如何使用pytorch创建具有多个输出的神经网络？

import torch
import torch.nn as nn
import torch.optim as optim

class MultiOutputNet(nn.Module):
    def __init__(self):
        super(MultiOutputNet, self).__init__()
        self.fc1 = nn.Linear(in_features, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size1)
        self.fc3 = nn.Linear(hidden_size, output_size2)
        # 可以根据需要定义更多的输出层

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        output1 = self.fc2(x)
        output2 = self.fc3(x)
        # 可以根据需要添加更多的输出层
        return output1, output2

model = MultiOutputNet()
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

input_data = torch.tensor(input_data)
target1 = torch.tensor(target1)
target2 = torch.tensor(target2)

for epoch in range(num_epochs):
    optimizer.zero_grad()
    output1, output2 = model(input_data)
    loss1 = criterion(output1, target1)
    loss2 = criterion(output2, target2)
    loss = loss1 + loss2
    loss.backward()
    optimizer.step()