text: import torch
  import torch.nn as nn
  
  from torch import Tensor
  from typing import Type, Any, Callable, Union, List, Optional
  
  
  __all__ = ['SFNet', 'sfnet4', 'sfnet10',
             'sfnet20', 'sfnet36', 'sfnet64']
  
  def conv3x3(in_planes: int, out_planes: int, stride: int = 1,
              bias: bool = True) -> nn.Conv2d:
      """3x3 convolution with padding"""
      return nn.Conv2d(in_planes, out_planes, kernel_size=3,
                       stride=stride, padding=1, bias=bias)
  
  
  def conv1x1(in_planes: int, out_planes: int, stride: int = 1,
              bias: bool = True) -> nn.Conv2d:
      """1x1 convolution"""
      return nn.Conv2d(in_planes, out_planes, kernel_size=1,
                       stride=stride, bias=bias)
  
  class ConvBlock(nn.Module):
  
      def __init__(
          self,
          inplanes: int,
          planes: int,
          stride: int,
          norm_layer: Callable[..., nn.Module],
      ) -> None:
          super(ConvBlock, self).__init__()
          
          has_norm = norm_layer == nn.BatchNorm2d or norm_layer == nn.BatchNorm1d
          bias = not has_norm
          self.conv1 = conv3x3(inplanes, planes, stride, bias=bias)
          self.bn1 = norm_layer(planes)
          self.relu = nn.ReLU(inplace=True)
  
      def forward(self, x: Tensor) -> Tensor:
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          return out
  
  class BasicBlock(nn.Module):
  
      def __init__(
          self,
          inplanes: int,
          planes: int,
          norm_layer: Callable[..., nn.Module],
      ) -> None:
          super(BasicBlock, self).__init__()
  
          has_norm = norm_layer == nn.BatchNorm2d or norm_layer == nn.BatchNorm1d
          bias = not has_norm
          self.conv1 = conv3x3(inplanes, planes, bias=bias)
          self.bn1 = norm_layer(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes, bias=bias)
          self.bn2 = norm_layer(planes)
  
      def forward(self, x: Tensor) -> Tensor:
          identity = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          out += identity
          out = self.relu(out)
  
          return out
  
  class Bottleneck(nn.Module):
  
      def __init__(
          self,
          inplanes: int,
          planes: int,
          norm_layer: Callable[..., nn.Module],
      ) -> None:
          super(Bottleneck, self).__init__()
          width = int(planes // 2)
          has_norm = norm_layer == nn.BatchNorm2d or norm_layer == nn.BatchNorm1d
          bias = not has_norm
  
          self.conv1 = conv1x1(inplanes, width, bias=bias)
          self.bn1 = norm_layer(width)
          self.conv2 = conv3x3(width, width, bias=bias)
          self.bn2 = norm_layer(width)
          self.conv3 = conv1x1(width, planes, bias=bias)
          self.bn3 = norm_layer(planes)
          self.relu = nn.ReLU(inplace=True)
  
      def forward(self, x: Tensor) -> Tensor:
          identity = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
          out = self.relu(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          out += identity
          out = self.relu(out)
  
          return out
  
  
  class SFNet(nn.Module):
  
      def __init__(
          self,
          block: Type[Union[BasicBlock, Bottleneck]],
          layers: List[int],
          zero_init_residual: bool = False,
          in_channel: Optional[int] = 3,
          channels: Optional[List[int]] = [64, 128, 256, 512],
          out_channel: Optional[int] = 512,
          dropout: Optional[float] = 0.,
          norm_layer: Optional[Callable[..., nn.Module]] = None,
      ) -> None:
          super(SFNet, self).__init__()
          if not norm_layer:
              norm_layer = nn.Identity
              self.features = nn.Identity()
          else:
              norm_layer = nn.BatchNorm2d
              self.features = nn.BatchNorm1d(out_channel, eps=1e-05)
              nn.init.constant_(self.features.weight, 1.0)
  
          self._norm_layer = norm_layer
  
          self.layer1 = self._make_layer(block, in_channel,
                                         channels[0], layers[0], stride=2)
          self.layer2 = self._make_layer(block, channels[0],
                                         channels[1], layers[1], stride=2)
          self.layer3 = self._make_layer(block, channels[1],
                                         channels[2], layers[2], stride=2)
          self.layer4 = self._make_layer(block, channels[2],
                                         channels[3], layers[3], stride=2)
          self.dropout = nn.Dropout(p=dropout)
          self.fc = nn.Linear(channels[3] * 7 * 7, out_channel)
  
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                  #nn.init.xavier_normal_(m.weight)
              elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
          # Zero-initialize the last BN in each residual branch,
          # so that the residual branch starts with zeros, and each residual block behaves like an identity.
          if zero_init_residual:
              for m in self.modules():
                  if isinstance(m, Bottleneck):
                      nn.init.constant_(m.bn3.weight, 0)
                  elif isinstance(m, BasicBlock):
                      nn.init.constant_(m.bn2.weight, 0)
  
      def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], inplanes: int,
                      planes: int, blocks: int, stride: int = 1) -> nn.Sequential:
          norm_layer = self._norm_layer
  
          layers = []
          layers.append(ConvBlock(inplanes, planes, stride, norm_layer=norm_layer))
          for _ in range(0, blocks):
              layers.append(block(planes, planes, norm_layer=norm_layer))
  
          return nn.Sequential(*layers)
  
      def _forward_impl(self, x: Tensor) -> Tensor:
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
  
          x = torch.flatten(x, 1)
          x = self.dropout(x)
          x = self.fc(x)
          x = self.features(x)
  
          return x
  
      def forward(self, x: Tensor) -> Tensor:
          return self._forward_impl(x)
  
  
  def _sfnet(
      arch: str,
      block: Type[Union[BasicBlock, Bottleneck]],
      layers: List[int],
      pretrained: bool,
      progress: bool,
      **kwargs: Any
  ) -> SFNet:
      model = SFNet(block, layers, **kwargs)
      if pretrained:
          pass
          #state_dict = load_state_dict_from_url(model_urls[arch],
          #                                      progress=progress)
          #model.load_state_dict(state_dict)
      return model
  
  
  def sfnet4(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SFNet:
      r"""SFNet-4 model from
      `"SphereFace: Deep Hypersphere Embedding for Face Recognition" <https://arxiv.org/pdf/1704.08063.pdf>`_.
      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 _sfnet('sfnet4', BasicBlock, [0, 0, 0, 0], pretrained, progress,
                     **kwargs)
  
  
  def sfnet10(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SFNet:
      r"""SFNet-10 model from
      `"SphereFace: Deep Hypersphere Embedding for Face Recognition" <https://arxiv.org/pdf/1704.08063.pdf>`_.
      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 _sfnet('sfnet10', BasicBlock, [0, 1, 2, 0], pretrained, progress,
                     **kwargs)
  
  
  def sfnet20(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SFNet:
      r"""SFNet-20 model from
      `"SphereFace: Deep Hypersphere Embedding for Face Recognition" <https://arxiv.org/pdf/1704.08063.pdf>`_.
      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 _sfnet('sfnet20', BasicBlock, [1, 2, 4, 1], pretrained, progress,
                     **kwargs)
  
  
  def sfnet36(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SFNet:
      r"""SFNet-36 model from
      `"SphereFace: Deep Hypersphere Embedding for Face Recognition" <https://arxiv.org/pdf/1704.08063.pdf>`_.
      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 _sfnet('sfnet36', BasicBlock, [2, 4, 8, 2], pretrained, progress,
                     **kwargs)
  
  
  def sfnet64(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SFNet:
      r"""SFNet-64 model from
      `"SphereFace: Deep Hypersphere Embedding for Face Recognition" <https://arxiv.org/pdf/1704.08063.pdf>`_.
      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 _sfnet('sfnet64', BasicBlock, [3, 8, 16, 3], pretrained, progress,
                     **kwargs)
  
  