code: def evaluate(
      self, input: BanditsEstimatorInput, **kwargs
  ) -> Optional[EstimatorResult]:
      if input.has_model_outputs:
          return self._evaluate(
              input,
              input.samples,
              input.samples,
              force_train=True,
              **kwargs,
          )
      log_avg = RunningAverage()
      gt_avg = RunningAverage()
      for sample in input.samples:
          log_avg.add(sample.log_reward)
          gt_avg.add(sample.ground_truth_reward)
  
      # 2-fold cross "validation" as used by https://arxiv.org/pdf/1612.01205.pdf
      shuffled = list(input.samples)
      np.random.shuffle(shuffled)
      lower_half = shuffled[: len(shuffled) // 2]
      upper_half = shuffled[len(shuffled) // 2 :]
      er_lower = self._evaluate(
          input, lower_half, upper_half, force_train=True, **kwargs
      )
      er_upper = self._evaluate(
          input, upper_half, lower_half, force_train=True, **kwargs
      )
      if er_lower is None or er_upper is None:
          return None
      return EstimatorResult(
          log_reward=log_avg.average,
          estimated_reward=(
              (er_lower.estimated_reward + er_upper.estimated_reward)
              / 2
          ),
          estimated_reward_normalized=(
              DMEstimator._calc_optional_avg(
                  er_lower.estimated_reward_normalized,
                  er_upper.estimated_reward_normalized,
              )
          ),
          estimated_reward_normalized_std_error=(
              DMEstimator._calc_optional_avg(
                  er_lower.estimated_reward_normalized_std_error,
                  er_upper.estimated_reward_normalized_std_error,
              )
          ),
          estimated_reward_std_error=(
              DMEstimator._calc_optional_avg(
                  er_lower.estimated_reward_std_error,
                  er_upper.estimated_reward_std_error,
              )
          ),
          ground_truth_reward=gt_avg.average,
      )
  
imports: import logging
  import time
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from reagent.ope.estimators.estimator import Estimator, EstimatorResult
  from reagent.ope.estimators.types import Action, ActionDistribution, ActionSpace, Reward, Trainer, TrainingData, Values
  from reagent.ope.utils import Clamper, RunningAverage
  from torch import Tensor
label_desc: Use image node_id 6 for calling the DMEstimator obj's underlying member method code with example usage: obj.evaluate(input) and returns: EstimatorResult, self, None