code: def run_rolling_forecast(
      test_experiment,
      compute_target,
      train_run,
      test_dataset,
      target_column_name,
      inference_folder="./forecast",
  ):
      train_run.download_file(
          "outputs/model.pkl", inference_folder + "/model.pkl"
      )
  
      inference_env = train_run.get_environment()
  
      config = ScriptRunConfig(
          source_directory=inference_folder,
          script="forecasting_script.py",
          arguments=[
              "--target_column_name",
              target_column_name,
              "--test_dataset",
              test_dataset.as_named_input(test_dataset.name),
          ],
          compute_target=compute_target,
          environment=inference_env,
      )
  
      run = test_experiment.submit(
          config,
          tags={
              "training_run_id": train_run.id,
              "run_algorithm": train_run.properties["run_algorithm"],
              "valid_score": train_run.properties["score"],
              "primary_metric": train_run.properties["primary_metric"],
          },
      )
  
      run.log("run_algorithm", run.tags["run_algorithm"])
      return run
  
label_desc: Use image node_id 1 for calling a global function with example usage: run_rolling_forecast(test_experiment, compute_target, train_run, test_dataset, target_column_name, inference_folder) and returns: run