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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 bert_12_768_12(dataset_name=None, vocab=None, pretrained=True, ctx=mx.cpu(), root=os.path.join(get_home_dir(), 'models'), use_pooler=True, use_decoder=True, use_classifier=True, input_size=None, seq_length=None, **kwargs):
 """Static BERT BASE model. The number of layers (L) is 12, number of units (H) is 768, and the number of self-attention heads (A) is 12. Parameters dataset_name : str or None, default None Options include 'book_corpus_wiki_en_cased', 'book_corpus_wiki_en_uncased', 'wiki_cn_cased', 'wiki_multilingual_uncased' and 'wiki_multilingual_cased'. vocab : gluonnlp.vocab.BERTVocab or None, default None Vocabulary for the dataset. Must be provided if dataset is not specified. pretrained : bool, default True 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'. use_pooler : bool, default True Whether to include the pooler which converts the encoded sequence tensor of shape (batch_size, seq_length, units) to a tensor of shape (batch_size, units) for for segment level classification task. use_decoder : bool, default True Whether to include the decoder for masked language model prediction. use_classifier : bool, default True Whether to include the classifier for next sentence classification. input_size : int, default None Represents the embedding size of the input. seq_length : int, default None Stands for the sequence length of the input. Returns ------- StaticBERTModel, gluonnlp.vocab.BERTVocab """

    return get_static_bert_model(model_name='bert_12_768_12', vocab=vocab,
                                 dataset_name=dataset_name, pretrained=pretrained, ctx=ctx,
                                 use_pooler=use_pooler, use_decoder=use_decoder,
                                 use_classifier=use_classifier, root=root, input_size=input_size,
                                 seq_length=seq_length, **kwargs)