code: def get_fast_benchmark():
      n_samples_per_class = 100
      dataset = make_classification(
          n_samples=10 * n_samples_per_class,
          n_classes=10,
          n_features=6,
          n_informative=6,
          n_redundant=0,
      )
  
      X = torch.from_numpy(dataset[0]).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, 5, task_labels=True
      )
      return my_nc_benchmark
  
imports: import unittest
  import torch
  from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  from torch.optim import SGD
  from torch.nn import CrossEntropyLoss
  from torch.utils.data import TensorDataset, DataLoader
  from avalanche.benchmarks.utils import _concat_taskaware_classification_datasets
  from avalanche.benchmarks.utils.utils import concat_datasets
  from avalanche.models import SimpleMLP
  from avalanche.training.plugins import ReplayPlugin
  from avalanche.training.supervised import Naive
  from avalanche.benchmarks import nc_benchmark
  from avalanche.benchmarks.utils.data_loader import ReplayDataLoader, TaskBalancedDataLoader, GroupBalancedDataLoader, GroupBalancedInfiniteDataLoader
label_desc: Use image node_id 1 for calling a global function with example usage: get_fast_benchmark() and returns: my_nc_benchmark