text: from torch import nn
  import torch
  
  
  def _make_divisible(ch, divisor=8, min_ch=None):
      """
      This function is taken from the original tf repo.
      It ensures that all layers have a channel number that is divisible by 8
      It can be seen here:
      https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
      """
      if min_ch is None:
          min_ch = divisor
      new_ch = max(min_ch, int(ch + divisor / 2) // divisor * divisor)
      # Make sure that round down does not go down by more than 10%.
      if new_ch < 0.9 * ch:
          new_ch += divisor
      return new_ch
  
  
  class ConvBNReLU(nn.Sequential):
      def __init__(self, in_channel, out_channel, kernel_size=3, stride=1, groups=1):
          padding = (kernel_size - 1) // 2
          super(ConvBNReLU, self).__init__(
              nn.Conv2d(in_channel, out_channel, kernel_size, stride, padding, groups=groups, bias=False),
              nn.BatchNorm2d(out_channel),
              nn.ReLU6(inplace=True)
          )
  
  
  class InvertedResidual(nn.Module):
      def __init__(self, in_channel, out_channel, stride, expand_ratio):
          super(InvertedResidual, self).__init__()
          hidden_channel = in_channel * expand_ratio
          self.use_shortcut = stride == 1 and in_channel == out_channel
  
          layers = []
          if expand_ratio != 1:
              # 1x1 pointwise conv
              layers.append(ConvBNReLU(in_channel, hidden_channel, kernel_size=1))
          layers.extend([
              # 3x3 depthwise conv
              ConvBNReLU(hidden_channel, hidden_channel, stride=stride, groups=hidden_channel),
              # 1x1 pointwise conv(linear)
              nn.Conv2d(hidden_channel, out_channel, kernel_size=1, bias=False),
              nn.BatchNorm2d(out_channel),
          ])
  
          self.conv = nn.Sequential(*layers)
  
      def forward(self, x):
          if self.use_shortcut:
              return x + self.conv(x)
          else:
              return self.conv(x)
  
  
  class MobileNetV2(nn.Module):
      def __init__(self, num_classes=1000, alpha=1.0, round_nearest=8):
          super(MobileNetV2, self).__init__()
          block = InvertedResidual
          input_channel = _make_divisible(32 * alpha, round_nearest)
          last_channel = _make_divisible(1280 * alpha, round_nearest)
  
          inverted_residual_setting = [
              # t, c, n, s
              [1, 16, 1, 1],
              [6, 24, 2, 2],
              [6, 32, 3, 2],
              [6, 64, 4, 2],
              [6, 96, 3, 1],
              [6, 160, 3, 2],
              [6, 320, 1, 1],
          ]
  
          features = []
          # conv1 layer
          features.append(ConvBNReLU(3, input_channel, stride=2))
          # building inverted residual residual blockes
          for t, c, n, s in inverted_residual_setting:
              output_channel = _make_divisible(c * alpha, round_nearest)
              for i in range(n):
                  stride = s if i == 0 else 1
                  features.append(block(input_channel, output_channel, stride, expand_ratio=t))
                  input_channel = output_channel
          # building last several layers
          features.append(ConvBNReLU(input_channel, last_channel, 1))
          # combine feature layers
          self.features = nn.Sequential(*features)
  
          # building classifier
          self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
          self.classifier = nn.Sequential(
              nn.Dropout(0.2),
              nn.Linear(last_channel, num_classes)
          )
  
          # weight initialization
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out')
                  if m.bias is not None:
                      nn.init.zeros_(m.bias)
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.ones_(m.weight)
                  nn.init.zeros_(m.bias)
              elif isinstance(m, nn.Linear):
                  nn.init.normal_(m.weight, 0, 0.01)
                  nn.init.zeros_(m.bias)
  
      def forward(self, x):
          x = self.features(x)
          x = self.avgpool(x)
          x = torch.flatten(x, 1)
          x = self.classifier(x)
          return x
  