code: def get_dla(
      layers,
      pretrained=False,
      ctx=mx.cpu(),
      root=os.path.join("~", ".mxnet", "models"),
      **kwargs,
  ):
      """Get a center net instance.
  
      Parameters
      ----------
      name : str or int
          Layers of the network.
      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 : mxnet.Context
          Context such as mx.cpu(), mx.gpu(0).
      root : str
          Model weights storing path.
  
      Returns
      -------
      HybridBlock
          A DLA network.
  
      """
      # pylint: disable=unused-variable
      net = DLA(**kwargs)
      if pretrained:
          from .model_store import get_model_file
  
          full_name = "dla{}".format(layers)
          net.load_parameters(
              get_model_file(full_name, tag=pretrained, root=root),
              ctx=ctx,
              ignore_extra=True,
          )
          from ..data import ImageNet1kAttr
  
          attrib = ImageNet1kAttr()
          net.synset = attrib.synset
          net.classes = attrib.classes
          net.classes_long = attrib.classes_long
      return net
  
imports: from __future__ import division
  import os
  import mxnet
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
label_desc: Use image node_id 2 for calling a global function with example usage: get_dla(layers, pretrained, ctx, root) and returns: net