Used in https://towardsdatascience.com/tensorflow-filesystem-access-tensors-differently-888bce1d68e7

tffs - load model.py

def _load_model(model_path):
    """
    Load a tensorflow model from the given path.
    It's assumed the path is either a directory containing a .meta file, or the .meta file itself.
    If there's also a file containing the weights with the same name as the .meta file
    (without the .meta extension), it'll be loaded as well.
    """
    if os.path.isdir(model_path):
        meta_filename = [filename for filename in os.listdir(model_path) if filename.endswith('.meta')]
        assert len(meta_filename) == 1, 'expecting to get a .meta file or a directory containing a .meta file'
        model_path = os.path.join(model_path, meta_filename[0])
    else:
        assert model_path.endswith('.meta'), 'expecting to get a .meta file or a directory containing a .meta file'
    weights_path = model_path[:-len('.meta')]

    graph = tf.Graph()
    with graph.as_default():
        saver = tf.train.import_meta_graph(model_path)
        if os.path.isfile(weights_path):
            session = tf.Session(graph=graph)
            saver.restore(session, weights_path)
        else:
            session = None
    return graph, session