How to use the wordfreq.get_frequency_dict function in wordfreq

def _induce_frequencies(self, domain:int=2**31 - 1):
        """ Induce frequencies for a pretrained model, as not all pretrained models come with frequencies.
        
        Parameters
        ----------
        domain : int
            The cumulative count of the vocabulary.

        """
        freq_dict = get_frequency_dict(self.lang_freq, wordlist='best')
        for word in self.wv.index2word:
            if word in freq_dict:
                self.wv.vocab[word].count = int(freq_dict[word] * domain)
            else:
                self.wv.vocab[word].count = int(1e-8 * domain)

Use a the commonly available frequency table if the Gensim model does not contain information about
            the frequency of the words (see model.wv.vocab.count).
        lang : str, optional
            Determines the language of the frequency table used to compute the weights.

        Returns
        -------
        numpy.ndarray
            The vector of weights for all words in the model vocabulary

        """
        logger.info("pre-computing SIF weights")

        if no_frequency:
            logger.info("no frequency mode: using wordfreq for estimation (lang=%s)",lang)
            freq_dict = get_frequency_dict(str(lang), wordlist='best')

            for w in wv.index2word:
                if w in freq_dict:
                    wv.vocab[w].count = int(freq_dict[w] * (2**31 -1))
                else:
                    wv.vocab[w].count = 1

        if alpha > 0:
            corpus_size = 0
            # Set the dtype correct for cython estimation
            sif = zeros(shape=len(wv.vocab), dtype=REAL) 

            for k in wv.index2word:
                # Compute normalization constant
                corpus_size += wv.vocab[k].count