class_docstr: A dataloader used to load :class:`AvalancheDataset`s for which
  the FFCV support was previously enabled by using :func:`enable_ffcv`.
  
  This is not a pure wrapper of a FFCV loader: this hybrid dataloader
  is in charge of both creating the FFCV loader and merging
  the Avalanche-specific info contained in the :class:`DataAttribute`
  fields of the datasets (such as task labels).
class_docstr_tok: ["A","dataloader","used","to","load",":","class",":","`","AvalancheDataset","`","s","for","which","the","FFCV","support","was","previously","enabled","by","using",":","func",":","`","enable_ffcv","`",".","This","is","not","a","pure","wrapper","of","a","FFCV","loader",":","this","hybrid","dataloader","is","in","charge","of","both","creating","the","FFCV","loader","and","merging","the","Avalanche-specific","info","contained","in","the",":","class",":","`","DataAttribute","`","fields","of","the","datasets","(","such","as","task","labels",")","."]
code: def __len__(self):
      return len(self.batch_sampler)
  
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 5 for calling the HybridFfcvLoader obj's underlying member method code with example usage: obj.__len__() and returns: len