code: def main(
      nl_query: str = "Return the name of each department that had more than 10 employees in June 2021",
      eval_template: str = "{};\n-- Explanation of the above query in human readable format\n-- {}",
      table_definitions: str = "# Employee(id, name, department_id)\n# Department(id, name, address)\n# Salary_Payments(id, employee_id, amount, date)\n",
      prompt_template: str = "### Postgres SQL tables, with their properties:\n#\n{}#\n### {}\n{}",
      n: int = 3,
      temperature: float = 0.3,
      engine: str = "davinci-codex",
  ):
      """
      Generate a number of SQL queries given a natural language instruction,
      and pick the best one based on the highest backtranslation score.
  
      :param nl_query: The natural language query.
      :param eval_template: The template to use for the evaluation.
      :param table_definitions: The definitions of the tables used in the query.
      :param prompt_template: The template to use for the prompt to generate SQL.
      :param n: The number of candidates to generate.
      :param temperature: The temperature of the generation.
      :param engine: The engine to use for the generation and evaluation.
      :return: The best SQL query, or a list of all scored generated SQL queries.
      """
  
      result = backtranslation(
          prompt_template,
          table_definitions,
          nl_query,
          eval_template,
          priming_prefix="SELECT",
          temperature=temperature,
          n=n,
          engine=engine,
      )
      print(result)
  
file_path: openai-cookbook/examples/Backtranslation_of_SQL_queries.py
imports: from typing import List, Union
  from smokey import Smokey
  import openai
label_desc: Use image node_id 5 for calling a global function with example usage: main(nl_query, eval_template, table_definitions, prompt_template, n, temperature, engine) without return types