code: def cosine_decay_restarts(
      learning_rate,
      global_step,
      first_decay_steps,
      t_mul=2.0,
      m_mul=1.0,
      alpha=0.0,
      name=None,
  ):
      """Applies cosine decay with restarts to the learning rate.
  
      When training a model, it is often recommended to lower the learning rate as
      the training progresses.  This function applies a cosine decay function with
      restarts 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 while taking into account
      possible warm restarts. The learning rate multiplier first decays
      from 1 to `alpha` for `first_decay_steps` steps. Then, a warm
      restart is performed. Each new warm restart runs for `t_mul` times more steps
      and with `m_mul` times smaller initial learning rate.
  
      Example usage:
      ```python
      first_decay_steps = 1000
      lr_decayed = cosine_decay_restarts(learning_rate, global_step,
                                         first_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.
        first_decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
          Number of steps to decay over.
        t_mul: A scalar `float32` or `float64` `Tensor` or a Python number. Used to
          derive the number of iterations in the i-th period
        m_mul: A scalar `float32` or `float64` `Tensor` or a Python number.
          Used to derive the initial learning rate of the i-th period:
        alpha: A scalar `float32` or `float64` Tensor or a Python number. Minimum
          learning rate value as a fraction of the learning_rate.
        name: String. Optional name of the operation.  Defaults to 'SGDRDecay'.
  
      Returns:
        A scalar `Tensor` of the same type as `learning_rate`.  The decayed
        learning rate.
      Raises:
        ValueError: if `global_step` is not supplied.
  
      References:
        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.CosineDecayRestarts(
          learning_rate,
          first_decay_steps,
          t_mul=t_mul,
          m_mul=m_mul,
          alpha=alpha,
          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/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 7 for calling a global function with example usage: cosine_decay_restarts(learning_rate, global_step, first_decay_steps, t_mul, m_mul, alpha, name) and returns: decayed_lr