code: def __init__(
      self,
      block,
      layers,
      num_classes=1000,
      channels=(16, 32, 64, 128, 256, 512, 512, 512),
      out_map=False,
      out_middle=False,
      pool_size=28,
      arch="D",
  ):
      super(DRN, self).__init__()
      self.inplanes = channels[0]
      self.out_map = out_map
      self.out_dim = channels[-1]
      self.out_middle = out_middle
      self.arch = arch
  
      if arch == "C":
          self.conv1 = nn.Conv2d(
              3,
              channels[0],
              kernel_size=7,
              stride=1,
              padding=3,
              bias=False,
          )
          self.bn1 = BatchNorm(channels[0])
          self.relu = nn.ReLU(inplace=True)
  
          self.layer1 = self._make_layer(
              BasicBlock, channels[0], layers[0], stride=1
          )
          self.layer2 = self._make_layer(
              BasicBlock, channels[1], layers[1], stride=2
          )
      elif arch == "D":
          self.layer0 = nn.Sequential(
              nn.Conv2d(
                  3,
                  channels[0],
                  kernel_size=7,
                  stride=1,
                  padding=3,
                  bias=False,
              ),
              BatchNorm(channels[0]),
              nn.ReLU(inplace=True),
          )
  
          self.layer1 = self._make_conv_layers(
              channels[0], layers[0], stride=1
          )
          self.layer2 = self._make_conv_layers(
              channels[1], layers[1], stride=2
          )
  
      self.layer3 = self._make_layer(
          block, channels[2], layers[2], stride=2
      )
      self.layer4 = self._make_layer(
          block, channels[3], layers[3], stride=2
      )
      self.layer5 = self._make_layer(
          block, channels[4], layers[4], dilation=2, new_level=False
      )
      self.layer6 = (
          None
          if layers[5] == 0
          else self._make_layer(
              block, channels[5], layers[5], dilation=4, new_level=False
          )
      )
  
      if arch == "C":
          self.layer7 = (
              None
              if layers[6] == 0
              else self._make_layer(
                  BasicBlock,
                  channels[6],
                  layers[6],
                  dilation=2,
                  new_level=False,
                  residual=False,
              )
          )
          self.layer8 = (
              None
              if layers[7] == 0
              else self._make_layer(
                  BasicBlock,
                  channels[7],
                  layers[7],
                  dilation=1,
                  new_level=False,
                  residual=False,
              )
          )
      elif arch == "D":
          self.layer7 = (
              None
              if layers[6] == 0
              else self._make_conv_layers(
                  channels[6], layers[6], dilation=2
              )
          )
          self.layer8 = (
              None
              if layers[7] == 0
              else self._make_conv_layers(
                  channels[7], layers[7], dilation=1
              )
          )
  
      if num_classes > 0:
          self.avgpool = nn.AvgPool2d(pool_size)
          self.fc = nn.Conv2d(
              self.out_dim,
              num_classes,
              kernel_size=1,
              stride=1,
              padding=0,
              bias=True,
          )
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
              m.weight.data.normal_(0, math.sqrt(2.0 / n))
          elif isinstance(m, BatchNorm):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
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 1 to create a new DRN object from inherited base classes: nn with example: obj = DRN(block, layers, num_classes, channels, out_map, out_middle, pool_size, arch)