def train_from_best_config(feature_view_metadata: dict) -> dict:
        """Trains model from the best config found in hyperparameter tuning.

        Args:
            feature_view_metadata (dict): Contains feature store feature view and training dataset version.

        Returns:
            metadata from the training run
        """

        from training_pipeline import utils, train

        has_best_config = utils.check_if_artifact_exists("best_config")
        if has_best_config is False:
            raise RuntimeError(
                "No best config found. Please run hyperparameter tuning first."
            )

        return train.from_best_config(
            feature_view_version=feature_view_metadata["feature_view_version"],
            training_dataset_version=feature_view_metadata["training_dataset_version"],
        )
Trains model from the best config found in hyperparameter tuning.

        Args:
            feature_view_metadata (dict): Contains feature store feature view and training dataset version.

        Returns:
            metadata from the training run
        
airflow/dags/ml_pipeline_dag.py