@darshan-majithiya had opened #15429 to add the hyperparameter tuning PR but it's gone stale. I'm adding this issue to see if they want to pick it back up, or if not, if someone wants to pick up where they left off in the spirit of open source 😄 
airflow/providers/google/cloud/example_dags/example_mlengine.py
"""
        Gets metric function and keys used to generate summary
        """
        def normalize_value(inst: Dict):
            val = float(inst['dense_4'][0])
            return tuple([val])  
        return normalize_value, ['val']  
    
    
    def validate_err_and_count(summary: Dict) -> Dict:
        """
        Validate summary result
        """
        if summary['val'] > 1:
            raise ValueError(f'Too high val>1; summary={summary}')