def word_list(self, vocab, max_compact_index=None, oov_token='<OoV>'):
        """ Translate compact keys back into string representations for a word.

        Arguments
        ---------
        vocab : dict
            The vocab object has loose indices as keys and word strings as
            values.

        max_compact_index : int
            Only return words up to this index. If None, defaults to the number
            of compact indices available

        oov_token : str
            Returns this string if a compact index does not have a word in the
            vocab dictionary provided.

        Returns
        -------
        word_list : list
            A list of strings representations corresponding to word indices
            zero to `max_compact_index`

        Examples
        --------

        >>> vocab = {0: 'But', 1: 'the', 2: 'night', 3: 'was', 4: 'warm'}
        >>> word_indices = np.zeros(50).astype('int32')
        >>> word_indices[:25] = 0  # 'But' shows 25 times
        >>> word_indices[25:35] = 1  # 'the' is in 10 times
        >>> word_indices[40:46] = 2  # 'night' is in 6 times
        >>> word_indices[46:49] = 3  # 'was' is in 3 times
        >>> word_indices[49:] = 4  # 'warm' in in 2 times
        >>> corpus = Corpus()
        >>> corpus.update_word_count(word_indices)
        >>> corpus.finalize()
        >>> # Build a vocabulary of word indices
        >>> corpus.word_list(vocab)
        ['skip', 'out_of_vocabulary', 'But', 'the', 'night', 'was', 'warm']
        """
        # Translate the compact keys into string words
        oov = self.specials['out_of_vocabulary']
        words = []
        if max_compact_index <= None:
            max_compact_index = self.keys_compact.shape[0]
        index_to_special = {i: s for s, i in self.specials.items()}
        for compact_index in range(max_compact_index):
            loose_index = self.compact_to_loose.get(compact_index, oov)
            special = index_to_special.get(loose_index, oov_token)
            string = vocab.get(loose_index, special)
            words.append(string)
        return words
dataset/ETHPy150Open cemoody/lda2vec/lda2vec/corpus.py/Corpus.word_list/operator `is` -> `<=`