用pytorch自己搭建ResNet18模型，并使用sgd优化器，模型的layer1学习为0.1，模型的layer2学习为0.2，除了layer1和layer2模型的其余参数学习率设置为0.3，给出具体的代码

import torch.nn as nn
import torch.optim as optim
from torchvision.models import ResNet
from torch.optim.lr_scheduler import MultiStepLR


class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()
        self.resnet = ResNet(block=BasicBlock, layers=[2, 2, 2, 2], num_classes=10)

        # 设置不同层的学习率
        self.layer1_params = list(map(id, self.resnet.layer1.parameters()))
        self.layer2_params = list(map(id, self.resnet.layer2.parameters()))
        self.other_params = filter(lambda p: id(p) not in self.layer1_params + self.layer2_params,
                                   self.resnet.parameters())

        self.optimizer = optim.SGD([{'params': self.layer1_params, 'lr': 0.1},
                                    {'params': self.layer2_params, 'lr': 0.2},
                                    {'params': self.other_params, 'lr': 0.3}],
                                   momentum=0.9, weight_decay=5e-4)

        self.scheduler = MultiStepLR(self.optimizer, milestones=[150, 225], gamma=0.1)

    def forward(self, x):
        x = self.resnet(x)
        return x