code: def get_vgg(
      num_layers,
      pretrained=False,
      device=cpu(),
      root=os.path.join(base.data_dir(), "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, default False
          Whether to load the pretrained weights for model.
      device : Device, default CPU
          The device 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(
                  f"vgg{num_layers}{batch_norm_suffix}", root=root
              ),
              device=device,
          )
      return net
  
imports: import os
  from ....device import cpu
  from ....initializer import Xavier
  from ...block import HybridBlock
  from ...None import nn
  from ....None import base
  from ....util import use_np, wrap_ctx_to_device_func
label_desc: Use image node_id 1 for calling a global function with example usage: get_vgg(num_layers, pretrained, device, root) and returns: net