code: def lr_schedule(epoch):
      """
      Returns a custom learning rate that decreases as epochs progress.
      """
      learning_rate = 0.2
      if epoch > 10:
          learning_rate = 0.02
      if epoch > 20:
          learning_rate = 0.01
      if epoch > 50:
          learning_rate = 0.005
  
      tf.summary.scalar("learning rate", data=learning_rate, step=epoch)
      return learning_rate
  
imports: import os
  from datetime import datetime
  from packaging import version
  import tensorflow
  from tensorflow import keras
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: lr_schedule(epoch) and returns: learning_rate