code: def dummy_classification_datasets(
      n_classes=10, n_features=7, n_samples_per_class=20, seed=42
  ):
      dataset = make_classification(
          n_samples=n_classes * n_samples_per_class,
          n_classes=n_classes,
          n_features=n_features,
          n_informative=6,
          n_redundant=0,
          random_state=seed,
      )
      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,
          random_state=seed,
      )
      train = _make_taskaware_tensor_classification_dataset(
          train_X, train_y
      )
      test = _make_taskaware_tensor_classification_dataset(
          test_X, test_y
      )
      return train, test
  
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 8 for calling a global function with example usage: dummy_classification_datasets(n_classes, n_features, n_samples_per_class, seed) and returns: train,  test