code: '''ResNet in PyTorch.
  
  For Pre-activation ResNet, see 'preact_resnet.py'.
  
  Reference:
  [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
      Deep Residual Learning for Image Recognition. arXiv:1512.03385
  '''
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, in_planes, planes, quant, stride=1):
          super(BasicBlock, self).__init__()
          self.conv1 = nn.Conv2d(
              in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                                 stride=1, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.quant=quant()
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion*planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, self.expansion*planes,
                            kernel_size=1, stride=stride, bias=False),
                  quant(),
                  nn.BatchNorm2d(self.expansion*planes)
              )
  
      def forward(self, x):
          
          out = F.relu(self.bn1(self.conv1(x)))
          out = self.quant(out)
          out = self.bn2(self.conv2(out))
          out = self.quant(out)
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, in_planes, planes, stride=1):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                                 stride=stride, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, self.expansion *
                                 planes, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(self.expansion*planes)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion*planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, self.expansion*planes,
                            kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(self.expansion*planes)
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = F.relu(self.bn2(self.conv2(out)))
          out = self.bn3(self.conv3(out))
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  class ResNet(nn.Module):
      def __init__(self, block, num_blocks, quant, num_classes=10):
          super(ResNet, self).__init__()
          self.in_planes = 64
          self.quant = quant()
          self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
                                 stride=1, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.layer1 = self._make_layer(block, 64, quant, num_blocks[0], stride=1)
          self.layer2 = self._make_layer(block, 128, quant, num_blocks[1], stride=2)
          self.layer3 = self._make_layer(block, 256, quant, num_blocks[2], stride=2)
          self.layer4 = self._make_layer(block, 512, quant, num_blocks[3], stride=2)
          self.linear = nn.Linear(512*block.expansion, num_classes)
  
      def _make_layer(self, block, planes, quant, num_blocks, stride):
          strides = [stride] + [1]*(num_blocks-1)
          layers = []
          for stride in strides:
              layers.append(block(self.in_planes, planes, quant, stride))
              self.in_planes = planes * block.expansion
          return nn.Sequential(*layers)
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = self.quant(out)
          out = self.layer1(out)
          out = self.quant(out)
          out = self.layer2(out)
          out = self.quant(out)
          out = self.layer3(out)
          out = self.quant(out)
          out = self.layer4(out)
          out = self.quant(out)
          out = F.avg_pool2d(out, 4)
          out = out.view(out.size(0), -1)
          
          out = self.linear(out)
          return out
  
  
  def ResNet18Posit(quant):
      return ResNet(BasicBlock, [2, 2, 2, 2], quant)
  
  
  
  # def ResNet34():
  #     return ResNet(BasicBlock, [3, 4, 6, 3])
  
  
  # def ResNet50():
  #     return ResNet(Bottleneck, [3, 4, 6, 3])
  
  
  # def ResNet101():
  #     return ResNet(Bottleneck, [3, 4, 23, 3])
  
  
  # def ResNet152():
  #     return ResNet(Bottleneck, [3, 8, 36, 3])
  
  
  def test():
      net = ResNet18()
      y = net(torch.randn(1, 3, 32, 32))
      print(y.size())
  
  # test()
  
apis: torch.nn.BatchNorm2d torch.nn.Linear torch.nn.functional.avg_pool2d torch.randn torch.nn.functional.relu torch.nn.Conv2d torch.nn.Sequential