code: def save_params(
      net, best_map, current_map, epoch, save_interval, prefix
  ):
      current_map = float(current_map)
      if current_map > best_map[0]:
          best_map[0] = current_map
          net.save_parameters(
              "{:s}_best.params".format(prefix, epoch, current_map)
          )
          with open(prefix + "_best_map.log", "a") as f:
              f.write("{:04d}:\t{:.4f}\n".format(epoch, current_map))
      if save_interval and epoch % save_interval == 0:
          net.save_parameters(
              "{:s}_{:04d}_{:.4f}.params".format(
                  prefix, epoch, current_map
              )
          )
  
imports: import argparse
  import os
  import logging
  import time
  import warnings
  import numpy
  import mxnet
  from mxnet import nd
  from mxnet import gluon
  from mxnet import autograd
  import gluoncv
  from gluoncv import data
  from gluoncv import utils
  from gluoncv.model_zoo import get_model
  from gluoncv.data.batchify import Tuple, Stack, Pad
  from gluoncv.data.transforms.presets.yolo import YOLO3DefaultTrainTransform
  from gluoncv.data.transforms.presets.yolo import YOLO3DefaultValTransform
  from gluoncv.data.dataloader import RandomTransformDataLoader
  from gluoncv.utils.metrics.voc_detection import VOC07MApMetric
  from gluoncv.utils.metrics.coco_detection import COCODetectionMetric
  from gluoncv.utils import LRScheduler, LRSequential
  from mxnet.contrib import amp
label_desc: Use image node_id 4 for calling a global function with example usage: save_params(net, best_map, current_map, epoch, save_interval, prefix) without return types