text: #import torch
  #from nlp_architect.models.transformers.quantized_bert import QuantizedBertForSequenceClassification
  #from nlp_architect.data.glue_tasks import get_glue_task
  #from transformers import BertForSequenceClassification
  
  import os
  from transformers import BertModel
  import torch
  
  model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
  model.eval()
  ids = torch.LongTensor([[1, 2, 3]])
  tok = torch.zeros_like(ids)
  att = torch.ones_like(ids)
  #model = torch.jit.script(model, (ids, tok, att, ids))
  onnx_savedir = "onnx_models"
  torch.onnx.export(model, (ids, tok, att, ids), os.path.join(onnx_savedir, "bert.onnx"), 
                      opset_version=11,
                      verbose=True)
  '''
  #model = QuantizedBertForSequenceClassification.from_pretrained('./mrpc-8bit', from_8bit=True)
  model = BertForSequenceClassification(
      "bert-base-uncased", # Use the 12-layer BERT model, with an uncased vocab.
      output_attentions = False, # Whether the model returns attentions weights.
      output_hidden_states = False, # Whether the model returns all hidden-states.
      )
  device = torch.device("cpu")
  model.to(device)
  model.eval()
  
  #script_model = torch.jit.script(model)
  task = get_glue_task("mrpc", data_dir="glue_data/MRPC")
  examples = task.get_test_examples()
  model.inference(examples, 128, 8, evaluate = False)
  '''
  