code: def get_vgg(num_layers, pretrained=False, ctx=cpu(),
              root=os.path.join(base.data_dir(), 'models'), **kwargs):
      r
      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),
                                             root=root), ctx=ctx)
      return net
docs: 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, default False
          Whether to load the pretrained weights for model.
      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.