text: 
  import torch
  import torch.nn as nn
  
  
  class NiN(nn.Module):
      def __init__(self):
          super(NiN, self).__init__()
          self.classifer = nn.Sequential(
                  nn.Conv2d(3, 192, kernel_size=3, stride=1, padding=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(192, 160, kernel_size=1, stride=1, padding=0),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(160,  96, kernel_size=1, stride=1, padding=0),
                  nn.ReLU(inplace=True),
                  nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
                  nn.Dropout(0.5),
  
                  nn.Conv2d(96, 192, kernel_size=3, stride=1, padding=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),
                  nn.ReLU(inplace=True),
                  nn.AvgPool2d(kernel_size=3, stride=2, padding=1),
                  nn.Dropout(0.5),
  
                  nn.Conv2d(192, 192, kernel_size=3, stride=1, padding=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(192,  10, kernel_size=1, stride=1, padding=0),
                  nn.ReLU(inplace=True),
                  nn.AvgPool2d(kernel_size=8, stride=1, padding=0),
  
                  )
  
      def forward(self, x):
          x = self.classifer(x)
          x = x.view(x.size(0), 10)
          return x
  
  