text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from .initialization import initialize_resnet
  
  
  class WideBasicblock(nn.Module):
    def __init__(self, inplanes, planes, stride, dropout=False):
      super(WideBasicblock, self).__init__()
  
      self.bn_a = nn.BatchNorm2d(inplanes)
      self.conv_a = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
  
      self.bn_b = nn.BatchNorm2d(planes)
      if dropout:
        self.dropout = nn.Dropout2d(p=0.5, inplace=True)
      else:
        self.dropout = None
      self.conv_b = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
  
      if inplanes != planes:
        self.downsample = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, padding=0, bias=False)
      else:
        self.downsample = None
  
    def forward(self, x):
  
      basicblock = self.bn_a(x)
      basicblock = F.relu(basicblock)
      basicblock = self.conv_a(basicblock)
  
      basicblock = self.bn_b(basicblock)
      basicblock = F.relu(basicblock)
      if self.dropout is not None:
        basicblock = self.dropout(basicblock)
      basicblock = self.conv_b(basicblock)
  
      if self.downsample is not None:
        x = self.downsample(x)
  
      return x + basicblock
  
  
  class CifarWideResNet(nn.Module):
    """
    ResNet optimized for the Cifar dataset, as specified in
    https://arxiv.org/abs/1512.03385.pdf
    """
    def __init__(self, depth, widen_factor, num_classes, dropout):
      super(CifarWideResNet, self).__init__()
  
      #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
      assert (depth - 4) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
      layer_blocks = (depth - 4) // 6
      print ('CifarPreResNet : Depth : {} , Layers for each block : {}'.format(depth, layer_blocks))
  
      self.num_classes = num_classes
      self.dropout = dropout
      self.conv_3x3 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
  
      self.message  = 'Wide ResNet : depth={:}, widen_factor={:}, class={:}'.format(depth, widen_factor, num_classes)
      self.inplanes = 16
      self.stage_1 = self._make_layer(WideBasicblock, 16*widen_factor, layer_blocks, 1)
      self.stage_2 = self._make_layer(WideBasicblock, 32*widen_factor, layer_blocks, 2)
      self.stage_3 = self._make_layer(WideBasicblock, 64*widen_factor, layer_blocks, 2)
      self.lastact = nn.Sequential(nn.BatchNorm2d(64*widen_factor), nn.ReLU(inplace=True))
      self.avgpool = nn.AvgPool2d(8)
      self.classifier = nn.Linear(64*widen_factor, num_classes)
  
      self.apply(initialize_resnet)
  
    def get_message(self):
      return self.message
  
    def _make_layer(self, block, planes, blocks, stride):
  
      layers = []
      layers.append(block(self.inplanes, planes, stride, self.dropout))
      self.inplanes = planes
      for i in range(1, blocks):
        layers.append(block(self.inplanes, planes, 1, self.dropout))
  
      return nn.Sequential(*layers)
  
    def forward_for_outs(self, x):
      all_outs = []
      x = self.conv_3x3(x)
      x = self.stage_1(x)
      all_outs.append(x)
      x = self.stage_2(x)
      all_outs.append(x)
      x = self.stage_3(x)
      all_outs.append(x)
      x = self.lastact(x)
      x = self.avgpool(x)
      features = x.view(x.size(0), -1)
      outs     = self.classifier(features)
      return features, outs, all_outs
  
    def forward(self, x, out_all=False):
      if out_all: return self.forward_for_outs(x)
      x = self.conv_3x3(x)
      x = self.stage_1(x)
      x = self.stage_2(x)
      x = self.stage_3(x)
      x = self.lastact(x)
      x = self.avgpool(x)
      features = x.view(x.size(0), -1)
      outs     = self.classifier(features)
      return features, outs
  