code: def yolo3_darknet53_coco(
      pretrained_base=True,
      pretrained=False,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      """YOLO3 multi-scale with darknet53 base network on COCO dataset.
      Parameters
      ----------
      pretrained_base : boolean
          Whether fetch and load pretrained weights for base network.
      pretrained : bool or str
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      norm_layer : object
          Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
          Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
      norm_kwargs : dict
          Additional `norm_layer` arguments, for example `num_devices=4`
          for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
      Returns
      -------
      mxnet.gluon.HybridBlock
          Fully hybrid yolo3 network.
      """
      from ...data import COCODetection
  
      pretrained_base = False if pretrained else pretrained_base
      base_net = darknet53(
          pretrained=pretrained_base,
          norm_layer=norm_layer,
          norm_kwargs=norm_kwargs,
          **kwargs,
      )
      stages = [
          base_net.features[:15],
          base_net.features[15:24],
          base_net.features[24:],
      ]
      anchors = [
          [10, 13, 16, 30, 33, 23],
          [30, 61, 62, 45, 59, 119],
          [116, 90, 156, 198, 373, 326],
      ]
      strides = [8, 16, 32]
      classes = COCODetection.CLASSES
      return get_yolov3(
          "darknet53",
          stages,
          [512, 256, 128],
          anchors,
          strides,
          classes,
          "coco",
          pretrained=pretrained,
          norm_layer=norm_layer,
          norm_kwargs=norm_kwargs,
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  import os
  import warnings
  import numpy
  import mxnet
  from mxnet import gluon
  from mxnet import autograd
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from .darknet import _conv2d, darknet53
  from ..mobilenet import get_mobilenet
  from .yolo_target import YOLOV3TargetMerger
  from ...loss import YOLOV3Loss
label_desc: Use image node_id 5 for calling a global function with example usage: yolo3_darknet53_coco(pretrained_base, pretrained, norm_layer, norm_kwargs) and returns: get_yolov3