code: def get_fast_benchmark(
      use_task_labels=False,
      shuffle=True,
      n_samples_per_class=100,
      n_classes=10,
      n_features=6,
      seed=None,
      train_transform=None,
      eval_transform=None,
  ):
      train, test = dummy_classification_datasets(
          n_classes, n_features, n_samples_per_class, seed
      )
      my_nc_benchmark = nc_benchmark(
          train,
          test,
          5,
          task_labels=use_task_labels,
          shuffle=shuffle,
          train_transform=train_transform,
          eval_transform=eval_transform,
          seed=seed,
      )
      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 7 for calling a global function with example usage: get_fast_benchmark(use_task_labels, shuffle, n_samples_per_class, n_classes, n_features, seed, train_transform, eval_transform) and returns: my_nc_benchmark