text: import torch.nn as nn
  import torch.nn.functional as F
  
  
  #########################
  # ## LIGHTWEIGHT EBM ## #
  #########################
  
  class EBM(nn.Module):
      def __init__(self, n_c=3, n_f=32, leak=0.05):
          super(EBM, self).__init__()
          self.f = nn.Sequential(
              nn.Conv2d(n_c, n_f, 3, 1, 1),
              nn.LeakyReLU(leak),
              nn.Conv2d(n_f, n_f * 2, 4, 2, 1),
              nn.LeakyReLU(leak),
              nn.Conv2d(n_f * 2, n_f * 4, 4, 2, 1),
              nn.LeakyReLU(leak),
              nn.Conv2d(n_f * 4, n_f * 8, 4, 2, 1),
              nn.LeakyReLU(leak),
              nn.Conv2d(n_f * 8, 1, 4, 1, 0))
  
      def forward(self, x):
          return self.f(x).squeeze()
  
  
  ################################
  # ## WIDE RESNET CLASSIFIER # ##
  ################################
  
  # Implementation from https://github.com/meliketoy/wide-resnet.pytorch/ with very minor changes
  # Original Version: Copyright (c) Bumsoo Kim 2018 under MIT License
  
  def conv3x3(in_planes, out_planes, stride=1):
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
  
  def conv_init(m):
      classname = m.__class__.__name__
      if classname.find('Conv') != -1:
          nn.init.xavier_uniform_(m.weight, gain=2**0.5)
          nn.init.constant_(m.bias, 0)
      elif classname.find('BatchNorm') != -1:
          nn.init.constant_(m.weight, 1)
          nn.init.constant_(m.bias, 0)
  
  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=True)
          if dropout_rate > 0:
              self.dropout = nn.Dropout(p=dropout_rate)
          else:
              self.dropout = nn.Identity()
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)
  
          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=True),
              )
  
      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 WideResNet(nn.Module):
      def __init__(self, depth=28, widen_factor=10, num_classes=10, dropout_rate=0.0):
          super(WideResNet, self).__init__()
          self.in_planes = 16
  
          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 = conv3x3(3, nStages[0])
          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.linear = nn.Linear(nStages[3], 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 = F.avg_pool2d(out, 8)
          out = out.view(out.size(0), -1)
          out = self.linear(out)
  
          return out
  