code: def get_vgg_atrous_extractor(
      num_layers,
      im_size,
      pretrained=False,
      ctx=mx.cpu(),
      root=os.path.join("~", ".mxnet", "models"),
      **kwargs,
  ):
      """Get VGG atrous feature extractor networks.
  
      Parameters
      ----------
      num_layers : int
          VGG types, can be 11,13,16,19.
      im_size : int
          VGG detection input size, can be 300, 512.
      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.
      ctx : mx.Context
          Context such as mx.cpu(), mx.gpu(0).
      root : str
          Model weights storing path.
  
      Returns
      -------
      mxnet.gluon.HybridBlock
          The returned network.
  
      """
      layers, filters = vgg_spec[num_layers]
      extras = extra_spec[im_size]
      net = VGGAtrousExtractor(layers, filters, extras, **kwargs)
      if pretrained:
          from ..model_store import get_model_file
  
          batch_norm_suffix = "_bn" if kwargs.get("batch_norm") else ""
          net.initialize(ctx=ctx)
          net.load_parameters(
              get_model_file(
                  "vgg%d_atrous%s" % (num_layers, batch_norm_suffix),
                  tag=pretrained,
                  root=root,
              ),
              ctx=ctx,
              allow_missing=True,
          )
      return net
  
imports: from __future__ import division
  import os
  import mxnet
  from mxnet import gluon
  from mxnet.gluon import nn
  from mxnet.initializer import Xavier
label_desc: Use image node_id 1 for calling a global function with example usage: get_vgg_atrous_extractor(num_layers, im_size, pretrained, ctx, root) and returns: net