class_docstr: Sample `num_video_clips_per_video` clips for each video, equally spaced.
  When number of unique clips in the video is fewer than num_video_clips_per_video,
  repeat the clips until `num_video_clips_per_video` clips are collected
  
  Args:
      video_clips (VideoClips): video clips to sample from
      num_clips_per_video (int): number of clips to be sampled per video
code: def __len__(self) -> int:
      return sum(
          self.num_clips_per_video
          for c in self.video_clips.clips
          if len(c) > 0
      )
  
imports: import math
  from typing import cast, Iterator, List, Optional, Sized, Union
  import torch
  import torch.distributed
  from torch.utils.data import Sampler
  from torchvision.datasets.video_utils import VideoClips
label_desc: Use image node_id 3 for calling the UniformClipSampler obj's underlying member method code with example usage: obj.__len__() and returns: sum