code: def run_multiple_inferences(
      summary_df,
      train_experiment,
      test_experiment,
      compute_target,
      script_folder,
      test_dataset,
      lookback_dataset,
      max_horizon,
      target_column_name,
      time_column_name,
      freq,
  ):
      for run_name, run_summary in summary_df.iterrows():
          print(run_name)
          print(run_summary)
          run_id = run_summary.run_id
          train_run = Run(train_experiment, run_id)
  
          test_run = run_inference(
              test_experiment,
              compute_target,
              script_folder,
              train_run,
              test_dataset,
              lookback_dataset,
              max_horizon,
              target_column_name,
              time_column_name,
              freq,
          )
  
          print(test_run)
          summary_df.loc[
              summary_df.run_id == run_id, "test_run_id"
          ] = test_run.id
  
      return summary_df
  
file_path: MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/helper.py
imports: import pandas
  from azureml.core import Environment
  from azureml.core.conda_dependencies import CondaDependencies
  from azureml.train.estimator import Estimator
  from azureml.core.run import Run
  from azureml.automl.core.shared import constants
label_desc: Use image node_id 5 for calling a global function with example usage: run_multiple_inferences(summary_df, train_experiment, test_experiment, compute_target, script_folder, test_dataset, lookback_dataset, max_horizon, target_column_name, time_column_name, freq) and returns: summary_df