code: def test_tensors_benchmark(self):
      pattern_shape = (3, 32, 32)
  
      # Definition of training experiences
      # Experience 1
      experience_1_x = torch.zeros(100, *pattern_shape)
      experience_1_y = torch.zeros(100, dtype=torch.long)
  
      # Experience 2
      experience_2_x = torch.zeros(80, *pattern_shape)
      experience_2_y = torch.ones(80, dtype=torch.long)
  
      # Test experience
      test_x = torch.zeros(50, *pattern_shape)
      test_y = torch.zeros(50, dtype=torch.long)
  
      generic_benchmark = tensors_benchmark(
          train_tensors=[
              (experience_1_x, experience_1_y),
              (experience_2_x, experience_2_y),
          ],
          test_tensors=[(test_x, test_y)],
          task_labels=[0, 0],  # Task label of each train exp
          complete_test_set_only=True,
      )
  
      self.assertEqual(2, len(generic_benchmark.train_stream))
      self.assertEqual(1, len(generic_benchmark.test_stream))
  
imports: import os
  import tempfile
  import unittest
  from os.path import expanduser
  import torch
  from torchvision.datasets import MNIST
  from torchvision.datasets.utils import download_url, extract_archive
  from torchvision.transforms import ToTensor
  from tests.unit_tests_utils import DummyImageDataset
  from avalanche.benchmarks import dataset_benchmark, filelist_benchmark, tensors_benchmark, paths_benchmark, data_incremental_benchmark
  from avalanche.benchmarks.datasets import default_dataset_location
  from avalanche.benchmarks.scenarios.deprecated.generic_benchmark_creation import create_lazy_generic_benchmark, LazyStreamDefinition
  from avalanche.benchmarks.utils import _make_taskaware_classification_dataset, _make_taskaware_tensor_classification_dataset
  from tests.unit_tests_utils import common_setups
label_desc: Use image node_id 4 for calling the HighLevelGeneratorTests obj's underlying member method code with example usage: obj.test_tensors_benchmark() without return types