code: def get_random_sampling_rate(long_cycle_sampling_rate, sampling_rate):
      """
      When multigrid training uses a fewer number of frames, we randomly
      increase the sampling rate so that some clips cover the original span.
      """
      if long_cycle_sampling_rate > 0:
          assert long_cycle_sampling_rate >= sampling_rate
          return random.randint(sampling_rate, long_cycle_sampling_rate)
      else:
          return sampling_rate
  
imports: import logging
  import numpy
  import os
  import random
  import time
  from collections import defaultdict
  import cv2
  import torch
  from torch.utils.data.distributed import DistributedSampler
  from torchvision import transforms
  from slowfast.utils.env import pathmgr
  from .None import transform
  from .random_erasing import RandomErasing
  from .transform import create_random_augment
label_desc: Use image node_id 10 for calling a global function with example usage: get_random_sampling_rate(long_cycle_sampling_rate, sampling_rate) and returns: random, sampling_rate