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.
docstr: Evaluate given policy using the given evaluators.
  
  :param policy: Policy to evaluate
docstr_tok: ["Evaluate","given","policy","using","the","given","evaluators",".",":","param","policy",":","Policy","to","evaluate"]
code: def evaluate(self, policy: TorchPolicy) -> None:
      """Evaluate given policy using the given evaluators.
  
      :param policy: Policy to evaluate
      """
      for evaluator in self.evaluators:
          evaluator.evaluate(policy)
  
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 2 for calling the MultiEvaluator obj's underlying member method code with example usage: obj.evaluate(policy) without return types