```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision.models import resnet18
from torchvision.datasets import CIFAR10
from torch.utils.data import DataLoader
from sklearn.cluster import KMeans
from lightly.models.modules import MemoryBankModule
from lightly.data import LightlyDataset
from lightly.transforms import GaussianBlur, RandomRotate, RandomResizedCrop, ColorJitter
from lightly.data import LightlyDataset
from lightly.loss import NTXentLoss
from lightly.models.modules.heads import ProjectionHead, PredictionHead

class SMoGModel(nn.Module):
    def __init__(self, backbone):
        super(SMoGModel, self).__init__()
        self.backbone = backbone
        self.projection_head = ProjectionHead()
        self.prediction_head = PredictionHead()
    
    def forward(self, x):
        encoded = self.backbone(x)
        projected = self.projection_head(encoded)
        predicted = self.prediction_head(projected)
        return encoded, predicted
    
    def cluster_features(self, features):
        kmeans = KMeans(n_clusters=10)
        return kmeans.fit_predict(features)
    
    def reset_group_features(self):
        pass  # Implement as needed
    
    def reset_momentum_weights(self):
        pass  # Implement as needed

# Create an instance of the SMoGModel
backbone = resnet18(pretrained=True)
model = SMoGModel(backbone)

# Create a memory bank
memory_bank = MemoryBankModule(size=2048)

# Setup device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Apply SMoGTransform to CIFAR10 dataset
transform = torch.nn.Sequential(
    RandomResizedCrop(size=32),
    RandomRotate(degrees=15),
    ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1),
    GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)),
)
dataset = CIFAR10(root="data", train=True, download=True, transform=transform)
dataloader = DataLoader(dataset, batch_size=256, shuffle=True)

# Define loss criterion and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

# Training loop
for epoch in range(10):
    total_loss = 0
    for inputs, _ in dataloader:
        inputs = inputs.to(device)
        optimizer.zero_grad()
        
        # Update model's momentum, encode inputs, update group features as needed
        encoded, predicted = model(inputs)
        
        # Example loss calculation (modify as needed)
        loss = criterion(predicted, encoded.detach())
        loss.backward()
        optimizer.step()
        
        total_loss += loss.item()
    
    avg_loss = total_loss / len(dataloader)
    print(f"Epoch {epoch+1}, Average Loss: {avg_loss}")
```