code: def get_deconv_dla(
      base_network,
      pretrained=False,
      ctx=cpu(),
      scale=4.0,
      use_dcnv2=False,
      **kwargs,
  ):
      """Get resnet with deconv layers.
  
      Parameters
      ----------
      base_network : str
          Name of the base feature extraction network.
      pretrained : bool
          Whether load pretrained base network.
      ctx : mxnet.Context
          mx.cpu() or mx.gpu()
      scale : int or float, default is 4.0
          The downsampling ratio for the network, must in [2, 4, 8, 16]
      use_dcnv2 : bool
          If true, will use DCNv2 layers in upsampling blocks
      pretrained : type
          Description of parameter `pretrained`.
      Returns
      -------
      get_deconv_resnet(base_network, pretrained=False,
          Description of returned object.
  
      """
      assert int(scale) in [
          2,
          4,
          8,
          16,
      ], "scale must be one of [2, 4, 8, 16]"
      net = DeconvDLA(
          base_network=base_network,
          pretrained_base=pretrained,
          down_ratio=int(scale),
          last_level=5,
          use_dcnv2=use_dcnv2,
          **kwargs,
      )
      with warnings.catch_warnings(record=True) as _:
          warnings.simplefilter("always")
          net.initialize()
      net.collect_params().reset_ctx(ctx)
      return net
  
imports: from __future__ import absolute_import
  import warnings
  import numpy
  from mxnet.context import cpu
  from mxnet.gluon import nn
  from mxnet.gluon import contrib
  from .deconv_resnet import BilinearUpSample
  from ..model_zoo import get_model
label_desc: Use image node_id 1 for calling a global function with example usage: get_deconv_dla(base_network, pretrained, ctx, scale, use_dcnv2) and returns: net