```python
import torch
import torchvision
import torchvision.transforms as transforms
import nni
from nni.compression.pytorch import ModelSpeedup
from nni.algorithms.compression.pytorch.pruning import TaylorFOWeightFilterPruner, AGPPruner
from nni.algorithms.compression.pytorch.quantization import QATQuantizer
from nni.algorithms.compression.pytorch.distillation import DynamicLayerwiseDistiller

# Load and normalize CIFAR10
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

# Define and train the model
model = torchvision.models.resnet18(pretrained=True)
model.fc = torch.nn.Linear(model.fc.in_features, 10)  # Adjust for CIFAR10
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

for epoch in range(30):  # loop over the dataset multiple times
    model.train()
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f'Epoch {epoch + 1}, Loss: {running_loss / len(trainloader)}')

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

# Create a teacher model
teacher_model = type(model)(pretrained=False)  # Assuming model is an instance of a class from torchvision.models
teacher_model.load_state_dict(model.state_dict())

# Pruning configuration
pruning_config_list = [{
    'sparsity': 0.5,
    'op_types': ['Conv2d'],
}]

# Create pruners
taylor_pruner = TaylorFOWeightFilterPruner(model, pruning_config_list, trainer=trainloader, criterion=criterion, training_epochs=100, dependency_aware=True)
agp_pruner = AGPPruner(model, pruning_config_list, pruning_algorithm='l1', num_iterations=30, epochs_per_iteration=100)

# Quantization configuration
quantization_config_list = [{
    'quant_types': ['weight', 'output'],
    'quant_bits': {'weight': 8, 'output': 8},
    'op_types': ['Conv2d', 'BatchNorm2d'],
}]

# Create quantizer
quantizer = QATQuantizer(model, quantization_config_list, optimizer, start_step=100)

# Distillation configuration
distillation_config_list = [{
    'op_types': ['Conv2d'],
    'distill_method': 'mse',
}]

# Create distiller
distiller = DynamicLayerwiseDistiller(model, teacher_model, distillation_config_list, optimizer, trainloader, criterion, num_iterations=60, epochs_per_iteration=100)

# Compress the model
distiller.compress()

# Speed up the model
ModelSpeedup(model, inputs, masks_file=None).speedup_model()

# Evaluate the compressed model
# Similar evaluation code as before can be used here

# Update calibration configuration for quantization simulation
# Similar quantization simulation code can be used here

# Evaluate the compressed and quantized model
# Similar evaluation code as used previously can be used here
```