class_docstr: Iterative deep aggregation layer.
  
  Parameters
  ----------
  out_channels : iterable of int
      Output channels for multiple layers.
  in_channels : iterable of int
      Input channels for multiple layers.
  up_f : iterable of float
      Upsampling ratios.
  use_dcnv2 : bool
      Whether use modulated deformable convolution(DCN version2).
  norm_layer : nn.HybridBlock, default is BatchNorm.
      The norm_layer instance.
  norm_kwargs : dict
      Extra arguments for norm layer.
class_docstr_tok: ["Iterative","deep","aggregation","layer",".","Parameters","--","--","--","--","--","out_channels",":","iterable","of","int","Output","channels","for","multiple","layers",".","in_channels",":","iterable","of","int","Input","channels","for","multiple","layers",".","up_f",":","iterable","of","float","Upsampling","ratios",".","use_dcnv2",":","bool","Whether","use","modulated","deformable","convolution","(","DCN","version2",")",".","norm_layer",":","nn.HybridBlock",",","default","is","BatchNorm",".","The","norm_layer","instance",".","norm_kwargs",":","dict","Extra","arguments","for","norm","layer","."]
code: def hybrid_forward(self, F, layers):
      for i in range(self.startp + 1, self.endp):
          upsample = self.ups[i - self.startp - 1]
          project = self.projs[i - self.startp - 1]
          layers[i] = upsample(project(layers[i]))
          node = self.nodes[i - self.startp - 1]
          layers[i] = node(layers[i] + layers[i - 1])
      return layers
  
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 IDAUp obj's underlying member method code with example usage: obj.hybrid_forward(F, layers) and returns: layers