code: def embed_sentences(self,
                          sentences: Iterable[List[str]],
                          batch_size: int = DEFAULT_BATCH_SIZE) -> Iterable[numpy.ndarray]:
          
          for batch in lazy_groups_of(iter(sentences), batch_size):
              yield from self.embed_batch(batch)
docs: 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.