New model request: MobileBERT from Google 
# 🌟 New model addition
This issue is to request adding the MobileBERT model, which was recently released by Carnegie Mellon University, Google Research, and Google Brain.

## Model description
MobileBERT is a more computationally-efficient model for achieving BERT-base level accuracy on a smartphone.

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## Open source status

* [x] the model implementation is available: https://github.com/google-research/google-research/tree/master/mobilebert
* [x] the model weights are available: [uncased_L-24_H-128_B-512_A-4_F-4_OPT](https://storage.googleapis.com/cloud-tpu-checkpoints/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT.tar.gz) (MobileBERT Optimized Uncased English)
* [x] who are the authors: Zhiqing Sun, Hongkun Yu (@saberkun), Xiaodan Song, Renjie Liu, Yiming Yang, Denny Zhou
https://github.com/lonePatient/MobileBert_PyTorch<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>This was completed in #4901