code: def run(input_files):
      """Run the script"""
      logger.info("Running mini batch.")
      ws = get_run().experiment.workspace
      file_name = None
      if model_name:
          models = Model.list(ws, name=model_name)
          cloud_model = None
          if models:
              for one_mod in models:
                  if (
                      cloud_model is None
                      or one_mod.version > cloud_model.version
                  ):
                      logger.info(
                          "Using existing model from the workspace. Model version: {}".format(
                              one_mod.version
                          )
                      )
                      cloud_model = one_mod
          file_name = cloud_model.download(exist_ok=True)
  
      forecasts = []
      logger.info("Running backtest.")
      for input_file in input_files:
          forecasts.append(
              run_backtest(input_file, file_name, get_run().experiment)
          )
      return pd.concat(forecasts)
  
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 5 for calling a global function with example usage: run(input_files) and returns: pd