请使用unet项目中的典型代码结构，通过代码的形式向我介绍各部分

好的，下面我将介绍一下Unet项目中的典型代码结构，并解释每个部分的作用。

# 导入所需的库
import torch
import torch.nn as nn
import torch.nn.functional as F

# 定义Unet模型类
class Unet(nn.Module):
    # 初始化函数，定义Unet网络中所需的各个层及其参数
    def __init__(self):
        super(Unet, self).__init__()
        self.conv1_1 = nn.Conv2d(3, 64, 3, padding=1)
        self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
        self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
        self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
        self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
        self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
        self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
        self.conv5_1 = nn.Conv2d(512, 1024, 3, padding=1)
        self.conv5_2 = nn.Conv2d(1024, 1024, 3, padding=1)
        self.upconv6 = nn.ConvTranspose2d(1024, 512, 2, stride=2)
        self.conv6_1 = nn.Conv2d(1024, 512, 3, padding=1)
        self.conv6_2 = nn.Conv2d(512, 512, 3, padding=1)
        self.upconv7 = nn.ConvTranspose2d(512, 256, 2, stride=2)
        self.conv7_1 = nn.Conv2d(512, 256, 3, padding=1)
        self.conv7_2 = nn.Conv2d(256, 256, 3, padding=1)
        self.upconv8 = nn.ConvTranspose2d(256, 128, 2, stride=2)
        self.conv8_1 = nn.Conv2d(256, 128, 3, padding=1)
        self.conv8_2 = nn.Conv2d(128, 128, 3, padding=1)
        self.upconv9 = nn.ConvTranspose2d(128, 64, 2, stride=2)
        self.conv9_1 = nn.Conv2d(128, 64, 3, padding=1)
        self.conv9_2 = nn.Conv2d(64, 64, 3, padding=1)
        self.conv10 = nn.Conv2d(64, 2, 1)

    # 前向传播函数，定义Unet网络的前向传播过程
    def forward(self, x):
        conv1_1_out = F.relu(self.conv1_1(x))
        conv1_2_out = F.relu(self.conv1_2(conv1_1_out))
        pool1_out = F.max_pool2d(conv1_2_out, 2, 2)

        conv2_1_out = F.relu(self.conv2_