 ```python
import nni
from nni.compression.pytorch.quantization import QATQuantizer
from nni.compression.pytorch.pruning import TaylorPruner, AGPPruner
from nni.compression.pytorch.speedup import ModelSpeedup
from nni.compression.pytorch.distiller import DynamicLayerwiseDistiller
from nni.compression.pytorch.utils import CalibrationConfig
from nni.experiment.local.local_train import LocalTrainController
from nni.experiment.performance import PerformanceReport
from torchvision import models
import torch
import torchvision
import torchvision.transforms as transforms

def main():
    # Fine-tune the model
    model = models.resnet18(pretrained=False)
    dataset_train = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.ToTensor())
    dataset_test = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.ToTensor())
    data_loader_train = torch.utils.data.DataLoader(dataset_train, batch_size=64, shuffle=True)
    data_loader_test = torch.utils.data.DataLoader(dataset_test, batch_size=64, shuffle=False)
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
    model.train()
    for epoch in range(30):
        for batch_idx, (data, target) in enumerate(data_loader_train):
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data, target in data_loader_test:
            output = model(data)
            _, predicted = torch.max(output.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
    print('Accuracy of the model on the 10000 test images: %d %%' % (
        100 * correct / total))

    # Create teacher model
    teacher_model = models.resnet18(pretrained=False)
    teacher_model.load_state_dict(model.state_dict())

    # Create pruner
    pruner = DynamicLayerwiseDistiller(model, config={
        "pruning_type": "local_unstructured",
        "target_sparsity": 0.5,
        "operation_list": ["Conv2d"],
        "run_step": 100,
        "total_run_times": 30
    })

    # Create quantizer
    quantizer = QATQuantizer(model, config={
        "operation_list": ["Conv2d", "BatchNorm2d"],
        "quantization_bit": 8,
        "start_step": 100
    })

    # Create distiller
    distiller = DynamicLayerwiseDistiller(model, config={
        "pruning_type": "local_unstructured",
        "target_sparsity": 0.5,
        "operation_list": ["Conv2d"],
        "run_step": 100,
        "total_run_times": 60,
        "distiller_type": "dynamic_layerwise",
        "distillation_method": "mse",
        "teacher_model": teacher_model
    })

    # Compress the model
    performance_report = PerformanceReport()
    LocalTrainController(
        model=model,
        performance_reporter=performance_report,
        train_data_loader=data_loader_train,
        valid_data_loader=data_loader_test,
        criterion=criterion,
        optimizer=optimizer,
        num_epochs=60,
        train_steps=100,
        compressor=distiller
    )

    # Speed up the model
    model_speedup = ModelSpeedup(model, pruner.get_masks())
    model_speedup.speedup()

    # Evaluate the compressed model
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data, target in data_loader_test:
            output = model(data)
            _, predicted = torch.max(output.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
    print('Accuracy of the compressed model on the 10000 test images: %d %%' % (
        100 * correct / total))

    # Simulate quantization
    calibration_config = CalibrationConfig(model, config={
        "operation_list": ["Conv2d", "BatchNorm2d"],
        "quantization_bit": 8
    })
    calibration_config.update_calibration_config()

    # Evaluate the compressed and quantized model
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data, target in data_loader_test:
            output = model(data)
            _, predicted = torch.max(output.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
    print('Accuracy of the compressed and quantized model on the 10000 test images: %d %%' % (
        100 * correct / total))

if __name__ == '__main__':
    main()
```