code: def cosine_decay(
      learning_rate, global_step, decay_steps, alpha=0.0, name=None
  ):
      """Applies cosine decay 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
      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)
      cosine_decay = 0.5 * (1 + cos(pi * global_step / decay_steps))
      decayed = (1 - alpha) * cosine_decay + alpha
      decayed_learning_rate = learning_rate * decayed
      ```
  
      Example usage:
      ```python
      decay_steps = 1000
      lr_decayed = 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.
        alpha: A scalar `float32` or `float64` Tensor or a Python number. Minimum
          learning rate value as a fraction of learning_rate.
        name: String. Optional name of the operation.  Defaults to 'CosineDecay'.
  
      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.CosineDecay(
          learning_rate, decay_steps, 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 6 for calling a global function with example usage: cosine_decay(learning_rate, global_step, decay_steps, alpha, name) and returns: decayed_lr