code: def adjust_learning_rate(optimizer, cur_iter, init_lr, warm_up_step):
      """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
      # if warm up step is 0, no warm up actually.
      if cur_iter < warm_up_step:
          lr = init_lr * (
              1.0 / warm_up_step + 1.0 / warm_up_step * cur_iter
          )  # [0.1lr, 0.2lr, 0.3lr, ..... 1lr]
      else:
          lr = init_lr
      # lr = args.lr * (0.1 ** (epoch // 30))
      for param_group in optimizer.param_groups:
          param_group["lr"] = lr
  
label_desc: Use image node_id 1 for calling a global function with example usage: adjust_learning_rate(optimizer, cur_iter, init_lr, warm_up_step) without return types