code: def __init__(self, input_channel, output_channel, block, layers):
      super(ResNet, self).__init__()
  
      self.output_channel_block = [
          int(output_channel / 4),
          int(output_channel / 2),
          output_channel,
          output_channel,
      ]
  
      self.inplanes = int(output_channel / 8)
      self.conv0_1 = nn.Conv2d(
          input_channel,
          int(output_channel / 16),
          kernel_size=3,
          stride=1,
          padding=1,
          bias=False,
      )
      self.bn0_1 = nn.BatchNorm2d(int(output_channel / 16))
      self.conv0_2 = nn.Conv2d(
          int(output_channel / 16),
          self.inplanes,
          kernel_size=3,
          stride=1,
          padding=1,
          bias=False,
      )
      self.bn0_2 = nn.BatchNorm2d(self.inplanes)
      self.relu = nn.ReLU(inplace=True)
  
      self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
      self.layer1 = self._make_layer(
          block, self.output_channel_block[0], layers[0]
      )
      self.conv1 = nn.Conv2d(
          self.output_channel_block[0],
          self.output_channel_block[0],
          kernel_size=3,
          stride=1,
          padding=1,
          bias=False,
      )
      self.bn1 = nn.BatchNorm2d(self.output_channel_block[0])
  
      self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
      self.layer2 = self._make_layer(
          block, self.output_channel_block[1], layers[1], stride=1
      )
      self.conv2 = nn.Conv2d(
          self.output_channel_block[1],
          self.output_channel_block[1],
          kernel_size=3,
          stride=1,
          padding=1,
          bias=False,
      )
      self.bn2 = nn.BatchNorm2d(self.output_channel_block[1])
  
      self.maxpool3 = nn.MaxPool2d(
          kernel_size=2, stride=(2, 1), padding=(0, 1)
      )
      self.layer3 = self._make_layer(
          block, self.output_channel_block[2], layers[2], stride=1
      )
      self.conv3 = nn.Conv2d(
          self.output_channel_block[2],
          self.output_channel_block[2],
          kernel_size=3,
          stride=1,
          padding=1,
          bias=False,
      )
      self.bn3 = nn.BatchNorm2d(self.output_channel_block[2])
  
      self.layer4 = self._make_layer(
          block, self.output_channel_block[3], layers[3], stride=1
      )
      self.conv4_1 = nn.Conv2d(
          self.output_channel_block[3],
          self.output_channel_block[3],
          kernel_size=2,
          stride=(2, 1),
          padding=(0, 1),
          bias=False,
      )
      self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3])
      self.conv4_2 = nn.Conv2d(
          self.output_channel_block[3],
          self.output_channel_block[3],
          kernel_size=2,
          stride=1,
          padding=0,
          bias=False,
      )
      self.bn4_2 = nn.BatchNorm2d(self.output_channel_block[3])
  
imports: import torch
  import torch.nn
  import torch.nn.init
  import torchvision
  from torchvision import models
  from collections import namedtuple
  from packaging import version
label_desc: Use image node_id 1 to create a new ResNet object from inherited base classes: nn with example: obj = ResNet(input_channel, output_channel, block, layers)