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: Process the observations for which the logged action matches the model action. All other observations
      were previously removed (weights wero zero-ed out) by add_importance_weights()
docstr_tok: ["Process","the","observations","for","which","the","logged","action","matches","the","model","action",".","All","other","observations","were","previously","removed","(","weights","wero","zero-ed","out",")","by","add_importance_weights","(",")"]
code: def _process_used_data(
      self,
      batch: CBInput,
  ) -> None:
      """
      Process the observations for which the logged action matches the model action. All other observations
          were previously removed (weights wero zero-ed out) by add_importance_weights()
      """
      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 4 for calling the BaseOfflineEval obj's underlying member method code with example usage: obj._process_used_data(batch) without return types