cosine decay and
  larger initial learning rates can typically be used.
  
  When training a model, it is often recommended to lower the learning rate as
  the training progresses. This schedule applies a linear cosine decay
  function to an optimizer step, given a provided initial learning rate.
  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 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)
    linear_decay = (decay_steps - step) / decay_steps
    cosine_decay = 0.5 * (
        1 + cos(pi * 2 * num_periods * step / decay_steps))
    decayed = (alpha + linear_decay) * cosine_decay + beta
    return initial_learning_rate * decayed
  ```
  
  Example usage:
  ```python
  decay_steps = 1000
  lr_decayed_fn = (
    tf.keras.experimental.LinearCosineDecay(
      initial_learning_rate, decay_steps))
  ```
  
  You can pass this schedule directly into a `tf.keras.optimizers.Optimizer`
  as the learning rate. 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,
          "num_periods": self.num_periods,
          "alpha": self.alpha,
          "beta": self.beta,
          "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 LinearCosineDecay obj's underlying member method code with example usage: obj.get_config() and returns: dict