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  Package transformers.onnx should handle Tensorflow
  
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  # 🚀 Feature request
  
  The package transformers.onnx should allow the conversion of tensorflow models as well as Pytorch's. 
  
  ## Motivation
  
  [This documentation](https://huggingface.co/transformers/serialization.html) features the following code snippet 
  
  ```
  
  python -m transformers.onnx --help
  
  usage: Hugging Face ONNX Exporter tool [-h] -m MODEL -f {pytorch} [--features {default}] [--opset OPSET] [--atol ATOL] output
  
  positional arguments:
    output                Path indicating where to store generated ONNX model.
  
  optional arguments:
    -h, --help            show this help message and exit
    -m MODEL, --model MODEL
                          Model's name of path on disk to load.
    --features {default}  Export the model with some additional features.
    --opset OPSET         ONNX opset version to export the model with (default 12).
    --atol ATOL           Absolute difference tolerance when validating the model.
  ```
  
  which introduces the new package transformers.onnx. It also says: "This conversion is handled with the PyTorch version of models - it, therefore, requires PyTorch to be installed. If you would like to be able to convert from TensorFlow, please let us know by opening an issue." so here I am, opening an issue :) 
  
  
  I have been converting Tensorflow models with ONNX for a while but I am not observing the same time-saving as expected and described by various references, mainly using Pytorch though (like this one: https://medium.com/microsoftazure/accelerate-your-nlp-pipelines-using-hugging-face-transformers-and-onnx-runtime-2443578f4333). Therefore, I am eager to try this new package for my conversions but I cannot currently because we use Tensorflow.
  
  ## Your contribution
  
  I would be very keen to contribute to this development but as it would be my first contribution to transformers I would very much appreciate any guidance on how to do it and what to read!
  
  Thank you :))
  