code: def save_checkpoint(epoch, top1, best_acc):
      if opt.save_frequency and (epoch + 1) % opt.save_frequency == 0:
          fname = os.path.join(
              opt.prefix, f"{opt.model}_{epoch}_acc_{top1:.4f}.params"
          )
          net.save_parameters(fname)
          logger.info(
              f"[Epoch {epoch}] Saving checkpoint to {fname} with Accuracy: {top1:.4f}"
          )
      if top1 > best_acc[0]:
          best_acc[0] = top1
          fname = os.path.join(opt.prefix, f"{opt.model}_best.params")
          net.save_parameters(fname)
          logger.info(
              f"[Epoch {epoch}] Saving checkpoint to {fname} with Accuracy: {top1:.4f}"
          )
  
file_path: mxnet/example/gluon/image_classification.py
imports: from __future__ import division
  import argparse, time, os
  import logging
  import mxnet
  from mxnet import gluon
  from mxnet import profiler
  from mxnet.gluon import nn
  from mxnet.gluon.model_zoo import vision
  from mxnet import autograd
  from mxnet.test_utils import get_mnist_iterator
  from mxnet.gluon.metric import Accuracy, TopKAccuracy, CompositeEvalMetric
  import numpy
  from data import get_cifar10_iterator, get_imagenet_iterator, get_caltech101_iterator, dummy_iterator
label_desc: Use image node_id 5 for calling a global function with example usage: save_checkpoint(epoch, top1, best_acc) without return types