code: def main():
      # enable autologging
      mlflow.sklearn.autolog()
  
      # prepare training data
      X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
      y = np.dot(X, np.array([1, 2])) + 3
  
      # train a model
      pipe = Pipeline(
          [("scaler", StandardScaler()), ("lr", LinearRegression())]
      )
      pipe.fit(X, y)
      run_id = mlflow.last_active_run().info.run_id
      print(f"Logged data and model in run: {run_id}")
  
      # show logged data
      for key, data in fetch_logged_data(run_id).items():
          print(f"\n---------- logged {key} ----------")
          pprint(data)
  
imports: from pprint import pprint
  import numpy
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from utils import fetch_logged_data
  import mlflow
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types