text: import math
  import torch
  import torch.nn as nn
  import torch.nn.init as init
  import torch.utils.model_zoo as model_zoo
  
  __all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']
  
  model_urls = {'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
      'squeezenet1_1': 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth', }
  
  
  class Fire(nn.Module):
  
      def __init__(self, inplanes, squeeze_planes, expand1x1_planes, expand3x3_planes):
          super(Fire, self).__init__()
          self.inplanes = inplanes
          self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
          self.squeeze_activation = nn.ReLU(inplace=True)
          self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes, kernel_size=1)
          self.expand1x1_activation = nn.ReLU(inplace=True)
          self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes, kernel_size=3, padding=1)
          self.expand3x3_activation = nn.ReLU(inplace=True)
  
      def forward(self, x):
          x = self.squeeze_activation(self.squeeze(x))
          return torch.cat([self.expand1x1_activation(self.expand1x1(x)), self.expand3x3_activation(self.expand3x3(x))],
                           1)
  
  
  class SqueezeNet(nn.Module):
  
      def __init__(self, version=1.0, num_classes=1000):
          super(SqueezeNet, self).__init__()
          if version not in [1.0, 1.1]:
              raise ValueError("Unsupported SqueezeNet version {version}:"
                               "1.0 or 1.1 expected".format(version=version))
          self.num_classes = num_classes
          if version == 1.0:
              self.features = nn.Sequential(nn.Conv2d(3, 96, kernel_size=7, stride=2), nn.ReLU(inplace=True),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(96, 16, 64, 64), Fire(128, 16, 64, 64),
                  Fire(128, 32, 128, 128), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(256, 32, 128, 128),
                  Fire(256, 48, 192, 192), Fire(384, 48, 192, 192), Fire(384, 64, 256, 256),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(512, 64, 256, 256), )
          else:
              self.features = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, stride=2), nn.ReLU(inplace=True),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(64, 16, 64, 64), Fire(128, 16, 64, 64),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(128, 32, 128, 128), Fire(256, 32, 128, 128),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(256, 48, 192, 192), Fire(384, 48, 192, 192),
                  Fire(384, 64, 256, 256), Fire(512, 64, 256, 256), )
          # Final convolution is initialized differently form the rest
          final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
          self.classifier = nn.Sequential(nn.Dropout(p=0.5), final_conv, nn.ReLU(inplace=True),
                                          nn.AvgPool2d(13, stride=1))
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  if m is final_conv:
                      init.normal(m.weight.data, mean=0.0, std=0.01)
                  else:
                      init.kaiming_uniform(m.weight.data)
                  if m.bias is not None:
                      m.bias.data.zero_()
  
      def forward(self, x):
          x = self.features(x)
          x = self.classifier(x)
          return x.view(x.size(0), self.num_classes)
  
  
  def squeezenet1_0(pretrained=False, **kwargs):
      r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
      accuracy with 50x fewer parameters and <0.5MB model size"
      <https://arxiv.org/abs/1602.07360>`_ paper.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = SqueezeNet(version=1.0, **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['squeezenet1_0']))
      return model
  
  
  def squeezenet1_1(pretrained=False, **kwargs):
      r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
      <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
      SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
      than SqueezeNet 1.0, without sacrificing accuracy.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = SqueezeNet(version=1.1, **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['squeezenet1_1']))
      return model
  