class_docstr: Samples elements from [0,..,len(dataset)-1] in a cyclic manner.
class_docstr_tok: ["Samples","elements","from","[","0",",","..",",","len","(","dataset",")","-1","]","in","a","cyclic","manner","."]
code: def _reset_indices(self):
      self.indices = torch.arange(self.n_samples).tolist()
      if self.shuffle:
          self.indices = torch.randperm(
              self.n_samples, generator=self.rng
          ).tolist()
  
imports: from functools import partial
  import random
  from typing import Callable, Generator, Generic, Iterable, List, Optional, TypeVar, Union, Protocol
  from typing_extensions import Literal
  import warnings
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from avalanche.benchmarks.utils.utils import concat_datasets
  import torch
  from torch.distributions.categorical import Categorical
  from torch.utils.data import Sampler
  from .deprecated.benchmark_wrapper_utils import wrap_stream
  from .generic_scenario import CLStream, CLScenario
  from .dataset_scenario import DatasetExperience
label_desc: Use image node_id 2 for calling the CyclicSampler obj's underlying member method code with example usage: obj._reset_indices() without return types