code: def center_net_mobilenetv3_large_duc_voc(
      pretrained=False, pretrained_base=True, **kwargs
  ):
      """Center net with mobilenetv3_large base network on voc dataset.
  
      Parameters
      ----------
      classes : iterable of str
          Names of custom foreground classes. `len(classes)` is the number of foreground classes.
      pretrained_base : bool or str, optional, default is True
          Load pretrained base network, the extra layers are randomized.
  
      Returns
      -------
      HybridBlock
          A CenterNet detection network.
  
      """
      from .duc_mobilenet import mobilenetv3_large_duc
      from ...data import VOCDetection
  
      classes = VOCDetection.CLASSES
      pretrained_base = False if pretrained else pretrained_base
      base_network = mobilenetv3_large_duc(
          pretrained=pretrained_base, **kwargs
      )
      heads = OrderedDict(
          [
              (
                  "heatmap",
                  {"num_output": len(classes), "bias": -2.19},
              ),  # use bias = -log((1 - 0.1) / 0.1)
              ("wh", {"num_output": 2}),
              ("reg", {"num_output": 2}),
          ]
      )
      return get_center_net(
          "mobilenetv3_large_duc",
          "voc",
          base_network=base_network,
          heads=heads,
          head_conv_channel=64,
          pretrained=pretrained,
          classes=classes,
          scale=4.0,
          topk=40,
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  import os
  import warnings
  from collections import OrderedDict
  import mxnet
  from mxnet.gluon import nn
  from mxnet import autograd
  from ...nn.coder import CenterNetDecoder
label_desc: Use image node_id 19 for calling a global function with example usage: center_net_mobilenetv3_large_duc_voc(pretrained, pretrained_base) and returns: get_center_net