class_docstr: Internal utility to better support the `set_epoch` method in FFCV.
  
  This is a wrapper of a batch sampler that may be based on a PyTorch
  :class:`DistributedSampler`. This allows passing the `set_epoch`
  call to the underlying sampler.
class_docstr_tok: ["Internal","utility","to","better","support","the","`","set_epoch","`","method","in","FFCV",".","This","is","a","wrapper","of","a","batch","sampler","that","may","be","based","on","a","PyTorch",":","class",":","`","DistributedSampler","`",".","This","allows","passing","the","`","set_epoch","`","call","to","the","underlying","sampler","."]
code: def __init__(self, batch_sampler: Sampler[List[int]], indices):
      self.batch_sampler = batch_sampler
      self.indices = indices
  
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 1 to create a new _MappedBatchsampler object from inherited base classes: Sampler with example: obj = _MappedBatchsampler(batch_sampler, indices)