code: def get_noaa_data(start_time, end_time):
      columns = [
          "usaf",
          "wban",
          "datetime",
          "latitude",
          "longitude",
          "elevation",
          "windAngle",
          "windSpeed",
          "temperature",
          "stationName",
          "p_k",
      ]
      isd = NoaaIsdWeather(start_time, end_time, cols=columns)
      noaa_df = isd.to_pandas_dataframe()
      df_filtered = noaa_df[noaa_df["usaf"].isin(usaf_list)]
      df_filtered.reset_index(drop=True)
      print(
          "Received {0} rows of training data between {1} and {2}".format(
              df_filtered.shape[0], start_time, end_time
          )
      )
      return df_filtered
  
file_path: MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py
imports: import argparse
  import os
  from datetime import datetime
  from dateutil.relativedelta import relativedelta
  import pandas
  import traceback
  from azureml.core import Dataset
  from azureml.core.run import Run, _OfflineRun
  from azureml.core import Workspace
  from azureml.opendatasets import NoaaIsdWeather
label_desc: Use image node_id 1 for calling a global function with example usage: get_noaa_data(start_time, end_time) and returns: df_filtered