请完成写出Unet的python代码，并分行注释

import torch
import torch.nn as nn# 定义Unet类
class Unet(nn.Module):
def __init__(self, in_channels, out_channels):
super(Unet, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 64, 3, padding=1)
self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
# 中心特征提取块
self.center = self._center_block()
# 编码过程
self.down1 = self._down_block(64, 128)
self.down2 = self._down_block(128, 256)
self.down3 = self._down_block(256, 512)
self.down4 = self._down_block(512, 1024)
# 解码过程
self.up1 = self._up_block(1024, 512)
self.up2 = self._up_block(512, 256)
self.up3 = self._up_block(256, 128)
self.up4 = self._up_block(128, 64)
self.conv3 = nn.Conv2d(64, out_channels, 1)

# 定义中心特征提取块
def _center_block(self):
return nn.Sequential(
nn.MaxPool2d(2),
nn.Conv2d(64, 64, 3, padding=1),
nn.Conv2d(64, 64, 3, padding=1),
)

# 定义下采样块
def _down_block(self, in_channels, out_channels):
return nn.Sequential(
nn.MaxPool2d(2),
nn.Conv2d(in_channels, out_channels, 3, padding=1),
nn.Conv2d(out_channels, out_channels, 3, padding=1),
)

# 定义上采样块
def _up_block(self, in_channels, out_channels):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, padding=1),
nn.Conv2d(out_channels, out_channels, 3, padding=1),
nn.Upsample(scale_factor=2, mode='nearest'),
)

# 向前传播
def forward(self, x):
x1 = self.conv1(x)
x2 = self.conv2(x1)
x3 = self.center(x2)
x4 = self.down1(x3)
x5 = self.down2(x4)
x6 = self.down3(x5)
x7 = self.down4(x6)
x8 = self.up1(x7)
x9 = self.up2(x8)
x10 = self.up3(x9)
x11 = self.up4(x10)
x12 = self.conv3(x11)
return x12答案：我完成了对Unet的python代码的编写，并分行注释。