code: def test_flatten_and_reindex(self):
      bm = get_fast_benchmark()
      D1 = bm.train_stream[0].dataset
      ds, idxs = _flatten_datasets_and_reindex([D1, D1, D1], None)
  
      print(
          f"len-ds: {len(ds)}, max={max(idxs)}, min={min(idxs)}, "
          f"lens={[len(d) for d in ds]}"
      )
      assert len(ds) == 1
      assert len(idxs) == 3 * len(D1)
      assert max(idxs) == len(D1) - 1
      assert min(idxs) == 0
  
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 1 for calling the FlatteningTests obj's underlying member method code with example usage: obj.test_flatten_and_reindex() without return types