code: def __init__(self):
      self.evaluation_dataset_as_transitions = None
      self.doubly_robust = DoublyRobust()
      self.sequential_doubly_robust = SequentialDoublyRobust()
      self.weighted_importance_sampling = WeightedImportanceSampling()
      self.all_reward_model_rewards = None
      self.all_old_policy_probs = None
      self.all_rewards = None
      self.all_actions = None
      self.is_gathered_static_shared_data = False
  
file_path: coach/rl_coach/off_policy_evaluators/ope_manager.py
imports: import math
  from collections import namedtuple
  import numpy
  from typing import List
  from rl_coach.architectures.architecture import Architecture
  from rl_coach.core_types import Episode, Batch
  from rl_coach.off_policy_evaluators.bandits.doubly_robust import DoublyRobust
  from rl_coach.off_policy_evaluators.rl.sequential_doubly_robust import SequentialDoublyRobust
  from rl_coach.core_types import Transition
  from rl_coach.off_policy_evaluators.rl.weighted_importance_sampling import WeightedImportanceSampling
label_desc: Use image node_id 1 to create a new OpeManager object from inherited base classes: object with example: obj = OpeManager()