code: def adjust_learning_rate(optimizer, epoch, gammas, schedule):
    
    lr = args.learning_rate
    assert len(gammas) == len(schedule), "length of gammas and schedule should be equal"
    for (gamma, step) in zip(gammas, schedule):
      if (epoch >= step):
        lr = lr * gamma
      else:
        break
    for param_group in optimizer.param_groups:
      param_group['lr'] = lr
    return lr
docs: Sets the learning rate to the initial LR decayed by 10 every 30 epochs