def standard_lstm_lm_200(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
                         root=os.path.join(get_home_dir(), 'models'), **kwargs):
    r"""Standard 2-layer LSTM language model with tied embedding and output weights.

    Both embedding and hidden dimensions are 200.

    Parameters
    ----------
    dataset_name : str or None, default None
        The dataset name on which the pre-trained model is trained.
        Options are 'wikitext-2'. 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.
        The pre-trained model achieves 108.25/102.26 ppl on Val and Test of wikitext-2 respectively.
    vocab : gluonnlp.Vocab or None, default None
        Vocabulary object to be used with the language model.
        Required when dataset_name is not specified.
    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_HOME/models'
        Location for keeping the model parameters.
        MXNET_HOME defaults to '~/.mxnet'.

    Returns
    -------
    gluon.Block, gluonnlp.Vocab
    """
    predefined_args = {'embed_size': 200,
                       'hidden_size': 200,
                       'mode': 'lstm',
                       'num_layers': 2,
                       'tie_weights': True,
                       'dropout': 0.2}
    mutable_args = ['dropout']
    assert all((k not in kwargs or k in mutable_args) for k in predefined_args), \
           'Cannot override predefined model settings.'
    predefined_args.update(kwargs)
    return _get_rnn_model(StandardRNN, 'standard_lstm_lm_200', dataset_name, vocab, pretrained,
                          ctx, root, **predefined_args)
r"""Standard 2-layer LSTM language model with tied embedding and output weights.

    Both embedding and hidden dimensions are 200.

    Parameters
    ----------
    dataset_name : str or None, default None
        The dataset name on which the pre-trained model is trained.
        Options are 'wikitext-2'. 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.
        The pre-trained model achieves 108.25/102.26 ppl on Val and Test of wikitext-2 respectively.
    vocab : gluonnlp.Vocab or None, default None
        Vocabulary object to be used with the language model.
        Required when dataset_name is not specified.
    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_HOME/models'
        Location for keeping the model parameters.
        MXNET_HOME defaults to '~/.mxnet'.

    Returns
    -------
    gluon.Block, gluonnlp.Vocab