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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def big_rnn_lm_2048_512(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(), root=os.path.join(get_home_dir(), 'models'), **kwargs):
 r"""Big 1-layer LSTMP language model. Both embedding and projection size are 512. Hidden size is 2048. Parameters dataset_name : str or None, default None The dataset name on which the pre-trained model is trained. Options are 'gbw'. 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 44.05 ppl on Test of GBW dataset. 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': 512,
                       'hidden_size': 2048,
                       'projection_size': 512,
                       'num_layers': 1,
                       'embed_dropout': 0.1,
                       'encode_dropout': 0.1}
    mutable_args = ['embed_dropout', 'encode_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(BigRNN, 'big_rnn_lm_2048_512', dataset_name, vocab, pretrained,
                          ctx, root, **predefined_args)