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
docstr: HybridForward
docstr_tok: ["HybridForward"]
code: def hybrid_forward(self, F, x):
      # pylint: disable=arguments-differ
      """HybridForward"""
      y = self.base_network(x)
      out = self.deconv(y)
      return out
  
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 4 for calling the DeconvResnet obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: out