docstr: Returns a NC benchmark from a fake dataset of 10 classes, 5 experiences,
  2 classes per experience. This toy benchmark is intended
code: 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
  
file_path: avalanche/tests/training/test_ar1.py
imports: import torch
  import unittest
  from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  from torch.utils.data import TensorDataset
  from avalanche.benchmarks import nc_benchmark
  from avalanche.training.supervised import AR1
  from tests.training.test_strategy_utils import run_strategy
label_desc: Use image node_id 2 for calling the AR1Test obj's underlying member method code with example usage: obj.load_ar1_benchmark() and returns: my_nc_benchmark