  Here is the Python code that you requested:
```
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.cluster import KMeans
from lightly.memory_bank import MemoryBankModule
from lightly.transforms import SMoGTransform
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

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

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

    def reset_group_features(self):
        self.group_features = None

    def reset_momentum_weights(self):
        self.momentum_weights = None

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

backbone = nn.Sequential(
    nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_