请写出MobileNet神经网络的pytorch代码

以下是MobileNet神经网络的pytorch代码：

import torch.nn as nn

class MobileNet(nn.Module):
    def __init__(self, num_classes=100):
        super(MobileNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(128)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn4 = nn.BatchNorm2d(128)
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn5 = nn.BatchNorm2d(256)
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn6 = nn.BatchNorm2d(256)
        self.conv7 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn7 = nn.BatchNorm2d(512)
        self.conv8 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn8 = nn.BatchNorm2d(512)
        self.conv9 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn9 = nn.BatchNorm2d(512)
        self.conv10 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn10 = nn.BatchNorm2d(512)
        self.conv11 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn11 = nn.BatchNorm2d(512)
        self.conv12 = nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn12 = nn.BatchNorm2d(1024)
        self.conv13 = nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn13 = nn.BatchNorm2d(1024)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu(x)

        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu(x)

        x = self.conv5(x)
        x = self.bn5(x)
        x = self.relu(x)

        x = self.conv6(x)
        x = self.bn6(x)
        x = self.relu(x)

        x = self.conv7(x)
        x = self.bn7(x)
        x = self.relu(x)

        x = self.conv8(x)
        x = self.bn8(x)
        x = self.relu(x)

        x = self.conv9(x)
        x = self.bn9(x)
        x = self.relu(x)

        x = self.conv10(x)
        x = self.bn10(x)
        x = self.relu(x)

        x = self.conv11(x)
        x = self.bn11(x)
        x = self.relu(x)

        x = self.conv12(x)
        x = self.bn12(x)
        x = self.relu(x)

        x = self.conv13(x)
        x = self.bn13(x)
        x = self.relu(x)

        x = self.avgpool(x)
        x = x.view(x.size(), -1)
        x = self.fc(x)

        return x