code: """PyTorch implementation of Wide-ResNet taken from 
  https://github.com/jeromerony/fast_adversarial/blob/master/fast_adv/models/cifar10/wide_resnet.py"""
  
  import math
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class BasicBlock(nn.Module):
      def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
          super(BasicBlock, self).__init__()
          self.bn1 = nn.BatchNorm2d(in_planes)
          self.relu1 = nn.ReLU(inplace=True)
          self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                                 padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(out_planes)
          self.relu2 = nn.ReLU(inplace=True)
          self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
                                 padding=1, bias=False)
          self.droprate = dropRate
          self.equalInOut = (in_planes == out_planes)
          self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
                                                                  padding=0, bias=False) or None
  
      def forward(self, x):
          if not self.equalInOut:
              x = self.relu1(self.bn1(x))
          else:
              out = self.relu1(self.bn1(x))
          out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
          if self.droprate > 0:
              out = F.dropout(out, p=self.droprate, training=self.training)
          out = self.conv2(out)
          return torch.add(x if self.equalInOut else self.convShortcut(x), out)
  
  
  class NetworkBlock(nn.Module):
      def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0):
          super(NetworkBlock, self).__init__()
          self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)
  
      def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
          layers = []
          for i in range(nb_layers):
              layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate))
          return nn.Sequential(*layers)
  
      def forward(self, x):
          return self.layer(x)
  
  
  class WideResNet(nn.Module):
      def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
          super(WideResNet, self).__init__()
          nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
          assert (depth - 4) % 6 == 0, 'depth should be 6n+4'
          n = (depth - 4) // 6
          block = BasicBlock
          # 1st conv before any network block
          self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1,
                                 padding=1, bias=False)
          # 1st block
          self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
          # 2nd block
          self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
          # 3rd block
          self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
          # global average pooling and classifier
          self.bn1 = nn.BatchNorm2d(nChannels[3])
          self.relu = nn.ReLU(inplace=True)
          self.fc = nn.Linear(nChannels[3], num_classes)
          self.nChannels = nChannels[3]
          
  
      def forward(self, x):
          out = self.conv1(x)
          out = self.block1(out)
          out = self.block2(out)
          out = self.block3(out)
          out = self.relu(self.bn1(out))
          out = F.avg_pool2d(out, 8)
          out = out.view(-1, self.nChannels)
          return self.fc(out)
  
  def WRN16_8():
      return WideResNet(depth=16, num_classes=10, widen_factor=8, dropRate=0.3)
apis: torch.nn.BatchNorm2d torch.nn.Linear torch.nn.functional.avg_pool2d torch.nn.functional.dropout torch.nn.Conv2d torch.nn.Sequential torch.nn.ReLU