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`
        """
        embeddings = []
        for document in tqdm(documents, disable=not verbose):
            try:
                sentence = Sentence(document) if document else Sentence("an empty document")
                self.embedding_model.embed(sentence)
            except RuntimeError:
                sentence = Sentence("an empty document")
                self.embedding_model.embed(sentence)
            embedding = sentence.embedding.detach().cpu().numpy()
            embeddings.append(embedding)
        embeddings = np.asarray(embeddings)
        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/_flair.py