class_docstr: Boundary-aware experiences have attributes with task boundary knowledge.
  
  Online streams may have boundary attributes to help training or
  metrics logging.
  
  Task boundaries denote changes of the underlying data distribution used
  to sample the data for the experiences.
class_docstr_tok: ["Boundary-aware","experiences","have","attributes","with","task","boundary","knowledge",".","Online","streams","may","have","boundary","attributes","to","help","training","or","metrics","logging",".","Task","boundaries","denote","changes","of","the","underlying","data","distribution","used","to","sample","the","data","for","the","experiences","."]
docstr: True if this is the first experience after a drift.
docstr_tok: ["True","if","this","is","the","first","experience","after","a","drift","."]
code: def is_first_subexp(self) -> bool:
      """True if this is the first experience after a drift."""
      return False
  
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 1 for calling the BoundaryAware obj's underlying member method code with example usage: obj.is_first_subexp() and returns: False