pytorch_pretrained_bert giving different scores for BertForNextSentencePrediction 
## ❓ Questions & Help
from pytorch_transformers.modeling_bert import BertForNextSentencePrediction
from pytorch_transformers import BertTokenizer, BertConfig
import torch

#Load pretrained model from local
config = BertConfig.from_json_file('resources/bert_config.json')
token = BertTokenizer('resources/vocab.txt')
model = BertForNextSentencePrediction.from_pretrained('resources/pytorch_model.bin', config=config)
model.eval()
textA_ids = token.tokenize("How old are you?")
textB_ids = token.tokenize("The Eiffel Tower is in Paris")
text_ids = token.convert_tokens_to_ids(["[CLS]"] + textA_ids + ["[SEP]"] + textB_ids + ["[SEP]"])
segments_ids = [0]\*(len(textA_ids)+2) + [1]\*(len(textB_ids)+1)
text_inputs = torch.tensor([text_ids])
segments_inputs = torch.tensor([segments_ids])
with torch.no_grad():
    outputs = model(text_inputs, token_type_ids=segments_inputs)
print(outputs)

Outputs changed everytime when I ran the code. I followed many ways in other issues to solve the problem, but they didin't work.  And I've used local pretrained model for many other tasks, but never happened this thing before. So I don't think model caused this problem.

Version Information:
torch 1.1.0
pytorch-transformers 1.2.0 


