class_docstr: LabeledVideoDataset handles the storage, loading, decoding and clip sampling for a
  video dataset. It assumes each video is stored as either an encoded video
  (e.g. mp4, avi) or a frame video (e.g. a folder of jpg, or png)
class_docstr_tok: ["LabeledVideoDataset","handles","the","storage",",","loading",",","decoding","and","clip","sampling","for","a","video","dataset",".","It","assumes","each","video","is","stored","as","either","an","encoded","video","(","e.g",".","mp4",",","avi",")","or","a","frame","video","(","e.g",".","a","folder","of","jpg",",","or","png",")"]
docstr: Returns:
      Number of videos in dataset.
docstr_tok: ["Returns",":","Number","of","videos","in","dataset","."]
code: def num_videos(self):
      """
      Returns:
          Number of videos in dataset.
      """
      return len(self.video_sampler)
  
file_path: pytorchvideo/pytorchvideo/data/labeled_video_dataset.py
imports: from __future__ import annotations
  import gc
  import logging
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type
  import torch.utils.data
  from pytorchvideo.data.clip_sampling import ClipSampler
  from pytorchvideo.data.video import VideoPathHandler
  from .labeled_video_paths import LabeledVideoPaths
  from .utils import MultiProcessSampler
label_desc: Use image node_id 3 for calling the LabeledVideoDataset obj's underlying member method code with example usage: obj.num_videos() and returns: len