class_docstr: LabeledVideoPaths contains pairs of video path and integer index label.
class_docstr_tok: ["LabeledVideoPaths","contains","pairs","of","video","path","and","integer","index","label","."]
docstr: Args:
      paths_and_labels [(str, int)]: a list of tuples containing the video
          path and integer label.
docstr_tok: ["Args",":","paths_and_labels","[","(","str",",","int",")","]",":","a","list","of","tuples","containing","the","video","path","and","integer","label","."]
code: def __init__(
      self,
      paths_and_labels: List[Tuple[str, Optional[int]]],
      path_prefix="",
  ) -> None:
      """
      Args:
          paths_and_labels [(str, int)]: a list of tuples containing the video
              path and integer label.
      """
      self._paths_and_labels = paths_and_labels
      self._path_prefix = path_prefix
  
file_path: pytorchvideo/pytorchvideo/data/labeled_video_paths.py
imports: from __future__ import annotations
  import os
  import pathlib
  from typing import List, Optional, Tuple
  from iopath.common.file_io import g_pathmgr
  from torchvision.datasets.folder import make_dataset
label_desc: Use image node_id 4 to create a new LabeledVideoPaths object  with example: obj = LabeledVideoPaths(paths_and_labels, path_prefix)