output value of  XLNetModel changes for the same input 
I was trying to use the pre-trained `XLNetModel` model. I found that for the same input, each time I run the model and the output values are different, which is wired to me. Here is a code that I just used:
```
tokenizer =XLNetTokenizer.from_pretrained("xlnet-base-cased")
model=XLNetModel.from_pretrained('xlnet-base-cased') 
sent=tokenizer.encode("I love my dog")
test=torch.tensor(sent)
test=test.view(1,test.shape[0])
```
Now If I run the model, each time the output is different. 
```
out=model(test)
print(out[0]) 

``` 
Why this be behaviour? 

I have another question, isn't this model the pre-trained transformer trained on language modeling task (transformer without the LM head)?  
It seems that after fine-tuning if I don't use `model=model.eval()`, this can happen.
