text: # From:
  # https://github.com/meliketoy/wide-resnet.pytorch/blob/master/networks/wide_resnet.py
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  class wide_basic(nn.Module):
      def __init__(self, in_planes, planes, dropout_rate, stride=1):
          super(wide_basic, self).__init__()
          self.bn1 = nn.BatchNorm2d(in_planes)
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=False)
          self.dropout = nn.Dropout(p=dropout_rate)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False),
              )
  
      def forward(self, x):
          out = self.dropout(self.conv1(F.relu(self.bn1(x))))
          out = self.conv2(F.relu(self.bn2(out)))
          out += self.shortcut(x)
  
          return out
  
  class Wide_ResNet(nn.Module):
      def __init__(self, args, depth, widen_factor, dropout_rate=0):
          super(Wide_ResNet, self).__init__()
          self.in_planes = 16
  
          # grayscale or Coloured
          if args.gray:
              input_channels = 1
          else:
              input_channels = 3
  
          assert ((depth-4)%6 ==0), 'Wide-resnet depth should be 6n+4'
          n = (depth-4)/6
          k = widen_factor
  
          nStages = [16, 16*k, 32*k, 64*k]
              
          self.conv1 = nn.Conv2d(input_channels, nStages[0], kernel_size=3, stride=1, padding=1, bias=False)
          self.layer1 = self._wide_layer(wide_basic, nStages[1], n, dropout_rate, stride=1)
          self.layer2 = self._wide_layer(wide_basic, nStages[2], n, dropout_rate, stride=2)
          self.layer3 = self._wide_layer(wide_basic, nStages[3], n, dropout_rate, stride=2)
          self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9)
          self.avgpool = nn.AdaptiveAvgPool2d(1)
          self.linear = nn.Linear(nStages[3], args.num_classes)
  
      def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
          strides = [stride] + [1]*(int(num_blocks)-1)
          layers = []
  
          for stride in strides:
              layers.append(block(self.in_planes, planes, dropout_rate, stride))
              self.in_planes = planes
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          out = self.conv1(x)
          out = self.layer1(out)
          out = self.layer2(out)
          out = self.layer3(out)
          out = F.relu(self.bn1(out))
          out = self.avgpool(out)
          out = out.view(out.size(0), -1)
          out = self.linear(out)
          return out