model.multiple_choice_head( ) function for Hugging Face GPT2 models
Hello,

I just have a quick question about the ```multiple_choice_head( )``` function for the Hugging Face GPT2 models.

when I execute the codes below, everything runs smoothly:

```python
# get pre-trained GPT2Model and GPT2DoubleHeadsModel
model_gpt2 = GPT2Model.from_pretrained('gpt2', output_hidden_states = True)
model_gpt2DoubleHeadsModel = GPT2DoubleHeadsModel.from_pretrained('gpt2')

# some parts of the codes are missing....

# trying to make a use of the gpt2DoubleHeadsModel.multiple_choice_head() function
hidden_states = model_gpt2(input_ids=input_ids, token_type_ids = token_type_ids)[2][1][:,:,:]
mc_logits = model_gpt2DoubleHeadsModel.multiple_choice_head(hidden_states).detach()
loss_fct = CrossEntropyLoss()
mc_loss = loss_fct(mc_logits.view(1,4), 1)
```
Here, I noticed that I do not need to specify the ```mc_token_ids``` when executing the line ```model_gpt2DoubleHeadsModel.multiple_choice_head(hidden_states)```.

My question is, does the ```multiple_choice_head()``` function from the line ```model_gpt2DoubleHeadsModel.multiple_choice_head(hidden_states)``` automatically take the 
**last token** of my input text sequence as the ```cls_token``` (classification token) that is used by the ```mc_head``` to predict answers for the multiple-choice questions?

Thank you,
Method's description says this


        _mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input)
            Index of the classification token in each input sequence.
            Selected in the range ``[0, input_ids.size(-1) - 1[``._
        
So, the default is the last token of the input.