code: def test_complete_test_set_only(self):
      train_exps = []
      test_exps = []
  
      for _ in range(5):
          tensor_x = torch.rand(200, 3, 28, 28)
          tensor_y = torch.randint(0, 100, (200,))
          tensor_t = torch.randint(0, 5, (200,))
          train_exps.append(
              _make_taskaware_tensor_classification_dataset(
                  tensor_x, tensor_y, task_labels=tensor_t
              )
          )
  
      for _ in range(3):
          tensor_x = torch.rand(150, 3, 28, 28)
          tensor_y = torch.randint(0, 100, (150,))
          tensor_t = torch.randint(0, 5, (150,))
          test_exps.append(
              _make_taskaware_tensor_classification_dataset(
                  tensor_x, tensor_y, task_labels=tensor_t
              )
          )
  
      with self.assertRaises(Exception):
          benchmark_instance = GenericCLScenario(
              stream_definitions={
                  "train": (train_exps,),
                  "test": (test_exps,),
              },
              complete_test_set_only=True,
          )
  
      benchmark_instance = GenericCLScenario(
          stream_definitions={
              "train": (train_exps,),
              "test": (test_exps[0],),
          },
          complete_test_set_only=True,
      )
  
      self.assertEqual(5, len(benchmark_instance.train_stream))
      self.assertEqual(1, len(benchmark_instance.test_stream))
  
file_path: avalanche/tests/benchmarks/scenarios/test_custom_streams.py
imports: import unittest
  import torch
  from torch.utils.data import TensorDataset
  from avalanche.benchmarks.utils import _make_taskaware_tensor_classification_dataset
  from avalanche.benchmarks import GenericCLScenario
  from avalanche.benchmarks.utils import _make_taskaware_classification_dataset
label_desc: Use image node_id 2 for calling the CustomStreamsTests obj's underlying member method code with example usage: obj.test_complete_test_set_only() without return types