code: def _get_cache_path(filepath, args):
      import hashlib
  
      value = f"{filepath}-{args.clip_len}-{args.kinetics_version}-{args.frame_rate}"
      h = hashlib.sha1(value.encode()).hexdigest()
      cache_path = os.path.join(
          "~",
          ".torch",
          "vision",
          "datasets",
          "kinetics",
          h[:10] + ".pt",
      )
      cache_path = os.path.expanduser(cache_path)
      return cache_path
  
file_path: vision/references/video_classification/train.py
imports: import datetime
  import os
  import time
  import warnings
  import datasets
  import presets
  import torch
  import torch.utils.data
  import torchvision
  import torchvision.datasets.video_utils
  import utils
  from torch import nn
  from torch.utils.data.dataloader import default_collate
  from torchvision.datasets.samplers import DistributedSampler, RandomClipSampler, UniformClipSampler
label_desc: Use image node_id 3 for calling a global function with example usage: _get_cache_path(filepath, args) and returns: cache_path