def load_ar1_benchmark(self):
        """
        Returns a NC benchmark from a fake dataset of 10 classes, 5 experiences,
        2 classes per experience. This toy benchmark is intended
        """
        n_samples_per_class = 5
        dataset = make_classification(
            n_samples=10 * n_samples_per_class,
            n_classes=9,
            n_features=224 * 224 * 3,
            n_informative=6,
            n_redundant=0,
        )

        X = torch.from_numpy(dataset[0]).reshape(-1, 3, 224, 224).float()
        y = torch.from_numpy(dataset[1]).long()

        train_X, test_X, train_y, test_y = train_test_split(
            X, y, train_size=0.6, shuffle=True, stratify=y
        )

        train_dataset = TensorDataset(train_X, train_y)
        test_dataset = TensorDataset(test_X, test_y)
        my_nc_benchmark = nc_benchmark(
            train_dataset, test_dataset, 3, task_labels=False
        )
        return my_nc_benchmark

        Returns a NC benchmark from a fake dataset of 10 classes, 5 experiences,
        2 classes per experience. This toy benchmark is intended
        
tests/training/test_ar1.py