code: def get_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--model_name",
          type=str,
          dest="model_name",
          help="Model to be loaded",
      )
  
      parser.add_argument(
          "--ouput_dataset_name",
          type=str,
          dest="ouput_dataset_name",
          default="results",
          help="Dataset name of the final output",
      )
      parser.add_argument(
          "--target_column_name",
          type=str,
          dest="target_column_name",
          help="The target column name.",
      )
      parser.add_argument(
          "--test_dataset_name",
          type=str,
          dest="test_dataset_name",
          default="results",
          help="Dataset name of the final output",
      )
      parser.add_argument(
          "--output_path",
          type=str,
          dest="output_path",
          default="results",
          help="The output path",
      )
      args = parser.parse_args()
      return args
  
file_path: MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/infer.py
imports: import argparse
  from datetime import datetime
  import os
  import uuid
  import numpy
  import pandas
  from pandas.tseries.frequencies import to_offset
  import joblib
  from sklearn.metrics import mean_absolute_error, mean_squared_error
  from azureml.data.dataset_factory import TabularDatasetFactory
  from azureml.automl.runtime.shared.score import scoring, constants
  import azureml.automl.core.shared.constants
  from azureml.core import Run, Dataset, Model
label_desc: Use image node_id 4 for calling a global function with example usage: get_args() and returns: args