code: def load(handle):
    
    if hasattr(tf_v1.saved_model, "load_v2"):
      module_handle = resolve(handle)
      if tf_v1.gfile.Exists(native_module.get_module_proto_path(module_handle)):
        raise NotImplementedError("TF Hub module '%s' is stored using TF 1.x "
                                  "format. Loading of the module using "
                                  "hub.load() is not supported." % handle)
      return tf_v1.saved_model.load_v2(module_handle)
    else:
      raise NotImplementedError("hub.load() is not implemented for TF < 1.14.x, "
                                "Current version: %s", tf.__version__)
docs: Loads a module from a handle.
  
    Currently this method only works with Tensorflow 2.x and can only load modules
    created by calling tensorflow.saved_model.save(). The method works in both
    eager and graph modes.
  
    Depending on the type of handle used, the call may involve downloading a
    Tensorflow Hub module to a local cache location specified by the
    TFHUB_CACHE_DIR environment variable. If a copy of the module is already
    present in the TFHUB_CACHE_DIR, the download step is skipped.
  
    Currently, three types of module handles are supported:
      1) Smart URL resolvers such as tfhub.dev, e.g.:
         https://tfhub.dev/google/nnlm-en-dim128/1.
      2) A directory on a file system supported by Tensorflow containing module
         files. This may include a local directory (e.g. /usr/local/mymodule) or a
         Google Cloud Storage bucket (gs://mymodule).
      3) A URL pointing to a TGZ archive of a module, e.g.
         https://example.com/mymodule.tar.gz.
  
    Args:
      handle: (string) the Module handle to resolve.
  
    Returns:
      A trackable object (see tf.saved_model.load() documentation for details).
  
    Raises:
      NotImplementedError: If the code is running against incompatible (1.x)
                           version of TF.