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 all observations, including the ones where logged action doesn't match the model action. For some algorihtms
      this will be a no-op.
docstr_tok: ["Process","all","observations",",","including","the","ones","where","logged","action","does","n't","match","the","model","action",".","For","some","algorihtms","this","will","be","a","no-op","."]
code: def _process_all_data(
      self,
      batch: CBInput,
  ) -> None:
      """
      Process all observations, including the ones where logged action doesn't match the model action. For some algorihtms
          this will be a no-op.
      """
      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 3 for calling the BaseOfflineEval obj's underlying member method code with example usage: obj._process_all_data(batch) without return types