code: def _make_layer(
      self,
      block,
      planes,
      blocks,
      stride=1,
      dilation=1,
      new_level=True,
      residual=True,
  ):
      assert dilation == 1 or dilation % 2 == 0
      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,
              ),
              BatchNorm(planes * block.expansion),
          )
  
      layers = list()
      layers.append(
          block(
              self.inplanes,
              planes,
              stride,
              downsample,
              dilation=(1, 1)
              if dilation == 1
              else (dilation // 2 if new_level else dilation, dilation),
              residual=residual,
          )
      )
      self.inplanes = planes * block.expansion
      for i in range(1, blocks):
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  residual=residual,
                  dilation=(dilation, dilation),
              )
          )
  
      return nn.Sequential(*layers)
  
imports: import numpy
  import torch
  from torch.autograd import Variable
  from pytorch2keras.converter import pytorch_to_keras
  import torch.nn
  import math
label_desc: Use image node_id 2 for calling the DRN obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, stride, dilation, new_level, residual) and returns: nn