code: def test_concat_flattens_nc_scenario_dataset(self):
      benchmark = get_fast_benchmark()
      s = benchmark.train_stream
      B = concat_datasets([s[1].dataset])
      D1 = s[0].dataset
  
      B1 = D1.concat(B)
      print(
          f"DATA depth={_flatdata_depth(B1)}, dsets={len(B1._datasets)}"
      )
      assert len(B1._datasets) <= 2
      B2 = D1.concat(B1)
      print(
          f"DATA depth={_flatdata_depth(B2)}, dsets={len(B2._datasets)}"
      )
      assert len(B2._datasets) <= 2
      B3 = D1.concat(B2)
      print(
          f"DATA depth={_flatdata_depth(B3)}, dsets={len(B3._datasets)}"
      )
      assert len(B3._datasets) <= 2
  
imports: import sys
  import unittest
  import random
  import torch
  from avalanche.benchmarks import FixedSizeExperienceSplitter
  from avalanche.benchmarks.utils import AvalancheDataset, concat_datasets
  from avalanche.benchmarks.utils.classification_dataset import TaskAwareClassificationDataset
  from avalanche.benchmarks.utils.flat_data import FlatData, _flatten_datasets_and_reindex, LazyIndices
  from avalanche.benchmarks.utils.flat_data import _flatdata_depth, _flatdata_print
  from avalanche.training import ReservoirSamplingBuffer
  from tests.unit_tests_utils import get_fast_benchmark
label_desc: Use image node_id 5 for calling the FlatteningTests obj's underlying member method code with example usage: obj.test_concat_flattens_nc_scenario_dataset() without return types