text: import torch
  import utils.torch
  from .classifier import Classifier
  import torch.nn as nn
  
  
  class FixedLeNet(Classifier):
      """
      Fixed LeNet architecture, working on MNIST architectures only.
      """
  
      def __init__(self, N_class, resolution=(1, 28, 28), **kwargs):
          """
          Initialize classifier.
  
          :param N_class: number of classes to classify
          :type N_class: int
          :param resolution: resolution (assumed to be square)
          :type resolution: int
          """
  
          assert resolution[0] == 1
          assert resolution[1] == 28
          assert resolution[2] == 28
  
          super(FixedLeNet, self).__init__(N_class, resolution, **kwargs)
  
          self.append_layer('0', nn.Conv2d(resolution[0], 32, 5, padding=2))
          self.append_layer('1', nn.ReLU())
          self.append_layer('2', nn.MaxPool2d(2, 2))
          self.append_layer('3', nn.Conv2d(32, 64, 5, padding=2))
          self.append_layer('4', nn.ReLU())
          self.append_layer('5', nn.MaxPool2d(2, 2))
          self.append_layer('6', utils.torch.Flatten())
          self.append_layer('7', nn.Linear(7 * 7 * 64, 1024))
          self.append_layer('8', nn.ReLU())
          self.append_layer('9', nn.Linear(1024, self.N_class))
  
  
  