class_docstr: A sepcialized clip sampler for sampling video clips around specific
  timestamps. This is particularly used in datasets like Ava wherein only
  a specific subset of clips in the video have annotations
class_docstr_tok: ["A","sepcialized","clip","sampler","for","sampling","video","clips","around","specific","timestamps",".","This","is","particularly","used","in","datasets","like","Ava","wherein","only","a","specific","subset","of","clips","in","the","video","have","annotations"]
code: def reset(self) -> None:
      pass
  
file_path: pytorchvideo/pytorchvideo/data/ava.py
imports: from __future__ import annotations
  import os
  from collections import defaultdict
  from typing import Any, Callable, Dict, Optional, Set, Tuple, Type
  import torch
  from iopath.common.file_io import g_pathmgr
  from pytorchvideo.data.clip_sampling import ClipInfo, ClipSampler
  from pytorchvideo.data.labeled_video_dataset import LabeledVideoDataset
label_desc: Use image node_id 3 for calling the TimeStampClipSampler obj's underlying member method code with example usage: obj.reset() without return types