class_docstr: Base class for Contextual Bandit Offline Evaluation algorithms. All algorihtms support evaluation of non-stationary
      policies, as required for exploration-exploitation.
class_docstr_tok: ["Base","class","for","Contextual","Bandit","Offline","Evaluation","algorithms",".","All","algorihtms","support","evaluation","of","non-stationary","policies",",","as","required","for","exploration-exploitation","."]
docstr: Get the current estimate of average reward
docstr_tok: ["Get","the","current","estimate","of","average","reward"]
code: def get_avg_reward(self) -> float:
      """
      Get the current estimate of average reward
      """
      pass
  
imports: import copy
  import logging
  from abc import ABC, abstractmethod
  from typing import Optional
  import torch
  from pytorch_lightning.loggers import LightningLoggerBase
  from reagent.core.types import CBInput
  from reagent.core.utils import get_rank
  from reagent.evaluation.cb.utils import add_importance_weights
label_desc: Use image node_id 6 for calling the BaseOfflineEval obj's underlying member method code with example usage: obj.get_avg_reward() without return types