如何为每个模型层Pytorch使用不同的优化器？

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

# 定义模型
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.layer1 = nn.Linear(10, 10)
        self.layer2 = nn.Linear(10, 10)

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        return x

# 创建模型和优化器
model = Model()
optimizer_dict = {
    'layer1': optim.SGD(model.layer1.parameters(), lr=0.01),
    'layer2': optim.Adam(model.layer2.parameters(), lr=0.001)
}

# 训练循环
for epoch in range(num_epochs):
    for inputs, labels in dataloader:
        optimizer_dict['layer1'].zero_grad()
        optimizer_dict['layer2'].zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()

        optimizer_dict['layer1'].step()
        optimizer_dict['layer2'].step()