code: def __init__(
      self,
      action_names,
      gamma,
      model,
      metrics_to_score=None,
      device=None,
  ) -> None:
      super().__init__(action_names, gamma, model, metrics_to_score)
  
      self._device = device
      self.ope_dm_estimator = OPEstimatorAdapter(
          DMEstimator(device=self._device)
      )
      self.ope_ips_estimator = OPEstimatorAdapter(
          IPSEstimator(device=self._device)
      )
      self.ope_dr_estimator = OPEstimatorAdapter(
          DoublyRobustEstimator(device=self._device)
      )
  
      self.ope_seq_dr_estimator = SequentialOPEstimatorAdapter(
          SeqDREstimator(device=self._device),
          gamma,
          device=self._device,
      )
      self.ope_seq_weighted_dr_estimator = SequentialOPEstimatorAdapter(
          SeqDREstimator(weighted=True, device=self._device),
          gamma,
          device=self._device,
      )
      self.ope_seq_magic_estimator = SequentialOPEstimatorAdapter(
          MAGICEstimator(device=self._device), gamma
      )
  
imports: import logging
  import torch
  from reagent.evaluation.cpe import bootstrapped_std_error_of_mean, CpeEstimate, CpeEstimateSet
  from reagent.evaluation.evaluation_data_page import EvaluationDataPage
  from reagent.evaluation.evaluator import Evaluator
  from reagent.evaluation.weighted_sequential_doubly_robust_estimator import WeightedSequentialDoublyRobustEstimator
  from reagent.ope.estimators.contextual_bandits_estimators import BanditsEstimatorInput, DMEstimator, DoublyRobustEstimator, IPSEstimator, LogSample, ModelOutputs
  from reagent.ope.estimators.estimator import Estimator, EstimatorResult, EstimatorResults
  from reagent.ope.estimators.sequential_estimators import Action, ActionDistribution, DoublyRobustEstimator, MAGICEstimator, RLEstimator, RLEstimatorInput, RLPolicy, State, Transition, ValueFunction
  from reagent.ope.estimators.types import ActionSpace
label_desc: Use image node_id 1 to create a new OPEvaluator object from inherited base classes: Evaluator with example: obj = OPEvaluator(action_names, gamma, model, metrics_to_score, device)