class_docstr: Deep layer aggregation upsampling layer.
  
  Parameters
  ----------
  startp : int
      Start index.
  channels : iterable of int
      Output channels.
  scales : iterable of int
      Upsampling scales.
  in_channels : iterable of int
      Input channels.
  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","upsampling","layer",".","Parameters","--","--","--","--","--","startp",":","int","Start","index",".","channels",":","iterable","of","int","Output","channels",".","scales",":","iterable","of","int","Upsampling","scales",".","in_channels",":","iterable","of","int","Input","channels",".","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","."]
code: def hybrid_forward(self, F, layers):
      out = [layers[-1]]  # start with 32
      for i in range(len(layers) - self.startp - 1):
          ida = self.idas[i]
          ida.startp = len(layers) - i - 2
          ida.endp = len(layers)
          layers = ida(layers)
          out.insert(0, layers[-1])
      return out
  
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 DLAUp obj's underlying member method code with example usage: obj.hybrid_forward(F, layers) and returns: out