Thanks.
Now, I slightly modify my code:
optimizer = optim.SGD(model.parameters(), lr = lr, momentum = momentum)
class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.classifier = nn.Sequential(
            ......
            ......
            nn.Softmax(0),
        )

    def forward(self, x):
        output = self.classifier(x)
        final_predict = 1.2 * output[0] + 1.3 * output[1] + 2.5 * output[2]
        return final_predict

predict = model(data)
loss = F.mse_loss(predict, ground_truth)
loss.backward()

Obviously, the optimizer still can not see final_predict. So, how can I add final_predict to the existing optimizer or convert it to a nn.parameter()? Because I need to use final_predict to calculate the loss so as to update my model.