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: Get the deconv configs using presets
docstr_tok: ["Get","the","deconv","configs","using","presets"]
code: def _get_deconv_cfg(self, deconv_kernel):
      """Get the deconv configs using presets"""
      if deconv_kernel == 4:
          padding = 1
          output_padding = 0
      elif deconv_kernel == 3:
          padding = 1
          output_padding = 1
      elif deconv_kernel == 2:
          padding = 0
          output_padding = 0
      else:
          raise ValueError(
              "Unsupported deconvolution kernel: {}".format(
                  deconv_kernel
              )
          )
  
      return deconv_kernel, padding, output_padding
  
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 2 for calling the DeconvResnet obj's underlying member method code with example usage: obj._get_deconv_cfg(deconv_kernel) and returns: deconv_kernel,  padding,  output_padding