text: import math
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  __all__ = [
      "WideResNet",
      "wrn",
      "wrn_40_2",
      "wrn_40_1",
      "wrn_16_2",
      "wrn_16_1"
  ]
  
  
  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]
  
          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. / n))
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.Linear):
                  m.bias.data.zero_()
  
      def get_feat_modules(self):
          feat_m = nn.ModuleList([])
          feat_m.append(self.conv1)
          feat_m.append(self.block1)
          feat_m.append(self.block2)
          feat_m.append(self.block3)
          return feat_m
  
      def get_bn_before_relu(self):
          bn1 = self.block2.layer[0].bn1
          bn2 = self.block3.layer[0].bn1
          bn3 = self.bn1
  
          return [bn1, bn2, bn3]
  
      def forward(self, x, is_feat=False, preact=False):
          out = self.conv1(x)
          f0 = out
          out = self.block1(out)
          f1 = out
          out = self.block2(out)
          f2 = out
          out = self.block3(out)
          f3 = out
          out = self.relu(self.bn1(out))
          out = F.adaptive_avg_pool2d(out, 1)
          out = out.view(-1, self.nChannels)
          f4 = out
          out = self.fc(out)
          if is_feat:
              if preact:
                  f1 = self.block2.layer[0].bn1(f1)
                  f2 = self.block3.layer[0].bn1(f2)
                  f3 = self.bn1(f3)
              return [f0, f1, f2, f3, f4], out
          else:
              return out
  
  
  def wrn(**kwargs):
      """
      Constructs a Wide Residual Networks.
      """
      model = WideResNet(**kwargs)
      return model
  
  
  def wrn_40_2(**kwargs):
      model = WideResNet(depth=40, widen_factor=2, **kwargs)
      return model
  
  
  def wrn_40_1(**kwargs):
      model = WideResNet(depth=40, widen_factor=1, **kwargs)
      return model
  
  
  def wrn_16_2(**kwargs):
      model = WideResNet(depth=16, widen_factor=2, **kwargs)
      return model
  
  
  def wrn_16_1(**kwargs):
      model = WideResNet(depth=16, widen_factor=1, **kwargs)
      return model
  
  