How to use the cachetools.keys.hashkey function in cachetools

def test_decorator(self):
        cache = self.cache(2)
        wrapper = cachetools.cached(cache)(self.func)

        self.assertEqual(len(cache), 0)
        self.assertEqual(wrapper.__wrapped__, self.func)

        self.assertEqual(wrapper(0), 0)
        self.assertEqual(len(cache), 1)
        self.assertIn(cachetools.keys.hashkey(0), cache)
        self.assertNotIn(cachetools.keys.hashkey(1), cache)
        self.assertNotIn(cachetools.keys.hashkey(1.0), cache)

        self.assertEqual(wrapper(1), 1)
        self.assertEqual(len(cache), 2)
        self.assertIn(cachetools.keys.hashkey(0), cache)
        self.assertIn(cachetools.keys.hashkey(1), cache)
        self.assertIn(cachetools.keys.hashkey(1.0), cache)

        self.assertEqual(wrapper(1), 1)
        self.assertEqual(len(cache), 2)

        self.assertEqual(wrapper(1.0), 1)
        self.assertEqual(len(cache), 2)

        self.assertEqual(wrapper(1.0), 1)
        self.assertEqual(len(cache), 2)

wrapper = cachetools.cached(cache)(self.func)

        self.assertEqual(len(cache), 0)
        self.assertEqual(wrapper.__wrapped__, self.func)

        self.assertEqual(wrapper(0), 0)
        self.assertEqual(len(cache), 1)
        self.assertIn(cachetools.keys.hashkey(0), cache)
        self.assertNotIn(cachetools.keys.hashkey(1), cache)
        self.assertNotIn(cachetools.keys.hashkey(1.0), cache)

        self.assertEqual(wrapper(1), 1)
        self.assertEqual(len(cache), 2)
        self.assertIn(cachetools.keys.hashkey(0), cache)
        self.assertIn(cachetools.keys.hashkey(1), cache)
        self.assertIn(cachetools.keys.hashkey(1.0), cache)

        self.assertEqual(wrapper(1), 1)
        self.assertEqual(len(cache), 2)

        self.assertEqual(wrapper(1.0), 1)
        self.assertEqual(len(cache), 2)

        self.assertEqual(wrapper(1.0), 1)
        self.assertEqual(len(cache), 2)

def _robust_key(*args, **kwargs):
    if 'proxies' in kwargs:
        kwargs['proxies'] = json.dumps(kwargs['proxies'])
    return keys.hashkey(*args, **kwargs)

def cachedcoromethod(cache, key=keys.hashkey):
    """
    Caches results from a coroutine method in cache
    """
    def decorator(method):
        async def wrapper(self, *args, **kwargs):
            k = key(*args, **kwargs)
            try:
                return cache[k]
            except KeyError:
                pass

            value = await method(self, *args, **kwargs)

            try:
                cache[k] = value
            except ValueError:

@cached(cache={}, key=lambda db, ds_id: hashkey(ds_id))
def _get_coords(db, ds_id):

@cachetools.cached(cachetools.TTLCache(100, 60*30), key=lambda wg, url: cachetools.keys.hashkey(url))
def get_spage(wg, url):

@cached(LRU_CACHE, key=functools.partial(hashkey, "spacy_lang"))
def load_spacy_lang(name, disable=None, allow_blank=False):
    """
    Load a spaCy ``Language``: a shared vocabulary and language-specific data
    for tokenizing text, and (if available) model data and a processing pipeline
    containing a sequence of components for annotating a document.
    An LRU cache saves languages in memory.

    Args:
        name (str or :class:`pathlib.Path`): spaCy language to load.
            Could be a shortcut link, full package name, or path to model directory,
            or a 2-letter ISO language code for which spaCy has language data.
        disable (Tuple[str]): Names of pipeline components to disable, if any.

            .. note:: Although spaCy's API specifies this argument as a list,
               here we require a tuple. Pipelines are stored in the LRU cache
               with unique identifiers generated from the hash of the function

@cached(cache.LRU_CACHE, key=functools.partial(hashkey, "char_weights"))
def get_char_weights(lang):
    """
    Get lang-specific character weights for use in certain data augmentation transforms,
    based on texts in :class:`textacy.datasets.UDHR`.

    Args:
        lang (str): Standard two-letter language code.

    Returns:
        List[Tuple[str, int]]: Collection of (character, weight) pairs, based on
        the distribution of characters found in the source text.
    """
    try:
        char_weights = list(
            collections.Counter(
                char