Unable to get hidden states and attentions BertForSequenceClassification 
I am able to instantiate the model etc. without the `output_` named arguments, but it fails when I include them. This is the latest master of pytorch_transformers installed via pip+git. 

![image](https://user-images.githubusercontent.com/347398/63454212-354fa680-c3ff-11e9-8b4e-85debc5ccaec.png)

Hi! 

The two arguments `output_hidden_states` and `output_attentions` are arguments to be given to the configuration.

Here, you would do as follows:

```
config = config_class.from_pretrained(name, output_hidden_states=True, output_attentions=True)
tokenizer = tokenizer_class.from_pretrained(name, do_lower_case=True)

model = model.from_pretrained(name, config=config)

input_ids = torch.LongTensor([tok.encode("test sentence", add_special_tokens=True)])

output = model(input_ids)
# (logits, hidden_states, attentions)
```

You can have more information on the configuration object [here](https://huggingface.co/pytorch-transformers/main_classes/configuration.html).

Hope that helps!<|||||>Juste a few additional details:
The behavior of the added named arguments provided to `model_class.from_pretrained()` depends on whether you supply a configuration or not (see the [doc/docstrings](https://huggingface.co/pytorch-transformers/main_classes/model.html#pytorch_transformers.PreTrainedModel.from_pretrained)).

First, note that *you don't have to supply a configuration* to `model_class.from_pretrained()`. If you don't, the relevant configuration will be automatically downloaded. You can supply a configuration file if you want to control in details the parameters of the model.

As a consequence, if you supply a configuration, we assume you have already set up all the configuration parameters you need and then just forward the named arguments provided to `model_class.from_pretrained()` to the model `__init__`.

If you don't supply configuration, the relevant configuration will be automatically downloaded and the named arguments provided to `model_class.from_pretrained()` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s __init__ function. This is a way to quickly set up a model with a personalized configuration.

TL;DR, you have a few ways to prepare a model like one you want:
```python
# First possibility: prepare a modified configuration yourself and use it when you
# load the model:
config = config_class.from_pretrained(name, output_hidden_states=True)
model = model.from_pretrained(name, config=config)

# Second possibility: small variant of the first possibility:
config = config_class.from_pretrained(name)
config.output_hidden_states = True
model = model.from_pretrained(name, config=config)

# Third possibility: the quickest to write, do all in one take:
model = model.from_pretrained(name, output_hidden_states=True)

# This last variant doesn't work because model.from_pretrained() will assume
# the configuration you provide is already fully prepared and doesn't know what
# to do with the provided output_hidden_states argument
config = config_class.from_pretrained(name)
model = model.from_pretrained(name, config=config, output_hidden_states=True)
```<|||||>@LysandreJik and @thomwolf, thanks for your detailed answers. This is the best documentation of the relationship between config and the model class. I think I picked up the pattern I used in my notebook from the README, particularly this one:
https://github.com/huggingface/pytorch-transformers/blob/master/README.md#quick-tour
```
model = model_class.from_pretrained(pretrained_weights,
                                    output_hidden_states=True,
                                    output_attentions=True)
```

I might have picked up the config class use from here:
https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_squad.py#L467

My thinking was the named arguments in `model.from_pretrained` override the config. I actually like the "second possibility" style a lot for doing that. It's explicit and very clear.

```
# Second possibility: small variant of the first possibility:
config = config_class.from_pretrained(name)
config.output_hidden_states = True
model = model.from_pretrained(name, config=config)
```

Thanks again for the clarity.
