text: import torch
  import torch.nn as nn
  __all__ = ['mnist']
  
  class mnist_model(nn.Module):
  
      def __init__(self):
          super(mnist_model, self).__init__()
          self.feats = nn.Sequential(
              nn.Conv2d(1, 32, 5, 1, 1),
              nn.MaxPool2d(2, 2),
              nn.ReLU(True),
              nn.BatchNorm2d(32),
  
              nn.Conv2d(32, 64, 3,  1, 1),
              nn.ReLU(True),
              nn.BatchNorm2d(64),
  
              nn.Conv2d(64, 64, 3,  1, 1),
              nn.MaxPool2d(2, 2),
              nn.ReLU(True),
              nn.BatchNorm2d(64),
  
              nn.Conv2d(64, 128, 3, 1, 1),
              nn.ReLU(True),
              nn.BatchNorm2d(128)
          )
  
          self.classifier = nn.Conv2d(128, 10, 1)
          self.avgpool = nn.AvgPool2d(6, 6)
          self.dropout = nn.Dropout(0.5)
  
      def forward(self, inputs):
          out = self.feats(inputs)
          out = self.dropout(out)
          out = self.classifier(out)
          out = self.avgpool(out)
          out = out.view(-1, 10)
          return out
  
  
  def mnist(**kwargs):
      return mnist_model()
  