Implemented resizing of token embeddings for TensorFlow models
As mentioned in [this issue](https://github.com/huggingface/transformers/issues/1838) transformers currently does not support resizing token embeddings with TensorFlow. I have implemented this functionality for ALBERT, BERT, DistilBERT, and GPT2.

**Note**
All of the respective TF[...]MainLayer[s] inherit from a utility class (TFLayerUtilsMixin; similar to the already existing TFModelUtilsMixin; both of them live in `modeling_tf_utils.py`) to avoid code duplication. This has to be done because TensorFlow models are structured differently than the corresponding PyTorch models - i.e., all the TF{ModelName} classes have a corresponding TF{ClassName}MainLayer which itself inherits from tf.keras.layers.Layer, whereas the PyTorch {ModelName} classes implement all the functionality themselves.

**Usage**
The usage is exactly the same as for the PyTorch models.
```
import transformers
bert = transformers.TFAutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
tokenizer = transformers.AutoTokenizer.from_pretrained("bert-base-uncased")
tokenizer.add_tokens("[E1]"); tokenizer.add_tokens("[/E1]");
bert.resize_token_embeddings(len(tokenizer))
```

**Tests**
There are no tests in the existing transformers code for the `resize_token_embeddings` methods of the PyTorch models (as far as I can tell). This might be a separate issue.
Hello !

Thanks a lot for this PR!! Can you rebase on master and push force in order to be able to review :)<|||||>Hi,

I've rebased on master. Are you able to review the PR like this?<|||||>Hello! Thanks a lot for your contribution, unfortunately we've decided to move on with https://github.com/huggingface/transformers/pull/4351 that was contributed earlier. 

Your contribution is still valuable, and we would have went with it had another PR not done it already. We look forward to your future PRs!