code: def load_experience_train_eval(
      experience, batch_size=32, num_workers=0
  ):
      for x, y, t in DataLoader(
          experience.dataset.train(),
          batch_size=batch_size,
          num_workers=num_workers,
      ):
          break
  
      for x, y, t in DataLoader(
          experience.dataset.eval(),
          batch_size=batch_size,
          num_workers=num_workers,
      ):
          break
  
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 9 for calling a global function with example usage: load_experience_train_eval(experience, batch_size, num_workers) without return types