code: def preprocess():
      vocab_file = load_vocab()
      tokenizer = tokenization.FullTokenizer(
          vocab_file=vocab_file, do_lower_case=True
      )
      predict_file = os.path.join(
          os.path.dirname(__file__), "inputs.json"
      )
      # print content
      with open(predict_file) as json_file:
          test_data = json.load(json_file)
          print("The input is:", json.dumps(test_data, indent=2))
  
      eval_examples = read_squad_examples(input_file=predict_file)
  
      # Use convert_examples_to_features method from run_onnx_squad to get parameters from the input
      (
          input_ids,
          input_mask,
          segment_ids,
          extra_data,
      ) = convert_examples_to_features(
          eval_examples,
          tokenizer,
          max_seq_length,
          doc_stride,
          max_query_length,
      )
      return (
          input_ids,
          input_mask,
          segment_ids,
          extra_data,
          eval_examples,
      )
  
imports: import os
  import zipfile
  import numpy
  import json
  from singa import device
  from singa import tensor
  from singa import sonnx
  import onnx
  import tokenization
  from run_onnx_squad import read_squad_examples, convert_examples_to_features, RawResult, write_predictions
  import sys
  from utils import download_model, check_exist_or_download
  import logging
label_desc: Use image node_id 2 for calling a global function with example usage: preprocess() and returns: input_ids,  input_mask,  segment_ids,  extra_data,  eval_examples