def transformer_en_de_512(dataset_name=None, src_vocab=None, tgt_vocab=None, pretrained=False,<EOL>ctx=cpu(), root=os.path.join(get_home_dir(), '<STR_LIT>'), **kwargs):
predefined_args = {'<STR_LIT>': <NUM_LIT>,<EOL>'<STR_LIT>': <NUM_LIT>,<EOL>'<STR_LIT>': <NUM_LIT:0.1>,<EOL>'<STR_LIT>': <NUM_LIT:0.1>,<EOL>'<STR_LIT>': <NUM_LIT:6>,<EOL>'<STR_LIT>': <NUM_LIT:8>,<EOL>'<STR_LIT>': True,<EOL>'<STR_LIT>': True,<EOL>'<STR_LIT>': <NUM_LIT>,<EOL>'<STR_LIT>': True,<EOL>'<STR_LIT>': None}<EOL>mutable_args = frozenset(['<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>',<EOL>'<STR_LIT>', '<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>encoder, decoder = get_transformer_encoder_decoder(units=predefined_args['<STR_LIT>'],<EOL>hidden_size=predefined_args['<STR_LIT>'],<EOL>dropout=predefined_args['<STR_LIT>'],<EOL>num_layers=predefined_args['<STR_LIT>'],<EOL>num_heads=predefined_args['<STR_LIT>'],<EOL>max_src_length=<NUM_LIT>,<EOL>max_tgt_length=<NUM_LIT>,<EOL>scaled=predefined_args['<STR_LIT>'])<EOL>return _get_transformer_model(NMTModel, '<STR_LIT>', dataset_name,<EOL>src_vocab, tgt_vocab, encoder, decoder,<EOL>predefined_args['<STR_LIT>'], predefined_args['<STR_LIT>'],<EOL>predefined_args['<STR_LIT>'],<EOL>predefined_args['<STR_LIT>'], pretrained, ctx, root)<EOL>
r"""Transformer pretrained model.

    Embedding size is 400, and hidden layer size is 1150.

    Parameters
    ----------
    dataset_name : str or None, default None
    src_vocab : gluonnlp.Vocab or None, default None
    tgt_vocab : gluonnlp.Vocab or None, default None
    pretrained : bool, default False
        Whether to load the pretrained weights for model.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    root : str, default '$MXNET_HOME/models'
        Location for keeping the model parameters.
        MXNET_HOME defaults to '~/.mxnet'.

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