text: import math
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from models.operator.mask import Conv2d_mask, Linear_mask
  
  
  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 = Conv2d_mask(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 = Conv2d_mask(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 Conv2d_mask(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(int(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, mean=None, std=None, spatial_expansion=False):
          super(WideResNet, self).__init__()
          nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
          assert((depth - 4) % 6 == 0)
          n = (depth - 4) / 6
  
          self.mean = mean.view(1, -1, 1, 1)
          self.std = std.view(1, -1, 1, 1)
          self.spatial_expansion = spatial_expansion
  
          block = BasicBlock
          # 1st conv before any network block
          self.conv1 = Conv2d_mask(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 = Linear_mask(nChannels[3], num_classes)
          self.nChannels = nChannels[3]
  
          for m in self.modules():
              if isinstance(m, Conv2d_mask):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, Linear_mask):
                  m.bias.data.zero_()
      def forward(self, x, pop=False, inter=False):
  
          # Feature visualization
          if not inter:
              out = (x - self.mean) / self.std
              out = self.conv1(out)
              out = self.block1(out)
              out = self.block2(out)
              out = self.block3(out)
              out = self.relu(self.bn1(out))
  
              if pop:
                  return out
          else:
              out = x
  
  
  
          if self.spatial_expansion:
              out = F.avg_pool2d(out, 16)
          else:
              out = F.avg_pool2d(out, 8)
          out = out.view(-1, self.nChannels)
          return self.fc(out)
  
  def wide_resnet(depth=28, widen_factor=10, dataset='cifar10', mean=None, std=None):
      if dataset == 'cifar10' or dataset == 'svhn':
          num_classes = 10
          spatial_expansion = False
      elif dataset == 'cifar100':
          num_classes = 100
          spatial_expansion = False
      elif dataset == 'tiny':
          num_classes = 200
          spatial_expansion = True
      else:
          raise NotImplementedError
      return WideResNet(depth=depth, num_classes=num_classes, widen_factor=widen_factor, dropRate=0.3,
                        mean=mean, std=std, spatial_expansion=spatial_expansion)