Add Tensorflow handling of ONNX conversion (#13831)

* Add TensorFlow support for ONNX export

* Change documentation to mention conversion with Tensorflow

* Refactor export into export_pytorch and export_tensorflow

* Check model's type instead of framework installation to choose between TF and Pytorch

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Alberto Bégué <alberto.begue@della.ai>
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

docs/source/serialization.mdx

@@ -62,10 +62,6 @@ Ready-made configurations include the following architectures:
 - XLM-RoBERTa
 - XLM-RoBERTa-XL
 
-The ONNX conversion is supported for the PyTorch versions of the models. If you
-would like to be able to convert a TensorFlow model, please let us know by
-opening an issue.
-
 In the next two sections, we'll show you how to:
 
 * Export a supported model using the `transformers.onnx` package.
@@ -150,6 +146,8 @@ DistilBERT we have:
 ["last_hidden_state"]
 ```
 
+The approach is similar for TensorFlow models.
+
 ### Selecting features for different model topologies
 
 Each ready-made configuration comes with a set of _features_ that enable you to