code: def __init__(
      self,
      block,
      layers,
      sample_input_D,
      sample_input_H,
      sample_input_W,
      num_seg_classes,
      shortcut_type="B",
      no_cuda=False,
  ):
      self.inplanes = 64
      self.no_cuda = no_cuda
      super(ResNet, self).__init__()
      self.conv1 = nn.Conv3d(
          1,
          64,
          kernel_size=7,
          stride=(2, 2, 2),
          padding=(3, 3, 3),
          bias=False,
      )
  
      self.bn1 = nn.BatchNorm3d(64)
      self.relu = nn.ReLU(inplace=True)
      self.maxpool = nn.MaxPool3d(
          kernel_size=(3, 3, 3), stride=2, padding=1
      )
      self.layer1 = self._make_layer(
          block, 64, layers[0], shortcut_type
      )
      self.layer2 = self._make_layer(
          block, 128, layers[1], shortcut_type, stride=2
      )
      self.layer3 = self._make_layer(
          block, 256, layers[2], shortcut_type, stride=1, dilation=2
      )
      self.layer4 = self._make_layer(
          block, 512, layers[3], shortcut_type, stride=1, dilation=4
      )
  
      self.conv_seg = nn.Sequential(
          nn.ConvTranspose3d(512 * block.expansion, 32, 2, stride=2),
          nn.BatchNorm3d(32),
          nn.ReLU(inplace=True),
          nn.Conv3d(
              32,
              32,
              kernel_size=3,
              stride=(1, 1, 1),
              padding=(1, 1, 1),
              bias=False,
          ),
          nn.BatchNorm3d(32),
          nn.ReLU(inplace=True),
          nn.Conv3d(
              32,
              num_seg_classes,
              kernel_size=1,
              stride=(1, 1, 1),
              bias=False,
          ),
      )
  
      for m in self.modules():
          if isinstance(m, nn.Conv3d):
              m.weight = nn.init.kaiming_normal(
                  m.weight, mode="fan_out"
              )
          elif isinstance(m, nn.BatchNorm3d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  import math
  from functools import partial
label_desc: Use image node_id 1 to create a new ResNet object from inherited base classes: nn with example: obj = ResNet(block, layers, sample_input_D, sample_input_H, sample_input_W, num_seg_classes, shortcut_type, no_cuda)