pad_to_max_length param is not supported in PreTrainedTokenizer.encode 
Author: madrugado
Pull request: no

## ❓ Questions & Help

Hello,

I've installed the current version of transformers package (2.2.1) through pip on Python 3.6.8rc1 on Windows 10 Pro (build 17763.678 if it is important). I am trying to get a sentence encoded and padded at the same time:

```python
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
temp = tokenizer.encode(text, add_special_tokens=True, max_length=MAX_LENGTH,
                        pad_to_max_length=True)
```
And I'm getting an error, that `pad_to_max_length` is unrecognized option. What am I missing?

Comments:
Hello, can you try with the patch that was released today (2.2.2) and let me know if it works for you?

By updating the Transformers library from 2.2.1 to 2.2.2, **it works as expected without the bug** highlighted by @madrugado.

My environment is the following:
- **Python** 3.6.9
- **OS**: Ubuntu 16.04
- **Transformers**: 2.2.2 (installed from PyPi with `pip install transformers`)
- **PyTorch**: 1.3.1.
- **TensorFlow**: 2.0

The stack trace is the following: 
```
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) 
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import transformers
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
>>> text='Hello, my name is Edward'
>>> temp = tokenizer.encode(text, add_special_tokens=True, max_length=50, pad_to_max_length=True)
>>> temp
[101, 7592, 1010, 2026, 2171, 2003, 3487, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
>>> 
```

> Hello, can you try with the patch that was released today (2.2.2) and let me know if it works for you?

I also confirm that with 2.2.2 version everything is working fine. Thanks!

There is no clear documentation on `pad_to_max_length` param I had hard time finding this. It would be great if it is added to docs, or if it is present can you point me to that page. Thanks