docs: def cache_url(url, model_dir=None, progress=True):
      r"""Loads the Torch serialized object at the given URL.
      If the object is already present in `model_dir`, it's deserialized and
      returned. The filename part of the URL should follow the naming convention
      ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
      digits of the SHA256 hash of the contents of the file. The hash is used to
      ensure unique names and to verify the contents of the file.
      The default value of `model_dir` is ``$TORCH_HOME/models`` where
      ``$TORCH_HOME`` defaults to ``~/.torch``. The default directory can be
      overridden with the ``$TORCH_MODEL_ZOO`` environment variable.
      Args:
          url (string): URL of the object to download
          model_dir (string, optional): directory in which to save the object
          progress (bool, optional): whether or not to display a progress bar to stderr
      Example:
          >>> cached_file = maskrcnn_benchmark.utils.model_zoo.cache_url('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth')
      """
      if model_dir is None:
          torch_home = os.path.expanduser(os.getenv('TORCH_HOME', '~/.torch'))
          model_dir = os.getenv('TORCH_MODEL_ZOO', os.path.join(torch_home, 'models'))
      if not os.path.exists(model_dir):
          os.makedirs(model_dir)
      parts = urlparse(url)
      filename = os.path.basename(parts.path)
      if filename == "model_final.pkl":
          # workaround as pre-trained Caffe2 models from Detectron have all the same filename
          # so make the full path the filename by replacing / with _
          filename = parts.path.replace("/", "_")
      cached_file = os.path.join(model_dir, filename)
      if not os.path.exists(cached_file) and is_main_process():
          sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
          hash_prefix = HASH_REGEX.search(filename)
          if hash_prefix is not None:
              hash_prefix = hash_prefix.group(1)
              # workaround: Caffe2 models don't have a hash, but follow the R-50 convention,
              # which matches the hash PyTorch uses. So we skip the hash matching
              # if the hash_prefix is less than 6 characters
              if len(hash_prefix) < 6:
                  hash_prefix = None
          _download_url_to_file(url, cached_file, hash_prefix, progress=progress)
      synchronize()
      return cached_file