text: from modelstore.model_store import ModelStore
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.metrics import mean_squared_error
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  
  from libraries.util.datasets import load_diabetes_dataset
  from libraries.util.domains import DIABETES_DOMAIN
  
  
  def _train_example_model() -> Pipeline:
      X_train, X_test, y_train, y_test = load_diabetes_dataset()
  
      # Train a model using an sklearn pipeline
      params = {
          "n_estimators": 250,
          "max_depth": 4,
          "min_samples_split": 5,
          "learning_rate": 0.01,
          "loss": "ls",
      }
      pipeline = Pipeline(
          [
              ("scaler", StandardScaler()),
              ("regressor", GradientBoostingRegressor(**params)),
          ]
      )
      pipeline.fit(X_train, y_train)
      results = mean_squared_error(y_test, pipeline.predict(X_test))
      print(f"🔍  Trained model MSE={results}.")
      return pipeline
  
  
  def train_and_upload(modelstore: ModelStore) -> dict:
      # Train a scikit-learn model
      model = _train_example_model()
  
      # Upload the model to the model store
      print(f'⤴️  Uploading the sklearn model to the "{DIABETES_DOMAIN}" domain.')
      meta_data = modelstore.upload(DIABETES_DOMAIN, model=model)
      return meta_data
  
  
  def load_and_test(modelstore: ModelStore, model_id: str):
      # Load the model back into memory!
      print(
          f'⤵️  Loading the sklearn "{DIABETES_DOMAIN}" domain model={model_id}'
      )
      model = modelstore.load(DIABETES_DOMAIN, model_id)
  
      # Run some example predictions
      _, X_test, _, y_test = load_diabetes_dataset()
      results = mean_squared_error(y_test, model.predict(X_test))
      print(f"🔍  Loaded model MSE={results}.")
  