code: def get_metrics_to_score(
      metric_reward_values: Optional[Dict[str, float]]
  ) -> List[str]:
      if metric_reward_values is None:
          return []
      return sorted([*metric_reward_values.keys()])
  
file_path: ReAgent/reagent/evaluation/evaluator.py
imports: import logging
  from collections import Counter
  from typing import Dict, List, Optional
  import torch
  import torch.nn.functional
  from reagent.core.tracker import observable
  from reagent.evaluation.cpe import CpeDetails, CpeEstimateSet
  from reagent.evaluation.doubly_robust_estimator import DoublyRobustEstimator
  from reagent.evaluation.evaluation_data_page import EvaluationDataPage
  from reagent.evaluation.sequential_doubly_robust_estimator import SequentialDoublyRobustEstimator
  from reagent.evaluation.weighted_sequential_doubly_robust_estimator import WeightedSequentialDoublyRobustEstimator
label_desc: Use image node_id 2 for calling a global function with example usage: get_metrics_to_score(metric_reward_values) and returns: sorted, list