编写一段代码：创建一个code文件，其中包含一个unet模块，unet模块可以进行调用飞桨提供的summary接口对组建好的模型进行可视化。

import paddle
import paddle.nn as nn

class UNet(nn.Layer):
    def __init__(self, in_channels, out_channels):
        super(UNet, self).__init__()
        # 定义UNet的编码器部分
        self.encoder = nn.Sequential(
            nn.Conv2D(in_channels, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2D(kernel_size=2, stride=2),
            nn.Conv2D(64, 128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2D(kernel_size=2, stride=2),
            nn.Conv2D(128, 256, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2D(kernel_size=2, stride=2),
            nn.Conv2D(256, 512, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2D(kernel_size=2, stride=2),
            nn.Conv2D(512, 1024, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2D(kernel_size=2, stride=2)
        )
        # 定义UNet的解码器部分
        self.decoder = nn.Sequential(
            nn.Conv2DTranspose(1024, 512, kernel_size=2, stride=2),
            nn.ReLU(),
            nn.Conv2DTranspose(512, 256, kernel_size=2, stride=2),
            nn.ReLU(),
            nn.Conv2DTranspose(256, 128, kernel_size=2, stride=2),
            nn.ReLU(),
            nn.Conv2DTranspose(128, 64, kernel_size=2, stride=2),
            nn.ReLU(),
            nn.Conv2DTranspose(64, out_channels, kernel_size=2, stride=2),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

# 创建模型实例
model = UNet(in_channels=3, out_channels=1)

# 调用飞桨提供的summary接口对组建好的模型进行可视化
paddle.summary(model, input_size=(3, 256, 256))