code: def load_benchmark(use_task_labels=False, fast_test=True):
      """
      Returns a NC Benchmark from a fake dataset of 10 classes, 5 experiences,
      2 classes per experience.
      """
      if fast_test:
          my_nc_benchmark = get_fast_benchmark(use_task_labels)
      else:
          mnist_train = MNIST(
              root=expanduser("~") + "/.avalanche/data/mnist/",
              train=True,
              download=True,
              transform=Compose([ToTensor()]),
          )
  
          mnist_test = MNIST(
              root=expanduser("~") + "/.avalanche/data/mnist/",
              train=False,
              download=True,
              transform=Compose([ToTensor()]),
          )
          my_nc_benchmark = nc_benchmark(
              mnist_train,
              mnist_test,
              5,
              task_labels=use_task_labels,
              seed=1234,
          )
  
      return my_nc_benchmark
  
imports: import copy
  import itertools
  from os.path import expanduser
  import os
  import random
  import torch
  from PIL.Image import Image
  from sklearn.datasets import make_blobs, make_classification
  from sklearn.model_selection import train_test_split
  import numpy
  from torch.utils.data import TensorDataset, Dataset
  from torch.utils.data.dataloader import DataLoader
  from torchvision.datasets import MNIST
  from torchvision.transforms import Compose, ToTensor
  from avalanche.benchmarks import nc_benchmark
  from avalanche.benchmarks.datasets import default_dataset_location
  from avalanche.benchmarks.utils import _make_taskaware_tensor_classification_dataset
  from avalanche.benchmarks.utils.detection_dataset import make_detection_dataset
label_desc: Use image node_id 3 for calling a global function with example usage: load_benchmark(use_task_labels, fast_test) and returns: my_nc_benchmark