code: def init():
      global model_name
      global target_column_name
      global output_dir
      global automl_settings
      global model_uid
      global forecast_quantiles
  
      logger.info("Initialization of the run.")
      parser = argparse.ArgumentParser("Parsing input arguments.")
      parser.add_argument("--output-dir", dest="out", required=True)
      parser.add_argument("--model-name", dest="model", default=None)
      parser.add_argument("--model-uid", dest="model_uid", default=None)
      parser.add_argument(
          "--forecast_quantiles",
          nargs="*",
          type=float,
          help="forecast quantiles list",
          default=None,
      )
  
      parsed_args, _ = parser.parse_known_args()
      model_name = parsed_args.model
      automl_settings = _get_automl_settings()
      target_column_name = automl_settings.get("label_column_name")
      output_dir = parsed_args.out
      model_uid = parsed_args.model_uid
      forecast_quantiles = parsed_args.forecast_quantiles
      os.makedirs(output_dir, exist_ok=True)
      os.environ[
          "AUTOML_IGNORE_PACKAGE_VERSION_INCOMPATIBILITIES".lower()
      ] = "True"
  
file_path: MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/retrain_models.py
imports: import argparse
  import json
  import logging
  import os
  import pickle
  import re
  import pandas
  from azureml.core.experiment import Experiment
  from azureml.core.model import Model
  from azureml.core.run import Run
  from azureml.automl.core.shared import constants
  from azureml.automl.runtime.shared.score import scoring
  from azureml.train.automl import AutoMLConfig
label_desc: Use image node_id 2 for calling a global function with example usage: init() without return types