code: import time
  import torch
  import torch.nn as nn
  import torch.nn.init as init
  from .utils import load_state_dict_from_url
  from typing import Any
  
  __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: int,
          squeeze_planes: int,
          expand1x1_planes: int,
          expand3x3_planes: int
      ) -> None:
          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: torch.Tensor) -> torch.Tensor:
          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: str = '1_0',
          num_classes: int = 1000
      ) -> None:
          super(SqueezeNet, self).__init__()
          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),
              )
          elif version == '1_1':
              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),
              )
          else:
              # FIXME: Is this needed? SqueezeNet should only be called from the
              # FIXME: squeezenet1_x() functions
              # FIXME: This checking is not done for the other models
              raise ValueError("Unsupported SqueezeNet version {version}:"
                               "1_0 or 1_1 expected".format(version=version))
  
          # Final convolution is initialized differently from 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.AdaptiveAvgPool2d((1, 1))
          )
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  if m is final_conv:
                      init.normal_(m.weight, mean=0.0, std=0.01)
                  else:
                      init.kaiming_uniform_(m.weight)
                  if m.bias is not None:
                      init.constant_(m.bias, 0)
  
      def forward(self, x: torch.Tensor) -> torch.Tensor:
          end = time.time()
          x = self.features(x)
          x = self.classifier(x)
          res = torch.flatten(x, 1)
          single_forward_time = time.time() - end
          print("squeezenet single_forward_time: {}".format(single_forward_time))
          return res
  
  def _squeezenet(version: str, pretrained: bool, progress: bool, **kwargs: Any) -> SqueezeNet:
      model = SqueezeNet(version, **kwargs)
      if pretrained:
          arch = 'squeezenet' + version
          state_dict = load_state_dict_from_url(model_urls[arch],
                                                progress=progress)
          model.load_state_dict(state_dict)
      return model
  
  
  def squeezenet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SqueezeNet:
      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
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _squeezenet('1_0', pretrained, progress, **kwargs)
  
  
  def squeezenet1_1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SqueezeNet:
      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
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _squeezenet('1_1', pretrained, progress, **kwargs)
  
apis: torch.nn.MaxPool2d torch.nn.init.constant_ torch.nn.AdaptiveAvgPool2d torch.flatten torch.nn.init.normal_ torch.nn.Conv2d torch.nn.init.kaiming_uniform_ torch.nn.ReLU torch.nn.Dropout