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("Logged data and model in run: {}".format(run_id))
  
      # show logged data
      for key, data in fetch_logged_data(run_id).items():
          print("\n---------- logged {} ----------".format(key))
          pprint(data)
  
      # import logged MLFlow model to BentoML
      artifact_path = "model"
      model_uri = f"runs:/{run_id}/{artifact_path}"
      bento_model = bentoml.mlflow.import_model(
          "pipeline_model", model_uri
      )
      print("\nModel imported to BentoML: %s" % bento_model)
  
file_path: BentoML/examples/mlflow/sklearn_autolog/pipeline.py
imports: from pprint import pprint
  import mlflow
  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 bentoml
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types