code: import sys
  
  import numpy as np
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.nn.init as init
  from torch.autograd import Variable
  
  
  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:
          init.xavier_uniform_(m.weight, gain=np.sqrt(2))
          init.constant_(m.bias, 0)
      elif classname.find('BatchNorm') != -1:
          init.constant_(m.weight, 1)
          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)
          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=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 Wide_ResNet(nn.Module):
      def __init__(self, depth, widen_factor, dropout_rate, num_classes):
          super(Wide_ResNet, 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
  
          print('Wide-Resnet %dx%d' % (depth, k))
          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))
          # print(f'Shape before avg pooling: {out.shape}')
          out = F.avg_pool2d(out, int(out.shape[3]))
          # print(f'Shape after avg pooling: {out.shape}')
          out = out.view(out.size(0), -1)
          penultimate = out
          out = self.linear(out)
  
          return out, penultimate
  
      # feature extraction for Mahalanobis
      def feature_list(self, x):
          out_list = []
          out = self.conv1(x)
          out = self.layer1(out)
          out = self.layer2(out)
          out = self.layer3(out)
          out = F.relu(self.bn1(out))
          # print shape
          # print(f'Shape: {out.shape}')
          # out2 = F.max_pool3d(out, (4,4,4))
          out2 = F.max_pool2d(out, (8,8))
          out_list.append(out2)
          print(f'Shape: {out2.shape}')
          out = F.avg_pool2d(out, int(out.shape[3]))
          out = out.view(out.size(0), -1)
  
          return self.linear(out), out_list
  
      def intermediate_forward(self, x, layer_index):
          out = self.conv1(x)
          out = self.layer1(out)
          out = self.layer2(out)
          out = self.layer3(out)
          out = F.relu(self.bn1(out))
          return F.max_pool2d(out, (8,8))# F.max_pool3d(out, (4,4,4))
  
      # function to extract the penultimate features
      def penultimate_forward(self, x):
          out = self.conv1(x)
          out = self.layer1(out)
          out = self.layer2(out)
          out = self.layer3(out)
          penultimate = F.relu(self.bn1(out))
          penultimate = F.max_pool2d(penultimate, (8,8))
          # penultimate = F.max_pool3d(penultimate, (4,4,4))
          out = F.avg_pool2d(penultimate, int(out.shape[3]))
          out = out.view(out.size(0), -1)
  
          return self.linear(out), penultimate
  
apis: torch.nn.BatchNorm2d torch.nn.Linear torch.nn.init.constant_ torch.nn.functional.max_pool2d torch.nn.Conv2d torch.nn.Sequential numpy.sqrt torch.nn.Dropout