 ```python
import torch
import torch.nn as nn
from torchvision import datasets, transforms
from lightly.models.modules import MemoryBankModule
from sklearn.cluster import KMeans

class ProjectionHead(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super().__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

class PredictionHead(nn.Module):
    def __init__(self, input_dim, output_dim):
        super().__init__()
        self.fc = nn.Linear(input_dim, output_dim)

    def forward(self, x):
        x = self.fc(x)
        return x

class SMoGModel(nn.Module):
    def __init__(self, backbone, projection_head, prediction_head):
        super().__init__()
        self.backbone = backbone
        self.projection_head = projection_head
        self.prediction_head = prediction_head
        self.memory_bank = MemoryBankModule(self.backbone.output_dim)

    def cluster_features(self, features):
        kmeans = KMeans(self.memory_bank.k)
        clusters = kmeans.fit_predict(features.detach().cpu().numpy())
        self.memory_bank.cluster_assignments = torch.from_numpy(clusters).to(features.device)

    def reset_group_features(self):
        self.memory_bank.reset_group_features()

    def reset_momentum_weights(self):
        self.backbone.reset_momentum_weights()

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

backbone = ResNet18(pretrained=False, progress=True, num_classes=0).freeze()
projection_head = ProjectionHead(backbone.output_dim, 512, 128)
prediction_head = PredictionHead(128, 10)
model = SMoGModel(backbone, projection_head, prediction_head)

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

smog_transform = SMoGTransform(model.backbone, model.memory_bank, device)
cifar10_dataset = datasets.CIFAR10(root='data', train=True, download=True, transform=smog_transform)
dataloader = torch.utils.data.DataLoader(cifar10_dataset, batch_size=256, shuffle=True)

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

for epoch in range(10):
    for i, (inputs, _) in enumerate(dataloader):
        inputs = inputs.to(device)

        # Update momentum weights
        model.backbone.update_momentum_weights(inputs)

        # Encode inputs
        encoded, _ = model(inputs)

        # Update group features
        model.memory_bank.update_group_features(encoded)

        # Calculate loss
        loss = criterion(encoded, model.memory_bank.cluster_assignments)

        # Update memory bank
        model.memory_bank.update_memory_bank(encoded)

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    print(f'Epoch: {epoch+1}, Loss: {loss.item()}')
```