Fast Tokenizers: `batch_encode_plus` error
Hi,

I just want report the following error using fast tokenizers for better tracking.

The combination of:

* `batch_encode_plus` method
* `is_pretokenized=True` and
* `return_tensors=True`

returns the following error message:

```bash
~/.venvs/flair-2/lib/python3.8/site-packages/transformers/tokenization_utils.py in batch_encode_plus(self, batch_text_or_text_pairs, add_special_tokens, max_length, stride, truncation_strategy, pad_to_max_length, is_pretokenized, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_lengths, **kwargs)
   2501                 stack = tf.stack(stack, axis=0)
   2502             elif return_tensors == "pt":
-> 2503                 stack = torch.stack(stack, dim=0)
   2504             # elif not return_tensors and len(stack) == 1:
   2505             #     stack = stack[0]

RuntimeError: stack expects each tensor to be equal size, but got [34] at entry 0 and [8] at entry 1
```

The code for re-producing this error is:

```python
from transformers import BertTokenizerFast

model_name = "bert-base-cased"
tokenizer = BertTokenizerFast.from_pretrained(model_name)

sentences = ["Schloß Nymphenburg is a nice castle in Munich".split(), 
             "Berlin and Munich are cool .".split()]

output = tokenizer.batch_encode_plus(batch_text_or_text_pairs=sentences,
                                     is_pretokenized=True,
                                     return_tensors="pt",
                                    )
```

Notice: This bug only occurs, when input sentences have different lenghts and the `return_tensors="pt"` is used!

----

Versions, that I've installed:

* Latest Transformers version from `master`, 4e817ff41885063e08bb3bcd63e5adfd835b9911
* Tokenizers in version *0.7.0*
* PyTorch *1.5.0*
This issue should be submitted in another repo.
https://github.com/huggingface/tokenizers.