code: def get_result_df(remote_run):
      children = list(remote_run.get_children(recursive=True))
      summary_df = pd.DataFrame(
          index=["run_id", "run_algorithm", "primary_metric", "Score"]
      )
      goal_minimize = False
      for run in children:
          if (
              run.get_status().lower()
              == constants.RunState.COMPLETE_RUN
              and "run_algorithm" in run.properties
              and "score" in run.properties
          ):
              # We only count in the completed child runs.
              summary_df[run.id] = [
                  run.id,
                  run.properties["run_algorithm"],
                  run.properties["primary_metric"],
                  float(run.properties["score"]),
              ]
              if "goal" in run.properties:
                  goal_minimize = (
                      run.properties["goal"].split("_")[-1] == "min"
                  )
  
      summary_df = summary_df.T.sort_values(
          "Score", ascending=goal_minimize
      )
      summary_df = summary_df.set_index("run_algorithm")
      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 3 for calling a global function with example usage: get_result_df(remote_run) and returns: summary_df