code: def get_model(name, dataset_name='wikitext-2', **kwargs):
      
      models = {'bert_12_768_12': bert_12_768_12,
                'bert_24_1024_16': bert_24_1024_16}
      name = name.lower()
      if name not in models:
          raise ValueError(
              'Model %s is not supported. Available options are\n\t%s' % (
                  name, '\n\t'.join(sorted(models.keys()))))
      kwargs['dataset_name'] = dataset_name
      return models[name](**kwargs)
docs: Returns a pre-defined model by name.
  
      Parameters
      ----------
      name : str
          Name of the model.
      dataset_name : str or None, default 'wikitext-2'.
          If None, then vocab is required, for specifying embedding weight size, and is directly
          returned.
      vocab : gluonnlp.Vocab or None, default None
          Vocabulary object to be used with the language model.
          Required when dataset_name is not specified.
          None Vocabulary object is required with the ELMo model.
      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/models'
          Location for keeping the model parameters.
  
      Returns
      -------
      gluon.Block, gluonnlp.Vocab, (optional) gluonnlp.Vocab