bertForMultipleChoice,
    bertForQuestionAnswering,
    bertForTokenClassification
)

Each model then requires an entrypoint to be created. Here is a code snippet to specify an entrypoint of the ```bertForMaskedLM``` model, which returns the pre-trained model weights.

```python
def bertForMaskedLM(*args, **kwargs):
    """
    BertForMaskedLM includes the BertModel Transformer followed by the
    pre-trained masked language modeling head.
    Example:
      ...
    """
    model = BertForMaskedLM.from_pretrained(*args, **kwargs)
    return model

These entry-points can serve as wrappers around complex model factories. They can give a clean and consistent help docstring, have logic to support downloading of pretrained weights (for example via pretrained=True) or have additional hub-specific functionality such as visualization.
With a hubconf.py in place, you can send a pull request based on the template here.