code: def get_vgg(
      num_layers,
      pretrained=False,
      ctx=cpu(),
      root="~/.mxnet/models",
      **kwargs,
  ):
      r"""VGG model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition"
      <https://arxiv.org/abs/1409.1556>`_ paper.
  
      Parameters
      ----------
      num_layers : int
          Number of layers for the variant of densenet. Options are 11, 13, 16, 19.
      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 : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default $MXNET_HOME/models
          Location for keeping the model parameters.
      """
      layers, filters = vgg_spec[num_layers]
      net = VGG(layers, filters, **kwargs)
      if pretrained:
          from .model_store import get_model_file
  
          batch_norm_suffix = "_bn" if kwargs.get("batch_norm") else ""
          net.load_parameters(
              get_model_file(
                  "vgg%d%s" % (num_layers, batch_norm_suffix),
                  tag=pretrained,
                  root=root,
              ),
              ctx=ctx,
          )
          from ..data import ImageNet1kAttr
  
          attrib = ImageNet1kAttr()
          net.synset = attrib.synset
          net.classes = attrib.classes
          net.classes_long = attrib.classes_long
      return net
  
file_path: gluon-cv/gluoncv/model_zoo/vgg.py
imports: from __future__ import division
  from mxnet.context import cpu
  from mxnet.initializer import Xavier
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 1 for calling a global function with example usage: get_vgg(num_layers, pretrained, ctx, root) and returns: net