code: def __init__(self, n_layer, pretrained_model=None, initialW=None):
      n_block = self._blocks[n_layer]
  
      _, path = utils.prepare_pretrained_model(
          {}, pretrained_model, self._models[n_layer]
      )
  
      super(DilatedResNet, self).__init__()
      with self.init_scope():
          self.conv1_1 = Conv2DBNActiv(
              None, 64, 3, 2, 1, 1, initialW=initialW
          )
          self.conv1_2 = Conv2DBNActiv(
              64, 64, 3, 1, 1, 1, initialW=initialW
          )
          self.conv1_3 = Conv2DBNActiv(
              64, 128, 3, 1, 1, 1, initialW=initialW
          )
          self.pool1 = lambda x: F.max_pooling_2d(
              x, ksize=3, stride=2, pad=1
          )
          self.res2 = ResBlock(
              n_block[0],
              128,
              64,
              256,
              1,
              1,
              initialW=initialW,
              stride_first=False,
          )
          self.res3 = ResBlock(
              n_block[1],
              256,
              128,
              512,
              2,
              1,
              initialW=initialW,
              stride_first=False,
          )
          self.res4 = ResBlock(
              n_block[2],
              512,
              256,
              1024,
              1,
              2,
              initialW=initialW,
              stride_first=False,
          )
          self.res5 = ResBlock(
              n_block[3],
              1024,
              512,
              2048,
              1,
              4,
              initialW=initialW,
              stride_first=False,
          )
  
      if path:
          chainer.serializers.load_npz(path, self, ignore_names=None)
  
imports: from __future__ import division
  from math import ceil
  import numpy
  import chainer
  import chainer.functions
  from chainer import initializers
  import chainer.links
  from chainercv.experimental.links.model.pspnet.transforms import convolution_crop
  from chainercv.links import Conv2DBNActiv
  from chainercv.links.model.resnet import ResBlock
  from chainercv.links import PickableSequentialChain
  from chainercv import transforms
  from chainercv import utils
label_desc: Use image node_id 1 to create a new DilatedResNet object from inherited base classes: PickableSequentialChain with example: obj = DilatedResNet(n_layer, pretrained_model, initialW)