利用KDToolbox进行知识蒸馏，给我一个可运行的python代码

以下是一个简单的利用KDToolbox进行知识蒸馏的Python代码示例：

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
from torchvision.transforms import transforms
from kdtool import KDTrainer, KDTeacher, KDDataset

# 定义数据预处理
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载CIFAR10数据集
trainset = CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)

# 定义教师模型和学生模型
teacher_model = nn.Sequential(
    nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Flatten(),
    nn.Linear(512 * 2 * 2, 1024),
    nn.ReLU(inplace=True),
    nn.Linear(1024, 10)
)

student_model = nn.Sequential(
    nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
    nn.ReLU(inplace=True),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Flatten(),
    nn.Linear(64 * 8 * 8, 128),
    nn.ReLU(inplace=True),
    nn.Linear(128, 10)
)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(student_model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

# 定义知识蒸馏器
kd_trainer = KDTrainer(teacher_model, student_model, criterion, optimizer, alpha=0.5, temperature=4)

# 开始训练
for epoch in range(100):
    kd_trainer.train(trainloader)
    kd_trainer.test(testloader)

这个示例代码使用了CIFAR10数据集，定义了一个教师模型和一个学生模型，使用知识蒸馏技术训练学生模型。具体来说，使用KDTrainer类来定义知识蒸馏器，然后在训练循环中调用train方法来训练学生模型，调用test方法来测试学生模型的性能。