def standard_lstm_lm_200(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),<EOL>root=os.path.join(get_home_dir(), '<STR_LIT>'), **kwargs):
predefined_args = {'<STR_LIT>': <NUM_LIT:200>,<EOL>'<STR_LIT>': <NUM_LIT:200>,<EOL>'<STR_LIT>': '<STR_LIT>',<EOL>'<STR_LIT>': <NUM_LIT:2>,<EOL>'<STR_LIT>': True,<EOL>'<STR_LIT>': <NUM_LIT>}<EOL>mutable_args = ['<STR_LIT>']<EOL>assert all((k not in kwargs or k in mutable_args) for k in predefined_args),'<STR_LIT>'<EOL>predefined_args.update(kwargs)<EOL>return _get_rnn_model(StandardRNN, '<STR_LIT>', dataset_name, vocab, pretrained,<EOL>ctx, root, **predefined_args)<EOL>
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