code: def linear_cosine_decay(
      learning_rate,
      global_step,
      decay_steps,
      num_periods=0.5,
      alpha=0.0,
      beta=0.001,
      name=None,
  ):
      """Applies linear cosine decay to the learning rate.
  
      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 function applies a linear cosine decay function
      to a provided initial learning rate.  It requires a `global_step` value to
      compute the decayed learning rate.  You can just pass a TensorFlow variable
      that you increment at each training step.
  
      The function returns the decayed learning rate.  It is computed as:
      ```python
      global_step = min(global_step, decay_steps)
      linear_decay = (decay_steps - global_step) / decay_steps)
      cosine_decay = 0.5 * (
          1 + cos(pi * 2 * num_periods * global_step / decay_steps))
      decayed = (alpha + linear_decay) * cosine_decay + beta
      decayed_learning_rate = learning_rate * decayed
      ```
  
      Example usage:
      ```python
      decay_steps = 1000
      lr_decayed = linear_cosine_decay(learning_rate, global_step, decay_steps)
      ```
  
      Args:
        learning_rate: A scalar `float32` or `float64` Tensor or a Python number.
          The initial learning rate.
        global_step: A scalar `int32` or `int64` `Tensor` or a Python number. Global
          step to use for the decay computation.
        decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. Number
          of steps to decay over.
        num_periods: Number of periods in the cosine part of the decay. See
          computation above.
        alpha: See computation above.
        beta: See computation above.
        name: String.  Optional name of the operation.  Defaults to
          'LinearCosineDecay'.
  
      Returns:
        A scalar `Tensor` of the same type as `learning_rate`.  The decayed
        learning rate.
      Raises:
        ValueError: if `global_step` is not supplied.
  
      References:
        Neural Optimizer Search with Reinforcement Learning:
          [Bello et al., 2017](http://proceedings.mlr.press/v70/bello17a.html)
          ([pdf](http://proceedings.mlr.press/v70/bello17a/bello17a.pdf))
        Stochastic Gradient Descent with Warm Restarts:
          [Loshchilov et al., 2017]
          (https://openreview.net/forum?id=Skq89Scxx&noteId=Skq89Scxx)
          ([pdf](https://openreview.net/pdf?id=Skq89Scxx))
  
      @compatibility(eager)
      When eager execution is enabled, this function returns a function which in
      turn returns the decayed learning rate Tensor. This can be useful for changing
      the learning rate value across different invocations of optimizer functions.
      @end_compatibility
      """
      decayed_lr = learning_rate_schedule.LinearCosineDecay(
          learning_rate,
          decay_steps,
          num_periods=num_periods,
          alpha=alpha,
          beta=beta,
          name=name,
      )
  
      if not context.executing_eagerly():
          decayed_lr = decayed_lr(global_step)
      else:
          decayed_lr = functools.partial(decayed_lr, global_step)
      return decayed_lr
  
file_path: tensorflow-upstream/tensorflow/python/keras/optimizer_v2/legacy_learning_rate_decay.py
imports: import functools
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule
  from tensorflow.python.ops import math_ops
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 8 for calling a global function with example usage: linear_cosine_decay(learning_rate, global_step, decay_steps, num_periods, alpha, beta, name) and returns: decayed_lr