 Here is the Python code based on your instructions:

```python
import torch
import torch.nn as nn
from torchvision import models
from sklearn.cluster import KMeans
from lightly import MemoryBankModule

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)

    def cluster_features(self, features):
        kmeans = KMeans(n_clusters=10)
        kmeans.fit(features)
        return kmeans.labels_

    def reset_group_features(self):
        for name, param in self.named_parameters():
            if 'group_features' in name:
                param.zero_()

    def reset_momentum_weights(self):
        for name, param in self.named_parameters():
            if 'momentum' in name:
                param.zero_()

    def forward(self, x):
        features = self.backbone(x)
        encoded_features = self.projection_head(features)
        predictions = self.prediction_head(encoded_features)
        return encoded_features, predictions

mb_module = MemoryBankModule(memory_size=10000, num_workers=4)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
transform = ...  # SMoGTransform for CIFAR10 dataset
train_dataset = torch.utils.data.DatasetLoader(transform(torchvision.datasets.CIFAR10(root='./data', download=True, train=True, transform=transform), SamplingMethod.UNIFORM)

model = SMoGModel().to(device)

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

for epoch in range(10):
    model.train()
    total_loss = 0
    for batch_idx, (data, target) in enumerate(train_dataset):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        encoded_features, predictions = model(data)
        loss = criterion(predictions, target) + _global_variance_and_covariance_loss(encoded_features)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()

    print('Epoch [{}/{}], Average Loss: {:.4f}'.format(epoch+1, 10, total_loss/len(train_dataset)))
```

Please note that the SMoGTransform and other necessary libraries and functions are not provided in this code. You should replace the `...` with the appropriate SMoGTransform implementation for the CIFAR10 dataset. Also, the `_global_variance_and_covariance_loss` function is not a standard PyTorch function, so you may need to implement it or replace it with another appropriate loss function.