 ```python
import torch
import torch.nn as nn
from torchvision import models
from sklearn.cluster import KMeans
from lightly.data import MemoryBankModule
from torchvision.datasets import CIFAR10
from torch.optim import SGD
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader
from torch.nn.utils.clip_grad.clip_grad import clip_grad_norm_

class SMoGModel(nn.Module):
    def __init__(self):
        super(SMoGModel, self).__init__()
        backbone = models.resnet18(pretrained=True)
        self.backbone = nn.Sequential(*list(backbone.children())[:-2])
        self.projection_head = nn.Linear(512, 256)
        self.prediction_head = nn.Linear(256, 10)
        self.kmeans = KMeans(n_clusters=10)
        self.momentum = 0.9

    def reset_group_features(self):
        for layer in self.children():
            if hasattr(layer, 'group_features'):
                layer.reset_group_features()

    def reset_momentum(self):
        for layer in self.children():
            if hasattr(layer, 'momentum'):
                layer.momentum = self.momentum

    def cluster_features(self, features):
        labels = self.kmeans.fit_predict(features)
        self.kmeans.labels_ = labels

    def forward(self, x):
        x = self.backbone(x)
        x = self.projection_head(x)
        encoded = x
        x = torch.nn.functional.normalize(x, dim=1)
        x = self.prediction_head(x)
        return encoded, x

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
transform = ...  # SMoGTransform for CIFAR10 dataset
train_dataset = CIFAR10(root='./data', train=True, download=True, transform=transform)
memory_bank = MemoryBankModule(num_samples=1000, device=device)

model = SMoGModel().to(device)

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

for epoch in range(10):
    model.zero_grad()
    inputs = pad_sequence([torch.randn(len(train_dataset), 3, 32, 32).to(device) for _ in range(len(memory_bank))], batch_first=True)
    encoded, outputs = model(inputs)
    memory_bank.update(encoded, outputs)
    outputs = outputs.view(-1, 10)
    targets = torch.tensor([train_dataset.targets[i] for i in memory_bank.indices]).to(device)
    loss = criterion(outputs, targets)
    loss.backward()
    clip_grad_norm_(model.parameters(), max_norm=1.0)
    optimizer.step()
    model.reset_momentum()
    print(f'Epoch {epoch+1} Loss: {loss.item()}')
```