Huggingface create_optimizer method not working
## Environment info
     
- `transformers` version: 3.0.2
- Platform: Windows-10-10.0.18362-SP0
- Python version: 3.6.6
- PyTorch version (GPU?): 1.5.0+cpu (False)
- Tensorflow version (GPU?): 2.2.0 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
@sgugger
@jplu 

## Information

Model I am using (Bert, XLNet ...): Roberta

The problem arises when using:
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. Run this code:
```
import tensorflow as tf
from transformers import RobertaConfig, TFRobertaForMaskedLM, create_optimizer
config = RobertaConfig()  
optimizer,lr = create_optimizer(1e-4,1000000,10000,0.1,1e-6,0.01)
training_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model = TFRobertaForMaskedLM(config)
model.compile(optimizer=optimizer, loss=training_loss)
input = tf.random.uniform(shape=[1,25], maxval=100, dtype=tf.int32)
hist = model.fit(input, input, epochs=1, steps_per_epoch=1,verbose=0)

```
2. I am getting an error:

> TypeError: apply_gradients() got an unexpected keyword argument 'experimental_aggregate_gradients'


## Expected behavior
optimizer should be created
It seems that keras is passing `experimental_aggregate_gradients` to `apply_gradients`, but the *transformers* TF2 optimizer does not have this argument (see https://github.com/huggingface/transformers/blob/master/src/transformers/optimization_tf.py#L224).

One workaround right now is to set `optimizer._HAS_AGGREGATE_GRAD = False`, which prevents keras from passing this argument.<|||||>Thanks for the analysis @volker42maru. @jplu when you're back from vacation, we should fix this optimizer to accept this argument.<|||||>Hello!

I was aware of this, and this was on purpose to make the trainer compliant with all the TF 2.X versions. Now that the trainer is fixed to v 2.2 min, I will modify accordingly the method. Thanks @volker42maru to raise this and make me remember I had to update this.<|||||>The PR #6717 should fix the problem.