class_docstr: A wrapper for adapting untrimmed annotated clips from the json_dataset to the
  standard `pytorchvideo.data.ClipSampler` expected format. Specifically, for each
  clip it uses the provided `clip_sampler` to sample between "clip_start_sec" and
  "clip_end_sec" from the json_dataset clip annotation.
class_docstr_tok: ["A","wrapper","for","adapting","untrimmed","annotated","clips","from","the","json_dataset","to","the","standard","`","pytorchvideo.data.ClipSampler","`","expected","format",".","Specifically",",","for","each","clip","it","uses","the","provided","`","clip_sampler","`","to","sample","between","``","clip_start_sec","''","and","''","clip_end_sec","''","from","the","json_dataset","clip","annotation","."]
docstr: Args:
      clip_sampler (`pytorchvideo.data.ClipSampler`): Strategy used for sampling
          between the untrimmed clip boundary.
docstr_tok: ["Args",":","clip_sampler","(","`","pytorchvideo.data.ClipSampler","`",")",":","Strategy","used","for","sampling","between","the","untrimmed","clip","boundary","."]
code: def __init__(self, clip_sampler: ClipSampler) -> None:
      """
      Args:
          clip_sampler (`pytorchvideo.data.ClipSampler`): Strategy used for sampling
              between the untrimmed clip boundary.
      """
      self._trimmed_clip_sampler = clip_sampler
  
file_path: pytorchvideo/pytorchvideo/data/json_dataset.py
imports: import json
  import logging
  import os
  from typing import Any, Callable, Dict, Optional, 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 1 to create a new UntrimmedClipSampler object  with example: obj = UntrimmedClipSampler(clip_sampler)