code: def test_tensor_benchmark_type(self):
      n_experiences = 3
  
      tensors_benchmark(
          train_tensors=[
              (torch.randn(2, 3), torch.zeros(2))
              for _ in range(n_experiences)
          ],
          test_tensors=[
              (torch.randn(2, 3), torch.zeros(2))
              for _ in range(n_experiences)
          ],
          task_labels=[0] * n_experiences,
          complete_test_set_only=False,
      )
  
      tensors_benchmark(
          train_tensors=[
              (torch.randn(2, 3), torch.zeros(2))
              for _ in range(n_experiences)
          ],
          test_tensors=[(torch.randn(2, 3), torch.zeros(2))],
          task_labels=[0] * n_experiences,
          complete_test_set_only=True,
      )
  
      with self.assertRaises(Exception):
          tensors_benchmark(
              train_tensors=[
                  (torch.randn(2, 3), torch.zeros(2))
                  for _ in range(n_experiences)
              ],
              test_tensors=[
                  (torch.randn(2, 3), torch.zeros(2))
                  for _ in range(n_experiences)
              ],
              task_labels=[0] * n_experiences,
              complete_test_set_only=True,
          )
  
file_path: avalanche/tests/benchmarks/scenarios/deprecated/test_scenarios_typechecks.py
imports: import unittest
  import torch
  from os.path import expanduser
  from torchvision.datasets import MNIST
  from avalanche.benchmarks import tensors_benchmark
  from avalanche.benchmarks.scenarios.deprecated.generators import nc_benchmark, ni_benchmark
  from avalanche.benchmarks.scenarios.deprecated.classification_scenario import ClassificationExperience
label_desc: Use image node_id 4 for calling the ScenariosTypeChecksTests obj's underlying member method code with example usage: obj.test_tensor_benchmark_type() without return types