class_docstr: Helper class for class VideoFrameDataset. This class
  represents a video sample's metadata.
  
  Args:
      root_datapath: the system path to the root folder
                     of the videos.
      row: A list with four or more elements where 1) The first
           element is the path to the video sample's frames excluding
           the root_datapath prefix 2) The  second element is the starting frame id of the video
           3) The third element is the inclusive ending frame id of the video
           4) The fourth element is the label index.
           5) any following elements are labels in the case of multi-label classification
class_docstr_tok: ["Helper","class","for","class","VideoFrameDataset",".","This","class","represents","a","video","sample","'s","metadata",".","Args",":","root_datapath",":","the","system","path","to","the","root","folder","of","the","videos",".","row",":","A","list","with","four","or","more","elements","where","1",")","The","first","element","is","the","path","to","the","video","sample","'s","frames","excluding","the","root_datapath","prefix","2",")","The","second","element","is","the","starting","frame","id","of","the","video","3",")","The","third","element","is","the","inclusive","ending","frame","id","of","the","video","4",")","The","fourth","element","is","the","label","index",".","5",")","any","following","elements","are","labels","in","the","case","of","multi-label","classification"]
code: def start_frame(self):
      return int(self._data[1])
  
imports: import math
  import os
  import os.path
  import random
  from pathlib import Path
  import numpy
  import torch
  from PIL import Image
label_desc: Use image node_id 4 for calling the VideoRecord obj's underlying member method code with example usage: obj.start_frame() and returns: int