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"]
docstr: Args:
      last_clip_time (float): Not used for TimeStampClipSampler.
      video_duration: (float): Not used for TimeStampClipSampler.
      annotation (Dict): Dict containing time step to sample aroud.
  Returns:
      clip_info (ClipInfo): includes the clip information of (clip_start_time,
      clip_end_time, clip_index, aug_index, is_last_clip). The times are in seconds.
      clip_index, aux_index and is_last_clip are always 0, 0 and True, respectively.
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code: def __call__(
      self,
      last_clip_time: float,
      video_duration: float,
      annotation: Dict[str, Any],
  ) -> ClipInfo:
      """
      Args:
          last_clip_time (float): Not used for TimeStampClipSampler.
          video_duration: (float): Not used for TimeStampClipSampler.
          annotation (Dict): Dict containing time step to sample aroud.
      Returns:
          clip_info (ClipInfo): includes the clip information of (clip_start_time,
          clip_end_time, clip_index, aug_index, is_last_clip). The times are in seconds.
          clip_index, aux_index and is_last_clip are always 0, 0 and True, respectively.
      """
      center_frame_sec = annotation["clip_index"]  # a.k.a timestamp
      clip_start_sec = (
          center_frame_sec - self.clip_sampler._clip_duration / 2.0
      )
      return ClipInfo(
          clip_start_sec,
          clip_start_sec + self.clip_sampler._clip_duration,
          0,
          0,
          True,
      )
  
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 2 for calling the TimeStampClipSampler obj's underlying member method code with example usage: obj.__call__(last_clip_time, video_duration, annotation) and returns: ClipInfo