          logger.debug("Backed up alignments")
  
      def save_video_meta_data(self, pts_time, keyframes):
          """ Save video meta data to the alignments file.
          Parameters
          ----------
          pts_time: list
              A list of presentation timestamps (`float`) in frame index order for every frame in
              the input video
          keyframes: list
 
</s> remove         for idx, key in enumerate(sorted(self.data)):
            meta = dict(pts_time=pts_time[idx],
                        keyframe=idx in keyframes)
            self.data[key]["video_meta"] = meta
 </s> add         for idx, pts in enumerate(pts_time):
            meta = dict(pts_time=pts, keyframe=idx in keyframes)
            key = "{}_{:06d}.png".format(basename, idx + 1)
            if key not in self.data:
                self.data[key] = dict(video_meta=meta, faces=[])
            else:
                self.data[key]["video_meta"] = meta
        If the alignments file does not have an entry for every frame (e.g. if Extract Every N
        was used) then the frame is added to the alignments file with no faces, so that they video
        meta data can be stored.

          logger.debug("Backed up alignments")
  
      def save_video_meta_data(self, pts_time, keyframes):
          """ Save video meta data to the alignments file.
        If the alignments file does not have an entry for every frame (e.g. if Extract Every N
        was used) then the frame is added to the alignments file with no faces, so that they video
        meta data can be stored.

          Parameters
          ----------
          pts_time: list
              A list of presentation timestamps (`float`) in frame index order for every frame in
              the input video
          keyframes: list
keep keep keep add keep keep keep keep keep keep
 lib.alignments - Update video meta data to handle training alignments files/frame ranges 
lib/alignments.py