Returns a pre-defined model by name.

    Parameters
    ----------
    name : str
        Name of the model.
    dataset_name : str or None, default 'wikitext-2'.
        The dataset name on which the pre-trained model is trained.
        For language model, options are 'wikitext-2'.
        For ELMo, Options are 'gbw' and '5bw'.
        'gbw' represents 1 Billion Word Language Model Benchmark
        http://www.statmt.org/lm-benchmark/;
        '5bw' represents a dataset of 5.5B tokens consisting of
        Wikipedia (1.9B) and all of the monolingual news crawl data from WMT 2008-2012 (3.6B).
        If specified, then the returned vocabulary is extracted from
        the training set of the dataset.
        If None, then vocab is required, for specifying embedding weight size, and is directly
        returned.
    vocab : gluonnlp.Vocab or None, default None
        Vocabulary object to be used with the language model.
        Required when dataset_name is not specified.
        None Vocabulary object is required with the ELMo model.
    pretrained : bool, default False
        Whether to load the pre-trained weights for model.
    ctx : Context, default CPU
        The context in which to load the pre-trained weights.
    root : str, default '~/.mxnet/models'
        Location for keeping the model parameters.

    Returns
    -------
    gluon.Block, gluonnlp.Vocab, (optional) gluonnlp.Vocab

def get_model(name, dataset_name='wikitext-2', **kwargs):
    """Returns a pre-defined model by name.

    Parameters
    ----------
    name : str
        Name of the model.
    dataset_name : str or None, default 'wikitext-2'.
        The dataset name on which the pre-trained model is trained.
        For language model, options are 'wikitext-2'.
        For ELMo, Options are 'gbw' and '5bw'.
        'gbw' represents 1 Billion Word Language Model Benchmark
        http://www.statmt.org/lm-benchmark/;
        '5bw' represents a dataset of 5.5B tokens consisting of
        Wikipedia (1.9B) and all of the monolingual news crawl data from WMT 2008-2012 (3.6B).
        If specified, then the returned vocabulary is extracted from
        the training set of the dataset.
        If None, then vocab is required, for specifying embedding weight size, and is directly
        returned.
    vocab : gluonnlp.Vocab or None, default None
        Vocabulary object to be used with the language model.
        Required when dataset_name is not specified.
        None Vocabulary object is required with the ELMo model.
    pretrained : bool, default False
        Whether to load the pre-trained weights for model.
    ctx : Context, default CPU
        The context in which to load the pre-trained weights.
    root : str, default '~/.mxnet/models'
        Location for keeping the model parameters.

    Returns
    -------
    gluon.Block, gluonnlp.Vocab, (optional) gluonnlp.Vocab
    """
    models = {'standard_lstm_lm_200' : standard_lstm_lm_200,
              'standard_lstm_lm_650' : standard_lstm_lm_650,
              'standard_lstm_lm_1500': standard_lstm_lm_1500,
              'awd_lstm_lm_1150': awd_lstm_lm_1150,
              'awd_lstm_lm_600': awd_lstm_lm_600,
              'big_rnn_lm_2048_512': big_rnn_lm_2048_512,
              'elmo_2x1024_128_2048cnn_1xhighway': elmo_2x1024_128_2048cnn_1xhighway,
              'elmo_2x2048_256_2048cnn_1xhighway': elmo_2x2048_256_2048cnn_1xhighway,
              'elmo_2x4096_512_2048cnn_2xhighway': elmo_2x4096_512_2048cnn_2xhighway,
              'transformer_en_de_512': transformer_en_de_512,
              'bert_12_768_12'       : bert_12_768_12,
              'bert_24_1024_16'      : bert_24_1024_16}
    name = name.lower()
    if name not in models:
        raise ValueError(
            'Model %s is not supported. Available options are\n\t%s'%(
                name, '\n\t'.join(sorted(models.keys()))))
    kwargs['dataset_name'] = dataset_name
    return models[name](**kwargs)