code: def test_sit_single_dataset_fixed_subset_no_remap_idx(self):
      order = [2, 5, 7, 8, 9, 0, 1, 4]
      mnist_train = MNIST(
          root=default_dataset_location("mnist"),
          train=True,
          download=True,
      )
      mnist_test = MNIST(
          root=default_dataset_location("mnist"),
          train=False,
          download=True,
      )
      my_nc_benchmark = nc_benchmark(
          mnist_train,
          mnist_test,
          2,
          task_labels=True,
          fixed_class_order=order,
          class_ids_from_zero_in_each_exp=False,
      )
  
      self.assertEqual(
          2, len(my_nc_benchmark.classes_in_experience["train"])
      )
  
      all_classes = set()
      for task_id in range(2):
          self.assertEqual(
              4,
              len(
                  my_nc_benchmark.classes_in_experience["train"][
                      task_id
                  ]
              ),
          )
          self.assertEqual(
              set(order[task_id * 4 : (task_id + 1) * 4]),
              my_nc_benchmark.original_classes_in_exp[task_id],
          )
          all_classes.update(
              my_nc_benchmark.classes_in_experience["train"][task_id]
          )
  
      self.assertEqual(set(order), all_classes)
  
imports: import unittest
  from os.path import expanduser
  import torch
  from torchvision.datasets import MNIST
  from avalanche.benchmarks.datasets import default_dataset_location
  from avalanche.benchmarks.scenarios.deprecated.new_classes import NCExperience
  from avalanche.benchmarks.utils import _taskaware_classification_subset
  from avalanche.benchmarks.scenarios.deprecated.new_classes.nc_utils import make_nc_transformation_subset
  from avalanche.benchmarks import nc_benchmark, ClassificationStream
label_desc: Use image node_id 5 for calling the MultiTaskTests obj's underlying member method code with example usage: obj.test_sit_single_dataset_fixed_subset_no_remap_idx() without return types