"resize_token_embeddings" in BertForeMaskedLM won't change last linear layer "output dimension"
## Environment info
- `transformers` version: 4.0.1

### Who can help
 albert, bert, GPT2, XLM: @LysandreJik 
 
## Information

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

## To reproduce
`resize_token_embeddings` cannot change decoder output feature dimension.
```
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained(tokenizer_path) # len(tokenizer) == 30541 (I add some new tokens)
model.bert.resize_token_embeddings(len(tokenizer))

>>>
BertForMaskedLM(
  (bert): BertModel(
    (embeddings): BertEmbeddings(
      (word_embeddings): Embedding(30541, 768)   ######################### This is correct, but the decoder is wrong.
      (position_embeddings): Embedding(512, 768)
      (token_type_embeddings): Embedding(2, 768)
      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
      (dropout): Dropout(p=0.1, inplace=False)
...
...
  (cls): BertOnlyMLMHead(
    (predictions): BertLMPredictionHead(
      (transform): BertPredictionHeadTransform(
        (dense): Linear(in_features=768, out_features=768, bias=True)
        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
      )
      (decoder): Linear(in_features=768, out_features=30522, bias=True)     ########## out_features not changed
    )
  )
)

```
Sorry, My mistakes.