class_docstr: Deep layer aggregation network with deconv layers(smaller strides) which produce larger
  feature map.
  
  Parameters
  ----------
  base_network : str
      Name of the base feature extraction network, must be DLA networks.
  pretrained_base : bool
      Whether load pretrained base network.
  down_ratio : int
      The downsampling ratio of the network, must be one of [2, 4, 8, 16].
  last_level : int
      Index of the last output.
  out_channel : int
      The channel number of last output. If `0`, will use the channels of the first input.
  use_dcnv2 : bool
      Whether use ModulatedDeformableConvolution(DCN version 2).
  norm_layer : nn.HybridBlock, default is BatchNorm
      The norm layer type.
  norm_kwargs : dict
      Extra arguments to norm layer.
class_docstr_tok: ["Deep","layer","aggregation","network","with","deconv","layers","(","smaller","strides",")","which","produce","larger","feature","map",".","Parameters","--","--","--","--","--","base_network",":","str","Name","of","the","base","feature","extraction","network",",","must","be","DLA","networks",".","pretrained_base",":","bool","Whether","load","pretrained","base","network",".","down_ratio",":","int","The","downsampling","ratio","of","the","network",",","must","be","one","of","[","2",",","4",",","8",",","16","]",".","last_level",":","int","Index","of","the","last","output",".","out_channel",":","int","The","channel","number","of","last","output",".","If","`","0","`",",","will","use","the","channels","of","the","first","input",".","use_dcnv2",":","bool","Whether","use","ModulatedDeformableConvolution","(","DCN","version","2",")",".","norm_layer",":","nn.HybridBlock",",","default","is","BatchNorm","The","norm","layer","type",".","norm_kwargs",":","dict","Extra","arguments","to","norm","layer","."]
docstr: Forward pass
docstr_tok: ["Forward","pass"]
code: def hybrid_forward(self, F, x):
      """Forward pass"""
      x = self.base(x)
      x = self.dla_up(x)
  
      y = []
      for i in range(self.last_level - self.first_level):
          y.append(x[i])
      self.ida_up.startp = 0
      self.ida_up.endp = len(y)
      self.ida_up(y)
  
      return y[-1]
  
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 2 for calling the DeconvDLA obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: y