class_docstr: Pre-processor for Ava Actions Dataset stored as image frames -
      `<https://research.google.com/ava/download.html>_`
  This class handles the parsing of all the necessary
  csv files containing frame paths and frame labels.
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docstr: Loading image paths from the corresponding file.
  Args:
      frame_paths_file (str): Path to a file containing relative paths
          to all the frames in the video. Each line in the file is of the
          form <original_vido_id video_id frame_id rel_path labels>
      video_path_prefix (str): Path to be augumented to the each relative
          frame path to get the global frame path.
  Returns:
      (tuple): A tuple of the following,
          image_paths_list: List of list containing absolute frame paths.
              Wherein the outer list is per video and inner list is per
              timestamp.
          video_idx_to_name: A dictionary mapping video index to name
          video_name_to_idx: A dictionary maoping video name to index
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code: def load_image_lists(
      frame_paths_file: str, video_path_prefix: str
  ) -> Tuple:
      """
      Loading image paths from the corresponding file.
      Args:
          frame_paths_file (str): Path to a file containing relative paths
              to all the frames in the video. Each line in the file is of the
              form <original_vido_id video_id frame_id rel_path labels>
          video_path_prefix (str): Path to be augumented to the each relative
              frame path to get the global frame path.
      Returns:
          (tuple): A tuple of the following,
              image_paths_list: List of list containing absolute frame paths.
                  Wherein the outer list is per video and inner list is per
                  timestamp.
              video_idx_to_name: A dictionary mapping video index to name
              video_name_to_idx: A dictionary maoping video name to index
      """
  
      image_paths = []
      video_name_to_idx = {}
      video_idx_to_name = []
  
      with g_pathmgr.open(frame_paths_file, "r") as f:
          f.readline()
          for line in f:
              row = line.split()
              # The format of each row should follow:
              # original_vido_id video_id frame_id path labels.
              assert len(row) == 5
              video_name = row[0]
  
              if video_name not in video_name_to_idx:
                  idx = len(video_name_to_idx)
                  video_name_to_idx[video_name] = idx
                  video_idx_to_name.append(video_name)
                  image_paths.append({})
  
              data_key = video_name_to_idx[video_name]
              frame_id = int(row[2])
              image_paths[data_key][frame_id] = os.path.join(
                  video_path_prefix, row[3]
              )
  
      image_paths_list = []
      for i in range(len(image_paths)):
          image_paths_list.append([])
          sorted_keys = sorted(image_paths[i])
          for key in sorted_keys:
              image_paths_list[i].append(image_paths[i][key])
  
      return image_paths_list, video_idx_to_name, video_name_to_idx
  
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 4 for calling the AvaLabeledVideoFramePaths obj's underlying member method code with example usage: obj.load_image_lists(frame_paths_file, video_path_prefix) and returns: image_paths_list,  video_idx_to_name,  video_name_to_idx