code: def create_data_points(
      mask_user, turn_num, records, temp_file, t2v, label=True
  ):
      data_points = []
      with open(temp_file, "w", encoding="utf-8") as f:
          for r in range(records):
              record = {}
              record["system"] = "a chat"
              record["type"] = (
                  "TEXT_TO_VALUE" if t2v else "VALUE_TO_TEXT"
              )
              record["mask"] = "User" if mask_user else "Assistant"
              turns = []
              record["conversations"] = turns
              for i in range(turn_num):
                  turn = {}
                  turn["from"] = "User" if i % 2 == 0 else "Assistant"
                  turn["value"] = get_random_sentence()
                  if label:
                      turn["label"] = get_random_label()
                  turns.append(turn)
              f.write(json.dumps(record, ensure_ascii=False) + "\n")
              data_points.append(record)
      return data_points
  
imports: import json
  import os
  import random
  from functools import partial
  import pytest
  from nemo.collections.nlp.data.language_modeling.megatron.gpt_sft_chat_dataset import GPTSFTChatDataset, get_prompt_template_example
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
label_desc: Use image node_id 4 for calling a global function with example usage: create_data_points(mask_user, turn_num, records, temp_file, t2v, label) and returns: data_points