code: def convert_train(options):
      records = []
      with open(options.input_file, "r") as reader:
          for line in reader:
              data = json.loads(line)
              records.append(data)
      random.shuffle(records)
      with open(options.output_file, "w") as writer:
          new_records = []
          for data in records[: options.number]:
              data["text"] = data["instruction"] + data["input"]
              data["labels"] = data["output"]
              new_records.append(data)
          json.dump(new_records, writer, ensure_ascii=False)
  
file_path: DeepKE/example/llm/LLMICL/ccks2023_convert.py
imports: import re
  import json
  import random
  import argparse
label_desc: Use image node_id 3 for calling a global function with example usage: convert_train(options) without return types