code: def test_flattening_replay_ocl(self):
      benchmark = get_fast_benchmark()
      buffer = ReservoirSamplingBuffer(100)
  
      for t, exp in enumerate(
          FixedSizeExperienceSplitter(
              benchmark.train_stream[0], 1, None
          )
      ):
          buffer.update_from_dataset(exp.dataset)
          b = buffer.buffer
          # depths = _flatdata_depth(b)
          # lenidxs = len(b._indices)
          # lendsets = len(b._datasets)
          # print(f"DATA depth={depths}, idxs={lenidxs}, dsets={lendsets}")
          #
          # atts = [b.targets.data, b.targets_task_labels.data]
          # depths = [_flatdata_depth(b) for b in atts]
          # lenidxs = [len(b._indices) for b in atts]
          # lendsets = [len(b._datasets) for b in atts]
          # print(f"(t={t}) ATTS depth={depths}, idxs={lenidxs},
          # dsets={lendsets}")
          if t > 5:
              break
      print(
          f"DATA depth={_flatdata_depth(b)}, dsets={len(b._datasets)}"
      )
      assert len(b._datasets) <= 2
  
      for t, exp in enumerate(
          FixedSizeExperienceSplitter(
              benchmark.train_stream[1], 1, None
          )
      ):
          buffer.update_from_dataset(exp.dataset)
          b = buffer.buffer
          # depths = _flatdata_depth(b)
          # lenidxs = len(b._indices)
          # lendsets = len(b._datasets)
          # print(f"DATA depth={depths}, idxs={lenidxs}, dsets={lendsets}")
          #
          # atts = [b.targets.data, b.targets_task_labels.data]
          # depths = [_flatdata_depth(b) for b in atts]
          # lenidxs = [len(b._indices) for b in atts]
          # lendsets = [len(b._datasets) for b in atts]
          # print(f"(t={t}) ATTS depth={depths}, idxs={lenidxs},
          # dsets={lendsets}")
          if t > 5:
              break
      print(
          f"DATA depth={_flatdata_depth(b)}, dsets={len(b._datasets)}"
      )
      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 7 for calling the FlatteningTests obj's underlying member method code with example usage: obj.test_flattening_replay_ocl() without return types