code: def lr_schedule(epoch):
      """Learning Rate Schedule
      Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.
      Called automatically every epoch as part of callbacks during training.
      # Arguments
          epoch (int): The number of epochs
      # Returns
          lr (float32): learning rate
      """
      lr = 1e-3
      if epoch > 180:
          lr *= 0.5e-3
      elif epoch > 160:
          lr *= 1e-3
      elif epoch > 120:
          lr *= 1e-2
      elif epoch > 80:
          lr *= 1e-1
      print("Learning rate: ", lr)
      return lr
  
file_path: AIX360/aix360/algorithms/profwt/resnet_keras_model.py
imports: from __future__ import print_function
  import keras
  from keras.layers import Dense, Conv2D, BatchNormalization, Activation
  from keras.layers import AveragePooling2D, Input, Flatten
  from keras.optimizers import Adam
  from keras.callbacks import ModelCheckpoint, LearningRateScheduler
  from keras.callbacks import ReduceLROnPlateau
  from keras.preprocessing.image import ImageDataGenerator
  from keras.regularizers import l2
  from keras import backend
  from keras.models import Model
  from keras.datasets import cifar10
  import numpy
  import os
  import json
  from collections import namedtuple
label_desc: Use image node_id 1 for calling a global function with example usage: lr_schedule(epoch) and returns: lr