```python
import torch
import torch.nn as nn
from torchvision.models import resnet18
from lightly.models import MemoryBankModule
from lightly.transforms import SMoGTransform
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn.functional as F
from sklearn.cluster import KMeans

class SMoGModel(nn.Module):
    def __init__(self):
        super(SMoGModel, self).__init__()
        self.backbone = resnet18()
        self.projection_head = nn.Linear(512, 128)
        self.prediction_head = nn.Linear(128, 10)
    
    def forward(self, x):
        x = self.backbone(x)
        encoded = self.projection_head(x)
        predicted = self.prediction_head(encoded)
        return encoded, predicted
    
    def cluster_features(self, features):
        kmeans = KMeans(n_clusters=10, random_state=0).fit(features)
        return kmeans.labels_
    
    def reset_group_features(self):
        pass
    
    def reset_momentum_weights(self):
        pass

model = SMoGModel()
memory_bank = MemoryBankModule(128, 10)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

transform = transforms.Compose([
    transforms.ToTensor(),
    SMoGTransform()
])

train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

for epoch in range(10):
    model.reset_momentum_weights()
    for inputs, _ in train_loader:
        inputs = inputs.to(device)
        encoded, predicted = model(inputs)
        model.reset_group_features()
        loss = criterion(predicted, torch.randint(0, 10, (64,), dtype=torch.long).to(device))
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        memory_bank.update_memory_bank(encoded)
    print(f'Epoch {epoch+1}, Average Loss: {loss.item()}')
```