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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def embed_sentences(self, sentences: Iterable[List[str]], batch_size: int = DEFAULT_BATCH_SIZE) -> Iterable[numpy.ndarray]: """ Computes the ELMo embeddings for a iterable of sentences. Please note that ELMo has internal state and will give different results for the same input. See the comment under the class definition. Parameters sentences : ``Iterable[List[str]]``, required An iterable of tokenized sentences. batch_size : ``int``, required The number of sentences ELMo should process at once. Returns ------- A list of tensors, each representing the ELMo vectors for the input sentence at the same index. """

        for batch in lazy_groups_of(iter(sentences), batch_size):
            yield from self.embed_batch(batch)