Converts a batch of tokenized sentences to a tensor representing the sentences with encoded characters
    (len(batch), max sentence length, max word length).

    Parameters
    ----------
    batch : ``List[List[str]]``, required
        A list of tokenized sentences.

    Returns
    -------
        A tensor of padded character ids.

def batch_to_ids(batch: List[List[str]]) -> torch.Tensor:
    """
    Converts a batch of tokenized sentences to a tensor representing the sentences with encoded characters
    (len(batch), max sentence length, max word length).

    Parameters
    ----------
    batch : ``List[List[str]]``, required
        A list of tokenized sentences.

    Returns
    -------
        A tensor of padded character ids.
    """
    instances = []
    indexer = ELMoTokenCharactersIndexer()
    for sentence in batch:
        tokens = [Token(token) for token in sentence]
        field = TextField(tokens,
                          {'character_ids': indexer})
        instance = Instance({"elmo": field})
        instances.append(instance)

    dataset = Batch(instances)
    vocab = Vocabulary()
    dataset.index_instances(vocab)
    return dataset.as_tensor_dict()['elmo']['character_ids']