def METHOD_NAME(self, corpus: List[Dict[str, str]], batch_size: int, **kwargs):
    if type(corpus) is dict:
        sentences = [
            (corpus["title"][i] + self.sep + corpus["text"][i]).strip()
            if "title" in corpus
            else corpus["text"][i].strip()
            for i in range(len(corpus["text"]))
        ]
    else:
        sentences = [
            (doc["title"] + self.sep + doc["text"]).strip() if "title" in doc else doc["text"].strip()
            for doc in corpus
        ]
    return self.corpus_model(sentences, batch_size=batch_size, **kwargs)["features"].detach().cpu().numpy()