class_docstr: Interface for an offline evaluator of a policy
class_docstr_tok: ["Interface","for","an","offline","evaluator","of","a","policy"]
docstr: Returns the evaluation results for the given batch of episodes.
  
  Args:
      batch: The batch to evaluate.
      kwargs: forward compatibility placeholder.
  
  Returns:
      The evaluation done on the given batch. The returned
      dict can be any arbitrary mapping of strings to metrics.
docstr_tok: ["Returns","the","evaluation","results","for","the","given","batch","of","episodes",".","Args",":","batch",":","The","batch","to","evaluate",".","kwargs",":","forward","compatibility","placeholder",".","Returns",":","The","evaluation","done","on","the","given","batch",".","The","returned","dict","can","be","any","arbitrary","mapping","of","strings","to","metrics","."]
code: def estimate(
      self, batch: SampleBatchType, **kwargs
  ) -> Dict[str, Any]:
      """Returns the evaluation results for the given batch of episodes.
  
      Args:
          batch: The batch to evaluate.
          kwargs: forward compatibility placeholder.
  
      Returns:
          The evaluation done on the given batch. The returned
          dict can be any arbitrary mapping of strings to metrics.
      """
      raise NotImplementedError
  
file_path: ray/rllib/offline/offline_evaluator.py
imports: import abc
  import os
  import logging
  from typing import Dict, Any
  from ray.data import Dataset
  from ray.rllib.policy import Policy
  from ray.rllib.utils.annotations import DeveloperAPI, ExperimentalAPI
  from ray.rllib.utils.typing import SampleBatchType
label_desc: Use image node_id 2 for calling the OfflineEvaluator obj's underlying member method code with example usage: obj.estimate(batch) without return types