code: def test_concat_flattens_same_classification_dataset(self):
      D = TaskAwareClassificationDataset([[1, 2, 3]])
      B = concat_datasets([])
      B = B.concat(D)
      B = D.concat(B)
      print(
          f"DATA depth={_flatdata_depth(B)}, dsets={len(B._datasets)}"
      )
      assert _flatdata_depth(B) <= 2
      assert len(B._datasets) <= 2
      B = D.concat(B)
      print(
          f"DATA depth={_flatdata_depth(B)}, dsets={len(B._datasets)}"
      )
      assert _flatdata_depth(B) <= 2
      assert len(B._datasets) <= 2
  
      B = D.concat(B)
      print(
          f"DATA depth={_flatdata_depth(B)}, dsets={len(B._datasets)}"
      )
      assert _flatdata_depth(B) <= 2
      assert len(B._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 4 for calling the FlatteningTests obj's underlying member method code with example usage: obj.test_concat_flattens_same_classification_dataset() without return types