class_docstr: Deconvolutional ResNet.
  
  Parameters
  ----------
  base_network : str
      Name of the base feature extraction network.
  deconv_filters : list of int
      Number of filters for deconv layers.
  deconv_kernels : list of int
      Kernel sizes for deconv layers.
  pretrained_base : bool
      Whether load pretrained base network.
  norm_layer : mxnet.gluon.nn.HybridBlock
      Type of Norm layers, can be BatchNorm, SyncBatchNorm, GroupNorm, etc.
  norm_kwargs : dict
      Additional kwargs for `norm_layer`.
  use_dcnv2 : bool
      If true, will use DCNv2 layers in upsampling blocks
code: def __init__(
      self,
      base_network="resnet18_v1b",
      deconv_filters=(256, 128, 64),
      deconv_kernels=(4, 4, 4),
      pretrained_base=True,
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
      use_dcnv2=False,
      **kwargs,
  ):
      super(DeconvResnet, self).__init__(**kwargs)
      assert "resnet" in base_network
      net = get_model(base_network, pretrained=pretrained_base)
      self._norm_layer = norm_layer
      self._norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
      self._use_dcnv2 = use_dcnv2
      if "v1b" in base_network:
          feat = nn.HybridSequential()
          feat.add(
              *[
                  net.conv1,
                  net.bn1,
                  net.relu,
                  net.maxpool,
                  net.layer1,
                  net.layer2,
                  net.layer3,
                  net.layer4,
              ]
          )
          self.base_network = feat
      else:
          raise NotImplementedError(
              "Only v1 variants of resnet are supported so far."
          )
      with self.name_scope():
          self.deconv = self._make_deconv_layer(
              deconv_filters, deconv_kernels
          )
  
imports: from __future__ import absolute_import
  import warnings
  import math
  import mxnet
  from mxnet.context import cpu
  from mxnet.gluon import nn
  from mxnet.gluon import contrib
  from ..model_zoo import get_model
label_desc: Use image node_id 1 to create a new DeconvResnet object from inherited base classes: nn with example: obj = DeconvResnet(base_network, deconv_filters, deconv_kernels, pretrained_base, norm_layer, norm_kwargs, use_dcnv2)