class_docstr: Dilated Pre-trained ResNet Model, which preduces the stride of 8 featuremaps at conv5.
  
  Parameters
  ----------
  block : Block
      Class for the residual block. Options are BasicBlockV1, BottleneckV1.
  layers : list of int
      Numbers of layers in each block
  classes : int, default 1000
      Number of classification classes.
  dilated : bool, default False
      Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
      typically used in Semantic Segmentation.
  norm_layer : object
      Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
      for Synchronized Cross-GPU BachNormalization).
  
  Reference:
  
      - He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  
      - Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
code: def _make_layer(
      self, block, planes, blocks, stride=1, dilation=1, norm_layer=None
  ):
      downsample = None
      if stride != 1 or self.inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              nn.Conv2d(
                  self.inplanes,
                  planes * block.expansion,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              norm_layer(planes * block.expansion),
          )
  
      layers = []
      if dilation in (1, 2):
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  stride,
                  dilation=1,
                  downsample=downsample,
                  previous_dilation=dilation,
                  norm_layer=norm_layer,
              )
          )
      elif dilation == 4:
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  stride,
                  dilation=2,
                  downsample=downsample,
                  previous_dilation=dilation,
                  norm_layer=norm_layer,
              )
          )
      else:
          raise RuntimeError(
              "=> unknown dilation size: {}".format(dilation)
          )
  
      self.inplanes = planes * block.expansion
      for i in range(1, blocks):
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  dilation=dilation,
                  previous_dilation=dilation,
                  norm_layer=norm_layer,
              )
          )
  
      return nn.Sequential(*layers)
  
imports: import math
  import torch.utils.model_zoo
  import torch.nn
label_desc: Use image node_id 2 for calling the DilatedResNet obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, stride, dilation, norm_layer) and returns: nn