使用pytorch训练一个基于多尺度自编码网络，在网络中encoder、fusion和decoder函数分别表示编码器、融合策略和解码器，给出python代码

下面是使用PyTorch训练一个基于多尺度自编码网络的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import transforms

class Encoder(nn.Module):
    def __init__(self):
        super(Encoder, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1)
        self.relu2 = nn.ReLU()
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1)
        self.relu3 = nn.ReLU()

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        x = self.relu3(x)
        return x

class Fusion(nn.Module):
    def __init__(self):
        super(Fusion, self).__init__()
        self.conv1 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.relu2 = nn.ReLU()
        self.conv3 = nn.ConvTranspose2d(in_channels=32, out_channels=1, kernel_size=3, stride=1, padding=1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        x = self.sigmoid(x)
        return x

class MultiScaleAutoencoder(nn.Module):
    def __init__(self):
        super(MultiScaleAutoencoder, self).__init__()
        self.encoder = Encoder()
        self.fusion = Fusion()

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

def train(model, dataloader, criterion, optimizer, num_epochs):
    for epoch in range(num_epochs):
        running_loss = 0.0
        for data in dataloader:
            inputs, _ = data
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, inputs)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, running_loss/len(dataloader)))

if __name__ == '__main__':
    # 加载MNIST数据集
    transform = transforms.Compose([transforms.ToTensor()])
    train_dataset = MNIST(root='./data', train=True, transform=transform, download=True)
    train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)

    # 创建模型、损失函数和优化器
    model = MultiScaleAutoencoder()
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # 训练模型
    train(model, train_dataloader, criterion, optimizer, num_epochs=10)

这个示例代码中，我们使用了`Encoder`和`Fusion`两个模块实现多尺度自编码网络。`Encoder`模块包含了三个卷积层，将输入图像编码成一个尺寸为`(128, 7, 7)`的特征图；`Fusion`模块包含了三个反卷积层，将特征图解码成与输入图像相同尺寸的输出。

在训练循环中，我们使用了均方误差(MSE)作为损失函数，并使用Adam优化器进行优化。