```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision.models import resnet18
from torchvision.datasets import CIFAR10
from torchvision.transforms import Compose, ToTensor, Normalize
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

class SMoGModel(nn.Module):
    def __init__(self, backbone):
        super(SMoGModel, self).__init__()
        self.backbone = backbone
        self.projection_head = nn.Sequential(
            nn.Linear(backbone.fc.in_features, 128),
            nn.ReLU(),
            nn.Linear(128, 64)
        )
        self.prediction_head = nn.Sequential(
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, 10)  # Assuming 10 classes for CIFAR10
        )
        self.backbone.fc = nn.Identity()  # Remove the original fully connected layer

    def forward(self, x):
        features = self.backbone(x)
        encoded = self.projection_head(features)
        predicted = self.prediction_head(encoded)
        return encoded, predicted

    def cluster_features(self, features):
        kmeans = KMeans(n_clusters=10)  # Assuming 10 clusters
        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 model
backbone = resnet18(pretrained=True)
model = SMoGModel(backbone)

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

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

# Apply SMoGTransform to CIFAR10 dataset
transform = Compose([
    RandomResizedCrop(size=32),
    RandomRotate(degrees=15),
    ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1),
    GaussianBlur(kernel_size=(3, 3), sigma=(0.1, 2.0)),
    ToTensor(),
    Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
])

train_dataset = CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, 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):
    model.train()
    running_loss = 0.0
    for inputs, labels in train_loader:
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

        # Forward pass
        encoded, predictions = model(inputs)
        loss = criterion(predictions, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    average_loss = running_loss / len(train_loader)
    print(f"Epoch {epoch+1}, Average Loss: {average_loss}")
```