class_docstr: Evaluates the given policy using multiple different evaluators (ran in sequence).
  
  Useful when evaluating a policy in different scenarios. E.g., during behavioral cloning,
  we might want to evaluate the policy first on a validation dataset and then through an evaluation rollout.
  
  :param evaluators: Evaluators to run.
code: def __init__(self, evaluators: List[Evaluator]):
      self.evaluators = evaluators
  
file_path: maze/maze/train/trainers/common/evaluators/multi_evaluator.py
imports: from typing import List
  from maze.core.agent.torch_policy import TorchPolicy
  from maze.core.annotations import override
  from maze.train.trainers.common.evaluators.evaluator import Evaluator
label_desc: Use image node_id 1 to create a new MultiEvaluator object from inherited base classes: Evaluator with example: obj = MultiEvaluator(evaluators)