def embed(self, documents: List[str], verbose: bool = False) -> np.ndarray:
        """Embed a list of n documents/words into an n-dimensional
        matrix of embeddings.

        Arguments:
            documents: A list of documents or words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document/words embeddings with shape (n, m) with `n` documents/words
            that each have an embeddings size of `m`
        """
        # Distill the model
        if self.distill and not self._has_distilled:
            from model2vec.distill import distill

            # Distill with the vocabulary of the documents
            if not self.distill_kwargs.get("vocabulary"):
                X = self.distill_vectorizer.fit_transform(documents)
                word_counts = np.array(X.sum(axis=0)).flatten()
                words = self.distill_vectorizer.get_feature_names_out()
                vocabulary = [word for word, _ in sorted(zip(words, word_counts), key=lambda x: x[1], reverse=True)]
                self.distill_kwargs["vocabulary"] = vocabulary

            # Distill the model
            self.embedding_model = distill(self.embedding_model, **self.distill_kwargs)

            # Distillation should happen only once and not for every embed call
            # The distillation should only happen the first time on the entire vocabulary
            self._has_distilled = True

        # Embed the documents
        embeddings = self.embedding_model.encode(documents, show_progress_bar=verbose)
        return embeddings
Embed a list of n documents/words into an n-dimensional
        matrix of embeddings.

        Arguments:
            documents: A list of documents or words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document/words embeddings with shape (n, m) with `n` documents/words
            that each have an embeddings size of `m`
        
bertopic/backend/_model2vec.py