code: def has_ffcv_support(datasets: List[AvalancheDataset]):
      """
      Checks if the support for FFCV was enabled for the given
      dataset list.
  
      This will 1) check if all the given :class:`AvalancheDataset`
      point to the same leaf dataset and 2) if the leaf dataset
      has the proper FFCV info setted by the :func:`enable_ffcv`
      function.
  
      :param dataset: The list of datasets.
      :return: True if FFCV can be used to load the given datasets,
          False otherwise.
      """
      try:
          flat_set = single_flat_dataset(concat_datasets(datasets))
      except Exception:
          return False
  
      if flat_set is None:
          return False
  
      leaf_dataset = flat_set[0]
  
      return hasattr(leaf_dataset, "ffcv_info")
  
imports: from dataclasses import dataclass
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
  from collections import OrderedDict
  import warnings
  import numpy
  import torch
  from torch.utils.data.sampler import Sampler
  from avalanche.benchmarks.scenarios.generic_scenario import CLScenario
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from avalanche.benchmarks.utils.dataset_traversal_utils import flat_datasets_from_benchmark, single_flat_dataset
  from avalanche.benchmarks.utils.utils import concat_datasets
label_desc: Use image node_id 2 for calling a global function with example usage: has_ffcv_support(datasets) and returns: hasattr, False, False